IDENTIFYING ENERGY
EFFICIENCY IMPROVEMENTS
AND SAVING POTENTIAL IN
ENERGY NETWORKS,
INCLUDING ANALYSIS OF THE
VALUE OF DEMAND RESPONSE
In support of the implementation of article 15 of the energy efficiency directive
(2012/27/EU)
Final report
DG ENERGY - EUROPEAN COMMISSION |
BELGIUM
18 December 2015
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Comments:
IDENTIFYING ENERGY EFFICIENCY IMPROVEMENTS AND SAVING POTENTIAL IN ENERGY NETWORKS, INCLUDING
ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Identifying Energy Efficiency
improvements and saving potential in
energy networks, including analysis of the
value of demand response, in support of
the implementation of article 15 of the
energy efficiency directive (2012/27/EU)
December 2015
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Final report
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IDENTIFYING ENERGY EFFICIENCY IMPROVEMENTS AND SAVING POTENTIAL IN ENERGY NETWORKS, INCLUDING
ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Disclaimer
This document has been prepared for the European Commission however it reflects the
views only of the authors, and the Commission cannot be held responsible for any use
This document is the property of Tractebel Engineering S.A. Any duplication or transmission to third parties is forbidden without prior written approval
which may be made of the information contained therein.
© Tractebel Engineering, Ecofys 2015 by order of: European Commission
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IDENTIFYING ENERGY EFFICIENCY IMPROVEMENTS AND SAVING POTENTIAL IN ENERGY NETWORKS, INCLUDING
ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
FOREWORD
This report has been prepared for the European Commission by a consortium composed of
Tractebel Engineering and Ecofys.
The report has been organized in two parts.
The first part of the report focuses on the identification and assessment of measures to improve
the efficiency of electricity and gas networks.
The project has been led by Stéphane Rapoport and Vincenzo Giordano (Tractebel Engineering),
with the support of Georgios Papaefthymiou (Ecofys). The core team has included Gregoire
Lejeune (Tractebel), Sébastien Leyder (Tractebel), Farid Comaty (Ecofys), Rena Kuwahata
(Ecofys), Amelie Bonard (CRIGEN) and Pascal Vercamer (CRIGEN).
In addition to the core team valuable contributions have been made by several of our colleagues
at Tractebel Engineering and Ecofys.
The work has been followed by Mr Massimo Maraziti of the European Commission DG-Energy.
During the study, a survey has been carried out among European DSOs, thanks to the facilitation
role of European associations EURELECTRIC, CEDEC, GEODE, EDSO4SG.
All conclusions are the responsibility of the analysis team and do not necessarily reflect the views
of the European Commission, or any of the consulted stakeholders.
We are very grateful for all the valuable comments and suggestions received from the DG Energy
officers and the consulted organizations. Any remaining errors or omissions are the sole
responsibility of the analysis team.
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The second part specifically focuses on the use of demand response to improve the efficiency of
electricity networks, concerning reduction of losses and optimization of grid planning and
operation.
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IDENTIFYING ENERGY EFFICIENCY IMPROVEMENTS AND SAVING POTENTIAL IN ENERGY NETWORKS, INCLUDING
ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
TABLE OF CONTENTS
ACRONYMS .................................................................................................................... 11
PART 1 - IDENTIFYING ENERGY EFFICIENCY IMPROVEMENTS AND SAVING
POTENTIAL IN ENERGY NETWORKS............................................................................ 12
PART 1 - EXECUTIVE SUMMARY ....................................................................................... 13
1.1.
Objectives of this study .............................................................................. 17
1.2.
The Energy Efficiency Directive .................................................................. 18
1.3.
Reading guide to this document ................................................................. 19
1.3.1. Energy efficiency measures for electricity grids ....................................... 19
1.3.2. Energy efficiency measures for gas grids................................................ 20
2. POTENTIAL OF ENERGY EFFICIENCY MEASURES ON LOSS REDUCTION IN
ELECTRICITY GRIDS ................................................................................................. 21
2.1.
Understanding energy losses in electricity networks ................................. 22
2.1.1.
2.1.2.
2.1.3.
2.1.4.
2.1.5.
2.1.6.
2.1.7.
2.1.8.
2.2.
Energy efficiency measures in electricity networks: categorisation and
potential ...................................................................................................... 29
2.2.1.
2.2.2.
2.2.3.
2.2.4.
2.2.5.
2.2.6.
2.2.7.
2.2.8.
2.2.9.
2.3.
Type: Technical/non-Technical and Variable/Fixed .................................. 22
Voltage level: transmission and distribution networks .............................. 23
Components: Lines and Transformers .................................................... 24
Marginal vs Average losses ................................................................... 26
Role of DG and grid management ......................................................... 27
Reactive Losses................................................................................... 28
Imbalances......................................................................................... 29
Life cycle assessment: impact of losses on investment decisions ............... 29
Energy Efficient transformers ................................................................ 32
Increasing voltage level ....................................................................... 33
Increasing line capacity........................................................................ 33
Feed-in control.................................................................................... 34
Transformers switching ........................................................................ 36
Network reconfiguration ....................................................................... 37
Conservation voltage reduction ............................................................. 37
Voltage optimisation ............................................................................ 38
Balancing loading of lines ..................................................................... 40
Regulatory regimes to promote loss reduction in electricity grids ............ 40
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1. INTRODUCTION (PART 1).......................................................................................... 17
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
2.3.1. Regulatory incentives: basic options ...................................................... 40
2.3.2. State of play of regulatory mechanisms targeting loss reduction ............... 41
3. POTENTIAL OF MEASURES ON PLANNING/OPERATIONAL EFFICIENCY IN
ELECTRICITY GRIDS ................................................................................................. 44
Impact of selected energy efficiency measures on operational efficiency 45
3.1.1.
3.1.2.
3.1.3.
3.1.4.
3.1.5.
3.1.6.
3.1.7.
3.1.8.
3.1.9.
3.1.10.
High efficiency transformers ................................................................. 45
Expanding line capacity........................................................................ 46
Increasing voltage level ....................................................................... 47
Demand response ............................................................................... 49
Feed-in control of distributed generation & storage ................................. 50
Network reconfiguration ....................................................................... 51
Switching off redundant transformers .................................................... 52
Voltage optimisation ............................................................................ 53
Conservation Voltage Reduction (CVR)................................................... 54
Balancing 3-phase loads....................................................................... 55
4. COST-EFFECTIVENESS OF ENERGY EFFICIENCY MEASURES IN ELECTRICITY GRIDS ....... 56
4.1.
Assessment of cost-effectiveness ............................................................... 56
4.2.
Synthesis of the cost effectiveness of the measures .................................. 57
5. ASSESSMENT OF THE ENERGY EFFICIENCY POTENTIAL IN GAS GRIDS .......................... 61
5.1.
Understanding Energy Losses in gas networks .......................................... 61
5.1.1. Mapping of losses in gas transmission network ....................................... 64
5.1.2. Mapping of losses in compression stations.............................................. 65
5.1.3. Mapping of losses in gas distribution network ......................................... 65
5.2.
Energy efficiency measures in gas networks: categorisation and potential66
5.2.1. Gas transmission network: energy efficiency measures and reduction potential
67
5.2.2. Compressor stations: energy efficiency measures and reduction potential .. 68
5.2.3. Distribution grids: energy efficiency measures and reduction potential ...... 69
6. ASSESSMENT OF COST-EFFECTIVENESS OF SELECTED ENERGY EFFICIENCY
MEASURES - GAS ...................................................................................................... 71
6.1.
Gas Transmission Network ......................................................................... 71
6.2.
Gas Compressor Stations ............................................................................ 72
6.3.
Gas Distribution Network ........................................................................... 74
PART 2 - ASSESSING THE POTENTIAL OF DEMAND RESPONSE TO INCREASE THE
EFFICIENCY OF ELECTRICITY NETWORKS ................................................................... 76
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3.1.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
PART 2 - EXECUTIVE SUMMARY ....................................................................................... 77
7. INTRODUCTION (PART 2).......................................................................................... 84
8. OVERVIEW OF DEMAND RESPONSE CHARACTERISTICS ................................................ 85
8.1.
Benefits of demand response across the power system value chain ......... 85
8.2.
Factors affecting the value of Demand Response ...................................... 87
8.2.1.
8.2.2.
8.2.3.
8.2.4.
8.2.5.
Type of loads ...................................................................................... 87
Characteristics of demand response....................................................... 88
Activation mechanisms of demand response ........................................... 89
Price-based mechanisms (implicit DR).................................................... 89
Volume-based mechanisms (explicit DR) ................................................ 90
9.1.
Value streams of demand response for distribution system operators ...... 92
9.2.
Options for DSOs to procure demand response and capitalise the benefits94
9.2.1. Price-based procurement of demand response........................................ 94
9.2.2. Volume-based procurement of demand response ...................................101
9.2.3. Procurement options: Conclusions ........................................................105
10. ASSESSMENT OF THE VALUE OF DEMAND RESPONSE TO IMPROVE DISTRIBUTION
GRID EFFICIENCY AT PLANNING AND OPERATION......................................................106
10.1. Objectives and global approach................................................................106
10.2. Building the simulation models ................................................................108
10.3. Definition of scenarios and simulation results .........................................113
10.3.1.
10.3.2.
10.3.3.
10.3.4.
Base case ..........................................................................................114
Time-of-Use scenario ..........................................................................115
Demand response (volume-based) .......................................................117
Full-flex scenario ................................................................................121
10.4. Conclusions and key messages .................................................................123
11. REGULATORY EVOLUTIONS FOR DISTRIBUTION SYSTEM OPERATORS TO
PROCURE AND ACTIVATE DEMAND RESPONSE ...........................................................126
11.1. Price-based approach: DSOs’ use of tariff signals ....................................126
11.2. Volume-based approach: DSOs directly procure demand response .........128
11.2.1. Evolution of DSO remuneration based on TOTEX ...................................129
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9. VALUE STREAMS AND PROCUREMENT OPTIONS OF DEMAND RESPONSE FOR
DISTRIBUTION SYSTEM OPERATORS.......................................................................... 92
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11.3. Role of DSO regarding ownership and use of flexibility assets ................130
11.4. Role of DSO as validator of DR transactions at distribution level ............131
11.5. Regulatory evolutions: conclusions and key messages............................134
12. BIBLIOGRAPHY........................................................................................................136
13. ANNEX I - PARAMETERS INFLUENCING THE IMPLEMENTATION AND THE COSTEFFECTIVENESS OF THE MEASURES – DETAILED ANALYSIS.........................................142
14. ANNEX II ENERGY EFFICIENCY MEASURES IN EUROPEAN MEMBER STATES:
SURVEY ..................................................................................................................153
15. ANNEX III - SURVEY - QUESTIONNAIRE FORMAT ........................................................161
17. ANNEX V- DETAILED SIMULATION RESULTS...............................................................172
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16. ANNEX IV - TOOL FOR RANKING MEASURES ACCORDING TO THEIR COSTEFFECTIVENESS ......................................................................................................166
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Capital Expenditure
CAPEX
CHP
Combined Heat and Power
Prosumers
Consumers with local generation
DR
Demand Response
DER
Distributed Energy Resources
DG
Distributed Generation
DMS
Distribution Management System
DSO
Distribution System Operator
EE
Energy Efficiency
EV
Electrical Vehicle
NPV
Net Present Value
O&M
Operation and maintenance costs
OMS
Outage Management System
OPEX
Operational Expenditure
PV
Photo voltaic electricity generation
RES
Renewable Energy Sources
SAIDI
System Average Interruption Duration Index
SCADA
Supervisory Control and Data Acquisition
TSO
Transmission System Operator
VRES
Variable Renewable Energy Sources
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ACRONYMS
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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PART 1 - IDENTIFYING ENERGY EFFICIENCY
IMPROVEMENTS AND SAVING POTENTIAL IN
ENERGY NETWORKS
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
PART 1 - EXECUTIVE SUMMARY
Objective of the study
According to Article 15(2) of the Energy Efficiency Directive (2012/27/EU), Member States
have to ensure, by the 30th June 2015, that:
(a) an assessment is undertaken of the Energy Efficiency potentials of their gas and
electricity infrastructure, in particular regarding transmission, distribution, load
management and interoperability, and connection to energy generating installations, including
access possibilities for micro energy generators;
(b) concrete measures and investments are identified for the introduction of cost-effective
Energy Efficiency improvements in the network infrastructure, with a timetable for their
introduction.
The main objective of this study is therefore to support Member States which have not yet carried
out the assessment. The study also aims at defining common ground to carry out the review of the
assessment reports that will be submitted by Member States to the EC.
Perimeter of the study
Energy savings/loss reduction. Loss reduction is an important aspect of efficiency in energy
grids. To date, the electricity and gas consumption is responsible for 43% of the final energy
consumption in the EU and their infrastructure losses account for 1,5% and 0.3% respectively .
Losses levels vary among Members States. One key way to improve energy efficiency is to
reduce energy wastage. In the electricity sector this wastage is referred to as losses and in the gas
sector as shrinkage [2]. Energy efficiency potential therefore implies the minimisation of wastage
in both gas and electricity transmission and distribution. The study will assess the potential of
selected measures to meet this objective.
Planning/Operational Efficiency - The directive also considers that, in assessing grid energy
efficiency measures, planning/operational efficiency should also be considered. This entails
exploring opportunities to improve the efficient operation of available energy infrastructure and
reduce the need for investing in new infrastructures1.
This study will particularly consider the planning/operational efficiency potential in electricity
grids, where, thanks to new “Smart” measures allowing active grid management (Smart Grid
assets; flexibility of distributed energy resources), a higher potential for operation efficiency is
available.
1
DG
ENER
guidance
note
http://eur-lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:52013SC0450&from=EN point 57 and 58, Chapter E
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According to collected information at the moment of finalization of the interim report, some
Member States are finalising their assessment, while others presumably are not.
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
In particular, detailed analysis of the impact of demand response on energy efficiency (taking a
broad view on energy efficiency and encompassing losses, and planning/operational efficiency)
will be carried out in the second part of this study.
Methodological approach
The assessment of the energy efficiency potential in electricity and gas grids has been carried out
according to the following four-step approach:
3)
4)
Identification of key measures improving the energy efficiency of gas and electrical grids.
Identification and discussion of the potential of those measures in term of losses reduction,
through the identification the type of sources of losses in electricity and gas grids and a
mapping between the sources of losses and the corresponding measures that can mitigate
those losses
Discussion of the potential for planning/operational efficiency of each measure, looking
at aspects like investment deferral, RES hosting capacity, outage reduction, power quality
Identify the factors that affect the potential of each measure (e.g. load demand, pressure
level etc.)
The analysis is supported by examples of the observed impacts of the measures based on
literature review and a data collection survey carried out with European system operators’
associations (Eurelectric, EDSO, GEODE, CEDEC). Around 15 European distribution system
operators have participated to the survey and shared their experiences and best practices (more
details in ANNEX III and IV).
A key outcome of the study is the definition of a step-by-step methodology to guide Member
States and system operators in the definition of energy efficiency measures for electricity and
gas grids.
To this end an ad-hoc decision support tool has been set-up (ANNEX V).The goal of the tool is
to help project promoters in selecting good candidate measures and refine their Energy Efficiency
strategy before carrying out full detailed cost-benefit analyses.
Some of the key outcomes and insights are summarized here below.
Grid Energy Efficiency - State of play in Member States
European associations report that most of their members have not yet been actively involved
by Member States in addressing the provisions of article 15.2. The level of mobilization in EU
Member States in the implementation of the provisions of article 15.2 (definition of
investment plans to improve grid energy efficiency) appears to vary across Member States.
Based on collected information, a number of Member States are working on the topic and
would likely publish their official assessments in the period after the completion of this study.
According to information available at the time of publication of this study, Member States
seem mainly to consider loss reduction initiatives in their assessments.
Starting conditions of grids (e.g. voltage level; number of transformers) and potentials for
energy efficiency improvement vary widely. In particular, the level of losses varies
significantly among Member States.
Loss reduction is typically taken into account in grid investment decisions, however often it is
not the main driver for ad-hoc investments.
For electricity grids,
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1)
2)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
- System operators generally opt for traditional investments (replacement of assets;
reinforcements etc.) to improve grid energy efficiency (mainly targeting loss reduction)
- Smart Grid-related EE measures (e.g. use of flexibility of DER) to pursue energy
efficiency are less common; the existence of regulatory barriers hindering their viability.
A detailed categorization of losses in electricity grids have been carried out to map out where
and why losses occur in electricity grids.
A list of factors that have a key impact on the level of losses in the system have been
identified, including:
- Loading (including peak demand)
- Number of energized transformers
- Lengths of the feeders
- Level of power quality
- Presence of distributed energy resources
The main potential for reduction of electricity grids is at distribution level, where the majority
of losses occur.
The implementation of adequate metering systems at substations and at customers’ premises is
an important first step to identify where losses are actually occurring in the distribution grid
and to better target EE measures.
Mapping of losses – gas grids
A detailed mapping of losses in gas grids and compression stations has been carried out. Over
15 sources of losses have been identified.
A list of factors that have a key impact on the level of losses in the system have been
identified and analysed, including:
- Pressure level, volume of vented pipe
- Length and age of the grid, pipes and joints materials
- Number and efficiency of heaters, necessity of burners
- Base or peak load compressor
Planning/Operational efficiency improvement potential
The potential benefits of each measure in term of planning/operation efficiency have been
analysed, particularly for electricity grids where a higher potential of improvement exist. The
following dimensions of planning/operational efficiency have been considered:
- Investment deferral
- RES hosting capacity
- Security of supply (reduction of outages)
- Power quality
The factors impacting the effectiveness of each measure and the potential cost ranges have
also been analysed. The factors need to be tailored to specific grid conditions to assess the
cost-effectiveness of individual measures in a given context.
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Mapping of losses – electricity grids
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Drivers and regulation
Measures which require the exploitation of DER flexibility have still limited diffusion in
many Member States, often just at pilot level. Their implementation would require the
definition of new regulatory mechanisms over how DSOs can procure and activate DER
flexibility. On this topic, a specific focus on Demand Response will be carried out in the
second part of the study.
Energy efficiency (and particularly loss reduction) is the main driver of investments when
there is the need to comply with regulatory requirements and quality standards (e.g.
EcoDesign directive). In general, loss reduction is considered as one of the variables of a
larger investment optimization plan, but not the main driver for investments.
The incentive to pursue specific investments for loss reduction is strictly related to the
presence of regulatory incentives. A wide variety of regulatory frameworks are present in
European Member States, which in some cases do not explicitly support loss reduction
measures.
A detailed catalogue of energy efficiency measures together with an assessment of their costeffectiveness has been defined for electricity and gas grids.
As the potential of these measures is very much related to the specific local grid conditions,
the analysis has focused on the assessment of key factors affecting the benefits and the costs
of selected measures. System operators then need to customize these factors to their specific
local grid conditions.
An excel tool has been set-up to guide project promoters in the assessment of the costeffectiveness of different energy efficiency measures according to their local grid conditions.
The tool highlights the main factors affecting the benefits and cost of each measure and
provides examples for benchmark.
Cost-effectiveness of energy efficiency measures
A number of factors can greatly impact the cost-effectiveness of energy efficiency measures,
such as:
- Monetary value of losses, i.e. the average price of wholesale electricity (the higher, the
more cost-effective are energy efficiency measures)
- Loss reduction is in many cases a side-benefit of investments driven by other needs. The
cost-effectiveness of energy efficiency improvements should thus be analysed within the
context of a larger investment plan.
- Parallel grid developments (e.g. roll-out of smart meters; built-in smart functionalities in
DG converters etc.) make the enabling infrastructure for certain EE measures already
available, reducing their implementation costs.
- Energy efficiency measures could have conflicting objectives (e.g. loss reduction and
hosting capacity when implementing DG voltage optimization).
- External factors outside the control of system operators can also greatly impact the
potential of EE measures: e.g. the development and location of DERs.
A system approach to energy efficiency
A systemic approach should be adopted when assessing the impacts of the implementation of
a portfolio of EE measures. This could include:
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Catalogue of measures and decision-support tool
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
1.
INTRODUCTION (PART 1)
1.1.
Objectives of this study
According to Article 15(2) of the EE Directive (2012/27/EU), Member States have to ensure, by
the 30th June 2015, that:
(a) an assessment is undertaken of the Energy Efficiency potentials of their gas and
electricity infrastructure, in particular regarding transmission, distribution, load
management and interoperability, and connection to energy generating installations, including
access possibilities for micro energy generators;
(b) concrete measures and investments are identified for the introduction of cost-effective
Energy Efficiency improvements in the network infrastructure, with a timetable for their
introduction.
According to collected information at the moment of finalization of the interim report, some
Member States are finalising their assessment, while others presumably are not.
The main objective of this study is therefore to support Member States which have not yet carried
out the assessment. The study also aims at defining common ground to carry out the review of the
assessment reports submitted by Member States to the EC.
The analysis is supported by examples of the observed impacts of the measures based on
literature review and a data collection survey carried out with European system operators’
associations.
Finally the study aims at integrating the result of the analysis in an easy-to use decision support
tool, to be used by system operators to screen most cost-effective energy efficiency investments.
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- Synergies among EE measures, i.e. consider the whole EE program rather than individual
EE measures. This includes the assessment of possible negative influence of parallel
implementation of measures.
- Synergies with on-going parallel investments in the energy system (e.g. installation of
smart converters for DGs)
- Synergies with replacement/investment programs already scheduled (e.g. oversizing new
lines taking into account the loss reduction potential).
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1.2.
The Energy Efficiency Directive
The 2012 Energy Efficiency Directive (EED) establishes a set of binding measures to help the
EU reach its 20% energy efficiency target by 2020 [1]. The Directive states that 1.5% of the
average final energy consumption in the period 2010-2012 needs to be saved annually from 2014
onwards till 2020. To achieve that, energy efficiency improvements throughout the whole value
chain of the energy sector are required, that is from generation-transmission-distribution to
consumption of any energy carrier to be delivered to the industry, building or transportation
sector.
Loss reduction is a key aspect of energy efficiency in energy grids. To date, the electricity and
gas consumption is responsible for 43% of the final energy consumption in the EU and their
infrastructure losses account for 1,5% and 0.3% respectively2. Losses levels vary among
Members States. One key way to improve energy efficiency is to reduce energy wastage. In the
electricity sector this wastage is referred to as losses and in the gas sector as shrinkage [2].
Energy efficiency potential therefore implies the minimisation of wastage in both gas and
electricity transmission and distribution networks. Energy savings that can be provided by
investing in measures aiming at reducing those grid losses can be significant, depending on the
local situation.
Moreover, the directive also considers that grid energy efficiency measures should go beyond
loss reduction and also take into account planning/operational efficiency. This entails exploring
opportunities to improve the efficient operation of available energy infrastructure and reduce the
need for investing in new infrastructures3.
This study will particularly consider the planning/operational efficiency in electricity grids,
where, thanks to new “Smart” measures allowing active grid management, a higher potential for
improvement is available. In particular, a detailed analysis of the impact of demand response on
operational efficiency will be carried out in the second part of this study.
2
Ecofys analysis from EuroStat database
3
See
DG
ENER
guidance
note
http://eur-lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:52013SC0450&from=EN point 57 an d 58, Chapter E
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Article 15 of the EED targets specifically the transmission and distribution sector of electricity
and gas and requires thus Member states to perform an assessment of the energy efficiency
potential of their respective infrastructure.
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1.3.
Reading guide to this document
For sake of brevity and readability, the main text reports the key results and messages of the
analysis.
A more detailed description of all the analysis that has been carried out is reported in the
Annexes:
ANNEX I reports detailed analysis of the parameters affecting the potential and the costeffectiveness of EE measures.
ANNEX II and III report the results and the questionnaire of the stakeholders’ survey.
ANNEX IV describes in detail the decision support tool (excel file) that synthetises the
analysis in a step-by-step methodology.
1.3.1.
Energy efficiency measures for electricity grids
Energy efficiency measures for electricity grids are analysed in chapters 2 and 3. Chapter 4
provides a summary of the cost-effectiveness of the selected energy efficiency measures.
The study considers three categories of energy efficiency measures for electricity grids:
Traditional ones (replacement/reinforcement)
Network management (e.g. Smart Grid assets)
Flexibility of distributed energy resources (Feed-in control).
Table 1 - Perimeter of the study for energy efficiency measures in electricity grids provides a
mapping of the perimeter of the study for the energy efficiency measures in electricity grids.
The second part of the study carries out a dedicated focus on the feed-in control category,
especially on demand response. In particular, the use of DR flexibility in planning and operation
will be thoroughly explored.
EE Measures for electricity
grids
Grid energy efficiency – electricity grids
Loss reduction
Planning/Operational
efficiency
Traditional
(replacement/reinforcements)
Chapter 2
Chapter 3
Smart Network Management
Chapter 2
Chapter 3
Feed-in control (flexibility of
distributed energy resources)
Chapter 2
Chapter 3
Specific focus on Demand Response in second part of the report
Table 1 - Perimeter of the study for energy efficiency measures in electricity grids
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The interim report is organized in two main parts. A first part analyses the potential and the costeffectiveness of energy efficiency measures in electricity networks, whereas the second part
carries out the same analysis for gas networks.
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1.3.2.
Energy efficiency measures for gas grids
Energy efficiency measures for gas grids are analysed in chapters 5 and 6. Selected measures
have been categorized as follows:
Measures for transmission grids
Measures for compression stations
Measures for distribution grids
The analysis has mainly focused on the potential for reduction of gas shrinkage in the gas system.
EE Measures for gas grids
Potential for reduction of gas
shrinkage
Cost-effectiveness of measures
Transmission grids
Chapter 5
Chapter 6
Compression stations
Chapter 5
Chapter 6
Distribution grids
Chapter 5
Chapter 6
Table 2 - Perimeter of the study for energy efficiency measures in gas grids
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Table 2 reports the perimeter of the study for the energy efficiency measures in gas grids.
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2.
POTENTIAL OF ENERGY EFFICIENCY
MEASURES ON LOSS REDUCTION IN
ELECTRICITY GRIDS
This chapter presents the qualitative and quantitative assessment of the energy efficiency
potential in electricity infrastructure with particular reference to losses.
4
Figure 1 – Level of losses in electricity grids in selected EU Member States
4
Both technical and non-technical losses, based on the Ecofys analysis from EuroStat database
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Section 2.1 presents the key factors that are central to the understanding of how losses are created
in electricity networks. Based on this mapping, section 2.2 presents the key measures for
increasing energy efficiency, and key context indicators on how to assess their energy efficiency
potential. Figure 1 reports best estimations of the level of energy losses in electricity and gas
grids in a number of European Member States. The figure shows that the level of losses varies
significantly among Member States, hinting that potentials for energy efficiency improvement are
different and depends on local conditions (e.g. starting conditions of grids -e.g. voltage level;
number of transformers- and potentials for energy efficiency improvement are different).
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2.1.
Understanding energy losses in electricity
networks
Figure 2: Categorisation of losses in electricity networks: Mapping and key mechanisms [3], [2], [4]
2.1.1.
Type: Technical/non-Technical and Variable/Fixed
Electrical losses can be divided into technical losses, which refer to energy transformed to heat
and noise during the transmission and therefore physically lost, and non-technical losses, which
refer to energy delivered and consumed, but for some reason not recorded as sales (such as theft).
A general rule of thumb is that technical losses can rise up to 12%. When system losses exceed
this limit, they are most probably due to increased non-technical losses [5]. In general technical
losses can be split into [6]:
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Energy losses in electricity grids are created through a set of parallel mechanisms which affect
the different network components. In order to analyse the impacts and potential of energy
efficiency measures, one should understand how losses are created through these mechanisms
and how losses are mapped in electricity grids. Figure 2 presents the key factors on the mapping
of losses (to allow the quantification of where losses occur in the system), and further presents
the key mechanisms for losses creation (for a better understanding of how measures could lead to
losses reduction). In total there are 8 key dimensions to categorize energy losses in electricity
networks, presented in the following sections.
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Variable losses (also referred to as load or copper losses), occur due to the heating effect of
energy passing through conductors in lines and cables and also in the copper in transformers.
They vary in proportion to the square of the current and in proportion to the conductor
resistance. In essence, measures to reduce variable losses can be understood under these two
main influencing factors and how they apply in the global system: they either aim to reduce
the system power flows or to reduce the resistance of the transportation paths. In general,
variable losses contribute roughly to two thirds to three quarters of the total power system
technical losses.
Fixed losses (also referred to as non-load or iron losses), refer to energy needed for the
energisation of transformers 5 or conductors. These losses are invariant with current and
depend mainly on the number of energised components. In this respect, measures to reduce
fixed losses mainly aim to reduce the number of energised components or to increase their
efficiency. In general they contribute to roughly one quarter to one third of the total network
losses.6
Voltage level: transmission and distribution networks
Electrical losses are directly affected by the voltage level: increasing the voltage level leads to a
reduction of system currents and therefore of the respective energy losses. A consultation on the
treatment of losses by network operators was conducted by the European Regulators’ Group for
Electricity and Gas7 for fourteen EU countries in 2008 and since, no update to the study has been
completed8. Table 3 illustrates the percentage of losses per system level in selected EU countries
from [7]. The average losses in transmission networks (TNs) in EU vary between 1-2.6% while
for distribution networks (DNs) between 2.3-13.4% [7]. This variation has mainly to do with the
historical development and current state of the grids in each country with regard to age, design
etc. In general, distribution networks present the highest losses and the highest energy efficiency
potential and therefore are the focus of this study.
5
This refers to the energy needed for the operation of the magnetic fields that enable the operation of
transformers.
6
Another source of fixed losses are corona losses, which occur in high voltage lines. They vary with
the voltage level and the physical wire diameter and with weather conditions such as rain and fog and
are generally a very small percentage of the overall system losses.
7
Referred to today as the Council of European Energy Regulators
8
Eurostat provides data on for total system losses for each EU country in 2014 but the breakdown of this
data does not exist
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2.1.2.
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Table 3: Percentage of losses in transmission and distribution networks in selected EU countries.
Components: Lines and Transformers
Power lines and transformers are the primary components of the infrastructure in electricity
networks. A key difference is that only variable losses occur in lines, while transformers are
responsible for both fixed and variable losses. Figure 3 presents how losses are distributed
between these two key categories of components for the different voltage levels and different EU
countries. As can be seen, the majority of losses occur close to the customer, on low voltage (LV)
networks followed by medium voltage (MV) grids. A high variation in energy losses in
transformers can be observed, primarily due to the age of the assets.
Figure 3: Mapping of losses based on voltage level and components for different EU countries (FR: [8], [9]; GER: [10],
[11], [12], [13], [14]; UK: [2], [5], [15]; EU: [16], GIS Data)
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2.1.3.
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Transformers are the system components presenting both fixed and variable losses. Typically a
third of the total system technical losses occur in transformers while fixed losses account for
about two thirds of total transformer losses. The operational efficiency of transformers depends
on the loading. The peak efficiency of distribution transformers reaches 99.4% at approximately
40-50% of rated capacity [18]. In the EU the average loading of distribution transformers in
electricity distribution companies is 18,9%, far below optimal, rendering their efficiency at
98.38% [18]. Since transformers run round the clock for more than 40 years, a 0.1% marginal
increase in efficiency of a transformer population leads to significant loss reductions. [18]
conducted a unique study in 2008 comparing the operating efficiency of newly purchased versus
the existing fleet of distribution transformers in electricity distribution companies across the EU.
Figure 4 illustrates the results. On average, the efficiency of distribution transformers in the E.U
has the potential to increase by 0.35%.
Figure 4 Efficiency of distribution sector transformer population and market [18]
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In Figure 3, the sum of the losses of the EHV and HV grids in each country falls in the general
range of transmission losses for transformers and lines expressed in the top row of the figure. For
example, in Germany, transmission losses sum to 37% of the total grid losses which falls in the
range of 13% and 42%. The transmission losses are specifically high in this country compared to
France and the UK, both at 25%. This is due to high international transits and internal transits
from north to south [17]. Similarly, the distribution of losses is not uniform across countries and
depends on the specific grid conditions. For example, in France and UK line losses are higher in
the MV grid compared to the LV grid, and in France, losses in LV transformers are higher than in
LV lines, whereas in Germany losses are highest in LV lines. We discuss further the factors that
affect the losses in each component.
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Lines
Power lines are the primary source of losses as indicated in Figure 3. Power line losses can be
considered almost entirely as variable losses and are proportional to the conductor resistance and
to the square of the current. Resistance of the line is equal to
, where:
L is the length of the line, thus long lines have a higher R and higher losses
A is the cross sectional area , thus thicker lines lead to decreased line losses
the resistivity, which depends on the material of the line (copper or aluminium) and its
temperature, hence a heated line leads to higher losses
The current represents the power flow fluctuation and has a quadratic effect which is discussed
below.
Marginal vs Average losses
The currents in a system vary based on the system loading. The variable losses are the sum of the
losses for all operational instances, comprising of operational instances of high loading (marginal
losses) and of normal loading (average losses). Average line losses are more often used when
estimating power losses, due to the fact that they are measured by meters, while measuring
marginal line losses requires advanced and expensive meters [19]. Since variable losses are
proportional to the square of the current, marginal losses have a much higher contribution to the
total sum. This is shown as an example in Figure 5 where the losses from 3 cases of system
loading are presented. In all cases the same energy is transported, but the loading profiles present
a different variability. Smoother loading profiles lead to significant losses reduction, up to 20%
for a flat profile.
Figure 5 Impact of smoothing the system loading on the reduction of variable losses
Therefore, losses increase disproportionally when the system loading is higher, which is actually
when they cost more, since normally these are the cases when power prices are higher.
Furthermore they create an increase in the system loading exactly at the times when there are less
available generation resources. In this respect, efficiency measures that help reduce the marginal
loading of the system would have a higher contribution to the reduction of system losses [20].
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2.1.4.
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Role of DG and grid management
In the latest years, distribution networks experience increasing penetration levels of distributed
generation (DG), in the form of small-to-medium size generating units connected in medium and
low voltage networks. A large share of these units consists of renewable generators, namely wind
turbines, CHP or PV panels. The general expectation has been that locating generation closer to
demand could reduce losses since the distance over which electricity is transported is shortened
and the number of voltage transformation levels is reduced. [21] However, this is only true when
the energy generated by DG and variable RES (VRES) units is consumed locally (synchronised
to load, local balancing). [22] In reality, such local balancing does not occur, either due to lack of
a proper operational market framework or due to the stochastic nature of the prime energy mover
(in the case of VRES). This unbalanced operation can lead to increased network flows (and thus
losses), often translated into reverse flows from distribution networks to transmission systems.
Figure 6 presents the expected effect of increasing shares of non-synchronised DG to distribution
network losses. In particular, losses are expected to decrease for low penetration levels, due to the
fact that no significant reverse flows are expected but after a certain level they increase due to the
increased power flows they incur to the system. For example, [23] shows a study case in a LV
grid in Switzerland where grid losses reach a maximum reduction of 20% at 25% PV penetration.
At a penetration level of 50%, they are equivalent to the case when there was no PV and further
increase if more PV panels are installed.
Figure 6: Impact of increasing DG penetration level and asynchronism of generation and demand to grid losses [12]
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2.1.5.
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2.1.6.
Reactive Losses
.
Figure 7: Voltage profile of the test feeder for different generating scenarios [25]
Figure 7 demonstrates that all possible load/DG scenarios need to be studied to assess the
decrease of reactive losses in the system. In this specific case a 3.2 MW DG capacity
synchronised with the load would lead to almost no voltage deviation, hence minimizing
reactive losses. However, in reality, utilities do not have control on the allocation or the size of
DG that will be installed in their distribution grid. Therefore different voltage support actions
with different impacts are used. We discuss them in the next section on voltage optimisation.
9
Power factor is a ratio between the real power and apparent power flowing through a conductor.
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Currents in electricity systems comprise of the vector sum of two components, active
(responsible for the transmission of active power) and reactive currents (responsible for the
energy alternately stored and released by inductors and/or capacitors). Reactive losses are due to
the circulation of reactive currents, which are increased when the power factor9 is deviating from
unity, and when voltage deviations are observed. Local voltage control actions (reactive power
injection or absorption) are the most widely accepted means for supporting voltages and reducing
reactive losses in distribution networks. In traditional distribution networks (comprising mainly
of loads), the voltage drops at the end of the line due the consumption of reactive power by the
line when it is highly loaded. The placement of capacitors for local injection of reactive power to
compensate for those losses is the state of the art solution for voltage support. In [24] it is
demonstrated that the use of such device reduce reactive losses by up to 45% compared to the
case where reactive power is transmitted from an external grid to compensate for the voltage
deviation. In distribution networks comprising of loads and DG, situations of over-voltages are
observed if the DG output exceeds the load. In [25] a case study analyses the effect of DG on the
voltage deviations of a 10 kV test distribution feeder with five equally distributed load of 0.5
MW each. Variable capacities of DG are connected to node 4.
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2.1.7.
Imbalances
Electricity networks comprise three phase systems which (especially in distribution grids) serve
single or dual phase loads. Imbalances in the loading between the three phases lead unavoidably
to increased currents in at least one phase, which increase marginal losses. Typically,
imbalances occur on all parts of the low voltage network due to customers who use one or two
phases with different load consumptions. In order to rebalance the network it is necessary to
reconfigure phase loading to ensure that this captures the development of loads. This is
normally done based on maximum customer load and cannot capture the fluctuations of load
profiles over time. In this respect, some imbalance is unavoidable [3].
Life cycle assessment: impact of losses on investment
decisions
Losses are one key contributor to operational expenditures in power networks. Typically, optimal
investment decisions translate in finding the components configuration that minimise the sum of
initial investments and lifetime costs (Lifecycle costing – LCC). Due to the long lifetime of the
network assets, adopting a LCC approach that includes losses can steer decision making when
included in investment analysis. In the case of transformers, LCC shows that the purchase of
energy efficient equipment could be optimal decision regardless of the higher initial capital costs.
[18] Similarly, in the case of lines, and due to the characteristics of variable losses, a LCC
optimal decision leads to increased line capacities for reduction of losses. In distribution network
design, the inclusion of the cost of losses leads to much lower utilisation rates of the assets (thus
higher installed capacities) in contrast to the practice of using the assets as much as possible to
increase capital efficiency. [26] Consequently, there is a trade-off between the cost of financing
surplus capacity and the cost of losses.
2.2.
Energy efficiency measures in electricity
networks: categorisation and potential
Figure 8 presents the key energy efficiency measures and their categorisation in two main
categories, a) the ‘traditional’ measures of component replacement, and b) measures that achieve
a reduction of losses by an improved management of the power system, either by controlling the
network infeeds (local balancing) or by grid management actions (such as network
reconfiguration, voltage control, or elimination of imbalances).
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2.1.8.
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E-EE grid
Measures
Component
replacement
System
management
Feed-in
EE
Transformers
Increase Line
capacity
Increase
Voltage level
Grid Mgmt
Control
DG
Demand
Response
Energy
Network
Storage
Mgmt
Trafo
VRES
switching
Controllable
(micro-chp)
Network
reconfiguration
CVR
Voltage
Balance 3-ph
line loading
Control
On Load Tap
Changing Trafo
Reactive power
compensation
(e.g. DFACTS)
Figure 8: Categorisation of key energy efficiency measures in electricity networks
The loss reduction potential of each measure can be in principle analysed by the factors presented
in the previous section. Table 4 presents a mapping of the energy efficiency potential of each
measure with respect to the categorisation of losses presented in section 2.1 (table refers to
distribution networks, as they present the highest share of losses in the electricity system). As can
be seen, each measure affects a set of factors and therefore the potential of each measure will
depend on how losses are mapped in the specific grid. The table presents two dimensions which
together map the potential, firstly the applicability of each measure on different voltage levels
and secondly the effectiveness for loss reduction when the measure is applied. The combination
of the two dimensions allows the estimation of the potential, e.g. measures of high effectiveness
but of poor applicability would rank low on potential.
Concerning variable losses, a key differentiator is the point where the measure is applied. System
loading at each voltage level corresponds to the sum of the loadings from the networks in
underlying voltage levels. Therefore, measures generally affect losses from the network level
where applied and upwards, making energy efficiency measures that apply to lower voltage
levels the most efficient. A typical example on this is demand response and demand flattening
actions: applying such measure to LV networks will lead to a flattening of profiles to the higher
system levels also, inducing a chain of loss reduction towards the upper levels. This also means
that losses at the LV grid will only be influenced by measures at this voltage level, such as
customer side demand response.
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DER Voltage
control
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Table 4: Mapping of the energy efficiency potential of each measure (focus on loss reduction in electrical distribution
grids)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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2.2.1.
Energy Efficient transformers
The key mechanism is the replacement of transformers with energy efficient ones 10, which in
most cases are larger and with higher capital cost. As transformers are responsible for both
variable and fixed losses in the system and their replacement is easier than changing cables or
lines, this option presents a good loss reduction potential, and affects almost all factors of losses,
as shown in Table 4. As shown in Figure 3 most losses in transformers occur in distribution grids,
therefore the potential of the measure in lower voltage levels is higher.
The lifetime of transformers is typically around 40 years. In many cases however, assets are still
operational beyond this point, but with significantly lower efficiency levels. As shown in [18],
20% of the oldest distribution transformers (installed before 1974) are responsible for 35% of the
fixed losses and 30% of the variable losses in the EU, approximately a total of 38 TWh of losses
per year. Replacing them with energy efficient transformers can help reducing these losses by
80% [5], leading to an annual saving of 30TWh, equivalent to the annual electricity consumption
of Denmark [27]. Furthermore, [28] quantified the impact of implementing the MEPS 11 of the
Ecodesign Directive till 2025 and found that energy savings of 84 TWh could be achieved
compared to a business as usual scenario.
Western Power Distribution (DSO in UK) conducted a cost and benefit analysis including the
values of losses to know whether their transformers needs to be replaced. [3] The outcome of the
study shows that it is only beneficial to oversize their smallest units (pole or ground mounted)
and to replace earlier than planned all pre-1958 ground mounted distribution transformers. The
expected savings per annum reaches 2.76 GWh, 97% coming from the replacement of the old
transformers.
1010
Energy efficient transformers have reduced variable and fixed losses. To reduce variable losses, the resistance
of the wires needs to be decreased, which can be done by increasing the cross sectional area or by using materials
with a lower resistance. To reduce fixed losses, one way is to use materials with better magnetic properties.
11
This option of the Mandatory Energy Performance Standards requires newly installed transformers to follow Top
Efficient level of EN 50464-1 for Tier 1 (2015) and Least Life Cycle Cost for Tier 2 (2020).
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The asset replacement strategy depends on the state of the population, characterized by the age,
size and type of transformers. For an optimal investment decision the cost of losses should be
taken into account in a full LCC analysis. Such LCC estimation shows that efficient transformers
have a payback period of 2-13 years compared to less-efficient models when the cost of the lost
energy is included to the cost of purchase [5].
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2.2.2.
Increasing voltage level
Increasing voltage level reduces the currents required to distribute the same amount of electricity,
increases current capacity of the grid and reduces substantially voltage drops and line losses. It
presents further indirect benefits such as reduction of probability of short circuits, increase of
quality of supply and it increases the DG hosting capacity. The measure typically applies to MV
networks, where multiple voltage levels are possible (e.g. 10kV and 20kV). In cases where the
insulation coordination allows both voltage levels, it can be applied by changing only the
transformers. In most cases however this is not the case and a full renewal of all network
components is necessary, which implies high costs. The measure is particularly interesting to
aging MV networks as it comes at little extra cost to up-rate the voltage [5].
The measure was applied in large scale in Ireland for upgrading the MV rural networks from
10kV to 20kV [29]. These networks were designed in the 50’s for lower typical household
consumptions and load densities, and were severely loaded and experiencing very poor voltage
regulation. The costs of 20kV conversion were little more than those of rebuilding in 10kV, yet
the voltage drop were halved, thermal capacity doubled and losses reduced by 75%.
2.2.3.
Increasing line capacity
Increasing cross-sectional area of cables leads to reduction of variable losses. Typically, doubling
cable rating leads to a reduction of losses by a factor of four. However, once a cable is installed,
it becomes expensive to make alterations to the cable, since civil works cost element of replacing
a cable outweighs any loss reduction benefits. The key opportunity to reduce losses therefore
exists at the time that the cable is initially installed or replaced [3]. An alternative approach is to
make use of high-temperature superconductors (HTS) which present no resistance when they are
cooled down at -180°C and can carry five times the current of a conventional cable system with
the same outer dimensions. The losses from this system are due to the energy needed to operate
the cooling mechanism. [30]
As shown in Table 4, the applicability of cable up-rating is deemed medium, and the measure
should be applied in cases on new line installations or replacements. Applicability of HTS is
deemed rather low, mainly for cases of high currents that need to be transported over relatively
short distances. Both measures have a high potential to reduce variable losses.
A CBA conducted by Western Power Distribution showed that is beneficial to uprate all the
existing MV underground cables from 6.6kV to 11 kV. The planning is to replace 700 km of
cables per year and savings are estimated at 3GWh per year. The study also showed that it is not
beneficial to uprate the 11kV network as these higher voltage network cables support adjacent
networks in times of fault. [3]
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As shown in Table 4, the applicability of the measure is mainly on MV grids and is deemed
rather low since only a limited number of networks will fulfil the respective conditions for
applying this measure. On the other hand, the potential of the method on reducing variable losses
is high, in the cases this solution is chosen.
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A 1 km 10 kV HTS cable was installed in 2014 to replace several 110kV underground cables
connecting two 10 kV substations in Essen Germany. The cost of the energy needed to cool the
cable down to eliminate its resistance over its lifecycle is found to be 15% lower than the cost of
compensating losses in conventional 110 kV cables. HTS are mentioned as the best technical and
economically viable solution to avoid the necessary extension of the 110 kV grid in urban areas.
[30]
Feed-in control
This measure refers to applying control actions for local balancing of demand and supply in
distribution grids, which leads to reduction of variable losses due to two key factors, the
reduction of energy transportation distance (energy is consumed locally) and the reduction of
marginal losses (by a flattening of flow profiles). This local balancing can be performed by three
options for control actions, of supply (DG), of demand (demand response actions) or of energy
storage (can be considered as supply and demand)12. Under this measure the control of active
power is considered. (Control of reactive power of DGs is analysed under the Voltage
Optimisation section).
The potential of this measure depends on three key factors, namely the ability for spatial and
temporal matching and the underlying technical limitations.
Spatial matching: it relates to the system architecture, namely DG penetration levels and
relative position of dispersed supply and demand; a general rule of thumb is that the measure
is more effective when supply and demand are close and when it is of similar rating.
Temporal matching: it refers to the operational framework that should coordinate local
balancing; such a framework is most of the times non-existent. Network losses are generally
reduced for low DG penetration levels, mainly due to the fact that independent of the
coordination scheme generation does not exceed demand. For higher penetration levels
however, this lack of coordination translates into reverse power flows and to a gradual
increase of system losses (see Figure 6).
Technical limitations: refer to the specific flexibility characteristics of the technology which
reveal the degree of local coordination that can be achieved. Such limitations include DG
controllability, demand elasticity and potential for peak shifting/clipping and the size and
flexibility of the storage devices.
12
New demand such as Electric Vehicles and Heat pumps are included in these categories, but with specific
flexibility characteristics and limitations due to their primary operation.
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2.2.4.
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This impact is discussed in [31], where the potential of demand response from residential
customers in reducing marginal losses of a Swedish distribution network is investigated. Results
show that this reduction varies from 4%, (if 10% of the peak load is shifted) to a maximum of
18%, if the load profile is flattened. The Model City Mannheim project proved effectively after
having developed, installed and tested smart control systems in 1000 households that the price
elasticity of customers to variable tariffs was 10% [32], which means that 10% of the demand
could be shifted when the price of electricity increased by 10%. In other terms, the 4% reduction
of marginal losses mentioned in [31] is feasible. This translates into annual savings of 350 MWh,
equivalent to the electricity consumption of 55 households in Sweden [33].
The IMPROGRESS project analysed the impact of distributed generation on three distribution
network in Germany, Spain and Netherlands [34]. The different characteristics of each area were
taken account: medium and low voltage networks, urban and semi-urban areas, number of
customer, shares of wind and PV and CHP and a forecast for load demand and DG penetration
till 2020 was applied. The areas studied are Aranjuez in Spain (industrial, wind and CHP),
Mannheim in Germany (residential, lots of PV roof and micro-CHP) and Kop van Noord (lots of
wind and CHP) in the Netherlands. The feed-in control action consisted on one hand of curtailing
VRES or storing the excess power in an electric heat boiler and on the other shifting the demand
from periods with low DG penetration to those where most CHP units were running. The study
showed that if feed-in control is practiced instead of expanding the LV grid to cope with the
forecast growth of DG until 2020, 97 €/kWDG in Netherlands can be saved, 77 €/kWDG in
Germany and 73 €/kWDG in Spain. The potential savings calculation includes the Net Present
Value of the electrical losses that would have been caused by the increasing penetration levels of
DG in each distribution grids.
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As can be seen in Table 4, although VRES are generally present in MV and LV grids, their
effectiveness to perform local balancing actions is deemed rather low, mainly due to the fact that
such actions translate into reduction/curtailment of active power production. Controllable DG
present a medium effectiveness, since although they are technically capable, they cannot perform
such actions due to lack of enabling operational framework. Demand response presents a high
applicability and high effectiveness on reduction of technical losses, which however depends on
the technical potential of demand (peak shifting/clipping potential). This option presents also a
medium potential for reduction of non-technical losses, since they are most of the time combined
to smart metering infrastructure that enables better monitoring of the creation of non-technical
losses. Finally, energy storage presents a high effectiveness on local balancing but a rather low
applicability due to the low implementation of storage in distribution grids.
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Within [14] the German Federal Ministry of Economics (BMWi) examines various measures to
improve the integration of VRES into the distribution grids. One of the key findings is that the
curtailment of 1% of the annual feed-in of VRES reduces the necessary grid extensions by 30%.
A curtailment of 3% of the energy annually produced by VRES would be sufficient to avoid 40%
of the required grid extensions. The effectiveness of curtailment measures rapidly decreases if
more than 3% of the annual feed-in of VRES is curtailed.
2.2.5.
Transformers switching
Fixed losses can be reduced without replacing equipment by reducing the number of energised
transformers in the system. This can be achieved by eliminating transformation steps or by
switching off transformers. Elimination of transformation steps could be achieved by direct
coupling of higher voltage levels to lower ones without the use of intermediate transformers (use
of a single transformation step). Alternatively, switching off transformers could be possible in
periods of low demand for configurations where multiple transformers are required in a
substation to meet peak load or for redundancy. This can be achieved via an ‘Auto Stop Start’
System that allows to periodically de-energise redundant transformers in substations.
As shown in Table 4, the potential of the measure is high on reduction of fixed losses. In addition
to reducing the fixed losses, this measure also reduces the variable (and marginal) losses by
improving of the loading of the energised transformers. The effectiveness of the measure in
different voltage levels depends on the redundancy configurations used. In this respect, the
measure is especially effective in MV or LV networks where redundant transformers are often in
use.
Case studies show a 75% loss reduction in MV networks, equivalent to the loss reduction
achieved by upgrading the 11kV network to 20 kV, however at about 1% of the upgrade cost.
The project is in execution phase led by the Scottish and Southern Electric Power Distribution
and will end in 2018 to validate the scale of loss reductions (640 MWh/year) and to confirm that
repeated switching did not impact the asset lives and the supply security. [36].
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In [35], energy storage is used to shift load from peak to off-peak period and the impact on
reducing transmission and distribution losses is analysed. The authors developed a set of
normalized charts to quantify the saved T&D losses as a function of the size of the energy storage
device, its efficiency and the load shifting potential (i.e. the ratio of the off-peak to peak load).
Based on an existing 1 MW battery storage system installed for shifting a peak load from 20 MW
to 6 MW in a 12 kV distribution feeder in Virginia USA, the authors estimate that 1 to 3% of
T&D losses could be saved which translates in 180 to 330 MWh year of energy saved including
the storage losses. The authors demonstrate that it is more beneficial to distribute small size
storages at different locations rather than using a big size at a single site to maximize the
reduction of T&D losses.
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2.2.6.
Network reconfiguration
As shown in Table 4, the measure involves reduction of variable losses mainly in MV networks,
where such configurations are employed and its potential is defined as medium. The potential
depends on the reconfiguration frequency and the spatial and temporal variations of power
demand. A limitation may be if the switch-disconnector equipment is designed for a limited
number of operations per lifetime (e.g. 1.000) [15], which limits the applicability of control
actions. Typically, for systems with high penetration levels of intermittent DG (especially wind
generators), which experience higher and random spatial and temporal demand variations, higher
switching frequencies are necessary.
Results from field pilot projects reported in [15] show that network reconfiguration may lead to a
theoretical reduction of losses of 40% for an hourly reconfiguration (which however cannot be
applied due to limitations of the switching equipment), and up to 20%, 10%, 4% if the
reconfiguration is made on a weekly, seasonal or yearly basis respectively.
2.2.7.
Conservation voltage reduction
Conservation Voltage Reduction (CVR) is a reduction of active power delivered to demand
resulting from a reduction of feeder voltage within the operational ranges. CVR schemes
typically contain two fundamental components: a) reactive power compensation, achieved
through the operation of shunt capacitors in order to maintain the power factor at the substation
transformer within a prescribed band, and b) voltage optimization, achieved through the
operation of substation voltage regulators in order to regulate the voltage at specific end of line
points within a prescribed range. In this way the peak load and the annual energy consumption
are reduced. [9] When applied on LV networks, the measure can be applied by changing settings
at the primary substation [3].
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The measure involves changing network topology at the distribution level dynamically by using
automated switching actions. Typical implementations are Smart Feeder Switching (SFS) and
Advanced Distribution Management Systems (ADMS). The configuration of the network has an
effect on losses in terms of the distance the electricity is transported. MV networks are typically
configured as open loops and are controlled in order to be able to isolate faults and restore power.
As the demand changes spatially and in time, it is often the case that the configuration of a
network is not the optimal one for the specific demand situation. There might therefore be some
scope for reducing losses by reconfiguring the network, to provide shorter, more direct paths to
where highest demand is situated. SFS and ADMS are typical implementation for performing
automated switching actions on distribution feeder systems enabling a reduction of distribution
system losses [37], [15], [38].
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The applicability of the method depends on the type of load in the system. The method typically
applies well to ‘voltage dependent’ loads such as filament lamps and resistive heating13.
However, increasingly more load is ‘voltage independent’, as it is fed via a switched mode power
supply, which effectively changes its impedance based on voltage (such as HF fluorescent, LED,
PCs VFD fed motors). Therefore lowering voltage may not, in all circumstances, lead to the
demand savings as desired and could actually increase losses. [2] Therefore, the measure is
considered of low applicability and medium effectiveness, as presented in Table 4.
2.2.8.
Voltage optimisation
Voltage optimisation refers to reduction of reactive losses by the application of local voltage
control actions (reactive power injection or absorption) aiming at local correction of the power
factor. The placement of capacitors for local injection of reactive power is the state of the art
solution for voltage support for passive networks (networks including mainly loads). In
distribution networks comprising of loads and DGs, the fluctuating generation profiles lead to
situation of over-voltages (when local generation exceeds demand) or under-voltages (in
situations of high demand and no DG production). Voltage support actions should therefore be
performed dynamically and react to the conditions in each operational instance in order to
optimise the feeder voltage profiles.
This is made possible through recent improvements in sensors, communications, control
algorithms, and information processing technologies that allow monitoring voltage levels
throughout the distribution system. This information is sent to devices that can adjust voltage
regulating equipment and capacitor banks on distribution feeders in near-real time enabling quick
adjustments in response to constantly changing load and voltage conditions. Options to perform
such actions are installing smart reactive power compensation devices (e.g. D-FACTS, see [41]),
using on-load tap changing (OLTC) voltage control actions at the distribution transformers [42],
or by using the capabilities of converters for power factor correction [12] in the case of converterconnected DG14.
13
As the resistance of these devices is largely fixed, applying a lower voltage reduces the current
drawn, less power is transferred and hence overall load is reduced.
14
Typically converters can adjust the power factor at the point of connection, by changing the injection
or consumption of reactive power.
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According to [39], utilities applying CVR may expect to see 1% reductions in electricity
consumption for every 1% reduction in voltage. Detailed analysis of CVR schemes in US show
that they can lead to peak demand reduction of 1 to 2.5% [39] and annual energy reduction of 4%
[9] Western Power Distribution is planning a voltage reduction trial in the South Wales area on
their MV networks from 11.4kV to 11.3kV and anticipates reduction of 95MWh which will
reduce the customer bills by a calculated €13M each year and carbon emissions by 41 000 tonnes
a year. [40]
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In [39], the results from 26 voltage optimisation programs are reported. The voltage optimisation
analysis focused on CVR and on reduction of energy losses along feeders, by automated
switching of capacitor banks. Results reported that for the 31 feeders under investigation half of
witnessed line loss reductions in the range of 0% to 5%, and 5 feeders experienced loss
reductions greater than 5%. Feeders with the worst baseline power factors (i.e., those with the
highest amount of inductive loads) showed the greatest reductions in line losses.
Overcompensation was observed in some feeders, which resulted in line loss increases.
In [12] a study case is presented, investigating the role of reactive power compensation coming
from DG on decreasing grid losses in the North of Germany. The analysis involves two MV grid
areas of similar size in load and number of connected customers but with different DG
penetration levels with respect to load demand (Area 1 with 45% and Area 2 with 146%). The
DG mix analysed consisted of 90% wind power plants and 10% biomass plants. The results
shows that grid losses would decrease by 15% in Area 1 if DG plants were adapting their power
factor between a ±0,05 window but would increase by 3% in Area 2. The results confirm that the
effectiveness of reactive power provision from DG plants is dependent on the grid topology and
DG penetration level.
In [42] a study case is presented, investigating the role of dynamically controlled OLTC on
decreasing grid losses. Six additional wind generation sites of 14 MW are connected to a generic
MV radial distribution system in the UK which needs to supply an annual energy demand of 215
GWh and a peak load of 38MW. If the wind generation sites are operating at a fixed unit power
factor, the OLTC helps reduce grid losses annually from 2,8% to 2.3%, saving 1 GWh per year,
equivalent to the average annual consumption of 240 houses in the UK [43]. [14] states that a full
penetration of OLTC in Germany would reduce the average yearly grid expansion costs by 10%.
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As shown in Table 4, there are difference between the applicability and effectiveness of the
options. Smart reactive power compensation devices are applied mainly on MV grids but their
applicability is considered medium mainly due to cost limitations. Their effectiveness is high,
since they can be applied to specific grid points. The applicability of OLTC is considered
medium and their effectiveness is also deemed medium, since their operation can be limited in
cases when multiple feeders are connected to the transformer (voltage control actions from
OLTC affect all feeders at the same time and this may lead to conflicts). [25] DER voltage
control is considered as a highly effective option with medium applicability mainly due to the
fact that DER should be converter connected and because the effectiveness of controlling voltage
profiles by controlling reactive power from DG is best applied to feeders with low resistance to
reactance (R/X) ratio [23]. As shown in [23] reactive power control from PV panels in a LV grid
(<1kV) in Switzerland would not be effective if the line diameters connecting the panels to the
feeder were not larger than 50 mm2, i.e. the R/X ratio lower than 5.
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2.2.9.
Balancing loading of lines
As mentioned, imbalances are a common phenomenon in LV networks where single- or doublephase customers are connected to the three-phase system. These imbalances lead to having one
phase carrying higher currents, giving rise to variable losses. The measure is to statically or
dynamically transfer loads from one feeder to another to balance the total load across multiple
feeders and transformers. As the demand changes in time, optimally balancing should be
performed in short time periods and reacting to demand changes. [2]
As discussed in [3], imbalances in the LV network can lead to neutral currents of around 35% of
the phase current. The annual potential savings from imbalance correction in this respect are in
the order of €70 to €110 per kilometre of LV network. An analysis of the effect of load balancing
on the reduction of power losses in 12 feeders of a LV distribution power network in Russia is
presented in [44]. The results show that the reduction of technical power losses after load
balancing varies from 0% to 16% depending on each feeder configuration. A total of 13 MWh
could be saved per year equivalent on average to the annual consumption of 100 customers. It is
important to note that this specific regional power network already experienced high distribution
losses of 18% in 2007. Reference [45] suggests the use of three-phase balancing PV units that are
able to inject more power in one phase than in the other phases compared to single-phase PV
units that inject only in one phase. The case study of typical Belgian LV network proves that the
three phase balancing PV units could help decrease grid losses by 30% saving 7 GWh per year.
2.3.
Regulatory regimes to promote loss reduction
in electricity grids
2.3.1.
Regulatory incentives: basic options
Various regulatory incentives exist to motivate network operators in investing in energy
efficiency (particularly loss reduction) in their networks. Some key options are reported below.
2.3.1.1.
TECHNICAL STANDARDS FOR CONDUCTORS AND TRANSFORMERS
A very effective measure that leads to an overall improvement in efficiency is setting mandatory
standards on a European level. In such cases, the national regulatory design should allow a higher
CAPEX allowance to grid operators as savings in OPEX will over-compensate them in the long
run. Based on the EU Ecodesign Regulation, new power transformers installed after July 2015
need to meet a minimum energy efficiency standard.
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The applicability of the measure is considered high and is limited to LV networks, as presented in
Table 4. Its effectiveness is considered medium, depending on the frequency of balancing that
can be achieved.
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2.3.1.2.
FINANCIAL INCENTIVES
In general financial incentives to increase efficiency can be of various forms, e.g. grants, loans or
tax breaks. They can also be set via lower interest rates for energy-efficient investments. To
further motivate and simplify investments, specific energy-efficiency budgets can be set up.
Given that the transmission and distribution networks are regulated industries, the most
appropriate way to deliver financial incentives would seem to be via the existing regulations.
Another option regulators can use is setting individual targets for network losses for grid
operators which can be a mechanism that financially penalises or rewards deviations from the
target. This can be a strong incentive for grid operators to improve in operational performance
while leaving them free space to decide on the most effective measure.
OBLIGATION OR CERTIFICATE SCHEMES
With obligations or certificates for energy efficiency and energy savings, grid operators are given
specific shares of losses (and savings) that they have to meet, just like the option of financial
incentives. In many schemes, there is also the option to trade certificates to meet the obligation.
In case of not conforming to the obligation, penalties (financial or not) can be set to promote
actions to meet the obligation which is a key difference to financial incentives.
2.3.1.4.
VOLUNTARY AGREEMENTS
Regulators can start by setting voluntary agreements with grid operators which would comprise
non-binding guidelines e.g. for a maximum share of grid losses in power transmission and
distribution. It can acquire the market participants’ attention on the issue and provide alignments
on grid operation.
2.3.1.5.
INFORMATION CAMPAIGNS TO OVERCOME LACK OF MOTIVATION
There are two potential areas where lack of information might influence: with the regulators not
understanding completely the case for energy efficiency and not setting the proper regulatory
framework and with grid operators not having all the information for a business decision. An
information campaign about best practices set up by the regulator can overcome such barriers.
2.3.2.
State of play of regulatory mechanisms targeting loss
reduction
The following key insights regarding regulatory mechanisms to support grid energy efficiency
measures (particularly regarding loss reduction) have been derived from the survey with
European system operators carried out within this study (see ANNEX II):
The incentive to pursue specific investments for loss reduction is strictly related to the
presence of regulatory incentives. A wide variety of regulatory frameworks are present in
European Member States, which in some cases do not explicitly support energy efficiency
measures.
Energy efficiency (and particularly loss reduction) is the main driver of investments when
there is the need to comply with regulatory requirements and quality standards (e.g.
EcoDesign directive). In general, loss reduction is considered as one of the variables of a
larger investment optimization plan.
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2.3.1.3.
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Energy Efficiency measures which require the exploitation of DER flexibility have still
limited diffusion in many Member States, often just at pilot level. Their implementation
would require the definition of new regulatory mechanisms over how DSOs can procure and
activate DER flexibility.
Table 5 presents a synthesis of the different mechanisms in force among the respondents.
Bonus/malus following the level DSOs from the Slovak Republic, Sweden (for 2016), the
UK and Portugal report that they have specific incentives
of losses
that directly target loss reduction, in form of bonuses or
penalties depending on the perfomances.
Maximum loss threshold
DSOs from Bulgaria, Czech Republic, France and
Germany report that they have an obligation to respect a
given loss threshold, overpassing them leading to audit or
non-recognition of the costs. However, this gives no
incentives to go further if their current loss level is under the
limit.
Benchmark among other DSOs
DSOs from Spain report that loss reduction is addressed by a
benchmarking of all Spanish DSOs on their performance to
reduce losses.
Table 5 - Regulatory incentives for energy efficiency measures highlighted by survey respondents
More generally, it is clear that proper regulatory incentives are necessary to stimulate loss
reduction investments. Absence of such regulation often brings the opposite effect, and acts as
disincentive for network operators. There are two main elements in the tariff regulations which
may act as an incentive or disincentive to invest in energy efficiency measures of the grid:
1)
2)
the way the tariff is calculated, which determines the amount that can be charged to the
consumer; and
Whether there is an incentive regulation for minimising operational costs.
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Cost reduction (Revenue cap DSOs from Bulgaria and The Netherlands report that they
are incentivised to perform losses reductions since it is part
regulation)
of their costs, and since the regulation targets a general
reduction of the cost. Loss reduction is therefore not a first
driver if other cost reduction opportunities are found.
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Tariffs are usually calculated on the basis of all relevant operational costs of transmission and
distribution of electricity. In this respect, grid operators can transfer the costs for purchasing
energy to make up for losses directly to the consumers. A clear disincentive to invest in loss
reduction measures is typically the case when the regulatory framework does not introduce a cap
to the amount of losses that can be transferred to the customer, combined to financial penalties
for trespassing the limit. In such cases, infrastructure investment decisions are based on the
minimisation of capital costs only, without including of the operational life cycle costs. Hence
low cost, and subsequently non-energy efficient equipment is chosen over advanced efficient
equipment.
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3.
POTENTIAL OF MEASURES ON
PLANNING/OPERATIONAL EFFICIENCY IN
ELECTRICITY GRIDS
In this chapter we analyse the impact of measures proposed in chapter 2 on the broader aspects of
efficiency in infrastructure design and operation.
In particular we consider planning/operational efficiency in terms of:
In this respect, the new options offered by active grid management hold a significant potential in
optimizing the long term planning process. In particular, the use of flexibility of distributed
energy resources is currently being explored as an alternative to traditional grid reinforcements 15
16
. A specific focus on the use of demand flexibility to optimize grid planning decisions will be
carried out in the second part of this study.
Investments in assets replacements could also be optimized. Measures presented in chapter 2 can
in fact contribute to decrease stressful conditions in the grid that could lead to faults of
components and a reduction of their useful life.
Optimization of grid operation - The reduction of losses is one important aspect of the
optimization of grid operation. It has been extensively analysed in chapter 2 and will not be taken
into account in this chapter. Other value streams for optimization of grid operation (OPEX
reduction) include:
Reduction of outage times (e.g. via implementation of network automation)
Improvement of quality of supply and reduction of customer complaints
Reduction of maintenance costs (e.g. by minimizing stressful operating conditions for grid
assets)
…
In the following sections, we analyze in detail the factors affecting the cost-effectiveness of each
of the proposed measures considering key aspects of planning/operational efficiency.
Implementation examples and best practices collected in the stakeholder survey (ANNEX III)
and in related literature are used for illustration.
15
Regulatory Recommendations for the Deployment of Flexibility, EC Smart Grid Task Force Expert Group
3, January 2015
16
Study on the effective integration of Distributed Energy Resources for providing flexibility to the
electricity
system,
Report
to
The
European
Commission,
April
2015
https://ec.europa.eu/energy/sites/ener/files/documents/5469759000%20Effective%20integration%20of
%20DER%20Final%20ver%202_6%20April%202015.pdf
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Optimization of investment planning - Traditionally, new capacity investments (grid
reinforcement or expansion) have been directly related to the increase of demand. With the
growing penetration of distributed generation, increasing the hosting capacity of DG is also
becoming an important driver of grid planning decisions.
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3.1.
Impact of selected energy efficiency measures
on operational efficiency
3.1.1.
High efficiency transformers
Planning/Operational efficiency
Investment planning
Assets to deploy
Grid operation
Technology
standardization
Technology
standardisation
Lighter weight
Reduced fault rate
Hardware
Software
Communication
High efficiency
Transformers
Possible benefits on planning/operational efficiency
Some benefits could be achieved in term of investment
optimization. Indeed, limiting the number of
technologies present on the grid may lead to stronger
purchasing positions in the market, depending of the
local market organisation, and may also lead to O&M
cost reduction (less various types of operation).
In case of pole-mounted transformers, lighter
transformers might also reduce the need for
investments in pole replacements.
Enexis (The Netherlands) is replacing older
MV/LV transformers with existing but more
recent transformers (left over from other
regular replacements, such as capacity
increase). 150 transformers have been already
replaced out of their 53000 MV/LV
transformers, at an average cost of 5300
€/transformers. 0.1% of loss reduction is
estimated to results from those replacements,
which shows a significant potential since only
0.3% of the assets have been replaced.
Benefits on grid operations
Aging transformers might present a higher fault rate than up-to-date transformers. Replacing
most problematic assets will save O&M cost. According to the economic assessment carried out
by the Asia-Pacific Economic Cooperation [46], replacing transformers in order to comply with
current performance standards leads to long-term savings, including additional operation cost
saving (in each of the 20 studied countries, positive net present values were observed in their
2016-2030 plans).
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Assets to deploy - Investments costs
The costs of a transformer have a wide range of variability depending on the power rating. In
[47],a study from the Indian government, it was showed that cost differences are high between
their “1 star” and the “5 star” transformers of small power rating (50% of difference for a 25 kVA
transformer) but those difference get smaller at higher power (27% for a 200 kVA transformers,
for a cost estimated at 3390 €17 for 5 star transformer). However, according to a study carried out
in the Asia Pacific Economic Copperaiton [46], it appears that, installed capacity being equal, the
cost-effectiveness of switching to higher standard is higher when replacing small transformers.
Cost-effectiveness can be maximized if the measure is implemented within a planned asset
replacement campaign. For instance, when replacing transformers at the end of their useful life,
CEZ (DSO in Czech Republic), has found it economically convenient to opt for upper class
transformers despite a 20% higher price, thanks to energy efficiency improvements.
Expanding line capacity
Planning/Operational efficiency
Investment planning
Standardization
Assets to deploy
Grid operation
Reduced SAIDI
Hardware
Cables
Software
None
Communication
None
Improve voltage quality
Possible benefits on planning/operational efficiency
Benefits on investment planning
Some optimization of investment planning can be achieved via the limitation of the number of
the cables sizes in use. In fact this can lead to standardization of equipment on the network, and
thus to an average purchase price reduction.
Benefits on grid operations
Oversizing the conductors decreases the impedance of the line, resulting in lower voltage drop.
Therefore, a positive impact on voltage quality of the grid should be obtained.
17
Change rate $ to €: 1$ = 0.894 € (11/05/2015)
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3.1.2.
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A limitation of the current flowing through the line could reduce the probability of default (and
the aging of the line). Moreover, by standardizing the line type, higher standardization of field
works could be achieved, reducing the overall O&M costs.
It is difficult to give an accurate cost for such a measure,
since it strictly depends on the implementation
conditions (overhead lines or underground cables, size,
voltage level, type of conductors, etc.). However, the
cost of oversizing line sections can be relatively small in
the case of installation works (particularly in case of
cables) which are already planned. Western Power
Distribution (see [3]) decided to size up their current
standard LV cables to discontinue their smallest-size
MV cables. The study shows that sizing up 95mm²
cables to 185mm² adds a cost of 14€/m18, while
excavation costs range between 70€/m and 140€/m.
3.1.3.
Východoslovenská distribu ná (DSO
from Slovak republic) has increased the
maximal conductor section of their LV
network from 70mm² to 150mm². While the
first driver of this measure was to reach
higher voltage quality, they expect a
nominal losses reduction of up to 20% at
peak load, and 5% on average (depending
on the intensity of operation). The increase
in investment was minimized and managed
by process changes in the procurement
procedure. The asset costs represent up to
10% of the project costs. However, a full
roll-out was deemed to be too costly, due to
costs of other grid elements such as poles.
Increasing voltage level
Planning/Operational efficiency
Investment planning
Grid operation
Deferred capacity
investment
Improvement of voltage
quality
Life time extension
Reduction of DG
curtailment
Technology
standardisation
Assets to deploy
Improvement of continuity
of supply
Increase DG hosting
capacity
Hardware
Fitted lines cables
HV/MV
substations:
Transformers
MV cells
(protections,
switches…)
Rails
MV/LV
substations:
MV cells
(protections,
switches…)
Transformers
18
Change rate £ to €: 1£=1.3793 € (11/05/2015)
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Software
None
Communication
None
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Assets to deploy - Investments costs
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Possible benefits on planning/operational efficiency
Benefits on investment planning
As shown in [49], power line aging is mainly due to its
temperature profiles along its lifetime. This temperature
profile depends on the ambient air, the weather, type of
soil, etc. but also the temperature generated by the carried
current. Generally, less heavily utilized assets present
lower risks of overheating. This could extend their
lifetime.
A higher voltage level might also increase DG Hosting
capacity.
Vattenfal (Sweden) is carrying out an
optimisation of voltages levels in their
regional grids. The analysis is performed
area per area, taking into account the
network topology and the load level. 5 to
15% of loss reduction has been observed in
areas where voltage increase has been
adopted.
Finally, reducing the number of voltage levels across the grid allows stronger standardization of
network technologies. As discussed in the previous section, this can lead to benefits both in terms
of CAPEX and OPEX.
Benefits on grid operations
A higher voltage level typically leads to better voltage quality (less significant voltage drop along
the lines - for example [48] reports that a 10kV to 20kV conversion can halve the voltage
drop/rise for a given power withdrawn/injected).
According to [50], a decentralized grid (more small transformers closer to the consumer), beside
leading to high loss reduction (47% in reported case studies), also improves the reliability of the
system (failure hours/years 4-5 times lower).
Assets to deploy - Investments costs
The cost of this measure strictly depends on the specific implementation scenario. A detailed
study must be carried out to estimate the full cost of such a measure (e.g. assessment of which
additional assets need to be replaced to sustain the new voltage level etc.).
A plan for voltage level increase can also be pursued by targeting a progressive increase of the
share of the HV/MV grid with respect to the LV grid when undertaking new grid investments. In
their replies to the survey, the system operators HEDNO (Greece) and EDP (Portugal) indicate
that their plan of loss reduction foresees a progressive increase of the number of MV/LV
substations.
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Since increasing the voltage level reduces the current flowing through the conductors to meet the
same power, networks reinforcement can be deferred. This is particularly true if only a limited
number of assets need to be replaced. In their plan (see in [48]) to convert their rural 10kV
network to 20kV, the Irish DSO operator ESB Networks aims to double their thermal capacity
while reducing peak losses. Future investments to expand the grid could also be reduced thanks
to an optimization of the number of substations.
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3.1.4.
Demand response
Planning/Operational efficiency
Investment planning
Grid operation
Assets to deploy
Hardware
Software
Communication
Line extended lifetime
SAIDI reduction
Smart Meter
Radio
Deferred capacity
investment
Lower DG curtailment
Sensors for feeders
monitoring
Optic fibre
PLC
Ripple control
injector
DG hosting capacity
increase
GSM
Possible benefits on planning/operational efficiency
The implementation of demand response allows shaving peak loads, reducing the need of grid
capacity increase and possibly extending the lifetime of the assets (by reducing their utilisation).
Demand response could also increase the DG hosting capacity of the grid, by allowing local
balancing of DG units injection. Grid reinforcements might therefore be deferred/avoided.
Detailed analysis of the impact of demand response on grid planning will be carried out in the
second part of this study.
Benefits on grid operations
Demand response can be used to prevent loads in feeders or other assets to reach critical values
that might determine asset faults. In this way, the average SAIDI can be reduced. Moreover,
flexible load control synchronized with local DG units could reduce to a reduction of DG energy
curtailment due to over/under voltage or to line overloading.
Assets to deploy - Investments costs
The on-going implementation of smart metering infrastructure in most European Member States
is providing the enabling infrastructure to carry out demand response activation, increasing the
cost-effectiveness of this measure for energy efficiency.
Other necessary assets to deploy include grid sensors to increase the observability of the grid,
particularly in terms of feeder loading, to adapt the DSOs activation of demand response to actual
grid conditions.
Finally, another possible option for DSOs to activate demand response consists in adopting Timeof-Use tariffs based on ripple control. In this case, DSO’s might have to invest in local ripple
injectors.
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3.1.5.
Feed-in control of distributed generation & storage
Planning/Operational efficiency
Investment planning
Line extended lifetime
Grid operation
Power Quality problems
reduction
Deferred capacity
investment
Voltage problems reduction
Customers’ investments
Lower restoration operations
Assets to deploy
Hardware
Converter with
“smart”
functionalities
Communication
Radio
Optic fibre
PLC
Smart Meter
Storage system
DG hosting capacity
increase
Software
GSM
Extra connections
lines
Benefits on investment planning
An efficient feed-in control (including reactive power management) can lead to capacity
investment deferral, by reducing the peak demand from the MV/LV substation. Reducing peak
demand can also typically reduce the aging of the component.
Benefits on grid operations
As seen in [51], efficient DG converters might provide
compensation in term of harmonics, voltage, reactive
power and phase balancing control. This might reduce
complaints from clients and the number of field
interventions.
Assets to deploy - Investments costs
In the case of a control strategy with direct
communication link between DG units and the DSO
(for DSOs to send set-points for the DG control
system), an important cost item is related to the
communication infrastructure between the DG units
and the DSO.
ERDF
(France)
is
studying
the
implementation of local voltage regulator to
perform reactive power management of DG
on the MV grid. The choice of the final
system has been based on a trade-off between
the gain on hosting capacity and the gain on
voltage losses, and its optimized value of the
reactive to active power ratio and a reactive
power regulation based on voltage. The
system is currently in implemented two cases
on their MV grid. The expectation is a
reduction of 2-3% in losses, and to an overall
cost reduction of 5% compared to a case with
no reactive power control, thanks to avoided
reinforcement. OPEX reduction is also
expected, since the system self-adapts when
new DG are connected.
It is worth mentioning that DG converters on the market are increasingly equipped with built-in
Smart Functionalities, with limited cost premiums. Retrofitting costs need to be foreseen for
older-generation converters to enable them for more advanced feed-in control schemes.
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Storage system may also be coupled with DG units to provide a better feed-in control. In that
case, the cost accrues to the DG owner. According to [52], storage systems could also be installed
by DSOs when demand growth is uncertain, as a temporary solution to defer investment, letting
time to assess which reinforcement are really needed
3.1.6.
Network reconfiguration
Planning/Operational efficiency
Investment planning
Deferred capacity
investment
Grid operation
Reduced fault rate
Reduced restoration time
Line extended lifetime
Improved voltage quality
Assets to deploy
Hardware
Remote
controlled
switches and
reclosers in
substation
Software
Advance
SCADA/DMS
Communication
Radio
Optic fiber
Control
software
PLC
Possible benefits on planning/operational efficiency
Benefits on investment planning
The drivers of automatic grid reconfiguration are typically loss reduction and reduced number of
interrupted customers. However, a better balance between the different feeders reduces the peaks
on those lines, therefore potentially reducing the aging of the lines, and reducing replacement
costs.
Benefits on grid operations
Increasing the number of remotely controlled switches reduces the number of substations to be
inspected when a fault occurs. In a study ( [53]) carried out by the US Department of Energy
within 5 utilities, the use of remotely controlled switches reduced the number of interrupted
customer by 35%. The use of automatic control for fault management leads this number to 55%.
By better balancing the load in the feeders, voltage drop can also be reduced.
According to [54], a simulation of an automation algorithm to control the AES Sul grid (Brazil)
showed that a 8.2% reduction of interrupted customer per year can be achieved. Moreover, the
solution prevents fault occurrences by reconfiguring the grid before a critical value is reached.
The benefits are particularly high in the case of rural networks.
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GSM
Feeders
monitoring
system
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Assets to deploy - Investments costs
The main cost item of the measure is the installation of remotely controlled switches and
reclosers and (if not already available) of the associated communication infrastructure. According
to [37], various U.S. utilities report that they have implemented this measure at a cost ranging
between 18.750 € and 35.750 € per installed switch, including software, monitoring and
communication system. For example, Adams-Colombia Cooperative mentioned a cost of 20.400
€ for an overhead switch and 35.750 € for an underground one. Voltage level is one of the key
drivers of the cost value.
3.1.7.
Switching off redundant transformers
Planning/Operational efficiency
Investment planning
Grid operation
Asset extended lifetime
Assets to deploy
Hardware
Remotely
controlled
transformers
switches
Software
Advanced
SCADA/DMS
Control
software
Communication
Radio
Optic fiber
PLC
GSM
Possible benefits on planning/operational efficiency
Benefits on investment planning
Switching off redundant transformers reduces their use and aging, possibly deferring needs for
replacements.
Assets to deploy - Investments costs
Switching off/on redundant transformers can be performed manually. However, this critically
increases the restoration time in case of the loss of the energized transformer. Implementation of
remote and/or automatically controlled switches is thus to be preferred. The cost of transformer
switches may vary significantly, depending on their power rating.
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However, it is possible to implement network reconfiguration manually, without relying on a
remote communication system, and still capture interesting benefits, particularly on loss
reduction. Enexis (DSO in The Netherlands) (manually) reconfigured the open points in 300 MV
loops out of 44000km of their MV grid, achieving a loss reduction of 0.5%, at a reported cost of
670€/loop (according to their replies to the survey).
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3.1.8.
Voltage optimisation
Planning/Operational efficiency
Investment planning
Line extended lifetime
Increase network
capacity
Increase DG hosting
capacity
Grid operation
Power Quality problems
reduction
Voltage problems
reduction
Facilitate DG integration
Assets to deploy
Hardware
D-Facts: MV/LV
STATCOM,
SSSC…
Capacitor banks
On-Load Tap
change
transformers
Software
Advanced
SCADA/DMS
Communication
Radio
Optic fibre
Control
software
(centralized
system)
PLC
GSM
Voltage sensors
Possible benefits on planning/operational efficiency
Benefits on investment planning
Optimization of reactive power flows allows better
exploitation of line capacity, reducing lines loading and
possibly increasing their useful life. Voltage optimisation
can also compensate the voltage rise/drop due to DG
injection, allowing more units to be connected on the grid.
In [55], it was shown that voltage control through on-load
tap changing transformer was cost-efficient to increase the
grid hosting capacity, deferring expensive line
reinforcement.
Benefits on grid operations
Reactive power management systems can be equipped to
compensate harmonics injections.
CEZ (Bulgaria) plans to install
capacitors and harmonics filtering
equipment
in
two 110kV/20kV
substations and in one 110kV/20kV
substation. CEZ estimates an investment
costs 176k€/substation of expense and
CEZ expects a saving of 474k€/annum,
coming from less power quality
problems to be compensated to the
customers, less complaints over voltage
fluctuation, reduction
of losses,
reduction of equipment overheating and
their aging.
By regulating the voltage level of the grid, complains due to over/under-voltage can be mitigated
and DG curtailment reduced.
Assets to deploy - Investments costs
Different options exist to carry out voltage optimization. In a substation, On-Load Tap Changing
(OLTP) transformers can be installed to keep voltage within an acceptable range while
optimising the operation of the grid. This solution can be more cost-effectively pursued by
foreseeing an OLTP when replacing an existing transformer.
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Smart Meters
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Devices injecting or absorbing reactive power can also be installed in various places of the grid.
Ideally, the best reactive power reduction and power factor correction is performed by placing
compensation devices the closest to the load. However, due to installation and operation cost, a
trade-off is to be found between cost, loss reduction and voltage constraints [56]. The length of
the network and the voltage stability of the system are therefore key parameters.
Different technologies (fixed capacitors banks, STATCOM, etc. ) can be adopted. It should be
noticed that, according to [57], self-commutated compensators have the highest impact on losses
while having a lower cost than synchronous condenser or static compensator technologies.
Manually controlled/remote controlled capacitor banks could also be considered. The cost of the
system will depend on the KVAr rating of the installed devices.
To ensure more granular voltage optimisation, feeders voltage sensors or Smart Meters can also
be installed to adjust the voltage regulation to the actual grid voltage profiles.
Conservation Voltage Reduction (CVR)
Planning/Operational efficiency
Investment planning
Increased network
capacity
Extended lifetime
Grid operation
Reduced fault rate
Power Quality problems
reduction
Assets to deploy
Hardware
On-load tap
changing
transformers
Voltage regulator
Capacitors banks
Software
Advanced
SCADA/DMS
Communication
Radio
Optic fiber
Control
software
(centralized
system)
PLC
GSM
Voltage sensors
Smart meters
Expected benefits on operational efficiency
Benefits on the investment planning
CVR main goal is to reduce energy consumption. Results of such a measure depend on various
conditions such as consumers’ load, CVR technology used etc. On average, a peak reduction
between 0.5% and 4% (depending on the feeder) can be expected, as seen in Denmark ( [58]) and
in the US ( [59] [60]). With a lower peak, the utilization of the line is also reduced, possibly
extending its lifetime.
Benefits on grid operations
As stated in the previous point, the peak reductions due to CVR reduces the utilisation of the line
and its aging. In principle, the probability of default and the number of interventions could also
be reduced.
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Assets to deploy - Investments costs
Conservation Voltage Reduction (CVR) exists in various forms of implementation and is highly
scalable (e.g. it can be implemented feeder by feeder). [60] describes some practical cases in the
US that have shown its low cost to achieve significant benefits (total energy saving cost between
0.01 and 5 cents/kWh following Bonneville Power Administration). To provide an order of
magnitude, Adams-Columbia have deployed a CVR on 10 feeders (including 10 voltage
regulators, 30 monitoring sensors, 10 controlled capacitors banks and 40 solid state variable
capacitors) for 157k€, including software.
3.1.10. Balancing 3-phase loads
Investment planning
Assets to deploy
Grid operation
Hardware
Software
Line extended lifetime
Reduced neutral fault rate
Advanced
SCADA/DMS
Deferred capacity
investment
Power Quality
improvement
LV phases switches
in MV/LV
substation
Feeders load
monitoring system
Communication
Radio
Optic fibre
Control
software
(centralized
system)
PLC
GSM
Three-phases
connections
Phase switches at
connections
Smart meters
Expected benefits on operational efficiency
Benefits on the investment planning
Unbalanced feeders lead to higher currents flowing in one of the single-phase lines, which might
reach its limit faster than the others. Balancing phases might thus extend the lifetime of the lines.
Benefits on grid operations
Phase balancing reduces intervention and complaints due to Power Quality problems. Problems
resulting from current in neutral lines can be reduced (up to 35% of the phase current could
circulate in several lines according to [3]), since no zero current is flowing in a perfectly balanced
system.
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Planning/Operational efficiency
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Assets to deploy - Investments costs
Various techniques can be considered to perform phase balancing.
2)
3)
4)
Phase-switching between single-phase line can already be performed manually on a regular
basis (seasonal for instance) to create a more balanced three-phase system, based on long
term feeders load measurement. It is clearly the cheapest approach but the impact is limited.
The above technique can also be performed automatically, based on load measurements to
optimise the impact.
Switches at connections: a more effective phase balancing can be performed by dynamically
switching the phase a user is connected to, according to the user’s load. This requires to have
a three-phase connection for every single-phase user, and to have Smart Meters to measure
the current. This technique is however much more expensive than the previous one.
Balancing DG injection: according to [61], replacing three-phase inverters by three singlephase inverters can control the injection phase by phase to balance the network.
4.
COST-EFFECTIVENESS OF ENERGY
EFFICIENCY MEASURES IN ELECTRICITY
GRIDS
The goal of this chapter is to analyze in detail the cost-effectiveness of Energy Efficiency for
electricity grids. The excel tool described in annex IV provides a step-by-step decision support to
carry out the assessment.
4.1.
Assessment of cost-effectiveness
Cost-effectiveness of each measure is defined as the ratio between the associated benefits and
costs. This entails looking at the benefits and the implementation costs associated to each energy
efficiency measure. Apart from reducing energy losses, ad discussed in previous chapters, it is
considered that EE measures could also provide additional benefits in terms of reduction of
OPEX (e.g. restoration time, power quality issues etc.) for the DSO and of avoidance of grid
investments (CAPEX reduction).
The analysis consists in individual assessment of all potential benefits and of implementation
costs of energy efficiency measures. The output is a ranking of individual energy efficiency
measures according to their cost-effectiveness.
The proposed methodology is synthetized in Figure 9.
The assessment of the cost-effectiveness of energy efficiency measures has the following
characteristics:
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Ranking of EE measures
Figure 9 - Methodology for assessment of cost-effectiveness of individual EE measures
4.2.
Synthesis of the cost effectiveness of the
measures
Table 6, Table 7, Table 8 – provide a synthetic qualitative indication of levels of benefits and
costs of the different measures. The assessment assumes the stand-alone implementation of a
given measure (no synergies with other measures or other investments) on a per-unit basis.
Clearly, as mentioned, the cost-effectiveness of each measure strongly depends on the specific
grid conditions where it is implemented. Provided qualitative value are only intended to provide
initial guidance in comparing the different measures.
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Focus on individual EE measures – The focus is on detailed assessment of individual EE
measures. The analysis does not consider the perspective of a larger investment plan which
also includes a set of EE measures.
Assessment of grid energy efficiency benefits according to the following key dimensions
- Reduction of grid losses
- Deferral/avoidance of grid investments
- Increase of RES hosting capacity
- Reduction of outages (security of supply)
- Power Quality
Analysis of implementation costs and of factors affecting costs - The assessment of the
investment costs of each measure is done considering the capital and operational costs and the
factors affecting them. Moreover, we also look into options for optimization of investments
(e.g. replacing assets at the end of useful life).
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Component replacement
Measure
EE
Transformers
Loss
reduction
Deferred
investment
++/+++
+/0
RES
hosting
capacity
Security
of supply
Power quality
improvement
Type
Cost
+
+
-Delta cost in case of
Level
Low to
high
planned replacement /
new installation
-Full cost in case of
proactive replacement
Oversizing line
capacity
+++
++
+
-Delta cost in case of
Low to
high
planned replacement /
new installation
-Full cost in case of
proactive replacement
Increase voltage
level
+++
+++
++
+
-Limited adaptation (in
Low to
very high
case of already voltage
-Massive replacement
of all equipment
Table 6 – Efficiency & cost of component replacement measures
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compliant equipment)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Grid management
Measure
Loss
reduction
Deferred
investment
++
+
Network
reconfig.
RES
hosting
capacity
++
Security
of supply
Power quality
improvement
++
Cost
Type
Level
-Mainly software in case
Low to
high
of existing remote
switches
-Full installation of
software and remote
switches
Transformer
switching
+/+++
++
-
-Labour work only in
case of manual switching
Very low
to
medium
-Full installation of new
remote switches
Voltage opt.
+++/++
+
+++
+++
- Mainly software in case
Medium
to high
-Full new equipment
deployment (e.g
capacitor/OLTP)
CVR
++
++
++
++
- Mainly software in case
Medium
of existing equipment
-Full new equipment
deployment (e.g
capacitor/OLTP)
Bal. 3-ph.
Load
++
+
+
++
-Labour work only in
Low to
high
case of manual switching
-Full installation of new
remote switches
Table 7 – Efficiency & cost of grid management measures
Feed-in control
Measure
Loss
reduction
Deferred
investment
+++
+/++
Demand
response
DG control
Security
of supply
+++
RES
hosting
capacity
++
++
++
++
Power quality
improvement
++
++
Cost
Type
Level
Depending on the
regulation
Low to
medium
Depending on the
regulation
Low to
medium
Table 8 –– Efficiency & cost of feed-in control measures
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of existing equipment
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Factors
impacting
the cost of
the
measure
X
X
Feed-in control & storage
Voltage optimisation
Conservation Voltage
Regulation
Balancing 3-phase load
Network reconfiguration
Increase voltage level
X
Demand response
X
X
X
Switching-off redundant
transformers
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Table 9 – Factors impacting the overall efficiency and the cost of the measures
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Factors
impacting
the
efficiency
of the
measure
Load rate
Age of the assets
Fault rate
High harmonics problems
Transformers power rating
Grid length
Voltage issues
Feeders load unbalance
Number of switches
Average restoration time
Presence of a repartition grid
Number of transformers in parallel
DG curtailment level
Distance between DG and consumers
Power factor
DG penetration rate
Number of voltage level
Voltage level
Neutral conductors issues
Overhead underground grid
Urban rural network
Transformers accessibility
Age of the assets
Other replacement/expansion plans
Technology standardization
Asset fitted for an upper voltage
Grid expansion / load growth
Voltage level
DG integration
Penetration of the communication
system
Flexibility market
Smart Meter roll-out plan
DG incentive to customer
Combination with CVR
LV consumers’ density
LV substation density
Expanding line capacity
High efficiency
transformers
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5.
ASSESSMENT OF THE ENERGY EFFICIENCY
POTENTIAL IN GAS GRIDS
5.1.
Understanding Energy Losses in gas networks
Table 10: Length of gas transmission and distribution pipelines in Europe (Source: Gas Infrastructure Europe)
In order to ensure that natural gas remains pressurized during its transmission (and more rarely
during its distribution), compression, by means of compressor stations, is required periodically
along the pipelines.
The following simplified diagram shows a typical natural gas transmission and distribution
network.
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The natural gas transmission and distribution system consists of a complex network of pipelines.
As detailed in Table 10, in the European Union, it represents more than 2150 thousand kilometres
of pipelines.
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Figure 10: Typical natural gas transmission and distribution network
Energy losses due to gas transmission and distribution have been considered in this study to be
natural gas shrinkage (gas losses and own consumption) and own electricity consumption. Figure
11 reports the losses level among representative Member States.
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Figure 11 – Level of losses
19
in gas grids in selected EU Member States
Compressor stations are the largest sources of gas use on a gas distribution and transmission
network. Electricity consumption can be important as well on compressor stations, when
electricity is used to run compressors.
Gas losses can arise from multiple causes. They have been grouped in three categories:
Vented emissions: Direct gas releases to the atmosphere of natural gas resulting from
equipment design, regular process operations, maintenance activities, or emergency releases
(e.g. process designed flow to the atmosphere through seals or vent pipes, equipment
blowdown for maintenance or an emergency, and direct venting of gas used to power
equipment (such as pneumatic devices)).
Fugitive emissions: Unintentional leaks of gas from piping and associated equipment
components (e.g. leaks from valve seals, packing or leaks resulting from corrosion, faulty
connections). Such emissions can occur on many different parts of the network and are very
difficult to estimate.
19
Both technical and non-technical losses, based on the Ecofys analysis from EuroStat database
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Emissions due to incidents: Inadvertent damages to pipelines may also cause gas emissions.
A mapping of energy losses due to gas distribution and transmission is presented in the three
following sections.
5.1.1.
Mapping of losses in gas transmission network
Gas transmission is achieved by means of high pressure underground pipelines. These pipelines
are usually made of steel that is coated and cathodically protected 20.
Figure 12: Mapping of losses in gas transmission network
20
Cathodic protection is a technique of running an electric current through the pipe to prevent corrosion and rusting.
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Figure 12 reports the mapping of mechanisms leading to losses in gas transmission network.
These losses can be either natural gas emissions from pipelines (vented, fugitive or caused by
incidents), electricity used for pipeline cathodic protection or gas to fuel heaters at regulation
stations.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
5.1.2.
Mapping of losses in compression stations
Along the transmission (and sometimes the distribution) networks, compressor stations are placed
at regular interval. When gas enters the compressor station, it is compressed by either natural gas
fuelled engines or turbines, or electric motors.
Figure 13: Mapping of losses in gas compressor stations
5.1.3.
Mapping of losses in gas distribution network
Natural gas is distributed to residential and small industrial customers through medium to low
pressure underground pipelines (distribution mains and service lines). Pipeline materials can be
very different depending on the localization, the pressure and the age of the network. For
example, oldest distribution mains were constructed from cast iron pipes and yarn/lead joints or
unprotected steel. New materials are mainly cathodically protected steel and plastic (e.g.
polyethylene).
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Figure 13 reports the mapping of mechanisms leading to losses in gas compressor stations. Fuel
used to compress gas is the most important source of gas and electricity consumption. Gas losses
can be due to vented emissions from compressors, valves and pneumatic-driven devices or
fugitive emissions.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Figure 14: Mapping of losses in gas distribution grids
5.2.
Energy efficiency measures in gas networks:
categorisation and potential
Energy efficiency measures in gas networks are considered in this study to be minimisation of
gas losses and own gas and electricity consumption. The three following sections present various
energy efficiency measures associated to transmission network, compressor stations and
distribution network.
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Figure 14 reports the mapping of mechanisms leading to losses in gas transmission grids. Losses
can be due to natural gas emissions and gas or electricity consumption. Gas emissions can vary
widely depending on the pipeline material as fugitive emissions from older networks are much
higher than more recent ones. Gas or electricity consumption can differ from one network to
another, depending on situations (e.g. existence of cathodically protected steel, heaters at
regulation stations).
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
5.2.1.
Gas transmission network: energy efficiency measures and
reduction potential
Table 11 reports the list of measures addressing the sources of loss in gas transmission network
identified in the previous section. It also describes the reduction potential of each measure and
identifies the key factors affecting this potential.
Opportunities to improve energy efficiency measures in gas distribution networks are mainly
related to natural gas losses. Measures to reduce vented gas during maintenance operation (e.g.
allow customers to use natural gas in order to lower the pressure in the affected section of pipe or
gas recompression) can potentially lead to substantial minimisation of losses. For example, a
80% reduction of gas losses can be achieved when gas is recompressed instead of vented before
maintenance ( [62]).
Type of losses
Associated measures
Qualitative
potential
Reduction of vented gas
++
Recompression
++
Inspection and
maintenance programs
+
Maintenance
operations
Permanent losses
due to age and gas
grid type
Natural gas
losses
Mapping, relation with
contractors, emergency
call centers…
+
Automatic shut-off
valves
+
Improved burner design
and avoid heating when
possible
+
(-)
(-)
Third party damages
Gas or
electricity own
consumption
Fuel to heat
facilities (at
regulation stations)
Electricity for
cathodic protection
Key factors
Pressure level,
volume of vented
pipe
Pressure level,
volume of vented
pipe
Length and age of the
network
Length of the
network, pressure
level, sensitivity of
the pipe area (urban
or rural, works
area…)
Length of the
network, pressure
level, sensitivity of
the pipe area (urban
or rural, works
area…)
Number and
efficiency of heaters,
necessity of burners
(-)
Table 11: Mapping of energy efficiency measures –Transmission
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Category of
losses
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
5.2.2.
Compressor stations: energy efficiency measures and
reduction potential
Table 12 reports the list of measures addressing the sources of loss in compressor stations
identified in the previous section. It also describes the reduction potential of each measure and
identifies the key factors affecting this potential.
Type of losses
Compressors starts /
shutdowns and
blowdowns
Natural
gas
losses
Pressure safety
valves and gas-driven
pneumatic devices
Fugitive emissions
from compressors
(seals…)
Fugitive emissions
from equipment’s not
in compression
service
Engines
(reciprocating
internal combustion)
Gas
own
consum
ption
Associated measures
Optimal strategies for
cycling on- and off-line
Reduction of vented gas (no
depressurizing after
shutdown…)
Replacement of high bleed
devices
Installation of compressed
air/electric systems
Qualitative
potential
Specific key factors
+++
Base or peak load
compressor
+++
Base or peak load
compressor
+
++
Number of high bleed
devices
Number of gas-driven
pneumatic devices
Inspection frequency,
type of seals to be
replaced
Inspection and maintenance
and replacement of seals
++
Inspection and maintenance
+
Inspection frequency
Optimization of equipment
dispatch
++
Increased efficiency (retrofit
control)
++
Replacement with a more
efficient unit
+++
Optimization of equipment
dispatch
+
Replacement with a more
efficient unit
+
Number and capacity of
compressors
(Pressure and flow
regulation)
Compressor efficiency
(before and after engine
retrofit)
Compressor efficiency
(before and after engine
replacement)
Number and capacity of
compressors
(Pressure and flow
regulation)
Compressor efficiency
(before and after gas
turbine replacement)
Gas turbines
Table 12: Mapping of the energy efficiency potential of each measure –Compression
Measures to improve energy efficiency in compressor stations can be either technical solutions
(e.g. replacement of seals) or optimized work practices (e.g. optimal strategies for cycling
compressors on- and off-line).
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Catego
ry of
losses
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Various solutions can be implemented to reduce gas losses. For example, a Marcogaz report (
[62]) describes the replacement of pneumatic actuators with devices operated by compressed air
in a compressor station in Italy that enabled gas emissions to be reduced by 45%. Another
example is the reduction of gas emissions when taking compressor off-line. This can be achieved
by keeping compressors pressurized (estimated emission savings: around 30% for a base load
compressor ( [63])).
Gas and electricity consumption can be reduced when replacing old engines or gas turbines with
more efficient units. Another option is to optimize equipment dispatch (e.g. optimisation of
pipeline system operations in order to operate compressors at their nominal capacity and avoid as
much as possible compressor starts and shutdowns).
Distribution grids: energy efficiency measures and reduction
potential
Table 13 reports the list of measures addressing the sources of loss in gas distribution network
identified in the previous section. It also describes the reduction potential of each measure and
identifies the key factors affecting this potential.
The most efficient way to reduce gas losses is to replace or repair/reline old grey cast iron pipes
with yarn/lead joints, as they are responsible for the highest emissions. For example, a program
of replacement of old pipes in the UK is expected to achieve 10 times lower emissions than
previously ( [62]).
Third party damages to gas distribution pipes can be another important cause of gas losses. Safety
is the primary driver to reduce these gas losses. Emissions can be reduced when implementing
measures to prevent damages (e.g. enhancement of the grid mapping accuracy or training and
technical guidance provided to contractors in charge of digging works). For example, reduction
of the number of third party damages (with gas emissions) of around 30% during the period
2010-2014 has been achieved in a French gas network (GrDF, 2015).
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5.2.3.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Associated measures
Pneumatic emissions
Reduction of vented gas
+
Reduction of vented gas
+
Recompression
+
Inspection and maintenance
programs
++
Pressure management
NA
Permeability of
materials and joints
Replacement or
repair/relining of old pipes
+++
Third party damages
Mapping, relation with
contractors, emergency call
centers, protection of pipes
in sensitive areas, shut-off
valves
++
Length of the grid,
pressure level, sensitivity
of the pipe area (urban or
rural, works area…)
Improved burner design and
avoid heating when possible
+
Number and efficiency
of heaters, necessity of
burners
(-)
(-)
(-)
Maintenance
operations
Natural
gas
losses
Gas or
electric
ity own
consum
ption
Qualitative
potential
Type of losses
Permanent losses due
to age and gas grid
type
Fuel to heat facilities
(at regulation
stations)
Electricity for
cathodic protection
Key factors
Number of gas-driven
pneumatic devices
Pressure level, volume of
vented pipe
Pressure level, volume of
vented pipe
Length and age of the
network
Length and age of the
network
Length and age of the
grid, pipes and joints
materials
Table 13: Mapping of the energy efficiency potential of each measure –Distribution network
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Catego
ry of
losses
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6.
ASSESSMENT OF COST-EFFECTIVENESS OF
SELECTED ENERGY EFFICIENCY MEASURES GAS
In the following we analyze more in detail the cost-effectiveness of energy efficiency measures
with most relevant potential at transmission, distribution and compressor station level.
The excel tool described in ANNEX V provides a step-by-step decision support to carry out the
assessment.
Gas Transmission Network
Table 14 describes the type of implementation costs and an estimation of cost levels associated to
measures with a significant reduction potential in gas transmission network.
Avoiding emissions during maintenance has significant energy efficiency potential. However,
some measures can be quite expensive and may be not economically relevant in all cases (e.g.
recompression of gas before maintenance with a mobile compressor unit). Less costly measures,
with minor facility modifications, can also be implemented (e.g. use inert gases and pigs to
perform pipeline purges instead of venting).
Additional benefits are less greenhouse gas emissions.
Category
of losses
Natural
gas losses
Type of
losses
Associated
measures
Maintenance
operations
Qualitative
reduction
potential
Reduction of
vented gas
++
Recompression
++
Type of costs
Capital costs (minor facility
modifications), operating costs
(maintenance), labour costs
Capital costs (pump-down
compressor), labour costs
Cost
level
+
+++
Table 14: Type of implementing costs and estimated cost levels – Transmission network
Examples of implementation costs are given in Table 15. These figures give an order of
magnitude and should be considered with caution since situations may differ from one network to
another.
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6.1.
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Measure
Reduction of vented gas (Use
inert gases and pigs to
perform pipeline purges)
Reduction of vented gas
(Inject blowdown gas into low
pressure mains or fuel gas
system)
Recompression (mobile
compressor unit)
Capital costs estimation
-
Operating costs estimation
$500 (for 2 miles of 10-inch diameter
pipeline) ( [63],2011)
$1,000 (minor facility
modifications: additional
piping) ( [63],2011)
-
3
200,000 € (for one case with 630,000 Nm natural gas saved from
venting) ( [62])
Table 15: Implementation costs of measures – Examples – Transmission network
Gas Compressor Stations
Table 16 describes the type of implementation costs and an estimation of cost levels associated to
measures with a significant reduction potential in compressor stations.
Possible measures that can be applied within compressor stations can vary widely. Some of them
can be implemented with no (or nearly no) costs (e.g. avoid compressor depressurization after
shutdown). Conversely, an engine replacement with a more efficient unit represents a very
expensive investment.
Overall, these measures can contribute to improve operational efficiency, less greenhouse gas
emissions and cost savings due to reduction of gas losses and own gas consumption.
Category of
losses
Type of losses
Compressors
starts /
shutdowns and
blowdowns
Natural gas
losses
Pressure safety
valves and gasdriven
pneumatic
devices
Fugitive
emissions from
compressors
(seals…)
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Qualitative
reduction
potential
Type of costs
Cost
level
+++
Labour costs
+
Reduction of vented
gas (no
depressurizing after
shutdown…)
+++
No costs (with the
option: no
depressurizing after
shutdown) or minor
capital and labour costs
with other options
+
Installation of
compressed
air/electric systems
++
Capital costs
(compressed air/electric
systems)
++
++
Capital costs (seals),
operating costs
(maintenance), labour
costs
++
Associated
measures
Optimal strategies
for cycling on- and
off-line
Inspection and
maintenance and
replacement of
seals
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Category of
losses
Associated
measures
Type of losses
Qualitative
reduction
potential
Type of costs
Cost
level
++
Labour costs
+
++
Capital costs (minor
engine modification),
operating costs
(maintenance), labour
costs
++
+++
Capital costs (new
compressor), operating
costs (gas or electricity +
maintenance), labour
costs
+++
Optimization of
equipment dispatch
Gas own
consumptio
n
Increased efficiency
(retrofit control)
Engines
(reciprocating
internal
combustion)
Replacement with a
more efficient unit
Table 16: Type of implementing costs and estimated cost levels – Compressor stations
Examples of implementation costs are given in Table 17. These figures give an order of
magnitude and should be considered with caution since situations may differ from one
compressor station to another.
Measure
Optimal strategies for cycling onand off-line
Capital costs estimation
-
Reduction of vented gas (no
depressurizing after shutdown)
Reduction of vented gas (Keep
compressor pressurized and route
gas to fuel system or install static
seal)
Installation of compressed
air/electric systems
Inspection and maintenance
(identification of leaks and costeffective repairs)
Replacement of seals (compressor
rod packing system replacement)
Replacement with a more efficient
unit (case of an electric compressor)
-
Operating costs estimation
Labour (very variable costs
depending on available
equipment and optimization
possibilities)
-
< $5,000 for one compressor ( [63], 2006)
$45,000 to $75,000 (per facility)
-
-
$26,248 (average value, for one
compressor station) ( [63],
2003)
-
$5,346 ($2008) (per
compressor) (US EPA, 2014)
$6,050,000 (per 5 reciprocating
compressors replaced) (EPA
Gas STAR Program, 2011)
$6,200,000 (per 5 reciprocating
compressors replaced) (EPA
Gas STAR Program, 2011)
Table 17: Implementation costs of measures – Examples – Compressor stations
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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6.3.
Gas Distribution Network
Table 18 describes the type of implementation costs and an estimation of cost levels associated to
measures with a significant reduction potential in gas distribution network.
As stated previously, replacement of grey cast iron pipes with polyethylene pipes can reduce
significantly gas emissions. This measure can be very expensive, especially in urban areas. This
should, however, be put in balance with associated benefits (higher safety level, less greenhouse
gas emissions, cost savings due to reduced gas losses and lower repair costs over the next
decades).
Category
of losses
Qualitative
reduction
potential
Type of losses
Associated
measures
Permanent losses
due to age and gas
grid type
Inspection and
maintenance
programs
++
Permeability of
materials and joints
Replacement or
repair/relining of
old pipes
+++
Third party damages
Mapping, relation
with contractors,
emergency call
centers, protection
of pipes in
sensitive areas,
shut-off valves
Natural
gas losses
++
Type of costs
Operating costs
(maintenance),
labour costs
Capital costs (new
pipes or liners),
operating costs
(maintenance),
labour costs
Mainly labour
costs
Capital costs (for
protection of pipes
and shut-off
valves)
Cost
level
+
+++
Variable
Table 18: Type of implementing costs and estimated cost levels – Distribution network
Examples of implementation costs are given in Table 19. These figures give order of magnitude
estimation and should be considered with caution since situations may differ from one network to
another.
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Less costly measures can be implemented as well (e.g. prevention of third party damages,
improvement in inspection and maintenance programs) and will contribute to a greater safety of
the network, less greenhouse gas emissions and cost savings due to reduction of gas losses.
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Measure
Capital costs estimation
Operating costs estimation
Improvement in inspection
and maintenance programs
(increased walking survey)
-
$11,000 for a pipeline system of
2,900 miles (labour costs: $10,000
and additional equipment: $1,000) (
[63], 2011)
Replacement of grey cast
iron pipes with PE pipes
Insert gas main flexible
liners (for cast iron and
unprotected steel)
Mapping, relation with
contractors, emergency call
centers
Shut-off valves (for service
lines)
200,000 € (per km) ( [64])
> $1,000,000 (for 1 mile of cast iron main replacement) ( [65])
$10,000 (for 1 mile of cast iron
main and 1 mile of unprotected
steel service lines) ( [63], 2011)
Mainly labour (Very variable costs
depending on implemented
measures)
$6,300 (for 350 valves installed) (
[63], 2011)
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Table 19: Implementation costs of measures – Examples – Distribution network
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PART 2 - ASSESSING THE POTENTIAL OF DEMAND
RESPONSE TO INCREASE THE EFFICIENCY OF
ELECTRICITY NETWORKS
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
PART 2 - EXECUTIVE SUMMARY
As described in the interim report, this study particularly considers the planning/operational
efficiency potential in electricity grids, thanks to new “Smart” measures (Smart Grid assets;
flexibility of distributed energy resources).
Accordingly, the focus of this second part of the report is to analyse the potential of demand
response (DR) in bringing efficiency in electricity grids for Distribution System Operators
(DSOs).
Key messages of the analysis are grouped into the following three main domains:
VALUE STREAMS AND PROCUREMENT OPTIONS OF DEMAND RESPONSE
FOR DISTRIBUTION SYSTEM OPERATORS
There are two main categories of DR benefits for DSOs: avoided CAPEX and avoided
OPEX
- Avoided capital expenditure (CAPEX): DSOs need to expand grid capacity and
upgrade transformers to cope with congestions. Depending on the demand and renewable
energy sources (RES-E) growth in an area, employing DR in the planning phase offer
DSOs a valuable solution to asset replacement. This strategy may postpone the need to
invest in infrastructure upgrades and allow using the assets for their full lifetime,
reducing short-term expenditures. 21
- Avoided operational expenditure (OPEX): local balancing leads to a more balanced
line loading and to a subsequent reduction of distribution grid losses which form a key
operational expenditure for DSOs. Moreover, by relieving grid congestion, DR allows
RES-E generation to be better integrated in the system. This means that curtailment of
RES-E can be avoided, and hence compensation payments for the curtailed renewable
energy are reduced (in countries where it is foreseen).
It is worth mentioning that, while DR can also be used to optimise operation of the wholesale and
balancing markets, DSOs cannot capitalise on these benefits, as unbundling provisions restrict
their participation to these markets as flexibility provider.
21
Grid assets must be replaced at the end of their lifespan regardless of the situation. Since large part of the
investment costs are related to installation, when replacing assets that have reached their lifetime, placing some
larger cables brings no significant additional cost. In this respect, CAPEX benefits from DR should be considered with
respect to the remaining lifetime of the assets.
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A.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
There are two main approaches for DSOs to procure demand flexibility: tariff-based
(implicit) DR and volume-based (explicit) DR
Tariff-based - Implicit DR
Under this approach, DSOs use tariff signals to trigger demand flexibility that is in line with the
needs of the distribution system. DR is implicit in the sense that a predefined volume of DR is
not agreed before the DR activation, rather it results from the users’ response to the tariff signal.
In this context, and throughout this report, we refer to “tariffs” as the regulated component of the
final price, related to distribution grid charges.
Basically the options are to vary the distribution grid tariff based on a) fixed timeframes (time of
use pricing); b) changing timeframes (dynamic pricing); c) changing timeframes and location
(locational dynamic pricing). The different options are best suited to different purposes.
Volume-based - Explicit DR
Under this approach, the DSO (financially) compensates a DR service provider to activate a
specific quantity of DR.
When the DSO identifies a need/potential benefit for DR in its network, it could in principle
procure it by:
1)
2)
3)
making a direct contract with the DR provider with customised conditions;
issuing a call for tenders for the DR service required, whenever it is required; or
establishing a local market for DR providers connected to the network with local price.
Which option is most efficient and effective depends on the number of DR providers that are
connected to the network (liquidity of the market). Forming bilateral agreements are most
efficient when there are only few, and forming a local market can be efficient when there are
many market players.
Reliability of DR delivery should be ensured, for example by applying penalties if there is a
deviation between services sold and delivered (except in an emergency). However, it must also
be kept in mind that very strict reliability standards will discourage service providers from
participating in such a DR scheme.
The correct implementation of explicit DR requires the definition of a mechanism to verify expost that the agreed amount of DR has been actually activated. Typically this is done via the
definition of a baseline which represents the load profile had the DR event not taken place.
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The limit of implicit DR is that it does not provide a guarantee to DSOs regarding the actual level
of DR that will be activated in response to tariff signals.
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B.
ASSESSMENT OF THE VALUE OF DEMAND RESPONSE TO IMPROVE
DISTRIBUTION GRID EFFICIENCY AT PLANNING AND OPERATION
An extensive simulation analysis has been carried out to assess how the integration of DR in
DSOs planning and operation could allow finding the optimal trade-off between CAPEX
(investment in equipment/asset replacement and upgrade) and OPEX spending (in particular cost
of thermal losses), particularly in presence of a growing pentration of distributed generation.
Tool for optimization of DSOs’ planning and operation in presence of flexibility
Tractebel’s proprietary tool Smart Sizing has been used for this analysis. The tool allows a global
optimization of DSOs’ OPEX and CAPEX by selecting the optimal mix of traditional and
flexibility-based investments. The optimization tool takes into account the following costs:
OPEX:
Technical losses costs
Activation cost of flexibility (e.g. procurement of volume-based flexibility by the DSO)
Network components (e.g. lines, substations)
ICT infrastructure for activation of flexibility
Grid models
Four different grid models have been built, iin terms of grid configuration, load patterns and
distributed generation profiles. They are intended to provide a constrasting set of operating
conditions to test the robustness of the results:
Distribution network in urban area located in Northern Europe;
Distribution network in rural area located in Northern Europe;
Distribution network in urban area located in Southern Europe;
Distribution network in rural area located in Southern Europe.
Scenarios representing available flexibility
For each model we have run four main scenarios with growing availability of flexibility:
1)
2)
3)
4)
Base case scenario – In this scenario, planning and operation rely only on traditional
investments, without the option of DR.
Time-of-Use scenario - In this scenario, it is assumed an (exogenous) reshaping of the load
profiles in the simulation thanks to the introduction of Time of Use (ToU) tariffs. The tool
does not optimize the load profiles, it just considers the reshaped load profiles as input to the
simulation.
Demand response scenario – In this scenario, the tool is able to arbitrate between
investments in traditional assets or DR (considering associated ICT investment costs and
activation costs ).
Full flex scenario: Demand response and DG curtailment options– In this scenario,
DSOs can opt for two competing sources of flexibility: (explicit) DR and DG Curtailment.
Within each scenario a sensitivity analysis has been carried out by varying (homotetically) the
penetration level of the different types of DG (PV, wind, CHP) in Low Voltage (LV) and
Medium Voltage (MV).
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CAPEX
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Main messages
In the following we provide a summary of the main messages resulting from the simulation
analysis, which are valid on average on a given grid. Deviations are possible on specific
feeders/substations depending on local specificities.
Demand response is effective in reducing the total cost of the distribution grid
If DSOs do not receive the right incentives to reduce losses, then the use of flexibility could
mainly target CAPEX reductions (i.e. size assets to their capacity limits) and possibly lead
to higher loss volumes.
The simulation analysis shows that DR activation targets specifically loss reduction only if
sufficiently high costs for losses are considered.
Regulation needs to pay attention to the costs of losses paid by the DSOs (or to provide the
right loss penalties) to avoid that DSOs have an incentive to use flexibility to downsize assets
(reduction of CAPEX), even if this leads to an increase of losses.
Explicit (Volume-based) Demand Response can bring higher benefits to the system
compared to implicit (tariff-based) Demand Response.
Both implicit and explicit DR can reduce the total cost of the system. Both approaches can
also be implemented together to maximize the impact (even if the two mecanisms can be in
competition with each other, as the presence of ToU reduces the amount of flexibility
exploitable by explicit DR).
Volume-based (explicit) DR can bring higher benefits as it can be more precisely activated
according to DSO’s needs.
For a volume-based flexibility market to develop, DSOs should make clearly accessible the
information about their flexibility needs (present and future). This would allow to reveal the
availability of flexibility resources and contribute to creation of a flexibility market.
Time of Use (ToU) tariff can provide significant system benefits, but they need to be
carefully tailored to specific local conditions.
The impact of ToU tariffs on TOTEX depends on the level of penetration of DGs and on local
conditions.
In general, ToU can provide significant benefits and facilitate the integration of DG at a lower
cost when the off-peak period is synchronized with DG generation. A location-based
component in the ToU could help better tailor the tariff design to local conditions.
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Demand response is an effective means to reduce the TOTEX (Total Expenditure) of the
system (CAPEX+OPEX), when integrated in grid planning and operation:
- Via reduction of direct and inverse peaks in the network and thus the need for
infrastructure costs (CAPEX).
- Via reduction of technical losses (OPEX) by performing a local balancing with DG and
reducing the flows in the system.
The reduction of TOTEX is achieved by finding the optimal balance between CAPEX and
OPEX (in certain conditions, the level of technical losses could also increase, if off-set by a
decrease of CAPEX). Of course, regulation should specifically incentivize DSOs to reduce
TOTEX (please refer to chapter 5).
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Poorly designed ToU timeframes, under certain conditions, could also have a negative effect.
For example, with high DG penetration, simple day-night ToU might actually worsen the
costs of DG hosting capacity.
The cost/benefit ratio of Demand Response for DSOs depends on local conditions
On the benefit side:
A key factor affecting the business case of DR for DSOs is the availability and cost of an ICT
infrastructure (smart meters).
In those regions where smart meters are already being deployed (driven by policy decisions),
the business case for DR is stronger both for residential and commercial/industrial customers
(as DR programs do not need to include ICT infrastructure costs)
In those regions where the cost of the ICT infrastructure should be borne by the DR program,
it is recommended to focus on DR at MV level get most of the benefits.
Demand Response is always part of the best flexibility mix to decrease the total cost of
distribution grid planning and operation
With limited penetration of DG, DR is the most economically viable flexibility option to drive
down the system cost. (This holds on average on a given grid. Deviations are possible on
specific feeders/substations depending on local specificities. For example in rural areas, with
limited load, curtailment of DG could be the best flexibility option to solve congestions on
specific feeders).
With very high penetration of DG, reverse peak (peak of reverse power flows) becomes the
main driver to size grid assets. Beyond that DG penetration level, generation curtailment
becomes an economically viable option together with DR.
Demand Response has typically a positive impact on grid technical losses, but reduction of
losses is not the main driver for its activation
Reduction of losses is typically not the main driver of the activation of DR, rather a side-effect
of the activation of DR to reduce the CAPEX.
Higher loss reduction level is typically achieved in rural areas, given the structure of rural
grids (longer feeders, overhead lines), for which cost of losses has a higher share of the
TOTEX.
Demand flexibility allows an higher DG hosting capacity at lower cost
DR increases the synchronicity of the demand with DG and mainly reduces CAPEX.
The positive impact of DR on DG hosting capacity becomes increasingly significant after a
certain DG penetration level.
In fact, a limited penetration of DG, even without any flexibility, could actually decrease the
TOTEX of the system compared to the case without any DG (thanks to DGs balancing local
demand and reducing network flows).
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The value of DR for DSOs strongly depends on the level of penetration of DG, on the
consumption patterns and on the grid characteristics.
Typically, the simulations shows that a limited number of activation of DR per year can
already provide significant benefits for the system
On the cost side:
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Strong penetration of residential PV will drive more rapidly the need for flexibility
A lack of variety in the types (PV, wind, CHP) and connection level (MV, LV) of DG has a
negative effect on grid efficiency, driving up CAPEX (reinforcements) and OPEX (technical
losses).
This means that in those areas where a single source of DG is mainly present (e.g. areas with
strong residential PV), demand flexibility can particularly provide positive system impact and
increase grid efficiency. This is of course dependent on the availability of sufficient level of
demand flexibility (in which case DG curtailment could be the main flexibility option to
explore).
C.
REGULATORY EVOLUTIONS FOR DISTRIBUTION SYSTEM OPERATORS
TO PROCURE AND ACTIVATE DEMAND RESPONSE
Under an implicit (tariff-based) DR approach, the regulator should define the required changes in
network tariffs to incentivise DR, by changing specific components of the tariffs (e.g. higher
capacity charges), changing time intervals for ToU, introducing location-based components or
allowing DSOs to adapt tariffs to grid conditions.
However, since distribution grid tariffs constitute just a component of the entire electricity price
paid by a consumer, in cases they are low they might fail to form an effective market signal to
grid users to change their consumption to meet grid needs.
DSOs can directly procure DR if DSOs remuneration is based on TOTEX
Under a volume based DR approach, DSOs procure DR services and remunerate the provided
service. Incentives to optimise operation need to be created by appropriate regulatory adjustments
in order to allow DSOs recovering the costs of procuring DR. This can be done by allowing DSO
remuneration to reflect the DSOs’ TOTEX (CAPEX+OPEX).
Regulation should ensure a neutral role for DSOs in the activation of flexibility, in order to
ensure market-based competition among different flexibility resources
As shown in the simulation analysis, economic benefits are achieved by enabling competition
between flexibility options for the provision of services at local or global level. To enable a
market-based approach, transparency and neutrality of DSOs is key. In this respect policy
questions are related to:
whether DSO should be allowed to own flexibility assets (e.g. storage)
How to address the risk that, if most of the value of flexibility is controlled directly by DSOs
(e.g. ownership of storage means; direct bilateral contracts with grid users), a local flexibility
market (targeting also other value streams, e.g. BRP; TSOs..) could never develop.
How to ensure DSO transparency in validating flexibility transactions at local level which are
driven by global market signals (e.g. flex transactions for TSOs)
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Regulatory evolutions should ensure that tariff signals provide sufficient incentives to grid
users to adapt their demand profiles to grid conditions
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In case of conflicts for flexibility activation, local distribution needs should prevail over
system transmission needs, unless when system security is at stake.
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Flexibilities in the distribution grid can be used for multiple purposes, a) in the system level
(participation to wholesale, intra-day and balancing markets) b) in transmission network level
(alleviation of transmission congestions) and c) in the distribution grid (alleviation of distribution
congestions). Congestions at any network level could “block” the provision of flexibility services
from the local level to the higher system levels. Respectively, conflicts on the activation of
flexibility between TSO and DSO are possible. Therefore, a prioritisation between local and
global requirements is needed, and local needs should prevail, unless when system security is at
stake and alternative options are not viable. In this respect, DSO/TSO coordination should be
applied to avoid such conflicts. To achieve this coordination a new process could be defined, the
DSO congestion forecast linked to the respective process at TSO level.
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7.
INTRODUCTION (PART 2)
As described in the first part of the report, this study particularly considers the
planning/operational efficiency potential in electricity grids, thanks to new “Smart” measures
allowing active grid management (Smart Grid assets; flexibility of distributed energy resources.
Accordingly, the focus of this second part of the report is to analyse the potential of demand
response in bringing efficiency in electricity grids for Distribution System Operators (DSOs).
The analysis has been organized in four main sections:
- Brief review of benefits of demand response for the whole power system
- Review of main factors affecting the value of demand response
DR characteristics (pre-activation time, duration, ..)
Type of consumers, type of loads etc..
- Activation mechanisms for demand response (Price-based/Implicit and volumebased/explicit)
b) Synthesis of the value of DR for DSOs
- Focus on the benefits of demand response specifically for DSOs
- Definition of DSO’s options to procure Demand Response for their needs: tariff-based
(implicit) demand response and volume-based (explicit) demand response
c) Simulation analysis of DR impact on grid efficiency at planning and operation
- Definition of four different network models to represent contrasted conditions (urban/rural;
North/south)
- For each model, definition of four different scenarios, each with a growing level of
available flexibility for the DSOs in planning and operating its grid:
Base case: no flexibility
ToU scenario: Demand flexibility triggered by (Static) ToU tariffs
Volume-based DR scenario: DSO has access to volume-based demand response
Full flex scenario: DSO has access to two options of flexibility: (volume-based)
demand response and DG curtailment
- The goal of the simulation analysis is to assess how the integration of flexibility options
can reduce the CAPEX (investment in assets) and OPEX (losses) investments of DSOs, in
presence of a growing penetration of DG.
d) Regulatory evolutions to promote DR, particularly for grid efficiency
- Four specific regulatory matters have been analysed
Evolutions of grid tariffs to trigger (price-based) demand response in line with the
needs of the system at local level
Enabling DSOs to integrate (volume-based) demand-response for planning and
operation. This requires the introduction of suitable procurement options (local markets,
tender etc) and of specific incentives to DSOs to use these options (e.g. TOTEX
remuneration).
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a) Synthesis of DR drivers, characteristics and value streams
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8.
OVERVIEW OF DEMAND RESPONSE
CHARACTERISTICS
This chapter describes briefly the main characteristics of Demand Response (DR).
The goal is to describe the general context of current DR implementations before focusing
specifically on how DR can be used by system operators to increase the planning and operation
efficiency of their grids. This will be the subject of the subsequent chapters.
Benefits of demand response across the
power system value chain
There are many potential benefits arising from DR across the whole power system value chain.
Figure 15 shows a wide spectrum of potential DR benefits, according to three main categories,
namely benefits for the system, the society and other, non-monetized ones.
Figure 15 - Classification of demand response benefits across the whole power system value chain
A few examples of current valorization of DR for different system services are reported below:
Ancillary services
DR is contracted by system operators to provide ancillary services to the power system (e.g.
frequency regulation, tertiary reserve). The capacity of DR contracted in these markets/programs
is on the rise in many countries, as an alternative to the use of traditional generation resources. A
few examples are reported below:
France - 400MW contracted DR by RTE (French TSO) for balancing market (tertiary
reserve) [66]. Since July 2014, DR can also access the frequency reserve market (10 MW
minimum bid).
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8.1.
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Italy - OOver 4000MW of Interruptible contracts for Sicily and Sardinia have been contracted
(based on auctions) by Terna (Italian TSO)
Belgium - 350 MW of DR were contracted in the ancillary reserves market in 2014
Capacity Market
PJM (USA) - 10GW of DR (mainly from Commercial & Industrial –C&I- sites) have been
contracted on PJM capacity market in the latest auction for 2017/2018. To provide an
indication of the value associated to the DR, the flexibility service provider EnerNOC is
managing 4GW of DR from C&I sites on PJM capacity market for total value of 185M$. [67]
[68]
Belgium – Since 2014 demand resources (with capacity larger than 50MW) are contracted
with 1 year contracts for provision of strategic reserve
France and the UK are also about to launch capacity markets which foresee the participation
of DR resources.
Distribution network
The integration of DR in distribution grid operations is starting to emerge in Europe, particularly
in pilot projects. In the US, particularly in regions where integrated utilities operate, DR
programs are in place to solve distribution congestion issues.
California (USA): in 2013, 923 MW of DR (direct load control) had been contracted by
distribution utilities to address congestions at distribution level.
Additionally, DR is also currently used as a resource by market actors (e.g. Balancing
Responsible Parties) to balance their portfolio.
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These markets value the contribution of DR resources to meet system peak demand and thus
ensure the security of supply of the system. The integration of DR resources can reduce the need
for investments in generation peak units. A few examples are reported below:
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8.2.
Factors affecting the value of Demand
Response
In the following, we describe a number of key variables affecting the potential and value of DR.
8.2.1.
Type of loads
Type of consumers
Currently, DR comes mainly from commercial and industrial sector (lower implementation cost
per MW of flexible demand). Residential DR is expected to increasingly become cost-effective
with the deployment of smart meters and the introduction of smart appliances (e.g. smart
thermostats) able to automatically respond to control signals, without the need for final
consumers to manually adapt their consumption.
Type of demand response resources
The flexibility potential of prosumers/consumers depends on the type of loads and generation
assets on their premises. A useful categorization is provided in [69]:
1)
2)
3)
Storable load
a) Battery storage
b) Electric Vehicles (smart charging)
c) Thermal storage (e.g. HVAC, heat pumps)
d) ..
Non-storable load
a) Shiftable load, for example:
i) Flexible industrial processes
ii) Dishwashers, laundry,..
iii) ..
b) Curtailable load, for example:
i) Non-essential lighting
ii) ..
c) Base load
i) Essential services/Processes
ii) ..
Self-generation
a) Use of generation at consumers’ premises to modify net-load profile (e.g. via PV; CHP)
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DR potential vary largely among sectors: Industrial (e.g. paper, steel, chemical industries etc.),
commercial (hospitals, commercial sites, ..), residential (at household level).
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Load patterns
Figure 16 – Example of load shapes for HVAC in office building and residential thermal loads
8.2.2.
Characteristics of demand response
In markets where DR is sold as a ‘product’, a number of characteristics are introduced to define
its ‘quality’ and thus its corresponding value.
Key parameters include:
Availability (e.g. weekday; summer period)
Maximum possible number of DR activations per year (e.g. maximum 10 demand response
events per year)
Hours of day required to respond (e.g. 10am – 18 pm)
Maximum duration of DR event (e.g. 4 hours)
Time to respond (e.g. 4 hours after instruction)
For example, the UK TSO National Grid’s program to procure short-term balancing reserves
(STOR), prescribed the following requirements for the provision of additional active power from
generation and/or demand reduction [70]:
3MW or more of generation or steady demand
Deliver within 4 hours from instruction
Provide for at least 2 hours
20 hours recovery
Ability to provide STOR at least 3 times a week
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Load patterns affect the potential to provide DR services. For example, in summer the potential
for DR of residential heating is zero. Another example is reported in Figure 16, where the load
patterns of a residential water heater has already been optimized to peak/off-peak tariffs (see
green profile) and thus does not leave any room for additional flexibility during the day.
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Figure 17 reports the requirements for different DR products (Limited DR, Extended Summer
DR, Annual DR) for the PJM capacity market in the US22 [71]:
8.2.3.
Activation mechanisms of demand response
Activation mechanisms of demand response can be categorised into price-based (implicit) and
volume-based (explicit) procurement.
8.2.4. Price-based mechanisms (implicit DR)
Under price-based procurement, DR is considered “implicit”, in the sense that the activated level
of flexibility is not stated in advance, but results from the response of consumers to price signals.
The main parameters to consider are:
Price : Variable or Fixed (known in advance)
Timing slots : variable or fixed
Occurrence: every day or on critical event
Capacity/Energy : Charges depending on cumulated energy consumption or on power level
The combinations of these parameters create a wide variety of tariff schemes. Most relevant ones
are briefly described below:
Inclining Block Rates
Fixed tariffs are applied by trench of consumed energy, hence the xxkWh are at x€/kWh, the
following yykWh at y€/kWh, etc.
Time-of-Use
Fixed periods of low and base tariffs all over the year. Typical example are day-night tariffs
22
DY stands for Delivery Year
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Figure 17 - Requirements of DR products on the PJM capacity market [PJM2012] -
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Peak Time rebate
The base tariff remains the same for all timeframes. However, if consumption is reduced
during critical events, customers can earn rebates
Critical peak pricing (CPP)
A high price is applied in a predefined number of days. The days where the CPP will be
applied are communicated in advance
Variable peak pricing
Mix of ToU and Real-Time pricing, where off- and on-peak periods are fixed, but the price of
the on-peak period may vary depending on market conditions.
Real Time Pricing
Each pricing scheme has advantages and drawbacks. From a wider perspective, they can be
ranked according to the level of Risk and Reward they provide. The higher the inherent risk, the
more rewarding they can be for consumers who adapt their energy usage patterns (see Figure 18).
Figure 18 - Risk/Reward levels are adaptable to consumer's risk aversion [72]
8.2.5. Volume-based mechanisms (explicit DR)
Under price-based mechanisms, there is no guarantee that a predetermined amount of DR will be
activated. On the other hand, in volume based DR a predetermined volume (MW) of DR is
contracted. In this case, DR is considered “explicit”, in the sense that the activated level of
flexibility needs to be explicitly defined in advance.
A key aspect of volume-based DR is the important role that control systems should play to
automatically control the load and make it “dispatchable’, in response to dispatch signals (e.g.
from an aggregator).
Volume-based DR products are typically contracted on centralized organized markets (capacity
markets, ancillary services, energy markets etc.), depending on the specific regulation in force.
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Price varies hourly or sub-hourly, on a day-ahead or hour-ahead basis.
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Verification of activated DR
Volume-based demand response requires the definition of a mechanism to evaluate ex-post the
level of DR that has been activated. For illustration purposes, three examples are provided below
related to the baseline calculation (i.e. the profile the load would have had, if the DR event had
not taken place) in the PJM market (US), NEBEF market (France), STOR program (UK):
PJM (USA) [71]
Different approaches are used by PJM to calculate baseline of remotely metered sites. The
general principle is that the error of the baseline (relative root mean square ) must be less than
20% so that the site can actually be paid for load reductions .
The most commonly used baseline (about 85 % of participants) for participants in "PJM
economic programs” is the average consumption measured during the same hours as those of
the event during the four days 5 previous busiest days of activation.
- Direct Load Control (DLC): In this case there is no need for tele-reading of meters or to
use a baseline. An impact study is carried out ex-ante to assess the volume of DR activated
by sending the DLC signal (i.e. testing the functioning of the load control loop)
- Firm Service Level (FSL): No use of baseline is needed, the verification consists in
monitoring (via remote metering) that the load has well been reduced to a predefined level.
- Guaranteed Load Drop (GLD): the load must in this case be reduced by a predetermined
amount (Guarantee) and a baseline must be calculated based on remote metering
measurements. Adopted baselines could be based on comparable days or the day before /
after
STOR (UK) [70]
Participants to the short term reserve program (STOR) of the UK TSO (National Grid) were
typically on call roughly between 7:00-13:00; 16:00-22:00.
The contribution of participating customers was judged relative to their level of consumption
immediately prior to the dispatch signal.
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Different methods are used for the different types of demand management capacity markets:
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9.
VALUE STREAMS AND PROCUREMENT
OPTIONS OF DEMAND RESPONSE FOR
DISTRIBUTION SYSTEM OPERATORS
This chapter focuses specifically on the use of DR by DSOs. We analyse the key DR value
streams for DSOs and present the options DSOs have to procure DR and capitalise on some of
these value streams.
Value streams of demand response for
distribution system operators
From the perspective of a power system operator, DR can be a useful tool to correct imbalances
between power supply and demand and to relieve congestions in the grid. Specifically, the
following functions can be considered:
Reduce peak power demand, thereby potentially reduce line overloads and relieve congestions
Increase demand in order to complement production of renewable energy sourced electricity
(RES-E) to reduce reverse power flows and avoid curtailment
Influence power flow in the network (quantity and direction) to balance 3-phase loading and
to eliminate the need for network reconfiguration
Figure 19 shows a general overview of benefits that can be derived from DR in the power system
in general, in terms of optimising operation and planning.
Figure 19 – Grid efficiency value streams deriving from DR for the power system
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Avoided capital expenditure (CAPEX): DSOs need to expand grid capacity and upgrade
transformers to cope with congestions. Depending on the demand and renewable energy
sources (RES-E) growth in an area, employing DR in the planning phase offers DSOs a
valuable solution to asset replacement. This strategy may postpone the need to invest in
infrastructure upgrades and allow using the assets for their full lifetime, reducing short-term
expenditures. 23
Avoided operational expenditure (OPEX): local balancing leads to a more balanced line
loading and to a subsequent reduction of distribution grid losses which form a key operational
expenditure for DSOs. Moreover, by relieving grid congestion, DR allows RES-E generation
to be better integrated in the system. This means that curtailment of RES-E can be avoided,
and hence compensation payments for the curtailed renewable energy are reduced (in
countries where it is foreseen).
It is worth mentioning that, while DR can also be used to optimise operation of the wholesale and
balancing markets (as discussed in the previous chapter), DSOs cannot capitalise on these
benefits, as unbundling provisions restrict their participation to these markets as flexibility
providers.
23
Grid assets must be replaced at the end of their lifespan regardless of the situation. Since large part of the
investment costs are related to installation, when replacing assets that have reached their lifetime, placing some
larger cables brings no significant additional cost. In this respect, CAPEX benefits from DR should be considered with
respect to the remaining lifetime of the assets.
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DSOs are facing a twofold challenge. On the one hand distribution grids should cope with
demand growth and changing demand patterns (due to the incorporation of new demand such as
electric vehicles and heat pumps as well as increasingly wholesale-price sensitive demand). On
the other hand, they should be able to host increasing shares of DG and especially renewable
energy sources (RES-E). DSOs are therefore faced with increased grid congestions caused either
due to overloads from new demand or due to reverse power flows from increased RES-E shares.
DR can provide cost effective solutions for both planning and operational purposes to relieve
such grid congestions by reducing demand in case of overloads or increasing demand to counterbalance reverse power flows. In this respect, there are two main categories of DR benefits for
DSOs:
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9.2.
Options for DSOs to procure demand response
and capitalise the benefits
Figure 20 – Overview of transaction options between DSO and DR service providers
9.2.1.
Price-based procurement of demand response
DSOs can use price-based incentives to procure DR. This means using the regulated component
of the final price which reflects distribution grid charges (hereafter denoted, “grid tariffs”) to
incentivise grid users to adjust their behaviour to meet grid conditions.
Figure 21 lists the options to use grid tariffs to trigger demand flexibility. Basically the options
are to vary the distribution grid tariff based on a) fixed timeframes (time of use pricing); b)
changing timeframes (dynamic pricing); or c) changing timeframes and location (locational
dynamic pricing). The different options are best suited to different purposes.
24
It is worth stressing that when referring to “price-based” options, since we are referring to DSOs, in this context
we refer to the “regulated component” of the final price for consumers (in most cases this refers to the distribution
tariff component of grid tariffs). In countries where market is not yet liberalized (where retail prices are still
regulated) “tariffs” refer to the whole price.
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Figure 20 shows an overview of DSOs’ options to procure DR within its control area, classified
into price-based and volume-based options 24. The individual options are explained in more detail
in the subsequent sections.
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Figure 21 – Options on how to vary distribution grid tariffs to incentivise DR
9.2.1.1.
TIME OF USE (TOU) TARIFFS
ToU tariffs apply fixed price differentiation of the network charges based on time. Consumers of
electricity are charged cheaper tariffs during low-demand periods (typically night time), while
more expensive tariffs apply during high-demand periods. The same tariff is charged to all
consumers in the DSO control area. This tariff structure is useful for reducing system peak
demand, when it is known to occur regularly around the same time every day.
Example of application: ToU tariffs have been used in France since 1960’s. Approximately 80%
of the hot water tanks in France are programmed to start during off peak, which is estimated to
shift 9 TWh of energy annually, corresponding to approximately 3 GW of installed peak load
capacity. Synergy effects are reduced investment needs in the grid of more than 100 MEUR/year.
[73]
Potential Challenges:
One observed side-effect from the example in France, was the so-called “bounce back effect,”
where all DR switch on simultaneously at the beginning of the cheaper tariff period, causing a
new peak. Pilot projects (e.g. NiceGrid project in France25) show that this can be successfully
dealt with by using more than one off-peak period (shown in Figure 22).
25
http://www.nicegrid.fr/
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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ToU tariffs are suited to ‘traditional’ power systems, where the daily patterns of (net) demand is
well defined, and the peaks occur consistently in the same timeframe. However, increasing shares
of Variable RES (VRE) in distribution systems alter the net demand patterns and the peaks do not
always occur in the same timeframe. This would make the ToU tariffs ineffective, and reduce the
predictability on what tariff should be applied when.
The same ToU tariffs apply to all consumers in the control area by the DSO, and act to solve
system peak demand. However, this does not guarantee a solution to local congestion issues. For
example, if there is a local congestion within the DSO area that requires a decrease in generation
output during the time when there is a system peak and instead demand reduction is encouraged,
a conflict between local grid conditions and system needs arise. In that event, solving the local
problem should be prioritised but this is not possible with ToU tariffs.
9.2.1.2. DYNAMIC TARIFFS
Dynamic tariffs apply price differentiation based on the current operational state of the system.
This means that, if the system has abundant supply, the price signal is lower to encourage more
use of electricity, and if there is a supply deficiency the price signal is increased to deter the use
of electricity.
The difference between the time of use (ToU) tariffs and the dynamic tariffs is mainly that the
ToU tariffs originate in fixed peak/off peak hours (easy to forecast even with long planning
horizon) whereas the dynamic tariffs consider the current state of the system, not necessarily
following the traditional peak/off-peak pattern. This makes them suitable for systems with VRE
penetration where the timing of the system peaks is varying from day to day.
The same tariff is charged to all consumers in the DSO control area, therefore, the price signals
can be coupled with the state of the system and with global system needs, which can be defined
as either:
a)
TSO system imbalance: Higher or lower price can be applied during timeframe where
supply-demand imbalance is forecasted (i.e., high consumption or high generation); or
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Figure 22 – Example of application of time of use tariffs by ERDF in France [74]
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b)
TSO regional imbalance: variable price can be applied when there is a foreseen transmission
grid congestion, based on congestion forecasts at a regular intervals (e.g. hourly).
Figure 23 – The Ecogrid EU market concept architecture: prices set by the TSO and its impact on the market actors [75]
Potential Challenges:
Even if the grid tariff is coupled with a price that represents the state of the system at a TSO
level, (i.e., wholesale price, balancing price) and acts to solve system imbalance, it does not
guarantee a solution to local congestion issues. For example, if there is a local congestion within
the DSO area that requires a decrease in demand during the time when there is a system
imbalance that signals the demand to increase instead, a conflict between local grid conditions
and system needs arise. In such cases the local problem should be prioritised, which is not
possible with this tariff system.
26
www.eu-ecogrid.net/
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Example of application: Dynamic tariffs are proposed by the Ecogrid EU26 market concept,
which is illustrated in Figure 23. In this example, when there is an imbalance (type a or b above)
detected in the system, the price would be set by a “real-time market operator,” which might be
the TSO or DSO (1). Therefore, depending on the state of the system, the real-time market
operator will increase or decrease the tariff in order to encourage consumers in the control area of
the DSO to decrease or increase electricity consumption. The price would be published and
retrieved by the market actors, which include the DR service providers (2). If no imbalance or
congestion exists, the real-time price will be equal to day-ahead price. The participating actors
would then adjust (3) the consumption according to the price signal and thus assist in maintaining
the power system balance. The “real-time market operator” can monitor the impact to verify that
equilibrium was established (4). The price would be updated frequently, every 5 minutes, to
utilise the potential for a dynamic response.
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Distribution grid tariffs in most countries constitute a part of the whole electricity price [76]. In
some cases, the impact it has on the end price-signal to customers could be limited to the point
that it becomes ineffective to steer changes in consumption behaviour.
9.2.1.3. LOCATIONAL DYNAMIC TARIFFS
Example of application: Locational dynamic tariffs are proposed by the Ecogrid EU market
concept27, which is explained in the example in the previous sub-section. A full-scale
demonstration of the concept is foreseen in a complete distribution system (Bornholm island),
owned and operated by the local DSO, Østkraft, in Denmark 28. The demonstration is run in three
phases, as shown in Figure 24. Basically system balancing, shown as Production & Consumption
in the centre of the figure, will consider three elements, in different phases. In the first phase, the
provision of global balancing services by DER and DR using energy and ancillary service price
signals is demonstrated. This will use the framework for price signals as described in the example
in the previous sub-section on dynamic pricing (the market perspective). In the second phase,
grid supervision and management technology is added to automate the operation of the
distribution system (the network perspective). In this phase, local congestions will be identified
and flagged. In the third and last phase, locational pricing is developed based on the local
congestion signals generated in the previous phase (combination of both market and network
perspective). Flexible resources would then be solicited with price signals to alleviate grid
congestion.
27
http://www.eu-ecogrid.net/ecogrid-eu/the-ecogrid-eu-market-concept
28
The Bornholm distribution system is part of the Nordic interconnected system and fully integrated in
the Nordic electricity market.
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Locational dynamic tariffs apply price differentiation based on time and location. This means
that, dynamic pricing is applied, but the price can be different for service providers at different
locations within the control area of the DSO. Therefore, a variable price is applied based on
where distribution grid congestion appears.
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Potential Challenges:
DSO would have to anticipate local congestions and set a price signal based on that forecast. This
means that DSOs should develop new tools and processes.
9.2.1.4. POWER BAND CONTRACT
Power band contracts apply a price differentiation on the energy charge component based on the
mean power of the peak hour. This means that the higher the power demand of the peak hour,
the higher the tariff for the customer. This dictates the EUR/kWh rate that a consumer has to pay
for a given period (e.g., could be applied for the entire consumption in a year, or only the hours
corresponding to that peak hour). This method encourages reduction of peak power by the
customer and essentially creates a dynamic link between the energy and capacity charge
components.
Example of application: Results from a pilot study in Finland [77] are shown in Figure 25 and
Figure 26. Normally, a customer is billed based on the main fuse size. For a main fuse of 3 x
25 A, the largest power band (utilised capacity) measured as an hourly power would be approx.
17 kW (red line in Figure 25). With a flat-rate EUR/kWh tariff which is indifferent to the level of
hourly consumption, there is no incentive to reduce consumption in a specific hour over another.
However, if the tariff is differentiated across the coloured bands (increasing with increased
power), the customer would be incentivised to minimise peaks and try to keep the hourly
consumption as low and even as possible. Figure 26 shows how the consumption pattern of a
customer was changed over a period of three years since their electricity bills were changed to a
power band contract. It can be seen that both the mean and peak power consumption have
decreased.
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Figure 24 – The Ecogrid EU demonstration phase [75]
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Figure 26 - Hourly electrical energy consumption over three consecutive years of a Finnish household with a power band
contract. [77]
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Figure 25 – Hourly electrical energy consumption of a Finnish household with a flat rate electricity tariff. Superimposed
with potential power bands [modified from [77]]
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Potential Challenges:
Consumer has to monitor/study his own consumption and understand when peak consumptions
occurs, and how to avoid it, which complicates the applicability of the approach.
9.2.2.
Volume-based procurement of demand response
Figure 27 – Volume-based procurement options of DR
Figure 27 lists the transaction options for DSOs to procure a certain volume of DR. When the
DSO identifies a need/potential benefit for DR in its network, it can procure it by:
making a direct contract (bilateral agreement) with the DR provider with customised
conditions;
issuing a call for tenders for the DR service required, whenever it is required; or
establishing a local market for DR providers connected to the network with local price.
Which option is most efficient and effective depends on the number of DR providers that are
connected to the network (liquidity of the market). Forming bilateral agreements are most
efficient when there are only few, and forming a local market can be efficient when there are
many market players.
Bilateral agreements and tenders establish a direct transaction between the DSO and local DR
service provider based on pre-defined conditions and price, while a local market would mean that
the DSO would source DR from a market which sells standardised products.
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DSOs can use volume-based incentives to procure DR. This means that the DSO will
(financially) compensate a DR service provider to activate a specific quantity of DR.
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DSOs as regulated entities have a non-discriminatory obligation and must carefully observe
market power effects. Therefore, it is important to ensure sufficient liquidity in a market.
Furthermore, reliability of DR delivery should be managed, for example by applying penalties if
there is a deviation between services sold and delivered (except in an emergency). However, it
must also be kept in mind that very strict reliability standards will discourage service providers
from participating in such a DR scheme.
Once a volume-based procurement method is established, the DSO could activate the actual
service in either of the following ways (see Figure 28):
Figure 28 – Curtailment and re-dispatching process aggregator DSO [78]
9.2.2.1. BILATERAL AGREEMENTS
Bilateral agreements are contracts signed between the DR service provider and DSO. The
contract would specify the conditions when and how the DR service provider would either
increase or decrease consumption. Key benefit is the ability to customise the service
requirements.
Such contracts may be standardised for simplicity, but should retain the freedom to formulate the
terms in order to fit the needs and preferences of the involved parties, e.g., in theory they could
have any time horizon or format. This is one of the benefits of bilateral contracting, compared
with other procurement designs. Such flexibility allows bilateral contracts to be used for a wide
range of services.
One example of a standardized approach to contracts is to revise the calculation method for the
fixed charge component of the distribution tariff, taking into account flexibility obligations. This
would apply to users (DG and consumers) connecting to the distribution network, paid either
directly to the DSO (i.e., customer has separate contracts to utility as energy supplier and DSO as
connection supplier) or paid by consumers to utilities, which then pay the DSOs.
Potential Challenges:
The nature of the product specifications in bilateral agreements makes it hard for traded services
to compare with other similar non-standardised products. Without clarity on how to develop
competitive products, it could discourage liquidity in the market. Furthermore, it may be seen by
the regulator as going against the transparency and market based approached directed by the EU.
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Curtail, which means uncompensated automatic control of DR-assets to curtail on pro-rata
across all assets; or
Re-dispatch, which is a more a market based solution, to compensate DR who can offer the
service at cheapest cost.
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Because the products are unique and not standardised, the value of the product becomes
subjective. The seller and buyer of the service need to have a good understanding of the service
offered, and also have an agreement about its value.
9.2.2.2. TENDERS
Call for tenders is a special procedure for generating competing offers from different bidders.
The DSO would announce the framework of the service contract to all interested service
providers. They would then assess the offers and offer binding service contracts to those that
comply with the stated requirements. Major benefit is the ability to customise the service
requirements.
The framework of a contract may include the following [78]:
Type of DR product (firm or optional, then notice of exercise)
Volume and duration;
Specified location;
Condition of delivery;
Invoicing and payment;
Measurement;
Penalties for failure to deliver;
Non-performance due to force majeure,
Confidentiality obligations;
Governing law and arbitration.
Example of application: In the United Kingdom, UK Power Networks has set-up bilateral
contract following a call for tenders with aggregators Flextricity, EDF Energy and Enernoc for
the provision of DR service to limit power congestions in selected distribution grid areas and
avoid reinforcements [79] [80]. UK Power Network’s business plan forecasts savings of around
£40 million [€52M] from DR schemes from 2015 until 2023 [81].
Potential Challenges:
To some degree, the potential challenges are similar to those faced by bilateral agreements. The
transparency of product description may be improved in the case of an open tender. However, the
ability to customise the technical requirements means that there will be a lack of standardisation
across different DSO areas. This could inhibit liquidity and competition from supply side.
Furthermore, the administrative burden to manage a call for tender could be considered high for a
DSO.
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The content of the contracts would be similar to that of a bilateral agreement, in that it would
specify the conditions when and how the DR service provider would either increase or decrease
consumption. The main difference is that with a tender, the content of the contracts are more
transparent compared to a bilateral agreement, due to the tendering process.
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9.2.2.3. LOCAL MARKETS
National markets in the form of system-wide balancing are already used for contracting DR.
However, no markets exist for solving issues from the DSO perspective only.
It could be possible to make use of this existing framework and treat the activation of DR for
DSO purposes as a “bonus” which is granted depending on the localisation of the flexibility and
its activation according to DSO’s needs
It is also possible to have local markets set up by the DSO or another regulated agent, to
exchange services locally. The design of the market could be very similar to the existing
wholesale markets. The local market could be obtained by applying a market splitting mechanism
from the TSO level market in case of a local congestion or problem. A local market approach
would be considered in the case of interconnected networks with a lot of local resources
connected to them and thus possible liquidities on the market.
The eTelligence project in the Cuxhaven region of Germany [82] developed and tested a complex
ICT-based system to balance the intermittent feed-in from wind energy generation by integrating
electricity into the grid and a regional market, while simultaneously enabling a more active
participation of small end-consumers into power markets of the region. A digital energy
marketplace was created, to enable small and medium-sized power-generating systems, as well as
medium to large electricity consumers, to trade energy products in a fully automated way. The
marketplace considers the availability of regional products under realistic conditions (grid29 and
generation capacity) and links together producers, commercial consumers with flexible loads and
energy service providers. A link to the wholesale market was also included, to ensure sufficient
liquidity on the marketplace. Some of the market participants included EWE NETZ GmbH,
various cogeneration plants and a virtual power plant consisting of a PV system, a wind farm and
two refrigerated warehouses.
Potential Challenges:
The more local a market is the less liquid it becomes. Besides, even if a local market is created, if
the DSO becomes the single service buyer, the local market would yield the same result as a call
for tenders. Therefore, the choice for a DSO between a call for tender or holding his own market
should be made based on the possible liquidities on the market and especially the market option
should be considered if there are other actors willing to buy the DR service. The only other actor
that would be willing to buy local DR services, besides the network operator, would be the
producers with regulated tariffs. Furthermore, in a meshed network with interconnections
between different DSOs, it would be possible for a DSO to become buyer on his neighbouring
DSO.
29
In order to understand the network behaviour, network measurement data (active and reactive power, voltages,
currents, frequency) was collected from 100 substations in the distribution network, saved and exported
on request. The data was recorded for the participation of the network in the regional marketplace. [100]
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Example of application: Local markets at pilot level (e.g. NiceGrid project in France; Etelligence project in Germany).
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If a DSO contracts DR to solve local constraints within a short timeframe, it will have to ensure
that the total balance at the point of connection with the TSO is not compromised, otherwise the
power flows at the TSO level and global system balance may be disrupted.
Furthermore, the administrative burden to manage a market would be considered high, especially
for small DSOs.
9.2.3.
Procurement options: Conclusions
Dynamic tariffs require the definition of a “real-time market operator” (i.e., DSO), and this entity
would have to set up a platform to send a signal to DR service providers about the tariff change
in a given time interval depending on grid conditions. This setup is complicated compared to
current practices, but would be fairly simple to implement from a technological point of view,
which would require a one-time effort to set up the system, and then could run as an automated
process.
Locational dynamic tariffs, which are based on the state of the system in terms of grid
congestions close to real-time, would require much more frequent forecasting and monitoring of
the power flows in the system compared to current practice by TSOs and DSOs. Therefore
intensive efforts would need to be invested to realise locational dynamic tariffs.
Bilateral agreements and tenders establish a direct transaction between the DSO and local DR
service provider based on pre-defined conditions and price, while a local market would mean that
the DSO would source DR from a market which sells standardised products. However, the
creation of local markets or even tendering would require liquidity in the market. Furthermore,
the additional administrative burden could be difficult for small DSOs to manage. In this respect,
bilateral agreements and tendering are probably the most accessible and effective options from
the perspective of a DSO.
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Static priced-based procurement options like the ToU tariff and power band contracts could be
relatively easy to implement. ToU tariffs would be defined ex-ante by the regulator, based on an
analysis of historical data about peak demand. Power band charges would be applied ex-post, and
could be integrated into existing billing processes.
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10.
ASSESSMENT OF THE VALUE OF DEMAND
RESPONSE TO IMPROVE DISTRIBUTION GRID
EFFICIENCY AT PLANNING AND OPERATION
10.1. Objectives and global approach
The main objective of this chapter is to study, via simulation analysis, how flexibility can
improve the efficiency of distribution networks, with an increasing DG penetration levels, while
respecting operation rules (Min – Max voltage limits, conductors and transformers load rate, N-1
security).
Figure 29 – Planning of distribution grids accounts for security constraints and OPEX/CAPEX trade-off
Figure 30 summarizes the three-step approach that has been used in the analysis.
Figure 30 – Followed approach for studying benefits brought by flexibility
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Specifically, we assess how the integration of DR in DSOs planning and operation could allow
finding the optimal trade-off between CAPEX (investment in equipment/asset replacement and
upgrade) and OPEX spending (in particular cost of thermal losses) (see Figure 29).
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Modelling approach: “Hosting capacity of existing grid” vs “Greenfield ideal network”
To carry out this kind of analysis, several studies follow a classical “hosting capacity” approach.
This consists in finding the maximum amount of DG that an existing grid can integrate safely
without operational security violation.
In this study, we follow a “greenfield” approach, which allows exploring the benefits of a
complete new approach of DSO grid planning and operation which includes flexibility. It consists
in designing the ideal network structure to integrate a long term target level of DG at least cost
(CAPEX+OPEX), given the load, generation and grid characteristics in specific regions. This
allows drawing general conclusions about the value of integrating DR into DSOs’ planning and
operation.
Smart sizing simulation tool
Figure 31 summarizes the Smart Sizing tool. In the model, the following cost items are included:
OPEX:
Technical losses costs
Activation cost of the flexibility (e.g. procurement of volume-based flexibility by the DSO)
CAPEX
Network components and installation (e.g. lines, substations)
ICT infrastructure for activation of flexibility
This tool allows a global optimization of OPEX and CAPEX by selecting the optimal mix of
traditional and flexibility-based investments.
30
www.plangridev.eu
31 https://gredor.be
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The Smart Sizing tool, developed by Tractebel in Smart Grid pilot projects like PlanGridEV30 and
Gredor31, has been used for this study. It allows modelling both technical (e.g. network
components, DG) and economic aspects (CAPEX and OPEX) when planning investments in the
distribution grid.
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10.2. Building the simulation models
The following realistic four distribution system models have been built based on consultants’
own collected data and experience in previous assignments.
Distribution network in urban area located in Northern Europe;
Distribution network in rural area located in Northern Europe;
Distribution network in urban area located in Southern Europe;
Distribution network in rural area located in Southern Europe.
These models are intended to provide a contrasting set of operating conditions to test the
robustness of the simulation results. Realistic load and decentralised generation data profiles,
coherent with grid characteristics and geographical locations, have been considered.
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Figure 31 - SMART SIZING TOOL: global optimization of OPEX and CAPEX
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The modelling phase has been organized in four specific steps:
a)
b)
c)
d)
Modelling the grid structure
Modelling distributed generation
Modelling the load
Analysis of representative days of the year
a)
Modelling the grid structure
The specificities of the grid are represented by parameters like
Type of conductor (overhead/underground)
Network structure (radial/meshed)
Average feeder lengths
Average number of feeders per substations
The associated costs of the assets (e.g. cost of cables per km) have then been defined.
b)
Modelling the distributed generation
Figure 33 summarizes the characteristics of DG in each model: the type (PV, wind, CHP)
and the associated voltage level (LV, MV, connection to HV/MV substation).
This repartition has been done based on the information given in [24]. Accordingly, urban
areas are mainly characterised by PV connected in LV while rural areas host a larger portion
of wind connected in Medium Voltage.
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Figure 32 - Four distribution systems models
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Figure 34 reports selected generation profiles, representative of local conditions (level of sun
irradiation, wind speed, district heating system, and individual cogeneration system). Urban areas
are mainly characterized by PV connected in LV (low voltage) and in MV (medium voltage),
while rural areas are characterised mainly by wind connected in MV. Figure 35 reports the share
of DG across voltage levels in the four models.
Figure 34 - Characteristics of generation for each model – Generation profiles
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Figure 33 - Characteristics of generation for each model – Peak load and share across voltage levels
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Figure 35 - Voltage level of connection of Distributed Generation
c)
Modelling load
Figure 36 – Characteristics of load for each model – Peak load and share across voltage levels
Figure 37 summarizes the different hourly load curves that have been considered to reflect the
local specificities of each model (density of customers, categories of customers, economical
sectors). The type of houses, heat & cooling types, thermic insulation, and seasonal variations
impact the residential load pattern.
The level of LV and MV commercial load depends of the area density (urban vs rural).
Industrial loads are mainly located in urban areas and connected in MV.
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Figure 36 reports the characteristics of the load considered for each model, in terms of peak
and load repartition across voltage levels.
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d)
Modelling representative days
To reduce computational complexity, simulations have been run on a sub-set of days
representative of the annual behaviour of the distribution system (Figure 38).
A total of 10 days (non-consecutive) have been selected. We have considered days with
“typical conditions”, and days with “extreme conditions”. The latter are days in which
generation and load profiles are particularly constraining for the grid in terms of peak load or
reverse peak.
Days with “Typical conditions”
Winter weekday, summer weekday, inter-season weekday;
Winter weekend, summer weekend, inter-season weekend;
Days with “Extreme conditions”
Total peak load (net load) and reverse peak (net load);
LV peak load (net load) and LV reverse peak (net load).
The probability of occurrence of the different days has then been considered to weigh the
simulation results associated to the corresponding day.
Figure 38 - Selection of 10 days to be representative of the annual behaviour
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Figure 37 - Characteristics of generation for each model – Load profiles
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10.3. Definition of scenarios and simulation results
1)
2)
3)
4)
Base case scenario – In this scenario, planning and operation rely only on traditional
investments, without the option of demand response. The impact of the total cost of the
system is then studied for the 4 models (North Urban, South Urban, North rural, South rural)
when increasing the Decentralised Generation (DG) penetration level.
Time-of-Use scenario - In this scenario, it is assumed a (exogenous) reshaping of the load
profiles in the simulation thanks to the introduction of ToU tariffs. The tool does not
optimize the load profiles, it just considered the ToU-based load profiles as input to the
simulation.
Demand response scenario – In this scenario, the tool is able to arbitrate between
investments in traditional assets or demand response (ICT investment costs and activation
costs).
Full flex scenario: Demand response and DG curtailment options– In this scenario,
DSOs can opt for two competing sources of flexibility competing on a cost basis: Demand
Response and Curtailment of DGs.
Within each scenario, the behaviour of the system has been assessed by increasing progressively
and homotetically the installed capacity of the different types of DG connected at LV and MV
(Figure 39).
In each scenario, DG penetration level varies from 0% to 400% of peak demand (considering
with 50% increment steps).
The same process is repeated for each of the four network models (North urban; North rural,
South Urban; South Rural) leading to a total of 16 simulation models (Figure 40). Considering
variations of parameters within each scenario (e.g. penetration level of DG), over 150 simulation
cases have been analysed.
Figure 39 – Simulation scenarios for each network model
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For each network model defined in the previous section, four different scenarios have been
defined according to the level of flexibility at DSOs’ disposal:
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Figure 40 – Global picture of the built models and studied scenarios to study all flexibility options
10.3.1. Base case
The base case does not consider any type of flexibility. The increase of DG penetration leads to
reverse power flows that can lead to a reverse peak. When the reverse peak is higher than the
direct peak load, it will size the system and will therefore directly impact the CAPEX (see
ANNEX V for additional detailed simulation results).
Figure 41 shows the evolution of the TOTEX (CAPEX + OPEX) from 0% to 400% DG
penetration level. The TOTEX is expressed in percentage of the case with 0% of DG penetration
of the same model (it means that by example, north rural case with generation is compared with
the north rural case without generation).
Key observations:
The increasing penetration of DG has first a positive impact on the TOTEX up to a minimum
cost value (optimal point). In fact, at first DG penetration allows to reduce flows in the
distribution grid (reduction of losses and of need for reinforcements).
Beyond a certain DG penetration level (break-even point) TOTEX becomes actually higher
than in the case without any DG: this is due to the need to increase CAPEX to reach higher
DG hosting capacity.
DG penetration level corresponding to the optimal and the break-even points strongly depends
on the characteristics of the load, of the share of generation and of the characteristics of the
network
- For example; in proposed case studies optimal cost and break-even points are reached
quicker in urban areas. This is due to the fact that reverse power flows in LV (which have
the highest investment impact as they affect the whole distribution system) first occur in
urban areas where it is assumed that a higher share of DG is connected in LV.
In rural areas, break even points are reached for a higher penetration level of DG compared to
the urban cases.
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Figure 41 – Evolution of the total cost for the 4 distribution systems
10.3.2. Time-of-Use scenario
In the “Time of Use scenario”, three types of ToU tariffs with different on peak/off-peak time
periods have been considered (Figure 42; Figure 43):
Night/Day: Off-Peak from 8pm to 8am
Mid-day: Off-Peak from 1pm to 4pm
Multi-period: Off-Peak from 8pm to 8am and from 1pm to 4pm
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- Rural areas are mainly characterised by overhead lines which are cheaper than
underground conductors. The optimal investment strategy is thus to oversize conductors to
reduce losses.
- Rural grids are more loss-driven, since the losses account for a higher share of the total
cost than in urban grids.
The cost of losses (€/MWh) strongly affects the optimal balance between CAPEX and OPEX.
With higher cost of losses, the investment decisions are steered toward oversizing the grid
(thus higher CAPEX) to minimize the OPEX of losses.
For a given DG penetration level, variety in the DG mix (in terms of voltage connection and
type) provides positive impacts on distribution system costs compared to the case of one type
of DG connected at a single voltage level (e.g. only PV connected in LV)
- Single connection point: In general, DG all connected at LV determines cost impact on all
upstream levels (both for CAPEX and OPEX)
- Single DG type (e.g. only PV or wind): the concentration of generation in the same time
periods could lead to an increase of the reverse peak and thus to higher infrastructure
investments.
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Midday ToU
16
14
12
10
8
6
4
2
0
Load [MW]
Load [MW]
Night/day ToU
0
10
20
16
14
12
10
8
6
4
2
0
Load
without
TOU
Load
with
TOU
0
5
10
15
20
For sake of simplicity; it has been assumed that the impact of ToU tariffs lead to a transfer of 5%
of the energy from the on-peak to the off-peak period. The optimized load profile is then used as
an input for the Smart Sizing simulation to assess the delta TOTEX compared with the base case
scenario.
Key observations are:
Night/day ToU (Figure 43)
Positive impact on TOTEX for low penetration of DG, thanks to the reduction of peak
demand and associated CAPEX investments
With higher DG penetration, TOTEX starts to actually increase compared to the base case.
Thi is due to the decrease of the load during the PV peak, leading to higher reverse flows
Mid-day ToU (Figure 43)
Reduction of TOTEX is achieved beyond a certain threshold of DG penetration, when higher
peak consumption provides benefits in balancing peak DG injection.
Multi-period ToU
It allows combining the benefits of both Night/Day and Mid/day ToU cases in a wide variety
of conditions (rural/urban, north/south, different DG penetration levels). Results are available
in Annex I.
Simulation results show that a well-defined ToU tariffs can indeed provide benefits in terms of
CAPEX and OPEX for the distribution grid. The level of impact strongly depends on the specific
characteristics of the grid and of the load/generation conditions.
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Figure 42 – Different ToU periods considered
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A key aspect is to well design time periods in a way to incentivize a synchronization of local load
and generation. Under certain conditions, ToU might actually lead to negative TOTEX impacts,
due to an increase of reverse power flows to a higher level than actual peak demand.
Night/day ToU
Midday ToU
4%
4%
3%
3%
2%
1%
North urban
0%
-1%
-2%
0%
50%
100%
150%
200%
250%
300%
350%
400%
400%
350%
300%
250%
200%
150%
50%
0%
100%
-2%
South rural
1%
0%
-1%
North rural
2%
South urban
10.3.3. Demand response (volume-based)
Under this scenario, DSOs have the option to select two types of investments:
Investments in traditional grid assets
Investments in Demand Response
Two costs are associated to Demand Response:
Demand Response CAPEX: cost of deploying the ICT infrastructure to activate Demand
Response (e.g. Smart Metering infrastructure)
Demand Response OPEX: cost of activation of demand response (which includes all the costs
borne by aggregators to provide demand response services: e.g. energy boxes, aggregation
platforms etc.)
Thus, the optimization strategy aims at selecting the investments that minimize the total CAPEX
(traditional assets + ICT assets) and OPEX (cost of losses + costs of DR activation).
In the following we assess how the availability of DR can reduce DSOs’ system costs compared
to the base case.
Clearly, these benefits will strictly depend on the costs (CAPEX and OPEX) associated to DR.
Therefore in the following we will carry out two different types of analysis:
Case 1: Sensitivity analysis on DR activation cost (DR OPEX)
DR OPEX: Analysis of 4 different activation costs for DR,
DR CAPEX: Negligible ICT infrastructure costs. This corresponds to a case in which smart
meters are already in place.
Case 2: Sensitivity analysis on DR ICT investment cost (DR CAPEX)
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Figure 43- TOTEX reduction with Night/Day and Mid-day ToU tariffs
DR CAPEX: Use of two different ICT infrastructure costs of DR,
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DR OPEX: Fixed activation cost of DR .
The proposed two cases represent the two extreme of cost allocation of the ICT infrastructure to
DR programs: in the first case DR program does not borne any cost (e.g. because smart meters
are supposed to be already in place) whereas in the second case it includes the whole ICT
infrastructure cost. This allows having robust results, as, depending on the conditions in different
countries, intermediary cost conditions will hold.
Case 1 – Demand response scenario - Sensitivity analysis on DR OPEX
Nearly free
1€/MWh
Equivalent to a 10% reduction
20€/MWh
Equivalent to a 25% reduction
50€/MWh
Equivalent to a 40% reduction
(=Night tariff)
80€/MWh
Equivalent to a 75% reduction
150€/MWh
Table 20 - Simulated Demand Response activation cost
Figure 44 reports the TOTEX variation as compared to the case without DG and without
flexibility, for different DR activation costs.
Key observations are:
DR has always a positive effect on the TOTEX in the different grid conditions
The value of DR in terms of TOTEX reduction increases with DG penetration
Even in presence of relatively high DR activation cost (150€/MWh), DR brings benefits. This
hints that a business case for DR services to DSOs could be reasonably pursued.
DR brings more values in the urban case, where DG is mainly represented by PV connected in
LV.
32
Source : https://ec.europa.eu/energy/en/statistics/market-analysis
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In this case, the cost of DR is limited to the activation cost and does not consider investments in
ICT. Several activation costs have been tested, based on an average end-user price of electricity
of 200 €/MWh32 and are presented in the Table 20.
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Figure 44 - TOTEX in % for different DR activation costs as compared to the case without DG and without flexibility
The implementation of DR could also lead in some circumstances to an increase of the losses
while providing a reduction of the TOTEX (e.g. see north-urban case in Figure 45, where the
optimal investment strategy leads to privilege a minimization of CAPEX, at the expenses of a
higher OPEX).
With growing penetration of DGs, the benefis of DR in terms of reduction of total costs and of
losses become increasingly significant (compare Figure 45 and Figure 46 which refer to a DG
penetration equal to 100% of peak demand and to 400% of pea demand respectively).
Figure 45 – Decrease of TOTEX and decrease of losses for different at 100% DG penetration level
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Figure 46 – Decrease of TOTEX and decrease of losses for different at 400% DG penetration level
The maximum daily DR (percentage of the load affected by DR activation) increases with the
DG penetration, as shown on the charts of the Figure 47. With high level of DG penetration, the
maximum daily demand response level is generally higher in urban areas. Again, this is due to
the different share of DGs among voltage levels and to the cost of cables (overhead
/underground).
Figure 47 – Maximum daily load demand response (in percentage of the total load) – results for a 80€/MWh demand
response cost
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The amount of load which is shifted during the year is generally low, but it has a large impact for
reducing the direct and/or the reverse peak..
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Case 2 – Demand response scenario - Sensitivity analyses on DR CAPEX
In this case, we test the impact of DR for different level of ICT infrastructure costs (see Table 21)
and a fixed DR activation cost of 80€/MWh .
LV grid
MV grid
Low deployment cost
50 k€/MW
0.5 k€/MW
High deployment cost
200 €/MW
2 k€/MW
Table 21 - Simulated ICT deployment costs
The result of the simulations is presented at the Figure 48.
ICT costs seem in general to have a limited impact on overall TOTEX reduction.
The ICT costs particularly influence the activation of DR at LV (residential consumers). In the
“high ICT deployment cost” case, DR in LV is rarely activated.
High ICT costs have a significant negative effect particularly in presence of high residential
PV penetration (urban areas) as they limit the availability of DR at residential level to balance
local PV generation).
The decrease of ICT costs in MV has a higher positive impact on TOTEX in those cases where a
large part of DG is connected in MV (rural areas)
Figure 48 – TOTEX decrease as compared with the base case without flexibility for two ICT infrastructure deployment
cost
10.3.4. Full-flex scenario
Under this scenario, DSOs can opt for two sources of flexibility for the optimization of their
planning and operation: DR and generation curtailment. The goal of the scenario is to assess the
benefits of DR in a more constraining situation, where a competing source of flexibility
(generation curtailment) is also at DSOs’ disposal.
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Key observations are:
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The compensation cost for the curtailed energy has been set at 120€/MWh, representing 150% of
the average price of the energy sold on the wholesale market (80€/MWh33). The cost hypotheses
are therefore the following:
DR activation cost
80€/MWh
ICT investment cost LV - MV
50€/MW – 0.05€/MW
Generation curtailment cost
120€/MWh
Figure 49 presents the TOTEX reduction in the full-flex scenario for different level of DG
penetration.
8%
7%
6%
5%
4%
3%
2%
1%
0%
100% DG
penetration
200% DG
penetration
300% DG
penetration
Figure 49 - Total cost decrease in the full-flex scenario
100
80
60
40
20
0
South urban
2000
1000
Generation
curtailment
0
0%
100%
200%
300%
400%
Energy [MWh]
Energy [MWh]
South rural
Demand
Response
% DG penetration
% DG penetration
Figure 50 - Use of Demand Response and generation curtailment
33
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TOTEX decrease (%)*
Figure 50 presents the activation level of DR and generation curtailment under different DG
penetration level.
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Key observations are:
Figure 51 – DR impact on loss reduction considering two different loss costs (80€/MWh and 120€/MWh)
10.4. Conclusions and key messages
In the following we provide a summary of the main messages resulting from the simulation
analysis, which are valid on average on a given grid. Deviations are possible on specific
feeders/substations depending on local specificities.
Demand response is effective in reducing the total cost of the distribution grid
Demand response is an effective means to reduce the TOTEX (Total Expenditure) of the
system (CAPEX+OPEX), when integrated in grid planning and operation:
- Via reduction of direct and inverse peaks in the network and thus the need for
infrastructure costs (CAPEX).
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DR is typically the preferred flexibility option: at first for reducing direct peak load and then
for reducing inverse net peak load for high DG penetration levels
At high level of DG penetration level, when reverse peak becomes the driver to size grid
assets, generation curtailment becomes also a viable flexibility option.
With low penetration of DG, DR is mainly used in MV rather than in LV for urban areas, due
to the low ICT costs considered in LV. This holds until a certain level of DG penetration (high
reverse flows from PV require more DR in LV) (detailed results are available in Annex I).
The cost of losses strongly affects the procurement strategy of DR. The simulation analysis
shows that DR activation specifically targets loss reduction only with a sufficiently high level
of loss costs (see Figure 51 – DR impact on loss reduction considering two different loss costs (80€/MWh and
120€/MWh)Figure 51, where a 50% increase in loss costs per MWh has been considered). This
is particularly the case in rural areas where the overall investment strategy is more loss-driven
as discussed previously.
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- Via reduction of technical losses (OPEX) by performing a local balancing with DG and
reducing the flows in the system.
The reduction of TOTEX is achieved by finding the optimal balance between CAPEX and
OPEX (in certain conditions, the level of technical losses could also increase, if off-set by a
decrease of CAPEX). Of course, regulation should specifically incentivize DSOs to reduce
TOTEX (please refer to subsequent chapters).
If DSOs do not receive the right incentives to reduce losses, then the use of flexibility could
mainly target CAPEX reductions (i.e. size assets to their capacity limits) and possibly lead
to higher loss volumes.
Explicit (Volume-based) Demand Response can bring higher benefits to the system
compared to implicit (tariff-based) Demand Response.
Both implicit and explicit DR can reduce the total cost of the system. Both approaches can
also be implemented together to maximize the impact (even if the two mechanisms can be in
competition with each other, as the presence of ToU reduces the amount of flexibility
exploitable by explicit DR).
Volume-based (explicit) DR can bring higher benefits as it can be more precisely activated
according to DSO’s needs.
For a volume-based flexibility market to develop, DSOs should make clearly accessible the
information about their flexibility needs (present and future). This would allow revealing the
availability of flexibility resources and contributing to creation of a flexibility market.
Time of Use (ToU) tariff can provide significant system benefits, but they need to be
carefully tailored to specific local conditions.
The impact of ToU tariffs on TOTEX depends on the level of penetration of DGs and on local
conditions.
In general, ToU can provide significant benefits and facilitate the integration of DG at a lower
cost when the off-peak period is synchronized with DG generation. A location-based
component in the ToU could help better tailor the tariff design to local conditions.
Poorly designed ToU timeframes, under certain conditions, could also have a negative effect.
For example, with high DG penetration, simple day-night ToU might actually worsen the
costs of DG hosting capacity.
The cost/benefit ratio of Demand Response for DSOs depends on local conditions
On the benefit side:
The value of DR for DSOs strongly depends on the level of penetration of DG, on the
consumption patterns and on the grid characteristics.
Typically, the simulations shows that a limited number of activation of DR per year can
already provide significant benefits for the system
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The simulation analysis shows that DR activation targets specifically loss reduction only if
sufficiently high costs for losses are considered.
Regulation needs to pay attention to the costs of losses paid by the DSOs (or to provide the
right loss penalties) to avoid that DSOs have an incentive to use flexibility to downsize assets
(reduction of CAPEX), even if this leads to an increase of losses.
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On the cost side:
A key factor affecting the business case of DR for DSOs is the availability and cost of an ICT
infrastructure (smart meters).
In those regions where smart meters are already being deployed (driven by policy decisions),
the business case for DR is stronger both for residential and commercial/industrial customers
(as DR programs do not need to include ICT infrastructure costs)
In those regions where the cost of the ICT infrastructure should be borne by the DR program,
it is recommended to focus on DR at MV level get most of the benefits.
With limited penetration of DG, DR is the most economically viable flexibility option to drive
down the system cost. (this holds on average on a given grid. Deviations are possible on
specific feeders/substations depending on localspecificities. For example in rural areas, with
limited load, curtailement of DG could be the best flexibility option to solve congestions on
specific feeders).
With very high penetration of DG, reverse peak (peak of reverse power flows) becomes the
main driver to size grid assets. Beyond that DG penetration level, generation curtailment
becomes an economically viable option together with DR.
Demand Response has typically a positive impact on grid technical losses, but reduction of
losses is not the main driver for its activation
Reduction of losses is typically not the main driver of the activation of DR, rather a side-effect
of the activation of DR to reduce the CAPEX.
Higher loss reduction level is typically achieved in rural areas, given the structure of rural
grids (longer feeders, overhead lines), for which cost of losses has a higher share of the
TOTEX.
Demand flexibility allows a higher DG hosting capacity at lower cost
DR increases the syncronicity of the demand with DG and mainly reduces CAPEX.
The positive impact of DR on DG hosting capacity becomes increasingly significant after a
certain DG penetration level.
In fact, a limited penetration of DG, even without any flexibility, could actually decrease the
TOTEX of the system compared to the case without any DG (thanks to DGs balancing local
demand and reducing network flows).
Strong penetration of residential PV will drive more rapidly the need for flexibility
A lack of variety in the types (PV, wind, CHP) and connection level (MV, LV) of DG has a
negative effect on grid efficiency, driving up CAPEX (reinforcements) and OPEX (technical
losses).
This means that in those areas where a single source of DG is mainly present (e.g. areas with
strong residential PV), demand flexibility can particularly provide positive system impact and
increase grid efficiency. This is of course dependent on the availability of sufficient level of
demand flexibility (in which case DG curtailment could be the main flexibility option to
explore).
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Demand Response is always part of the best flexibility mix to decrease the total cost of
distribution grid planning and operation
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11.
REGULATORY EVOLUTIONS FOR
DISTRIBUTION SYSTEM OPERATORS TO
PROCURE AND ACTIVATE DEMAND RESPONSE
This chapter presents the key regulatory evolutions that will allow DSOs to procure and activate
DR. We identify four key needed regulatory evolutions, in terms of how to:
use tariff signals (price-based approach – implicit DR),
directly procure DR (volume-based approach – explicit DR),
avoid conflicts with use and ownership of flexibility assets and
Validate transactions at distribution level.
11.1. Price-based approach: DSOs’ use of tariff
signals
In this section, we discuss the effectiveness of regulatory evolutions in using grid tariffs as a
lever for activating DR for DSOs’ needs.
Distribution grid tariffs are generally made of a number of components [83]:
Fixed component : fixed payment per customer per year (covers fixed costs like metering);
Capacity component : euro per kW (measured capacity) or per kVA (connection capacity);
Energy component: euro per kWh (proportionate, progressive or regressive).
Other components: reactive power, renewable energy charge, etc
A grid tariff may consist of one or more of the above components. Figure 52 shows which
components most commonly apply in European countries. As can be seen, key components are
the fixed and energy charges, followed by capacity and other charges. Reactive charges are
mostly not applied.
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a.
b.
c.
d.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Under a price-based approach for DR, the regulator should define the required changes in
network tariffs to incentivise DR. The following main regulatory evolutions can be considered:
Regulator defines the share of the different components, e.g. by defining a higher capacity
component. For example, power band contracts (see section 9.2.1, Power band contract)
modify the capacity charge component, while the fixed charge component could be subject to
negotiations as a contract clause in bilateral agreements.
Regulator defines different time intervals for ToU tariffs: For example, by introducing
time intervals which are aligned to PV production for areas with high PV penetration.
Regulator allows the DSOs to dynamically change the tariff according to grid
conditions: in order to pursue this option, measures should be taken to manage conflicts with
participation in wholesale energy market prices (distribution congestion needs vs wholesale
energy market needs).
To allow demand response actions, distribution network tariffs should be able to form clear
signals. However, distribution tariffs are only a component of the entire electricity tariff paid by a
consumer. Figure 53 shows the distribution grid tariff component of electricity tariffs in different
EU Member States. It can be seen that distribution grid tariffs constitute between 8% and 45% of
the entire electricity tariff. This means that the cost component that a DSO can influence may be
quite small om some cases, therefore the price signal could be insufficient to trigger DR. Where
the proportion is relatively large, distribution grid tariffs could be used as an effective price signal
to activate DR for DSOs’ needs.
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Figure 52 – Distribution tariff components in EU countries by consumer group: Household [83]
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Figure 53 – Distribution grid tariff component of final electricity tariffs in EU Member States (2014 S2) [Ecofys analysis
34
based on data from Eurostat]
11.2. Volume-based approach: DSOs directly
procure demand response
In general, DSOs would be willing to pay for DR if the avoided costs are larger than the cost of
contracting DR. Under a volume based procurement approach, DSOs procure directly the
required amount of DR services and remunerate the provided service.
There are three main value streams that could be considered for covering DR costs, which
however may not be sufficient to incentivise volume-based DR for the reasons discussed below:
1)
2)
Savings from avoided investment costs: these savings depend on the specific network
asset conditions and system evolution.
Savings from avoided payments for curtailing DG-RES to solve congestions: these
payments depend on the regulatory framework and in some countries are not applicable.
Due to the abovementioned reasons, in most cases, incentives to optimise operation are either
insufficient or not clearly visible to DSOs in its current form. Additional regulatory adjustments
could help to emphasise the incentives in optimising operation, and facilitate more freedom for
DSOS on how to recover the costs of procuring DR. To incentivise DSOs to capture benefits
from optimising operation and planning, an effective solution would be to allow them to capture
34
Note that data from Eurostat does not report consistently what constitutes distribution grid charge
across different countries. This means that, the renewables surcharge and some form of taxes like local
tax may be included in the percentage calculation for some countries. In this case, it would not
correspond to the actual percentage that the DSO could steer. Detailed review of each country in terms
of their distribution grid structure would be prudent, but is out of scope of this report.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
both CAPEX and OPEX savings. This can be done by allowing DSO remuneration to reflect
Total Expenditure (TOTEX, which encompasses both CAPEX and OPEX).35
11.2.1. Evolution of DSO remuneration based on TOTEX
Regulation based on TOTEX is suited to provide appropriate incentives for the use of “smarter”
solutions. This means regulation focusing on total costs (TOTEX) rather than capital cost
(CAPEX) and operational costs (OPEX) separately. Such a setup would allow more freedom to
the DSO to decide on approaches that could either affect CAPEX or OPEX, such as to use DER
to solve network related problems rather than investing in additional network infrastructure.
Concretely, TOTEX regulation could allow DSOs to arbitrate between traditional grid
investments (CAPEX) and procurement of flex-based solutions (OPEX).
For example, to that end, suitable mechanisms could be the application of bonus/malus regulation
for a given loss target or to explicitly encourage energy efficiency investments in the regulated
asset base.
Example case: UK regulation
Regulation in the UK is seen as the most progressive in allowing TOTEX to be reflected in
distribution grid tariffs and thus encouraging optimisation of operation as well as planning. The
key characteristics are presented below:
Calculation Method of Distribution Grid Tariffs
Distribution Use of System Charges are calculated by applying the Common Distribution
Charging Methodology (CDCM) which is approved by OFGEM. This methodology also implies
a cost component for losses but does not incentivise grid operators for a loss reduction.
Treatment of Losses in the price-control scheme
Distribution losses are calculated by OFGEM as an allowed loss percentage on an annual basis
for each distribution company. Distribution system operators are receiving a reward or penalty
based on the performance against the target. In 2009 the financial incentive for the reduction of
transmission losses has been 5p/kWh.
35
Another option is to allow DSOs to act as aggregators of DR, so that they can participate in wholesale
and balancing markets, where benefits are generated. However this option is not allowed due to
unbundling rules.
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Additionally, specific incentive regulation could be foreseen to steer DSOs’ CAPEX/OPEX
investment decisions toward desirable performance objectives, like reduction of losses.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Treatment of operational and capital costs
The regulation of both transmission and distribution system operators is designed with a certain
amount of allowed capital and operational costs for each company. Operational costs of
distribution grid operators are defined by the performance of their business operations and
maintaining their network. Capital costs are expenditure on investment in distribution assets with
a long lifetime, such as underground cables, overhead electricity lines and substations. As a
consequence, DSOs are remunerated for investments in efficient grid infrastructure as long as
they are part of their forecasts in the business plans and at the same time they are incentivised to
minimise operational costs. This design is an effective and efficient combination of output- and
input-based components to achieve loss reductions as it also takes into account the lifetime
operational costs.
The overall regulatory design of the UK has incentives for loss reductions. The incentives for
distribution grid operators are clear, as the regulatory authority calculates allowed loss
percentages annually. DSOs are penalised or rewarded based on their performance in losses.
Thus, they have strong financial incentives to improve in distribution efficiency. At the same
time, they are rewarded for their announced capital costs and incentivised to minimise
operational costs. This represents a combination of input- and output- based regulatory scheme.
11.3. Role of DSO regarding ownership and use of
flexibility assets
DSOs have the best overview of resources in its local network area. If the DSO has the option to
contract DR in its area, they could be activated to manage congestions in the distribution grid. In
cases when forecasted congestions do not finally take place, the DSO might be in the position to
have contracted flexibility which could be used for other purposes (surplus flexibility). In this
case, an option could be that the DSO sells this flexibility to other entities acting like an
aggregator. From a technical point of view, the following actions could be possible:
DSO can activate local DR to manage congestion in its own area, and where there is surplus
flexibility, sell the service at the balancing market.
DSO can activate local DR to manage congestion in its own area, and where there is surplus
flexibility, it could then sell the DR service to the TSO, if the TSO is willing to pay for DR to
solve congestion.
Alternatively, the DSO could sell the DR service to the DG in its area, to avoid curtailment, if
DG is willing to pay for DR to avoid curtailment, or DSO avoids paying compensation for
curtailment to RES-DG.
Since DSOs have the best overview on the grid situation, one could argue that they could make
optimal use of flexibility. However, DSOs are regulated entities and their operations in terms of
commercial transactions should be checked so that they do not impede competition. In this
respect, in most cases DSOs shall not be able to use the surplus flexibility, since he is not allowed
to trade in the market.
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Conclusion
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
A solution for allowing these resources to reach the market could be that the DSO frees the
specific market participants from their obligation, allowing them to reach other markets (use it or
lose it concept). In this respect, the flexibility resources could further reach to additional revenues
from offering services to the higher system levels. In this concept, it would be critical that the
DSO is responsible for approving activation of the specific DR that are in excess.
An application example where the DSO is controlling local flexibility for solving local
congestions exists in Italy. In the ENEL network, electrical storage systems (ESS) have been
identified as a promising flexibility resource which generates several benefits. For example, the
POI project is used to demonstrate how DSOs could use flexibility to solve congestions in both
the distribution grid and transmission grid, and also serves the balancing market. [84]
Flexibility from DR (or generally from Distributed Energy Resources) in the distribution grid can
be used for three key purposes: a) in the system level (participation to balancing markets) b) in
transmission grid level (alleviation of transmission grid congestions) and c) in the distribution
grid level (alleviation of distribution grid congestions). However, the way the resource is used by
the different levels may result in a conflict. This is why a prioritisation process is needed to use
DR for optimisation of grid operation and planning. Conflicting interests of grid and market
based use of flexibilities and between DSOs and TSOs need to be identified and access rights
need to be defined.
Market and grid based control signals and access rights to local flexibilities
Local conditions inevitably deviate from the global conditions at system level. In such cases, the
local grid situation could lead to different signals for flexibility services than the global market
situation,. An example of this is when the system is short of balancing power, and at the same
time a congestion appears at the DSO level due to local overproduction. In this case, the global
signal for DR is to reduce demand or increase generation, while the local conditions need the
opposite to happen to alleviate the congestion, i.e. increase local demand or reduce local
generation. In such cases, a clear prioritisation should be in place.
There are different options to coordinate market and grid based signals. One option is to limit
control signals, meaning that only one of either third market parties or grid operators get access
to the local flexibility. Another option is to prioritise one of the control signals, meaning that
both, third party marketer and the grid operator get access to local flexibility but in cases of
conflicting signals, one of the signals is prioritised. The second option allows more efficient use
of resources, as it is shared between two purposes, while the first option makes it mutually
exclusive.
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11.4. Role of DSO as validator of DR transactions at
distribution level
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For the second option, the priority of one of the signals has to be defined in advance. To ensure
secure grid operation, the alleviation of congestions has to be prioritised against market signals. 36
When there is grid congestion, the control signal of the grid operator must be prioritised and the
grid operator has to remunerate the flexibilities used.
TSO/DSO coordination process for accessing DR: Role of DSO as validator
Conflicts can be avoided by applying the following prioritisation process, starting from local
level and moving towards global (see Figure 54):
3)
DSO decides to reserve or release DR flexibility based on local congestion forecast
If no local congestions are foreseen, DR flexibility is handed over to TSO. This can be done
by:
a) DSO contracts DR reserve and sells it to TSO (however DSO in effect acts as
aggregator)
b) DSO lets DR providers (or aggregators) to offer directly their services to the TSO
TSO decides whether to use DR to resolve congestion or system imbalance.
Each step would be remunerated separately.
Figure 54 - Conflict of interest in using DR in distributed networks
36
This is also due to the fact that anyway, in cases of congestions, the grid capacity does not allow
power flows on the congestion direction, therefore the operation based on the conflicting market signal
would anyway not be feasible (e.g. reduce local demand (or increase local generation) on a situation of
local overproduction).
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1)
2)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
The coordination process would essentially require a technical validation by DSOs (step 1
mentioned above). This means that the DSO must decide what happens with the DR if network
constraints render impossible the participation to global markets. In such a case, the DR service
provider should request a validation from the DSO before activation for system balancing
purposes.
For the case of Switzerland, [85] recommended a coordination process as depicted in Figure 55.
Currently, TSOs are the only system operators that prepare congestion forecasts. Forecasts of the
network status are reviewed in three time intervals, 2-days ahead of dispatch, 1-day ahead, and
intraday. These activities define the need for flexibilities for a given timeframe, and can be linked
with different markets in the respective timeframes in order to enable solutions. To ensure that
congestions in the DSO areas are not compromised by the activities that currently exist, a new
process which includes a DSO congestion forecast is proposed. For DSOs, this is a new process
that consists in identification of the local grid situation and in definition of the need for local
flexibilities. This process should be linked and run in parallel to the Day-ahead TSO Congestion
Forecast. The DSO Congestion Forecast is a preventive action that reduces distribution grid
constraints and allows the early identification of critical situations in the distribution grid. The
assessment of the need for local flexibilities is based on the day-ahead and intraday market
results. The process ends before the TSO Intraday Congestion Forecast.
Figure 55 – DSO Congestion forecast timeline
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Example case: DSO/TSO coordination for Switzerland
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11.5. Regulatory evolutions: conclusions and key
messages
1.
Regulatory evolutions should ensure that tariff signals provide sufficient incentives to
grid users to adapt their demand profiles to grid conditions
Under an implicit (tariff-based) DR approach, the regulator should define the required changes in
network tariffs to incentivise DR, by changing specific components of the tariffs (e.g. higher
capacity charges), changing time intervals for ToU introducing location-based components or
allowing DSOs to adapt tariffs to grid conditions.
However, since distribution grid tariffs constitute just a component of the entire electricity price
paid by a consumer, in cases they are low they might fail to form an effective market signal to
grid users to change their consumption to meet grid needs.
DSOs can directly procure DR if DSOs remuneration accounts for it (e.g. use of
TOTEX remuneration)
Under a volume based DR approach, DSOs procure DR services and remunerate the provided
service. Incentives to optimise operation need to be created by appropriate regulatory adjustments
in order to allow DSOs recovering the costs of procuring DR. This can be done by allowing DSO
remuneration to reflect the DSOs’ TOTEX (CAPEX+OPEX).
3.
Regulation should ensure a neutral role for DSOs in the activation of flexibility, in
order to ensure market-based competition among different flexibility resources
As shown in the simulation analysis, economic benefits are achieved by enabling competition
between flexibility options (DR, storage, curtailment of DG) for the provision of services at local
or global level. To enable a market-based approach, transparency and neutrality of DSOs are key
elements. In this respect policy questions are related to:
whether DSO should be allowed to own flexibility assets (e.g. storage)
How to address the risk that, if most of the value of flexibility is controlled directly by DSOs
(e.g. ownership of storage means; direct bilateral contracts with grid users), a local flexibility
market (targeting also other value streams, e.g. BRP; TSOs..) could never develop.
How to ensure DSO transparency when taking the role of validator at local level of flexibility
transactions which are driven by global market signals (e.g. flex transactions for TSOs)
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2.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
4.
In case of conflicts for flexibility activation, local distribution needs should prevail over
system transmission needs, unless when system security is at stake.
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Flexibilities in the distribution grid can be used for multiple purposes, a) in the system level
(participation to wholesale, intra-day and balancing markets) b) in transmission network level
(alleviation of transmission congestions) and c) in the distribution grid (alleviation of distribution
congestions). Congestions at any network level could “block” the provision of flexibility services
from the local level to the higher system levels. Respectively, conflicts on the activation of
flexibility between TSO and DSO are possible. Therefore, a prioritisation between local and
global requirements is needed, and local needs should prevail, unless when system security is at
stake and alternative options are not viable. In this respect, DSO/TSO coordination should be
applied to avoid such conflicts. To achieve this coordination a new process could be defined, the
DSO congestion forecast linked to the respective process at TSO level.
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12.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
13.
ANNEX I - PARAMETERS INFLUENCING THE
IMPLEMENTATION AND THE COSTEFFECTIVENESS OF THE MEASURES –
DETAILED ANALYSIS
13.1.1.1.
HIGH EFFICIENCY TRANSFORMERS
Factors affecting benefits
Efficiency impact
Transformer load rate
The optimal efficiency of a transformer is reached at a loading of 30-40 of
rated capacity. The potential for energy savings can therefore higher by first
replacing overloaded/underloaded transformers and oversize/downsize the
new ones to reach the optimal operating point (clearly, oversizing costs must
be taken into account in the evaluation).
Age of the transformers
Old transformers are the main contributors to losses, since they had to meet
less stringent efficiency standards. The study on transformer replacement
carried the in the APEC ( [46]) regions show that for instance, an old
efficiency standard 25kVA transformer in Russia has a 98% efficiency,
compared to 98.9% for 1500 kVA. New standards allow them to reach
respectively an efficiency of 99.5% and 99.7%).
Transformers fault rate
Regularly defective transformers can be targeted to improve the quality of
supply and reduce the O&M cost.
High harmonics
problems
If a transformer encounters non-linear loading and therefore harmonics
problems, losses due to Eddy currents might appear and reduce the
efficiency of the transformers (Simulations carried out in [86] on 200 kVA
transformer showed an efficiency reduction from 98.1% to 96.5% due to
harmonics). New transformers equipped with harmonics filters, better core
structure, etc. might improve the operating efficiency.
Factors affecting costs
Work constraints
Cost impact
Transformers
accessibility
The conditions of installation works (outside, overhead, underground, in a
private building, etc.) can have a significant impact on implementation costs.
Transformer power
rating
According to the study made on the APEC countries [46], extra-cost related
to switching to higher standards is more cost-effective with small
transformers. However, in case of proactive replacement, transformers of
medium power rating replacement are more cost-effective than replacing
small or very large ones.
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Grid Parameters
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Investment
optimisation
Cost impact
Age of the transformer
The residual value of an old transformer can be close to zero. Its
replacement has thus a limited accounting impact.
Other replacement
plans / new
transformers
As already explained, transformers can be replaced for various reasons, and
it is cost-effective to seize those occasions to implement low-loss
transformers. Moreover, the implementation of a new transformer can be
coordinated with a voltage increase plan or plan to perform voltage control
(by installing on-load tap changing transformers).
In general, changing the minimal requirements for all new transformers
improve the long term efficiency of the asset population
Technology
standardization
opportunities
EXPANDING LINE CAPACITY
Factors affecting benefits
Grid Parameters
Efficiency impact
Age of the network
The use of new cables with up-to-date technological standards can reduce the
risks of defaults/malfunctioning and improve continuity of supply.
Load rate
Heavily loaded feeders generate higher losses. Heavily loaded feeders are
also the key target of replacement plans, to increase grid capacity. From an
energy efficiency perspective, the cost-effectiveness of this measure is
clearly maximized when carried out within a replacement plan which aims at
reducing congestions and takes also into account the minimization of losses.
Fault rate
When replacing lines presenting a high fault rate, due to previous faults,
isolation damages, etc., oversizing of the lines can be considered.
Factors affecting costs
Work constraints
Overhead
underground grid
Urban / rural areas
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Cost impact
/ Excavation works cost are much higher (depending on the area) than laying
lines overhead. All benefits being equal, targeting the replacement of
overhead lines is more cost-effective.
Cables or overhead lines installation can be made more quickly and more
easily in rural area, resulting in lower cost.
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13.1.1.2.
Limiting the number of technologies present on the grid leads to O&M cost
reduction (less variability in the types of operation; lower cost in
maintenance and procurement)
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Investment
optimisation
Cost impact
Age of the network
Targeting the replacement of old cables is more cost-effective thanks to their
lower residual value.
Replacement plan
already scheduled
As stated in the previous points, various drivers could lead to a line
replacement plan. Opportunities to introduce new line size standard have to
be taken into account. Synergies can here be found with an increase of the
voltage level, where lines replacement may be needed to fit the new voltage
level.
Technology
standardization
opportunities
Limiting the number of technologies present on the grid leads to O&M cost
reduction (less various types of operation; optimization of purchasing
operations)
INCREASING VOLTAGE LEVEL
Factors affecting benefits
Grid Parameters
Efficiency impact
Feeders length
An increase of voltage level could be particularly useful in rural areas, which
typically present longer feeders.
Feeders load rate
Current in the conductors is directly proportional to the voltage level.
Heavily loaded feeders generate the highest marginal losses and would
benefit the most from a voltage increase.
Fault rate
Lines with higher fault rate could be firstly addressed when considering this
measure.
Current voltage
problems
If there are known local voltage problems (due to DG for instance),
increasing voltage on those feeders could reduce clients complains and
O&M cost. If DG injection must be curtailed due to too high voltage rise,
increasing line voltage would have a mitigation effect.
Factors affecting the costs
Work constraints
Number of
fitted assets
Cost impact
already A key point in the cost-effectiveness of the measure is the number of assets
(feeders, transformers, etc.) to be replaced. If the insulation level of the asset
is sufficient to stand higher voltages, the measure can be less costly to
deploy. Otherwise the measure can be highly CAPEX intensive. In LV,
going from a 230V to 400V network represent more significant cost (need of
a four-lines network, three-phases user installation adaptation, etc.)
Underground /overhead
grid
If the implementation of the measure requires asset replacement (and
particularly cable replacements), an underground grid will much more
expensive to replace
Rural /urban area
Asset replacement is easier and less costly in rural area, where the
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13.1.1.3.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Investment
optimisation
Cost impact
Grid expansion / load
growth
If a grid expansion is envisaged to the future load growth, the voltage level
supplying those zones has to be considered, especially if the expansion will
require high feeder length. Feeders of higher voltage are cheaper (a 34.5kV
feeder can be 45% cheaper than a 12.5kV lines [87]), but on the other
substations equipment is more expensive. Substation density per feeder is a
key point when assessing the measure.
Age of the network
Targeting assets at the end of their lifetime is more cost-effective thanks to
their lower residual value.
Technology
standardization
opportunities
Limiting the number of technologies present on the grid leads to O&M cost
reduction (less various types of operation; optimization of purchasing
operations). A limitation of the number of voltages level also limits the
number of transformers to be installed in the upper voltage level.
Combination with
transformers,
substations or lines
replacement plans
All planned asset replacement actions represent an opportunity to increase
the number of asset fitted for higher voltages
DG integration plan
If it is planned to invest in the grid to improve the hosting capacity of
distributed generation, the opportunity to increase voltage level should be
assessed.
13.1.1.4.
DEMAND RESPONSE
Factors affecting the benefits
Grid Parameters
Efficiency impact
Feeder load rate
Critically loaded feeders can be firstly address by such a measure.
DG curtailment
Feeders where the DG’s are often curtailed will benefit from an interruptible
load, especially if curtailment compensation is to be paid by the grid
operator.
Factors affecting costs
Work constraints
Flexibility market
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Cost impact
How the flexibility can be purchased is a key component, its price will
impact the day-to-day flexibility activation
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accessibility of the installation is easier and where less work constraints are
generally to be encountered.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Investment
optimisation
Cost impact
Smart Meter roll-out
plan
When and to which extent the smart meters will be placed on the network is
to take into account to define which feeders will be targeted first.
Penetration of the
communication system
As the above, the extent to which the load can be easily controlled thanks to
an existing communication tool impact the cost of measure deployment.
13.1.1.5.
FEED-IN CONTROL OF DISTRIBUTED ENERGY GENERATION & STORAGE
Factors affecting the benefits
Efficiency impact
Level of harmonics
Harmonics injected in the network by consumer appliances can lead to
Power Quality problems and to an increase of losses in the transformers
Incentives to place efficient inverters can be made where high levels of
harmonics are met.
Feeder load rate
Loss reduction is always most important to be performed on highly loaded
feeder. However, depending on the situation, DG can sometimes increase the
load rate. Storage solution could also be envisaged.
Distance between DG
and consumers
The placement on a DG unit on a feeder can have a significant impact on the
increase/decrease of losses. In [88], it has been demonstrated that, depending
on the load pattern, an optimal placement on a radial feeder can lead to a loss
reduction between 71% and 86%.
Poor power factor
Advanced inverter can enhance the Power Factor by controlling the
injection/absorption of reactive power. Incentives to promote those inverters
where a local poor power factor is known can be made.
Voltage quality
problems
Following the above, good inverters can control the reactive power and
therefore the voltage. Local voltage problems, especially those leading to a
bad DG integration, can be solved by good inverters.
Factors affecting costs
Work constraints
Overhead
underground grid
Cost impact
/ If extra connections works are carried out, excavation works will increase the
costs.
Participation to the user Depending on the local regulation, DSO can participate to the cost of the
asset cost
inverter to promote efficient equipment.
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Investment
optimisation
Cost impact
Smart Meter roll-out
plan
With a complete access to the local consumption data, an inverter or its
centralised control can make the best forecast and control. A Smart Meter
roll-out is a good opportunity to improve the grid efficiency.
Penetration of the
communication system
The choice of the control solution (stand-alone, centralised or decentralised)
can be made depending on the existing communication system on the
targeted grid.
13.1.1.6.
NETWORK RECONFIGURATION
Grid Parameters
Efficiency impact
Number of switches /
substation on a feeder
The impact in terms of reduction of losses and of restoration times highly
depends on the number of remote switches which are included in the grid
reconfiguration. The optimal balance between number of switches and level
of benefits should be assessed depending on the specific grid conditions.
According to the Synergird study [89], moving the open-point in the middle
of the loop can lead to 66% of reduction compared to an end of loop openpoint on a feeder with 4 substations, but this reduction can go up to 73% in
the case of 16 substations loop.
Current unbalance
between feeders
A load repartition of 70%/30% between two feeders can lead to 1.4 times the
losses in a case of a 50%/50% repartition ( [90]). This makes it clear that the
first feeders to be addressed by such a measure should be feeders with strong
unbalances.
Current average
restoration time
Feeders with an high restoration time, due to the distance between switches,
the accessibility of the installations, the number of switches to be controlled,
etc. highly benefits from the implementation of remote switches.
EnergyUnited (North Carolina – see in [37]) reduced its Customer Minutes
Interrupted (CMI) of 90% on specific feeders thanks to automatically remote
controlled switches.
Current fault rate
Feeders with a high fault rate would particularly benefit from this solution,
reducing the total yearly interruption time.
Presence of subtransmission /
reparation grid
Repartition grids carry more load and impact more consumers than the final
grid on a same voltage level.
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Factors affecting benefits
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Factors affecting the costs
Work constraints
Overhead
underground grid
Cost impact
/ Installation of switches on overhead lines does not represent major work
Installation cost for underground grid would be significantly higher.
Urban / rural areas
Field deployment of remotely controlled switches is generally easier and less
costly in a more rural area, where installations can be more easily accessible.
Voltage level
Higher voltage levels require switches of higher insulation, which are more
expensive.
Cost impact
Existing assets
Feeders already equipped with remotely controlled switches would mainly
need software adaptation to integrate new functionalities (e.g. for balancing
load; for reducing restoration time). Investment will be much lower than in
the cases where the implementation and integration of new switches is to be
foreseen.
Substation renovation
plans
Old substations which must be restored can be easily targeted by such a
plan.
Penetration of the
communication system
The implementation cost of this solution could greatly be diminished if a
robust communication system already exits.
13.1.1.7.
SWITCHING OFF REDUNDANT TRANSFORMERS
Factors affecting the benefits
Grid Parameters
Efficiency impact
Number
of
transformers in parallel
Substations including more than two transformers working in parallel are
expected to have higher energy efficiency potential.
Load rate
At a certain load rate level (according to [89]), it might become more
efficient in term on losses to connect both transformers to feed the load at the
same time. Load pattern of one substation must be measured to assess the
best transformers operation mode.
Low transformers fault
rate / Low SAIDI
Switching off redundant transformers has the negative side effect to increase
the restoration time, since the transformer needs to be magnetised before
reaching its full operation mode. The measure will be more efficient in case
of feeders with a low transformer fault rate and a non-critical SAIDI. In the
case of manual switching, the restoration time and cost increase significantly.
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Factors affecting the costs
Investment
optimisation
Substation
plans
Cost impact
renovation
13.1.1.8.
Transformers switches replacement will be optimised if other works are to
be carried out in the targeted substation.
VOLTAGE OPTIMISATION
Factors affecting the benefits
Grid Parameters
Low power factor
Efficiency impact
Voltage stability
problems
DG penetration rate
Feeders with high levels of DG injection can be affected by voltage
problems and thus could represent good candidate for the implementation of
the measure.
Low (advanced) DG
penetration rate.
If DG units on the feeders already performed voltage control, CVR would be
redundant.
Feeders load rate
Power factor correction is expected to be particularly effective on heavily
loaded feeders, leading to higher benefits in terms of reduction of losses and
of congestions.
Number of voltage level
If power factor correction is performed in an upper level of the grid, the
effect will impact all the downstream levels. Implementing reactive power
compensation on a few places on a sub-transmission grid can be more cost
effective than implementing it in various locations at lower voltage level.
Length of the network
Following the above, the longer the path between the reactive consumption
and the compensation device, the higher the losses.
Line specific
characteristic
According to [23], a low R/X ratio leads to a less efficient reactive power
control by DG converters.
Factors affecting the costs
Grid Parameters
Efficiency impact
Underground/Overhead
grid
The installation cost is significantly higher in the case of an underground
grid. Installation of capacitors banks or voltage regulator on overhead lines
does not involve major works.
Urban / rural grid
Accessibility of the installation is easier in rural condition. Moreover, long
feeders in rural areas are the most subject to voltage drops.
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The lower the power factor, the higher the potential benefits of reactive
power compensation. Specific analysis shall be carried out to select most
suitable feeders to be targeted.
Feeders where known voltage problems exist could be firstly targeted.
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Voltage level
Investment
optimisation
Efficiency impact
Combination with CVR
Assets for voltage optimisation at the LV level can also be leveraged to
perform Conservation Voltage Reduction (CVR), as presented in the
following section.
Smart meter-roll out
plan
Implementation of Smart Meters would greatly increase the observability of
the grid (measures regarding voltage profile and reactive/active power flows)
supporting the implementation of voltage optimization measures.
Substation renovation
plan
Works to be carried out in substations can be opportunities to install
capacitors banks.
Penetration of the
communication system
Depending on where Volt/VAR control assets will be installed and on their
number, the availability of a robust communication system is important, and
must be taken into account.
13.1.1.9.
CONSERVATION VOLTAGE REDUCTION
Factors affecting benefits
Grid Parameters
Efficiency impact
Feeders load rate
CVR is highly scalable. Targeting only specific highly loaded feeders can
already lead to a major losses reduction (the U.S. Department of Energy
determined in [59] that if only 40% of all feeders would have been equipped
with CVR, 80% of the target can be reached, and 37% of the objective when
deployed only on 10% of the feeder). The main impact of CVR can be
achieved with a limited cost. Test can be made before deploying the
solution.
Voltage Stability
problems
Feeders encountering regularly voltage problems may be the first target of a
CVR deployment plan.
Number of final LV
customers
CVR objective is to reduce the end-user consumption by reducing voltage
level (while remaining in the required nominal voltage range). Therefore, the
more customers are connected on the LV, the more effective the impact will
be in term of losses reduction and on peak reduction, leading to customer’s
peak reduction and to investments deferral.
Factors affecting costs
Work constraints
Overhead
underground grid
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Cost impact
/ The installation cost will be significantly higher in the case of an
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Voltage level has a significant impact on the cost of one capacitor.
According to [91], purchase cost for 4.16kV capacitors is 20$/kvar and
25$/kvar at 13.8kV.
underground grid. Installation of capacitors banks or voltage regulator on
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overhead does not represent major works.
Urban / rural areas
Field deployment of CVR will be facilitated in a more rural area, where
installations can be easily accessible. Moreover, rural grids are usually not
meshed, allowing an easier voltage control.
Cost impact
Existing assets
Feeders already equipped with capacitors banks, feeder voltage sensors or
on-load tap changing transformers only needs automation to perform CVR.
Smart meter roll-out
plan
End-users voltage monitoring facilitates and optimizes the implementation of
the measure, and limits the number of voltage sensors to deploy.
Transformers
replacement plan
To be more effective, the first substation targeted by the measure might be
ones where transformers have to be replaced. On-load tap changing
transformers can therefore be installed.
Penetration of the
communication system
In the case of centralized CVR, the implementation will be facilitated if a
robust communication system already exits.
13.1.1.10.
BALANCING 3-PHASE LOADS
Factors affecting benefits
Grid Parameters
Efficiency impact
Feeders load rate
The higher the power demand on one feeder, the more the imbalance will
have an effect on the losses and on the cable capacity.
Neutral conductors
load rate
Current flowing in neutral conductors also results in losses in a 4-lines
system, and can cause protections to work in case of overloading. Feeders
with frequently overloaded neutral conductors can be firstly addressed.
Neutral conductors
fault rate
As the above, fault related to the neutral conductors can be reduced, as no
current would normally flow in it.
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Investment
optimisation
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Factors affecting the cost
Work constraints
/
Installing switches and replacing connections can be made cheaper in an
overhead grid.
Urban / rural grid
Switches installations can be cheaper if an easy access is available.
LV consumers’ density
The more LV consumers, the more adaptation work will be need to perform
an optimal balancing. Grid with less but more important connection will
require less works, but might be less accurate.
LV substation density
As the above, the number of LV substations is important to assess to which
extend and with accuracy the measure can be implemented.
Investment
optimisation
Cost impact
LV
substation
renovation plan
If LV substations are to be renovated, it presents an opportunity to installed
phase switches.
Connections
replacements plan
Every connection replacement is an opportunity to increase the number of
three-phases connection.
Smart Meters roll-out
plan
Phase connection balancing requires measuring the instantaneous load. If
Smart Meter are to be installed, such measures will be provided.
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Overhead
underground grid
Cost impact
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
14.
ANNEX II ENERGY EFFICIENCY MEASURES IN
EUROPEAN MEMBER STATES: SURVEY
In order to collect experiences and best practices from the field and to integrate the views of main
stakeholders in this study, a survey was carried out among stakeholders with respect to the
implementation of energy efficiency measures in gas and electricity grids. Mainly DSOs have
been targeted, as most of losses occur at distribution level. European DSO associations
(EURELECTRIC, GEODE, EDSO, CEDEC) have been involved to facilitate the distribution of
the questionnaire
Their experience with the implementation of grid energy efficiency measures
The Regulatory mechanisms in place in their country to incentivise the implementation of
energy efficiency measures.
Their view on the list of energy efficiency measures proposed in this study
The detailed description of the questions in the survey and the detailed list of respondents is
reported in ANNEX I.
Despite numerous attempts it was not possible to have CEER’s view on the regulatory
mechanisms to incentivise energy efficiency measures and their view on the level of mobilization
in the implementation of article 15.2 in their members’ countries.
A total of 16 replies have been collected, providing a good geographical coverage of EU Member
States. Since the great majority of respondents were electricity DSOs, in this chapter we will
mainly focus on the experiences of DSOs in electricity grids. Figure 56 reports the list of
respondents.
Collected responses have provided very useful insights in the implementation of grid energy
efficiency measures by DSOs in different Member States, facing different grid conditions.
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In particular, the survey aimed at collecting information about:
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Figure 56 - Overview of respondents to the survey
14.1.1. Level of mobilization in Member States regarding the
implementation of article 15.2
According to collected information, the level of mobilization in EU Member States in the
implementation of the provisions of article 15.2 (definition of investment plans to improve grid
energy efficiency) appears to be generally limited. European associations report that most of their
members have not yet been actively involved by Member States in addressing the provisions of
article 15.2. However, as was confirmed, a number of Member States are working on the topic
and would likely publish their assessments in the period after the completion of this study.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Examples of available information from some Member States include:
UK:
The proposal for the transposed legislation was discussed and handled in the Finnish
Parliament during autumn 2014 and the legislation has come in force beginning of 2015.
In May 2015, the Finnish Ministry of Employment and Economy together with the Energy
Authority have launched a survey on fulfilling the requirements of Article 15(2). Authorities
have also invited other stakeholders (like Finnish Energy Industries and Finnish Gas
Association) to join the survey. The survey is carried out by Lappeenranta University of
Technology (LUT) and will end in June 2015.
The main topics of the survey are:
- the technical losses and the overall efficiency of the system
- the technical losses of the Finnish electric system are around 1%- so the focus will be more
on the overall energy efficiency of the system, including distributed generation, demand
response and energy storage. New technology like smart meters, smart grids and LVDC
(Low Voltage Direct Current) distribution system will have a big role.
- the concrete measures and investment will also be identified
BELGIUM
In the context of the implementation of the article 15.2 by 30th June 2015, Synergrid
(federation of electricity and gas network operators in Belgium) carried out an assessment of
the energy efficiency potentials of gas and electricity infrastructure, together with the energy
regulators (FORBEG, Forum for Regulatory Bodies).
The scope of the study considers energy efficiency in a larger sense than just the reduction of
losses. In particular, the study looks at:
- 1. Potential for reduced grid losses and reduced energy consumption by the network
operators.
- 2. Potential to improve the efficient operation of available energy infrastructure, which in
turn could allow reducing the need for investing in new infrastructure
Categories of measures that will be considered:
- Investing measures by the network operators
- Operational measures by the network operators
- Changing the behaviour of the consumers, considering incentivizing Mechanisms (e.g.
tariffs by Time-of-Use, or flexibility vs. capacity tariffs)
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OFGEM (UK regulator) is carrying out a study to comply with the requirements of the
regulation [2]. A separate assessment of energy networks in Northern Ireland is being
prepared by the Northern Ireland Authority for Utility Regulation.
In this context, OFGEM has launched a survey among UK system operators to collect best
practices and information to
- Assess the energy efficiency potentials of the gas and electricity infrastructure of Great
Britain
- Identify a list of concrete measures and investments for the introduction of cost-effective
energy efficiency improvements in the network infrastructure, with a timetable for their
introduction.
Findings have to be delivered before or on 30th June 2015 to the Secretary of State.
FINLAND:
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
A public (shorter) version of the report has been made available. The document will serve as
support for regulator to review the investment plans from the grid operators.
The Swedish Energy Agency has carried out a study to assess the energy efficiency potential
of gas and electricity grid in Sweden.
The energy saving potential in grid infrastructure is considered to be relative low, although
not to be neglected
- The main share of the loss reduction potential in electricity grids by 2030 could be
obtained by changing the location of electricity generation units. This is however outside
the responsibility of grid operators.
- The potential that is achievable through measures conducted by the grid companies
themselves is around 175GWh/year by 2020 and 400GWh/year by 2030. In relation to
total losses, these potentials are quite small: 4% by 2020 and 7% by 2030.
- Standards for new transformers through the EcoDesign Directive are expected to
incentivise energy efficiency improvements.
- Losses in gas grids are considered very limited and therefore investments in energy
efficiency measures would not be justified.
A key remark made by the study is that reduction of losses should not be the main driver of
grid investments, as that can lead to sub-optimal solutions. It is important to adopt a systems
approach for energy efficiency.
In this respect, the introduction of regulatory incentives to improve more efficient network
operations are expected to support investment plans which take also loss reduction into
account.
14.1.2.
Summary of key insights of the survey
The following insights have been derived from the analysis of the survey respondents. Further
literature analysis has been carried out to further validate received replies:
State of play in Member States
Significant differences across Member States concerning the level of losses.
Starting conditions of grids (e.g. voltage level; number of transformers) and potentials for
energy efficiency improvement are different. Transposition of best practices should be done
with care.
Drivers
Energy efficiency within larger grid investment decision plans - Energy efficiency is
typically taken into account in grid investment decisions, however often it is not the main
driver. Energy efficiency is considered as one of the variables of a larger investment
optimization plan. The importance of energy efficiency as a criterion in grid investment
decisions clearly depends on the existence of regulatory incentives to reduce losses.
Compliance with energy efficiency obligations: In specific cases, energy efficiency
investments (particularly concerning loss reduction) are undertaken to comply with regulatory
requirements and quality standards (e.g. compliance with the EcoDesign directive leading to
installation of higher efficiency transformers)
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SWEDEN:
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Mapping of losses
Electricity losses mainly occur at distribution level
The implementation of adequate metering systems at substations and at customers’ premises is
an important first step to identify where losses are actually occurring in the distribution grid.
The following factors have a key impact on the level of losses in the system
- Loading (including peak demand)
- Number of energized transformers
- Lengths of the feeders and grid voltage levels
- Level of power quality
- Presence of distributed energy resources
A variety of energy efficiency measures exist, and their potential is very much related to the
specific local grid conditions.
In general, system operators opt for traditional investments (replacement of assets;
reinforcements etc.) to improve grid energy efficiency.
Factors impacting grid energy efficiency potential outside control and responsibility of
system operators
Smart Grid-related EE measures (e.g. feed-in control) are less common: in many cases the
right regulatory framework is not in place to make them a viable option.
External factors outside the control of system operators can also greatly impact the potential
of EE measures: e.g. the development and location of DERs.
Cost-effectiveness
In general, a number of factors can greatly impact the cost-effectiveness of energy efficiency
measures, including:
Monetary value of losses, i.e. the average price of wholesale electricity (the higher, the more
cost-effective are EE measures)
Loss reduction is a side-benefit of investments driven by other needs. Therefore the costeffectiveness of an energy efficiency measure depends on additional benefits in terms of
CAPEX/OPEX reduction.
When implementing EE measures, additional benefits in terms of reduction of CAPEX and
OPEX should be considered. However, for some measures, a trade-off needs to be found
between improvement of energy losses and other benefits (e.g. trade-off with hosting capacity
for DER voltage measure).
Parallel developments (e.g. roll-out of smart meters; built-in smart functionalities in DG
converters etc.) make the enabling infrastructure for certain EE measure already available,
reducing their implementation costs.
Most adopted EE measures concern implementation of higher efficiency assets (e.g. higher
capacity cable; higher efficiency transformers) within a planned asset replacement strategy.
An overall investment optimization strategy is therefore key to ensure that most cost-effective
energy efficiency measures are implemented.
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Catalogue of energy efficiency measures
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
14.1.3. Energy efficiency measures implemented by survey
respondents
Figure 57 provides a brief synthesis of grid energy efficiency measures implemented by
respondents. The survey results show that classical measures such as transformers replacement
and line capacity increase are the most commonly adopted measures, as their implementation fits
with traditional replacement programs, where old or inefficient assets are targeted. Reactive
power Management is also well adopted, since voltage quality and distributed generation can also
be addressed.
12
10
8
6
4
2
0
Figure 57 - Grid energy efficiency measures in electricity grids adopted by survey respondents
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Number of DSO adopting the measure
Number of DSOs adopting the measure
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Measures
Implementation
example
Highlights
reduction
of
loss
High efficiency
transformers
VSD is replacing 100
transformers/year since
2011 – 10% have been
replaced so far.
CEZ Bulgaria replaced
their four 110kV/20kV
transformers with the
highest losses.
Enexis is replacing their
oldest
MV/LV
transformers by more
recent
re-use
transformers.
VSD expects 8.5% of
losses reduction.
CEZ Bulgaria saved
6.7 k€/year on losses
per
replaced
transformer.
In the UK, low loss
transformers appear
as one the most
efficient solution.
Increasing line
capacity
VSD increase their
maximum cross section
of their LV lines from
70mm² to 150mm².
VSD expects on
average a 5% (locally
up to 20%) reduction
of losses,
Costs
Additional
benefits
For CEZ Czech
Republic, costs
increase of at
least 20%.
Enexis
is
replacing
old
MV/KV
transformers for
5.3k€/transfo
Higher quality
of
supply,
lower
overheating
Higher voltage
quality, lower
probability of
fault, increase
capacity
Belgian DSO’s are
developing a tool to
incorporate losses in the
selection of the minimal
cable section.
Voltage
optimisation /
Reactive power
management
CEZ Bulgaria equipped
three of their 110kV/1020kV substation with
capacitors
and
harmonics filtering units.
CEZ Czech Republic is
conducting
a
pilot
project
where
20
synchronous generators
in 110kV receive set
point in voltage and
reactive power.
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ErDF implemented
Volt/Var control of
distributed
generation. 2-3% of
loss reduction is
reported.
CEZ
Czech
Republic performed
a loss reduction of
7% per line, leading
to
2.5%
overall
reduction.
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CEZ Bulgaria
equipped
3
110kV/MV
substation at a
cost of 528k€
Distributed
Generation
integration
Optimisation
of the voltage
profile
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Table 22 shows examples of achieved energy efficiency results thanks to implemented measures:
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Measures
Implementation
example
Highlights
reduction
Increase
voltage level
Vattenfal is currently
studying area per area at
optimising the voltage
level in their regional
grid.
Vattenfal expects a
5% to 15% loss
reduction from the
increase in voltage
levels, with locally
25-30%
Conservation
Voltage
Reduction
Stromnetz Berlin has
already
conducted
voltage reduction
loss
Costs
Additional
benefits
HEDNO and EDP
increased the number
of higher voltage
transformers,
thus
increasing the share
of higher voltage
portion of the grid.
According to an
OFGEM study, the
increase share of
voltage-insensitive
loads is undermining
the potential of this
measure.
Table 22 - Highlights of energy efficiency measures in electricity grids implemented by survey respondents
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HEDNO and
EDP
increased the number of
higher
voltage
transformers,
thus
increasing the share of
higher voltage portion of
the grid.
of
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
ANNEX III - SURVEY - QUESTIONNAIRE
FORMAT
A.
Project CONTEXT and goal of the QUESTIONNAIRE
According to Article 15(2) of the Directive, Member States shall ensure, by 30 June 2015,
that:
- (a) an assessment is undertaken of the energy efficiency potentials of their gas and
electricity infrastructure, in particular regarding transmission, distribution, load
management and interoperability, and connection to energy generating installations,
including access possibilities for micro energy generators;
- (b) concrete measures and investments are identified for the introduction of costeffective energy efficiency improvements in the network infrastructure, with a timetable
for their introduction.
The European Commission has recently launched a study to provide support to Member
States in fulfilling the requirements of article 15.2 of the Energy Efficiency Directive
- The study is titled “Measures for grid energy efficiency in gas and electricity grids”
- The European Commission considers that cooperation of relevant stakeholders (system
operators, regulatory authorities) is a key element to maximize the pertinence and impact
of the study.
The goal of this questionnaire is to collect data and best practices from system
operators:
- Description of energy efficiency measures for electricity grids (measures for reducing
technical losses)
- Description of energy efficiency measures for gas grids (measures for reducing gas losses
and for optimizing use of energy in grid operations)
B.
1)
Survey Part 1 - Energy efficiency in electricity grids
Please provide a description of your specific grid conditions :
Average number of customers per
KM2
Average length of feeders
Number of customers
Number of voltage levels in the
grid?
Length of the network per voltage
level?
Number of transformers?
Average age of transformers?
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15.
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Level of losses on the grid in the
last five years?
2)
Please provide a short description of the on-going and planned energy efficiency
measures (measures for reducing technical losses):
Are there any regulatory incentives
to reduce grid energy losses?
Which energy efficiency measures
have been taken so far?
Which energy efficiency measures
are you planning?
Please find below a proposed list of energy efficiency measures. Could you please
provide your comments?
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3)
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4)
Please provide a description, as detailed as possible, of energy efficiency measures you
have implemented or are planning to implement.
DESCRIPTION OF ENERGY EFFICIENCY MEASURES –ELECTRICITY GRIDS
Which measure? Please describe.
Level of penetration of the measure?
(e.g. number of impacted feeders,
transformers…)
Which factors have a high
sensitivity on the cost-effectiveness
of the measure? (e.g. age of asset,
peak load,…)
Additional Benefits? (reduction of
CAPEX/OPEX,…)
Estimation of the cost of the
measure?
Which lessons learned to optimize
the implementation of this measure?
C.
1)
Survey Part 2 - Energy efficiency in gas grids
Please provide a description of your specific grid conditions by filling in this table:
Average number of customers per
Km2
Number of customers
Number of pressure levels in the
grid?
Length of the network per pressure
level?
Number of pressure reduction
stations?
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Which losses reduction has been
observed (%)?
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Average age of gas heaters in
pressure reduction stations?
Level of losses on the grid in the last
five years?
2)
Please provide a short description of the on-going and planned energy efficiency
measures (measures for reducing gas shrinkage and for optimizing use of energy in
grid operations)
Are there any regulatory incentives
to improve grid energy efficiency
(i.e. reduction of gas losses and of
use of energy)?
Which energy efficiency measures
are you planning?
3)
Please find below a proposed list of energy efficiency measures. Could you please
provide your comments?
Measures for reducing energy use for grid operation (e.g. optimization of compression; pressure profiles)
Gas heating
Cathodic protection
Quantity of gas delivered to LP users
Reduce transmission needs by using decentralized
generation
Scheduled for operation
Unscheduled (leakages)
Assessment of losses
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Increase the use of multistage regulator lines, lines insulation, heat exchanger…
Use of high-efficiency gas heaters in pressure reduction stations
Use of CHP
Autonomous generation through solar panels
Optimization of the delivered pressure
Biogas plants
Storage
Power-to-gas
Measures for reducing gas losses
Lower pressure through a closed by LP-grid
Optimization of the valves placed on the grid to reduce the volume to evacuate
Increase the use of mobile flow interrupter
Replacement of old pipes to PE pipes
% of steel pipes cathodically protected
Number of valves on the network
Use of automatic flow-stopper on grid and connections
Limitations of the operating pressure
Smart Meters
Frequency of leakage detections
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Which energy efficiency measures
have been taken so far?
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
4)
Please provide a description, as detailed as possible, of energy efficiency measures you
have implemented or are planning to implement. Please include additional slides to
describe additional measures.
DESCRIPTION OF ENERGY EFFICIENCY MEASURES –ELECTRICITY GRIDS
Which measure? Please
describe.
Level of penetration of the
measure? (e.g. number of
impacted pipelines/pressure
stations)
Which losses reduction has
been observed (%)?
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Which factors have a high
sensitivity on the costeffectiveness of the measure?
(e.g. pressure level, age of
asset,…)
Additional Benefits?
(reduction of
CAPEX/OPEX,…)
Estimation of the cost of the
measure?
Which lessons learned to
optimize the implementation
of this measure?
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
16.
ANNEX IV - TOOL FOR RANKING MEASURES
ACCORDING TO THEIR COST-EFFECTIVENESS
An excel tool has been set-up to guide project promoters in the assessment of the costeffectiveness of different energy efficiency measures according to their local grid conditions. The
tool highlights the main factors affecting the benefits and cost of each measure and provides
examples for benchmark.
The goal of the tool is to help project promoters in selecting good candidate measures and come
up with an overall Energy Efficiency strategy.
The analysis provided through the tool is divided in two mains steps:
1)
2)
Measure by measure impact and cost assessment
Ranking of those measure in term of efficiency and cost
The figure here below shows the schematic organisation of the tool:
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This document is the support to an Excel tool, whose goal is to help the user to make up a
qualitative cost-efficiency ranking. Those forewords aim to present the general methodology
behind this tool.
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Step 1 – Measure by measure analysis
The goal of this step is to assess, on one hand, the impact of one measure on the user’s specific
grid losses, investment planning and grid operations, and, on the other hand, to assess the cost of
its implementation. It is important to note here that all the final assessment is on the user’s hands,
with strong guidelines provided by the Task 1 and 2, presented in this report. The analysis to be
done for one measure is presented in figure 2.
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Grid factors
Loss impact
Grid factors
Invest. Planning impact
Grid factors
Grid op. impact
Implementation
constraints
Cost
assessment
Planned
investments
The elements providing by the present report and being the guidelines of the tool are classified as
follow:
Grid factors: losses, CAPEX and OPEX reduction highly depends of the specific conditions
of each grid, such as the current technology in use, the fault rate, the average of assets,…
Implementation constraints: present the elements of the grid how influence the cost of work,
such as the situation of the lines (underground/overhead), facility of access, age of the assets
Planned investments: list other investments which may provide opportunities for synergies
and reduce implementation costs.
Asset list: list of the assets which should be implemented for the measure.
Step 1.1 – Impact assessment
The users will be guided in the tool following open question, based on open questions, in order to
allow the user to build a complete analysis in function of his own grid factors. Each measure
should be assessed independently from the others, as well as each factor, following an exhibit as
presented down here:
MEASURE N
Loss reduction
Current load rate?
Open question to
guide
experts
reflexions
Expected impact
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Investment planning
impact
Future
needs?
capacity
Grid operation
impact
Fault rate?
Possible penetration?
…
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…
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Assets list
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
It is important to repeat at this point that the expected impact here is a totally free judgement
from the user. It will serve as basis for the ranking of the measure in the next step.
Step 1.2 – Cost assessment
Secondly, the cost assessment of the measure has to be performed. As for the impact assessment,
open questions guide the user to assess his costs in his own case, based on the local work
constraints, the asset list for a measure and the current investments already planned for the
concerned assets. It is especially for this last point that the final exhibit (presented here below) of
this cost assessment make a parallelism between all the measure and the planned investment,
asset by asset, in order the identify replacement already in the pipe and synergies between
measures. If such synergies appear, the measure list should be adapted, and a new assessment
should be done, taking this time the merged measures into account.
currently
Measure 1
Measure 2
Asset 1
Asset 2
Asset …
Step 2 – Ranking of the measures (pair-wise comparison)
Based on the step 1 analysis, the user should rank the measures between them, for each KPI and
for the cost. This is performed in the tool through a Multi-Criteria Analysis. The principle is the
following:
1)
2)
For one KPI, each measure is weighted in front of the others, between 0.1 (very low
efficiency compared to the other measure) and 9 (highly efficient).
A relative priority is then given for each measure (the higher the score, higher was the
efficiency of the measure in front of all the others). An exemple of those two first step is
given below
Relative
priorities
Measure 1
Measure 2
Measure 3
Measure…
Measure
1
1
1/3
1/2
…
0,3
Measure
2
3
1
2
…
0,17
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Investment
planned
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Measure
3
2
0.5
1
…
0,04
Measure
…
…
…
…
1
…
2)
A priority should also be given to the KPIs. A MCA comparison is also perform between
each KPI. The ranking is made by taking into account the current level of losses, the targeted
level, the regulatory framework, the presence of incentive aiming to reduce losses, CAPEX,
OPEX… The relative priority calculated for each KPI is used to weight the different
priorities given to one measure between them.
The sum of the weighted priorities gives the overall ranking of the efficiency of the
measures, such as showed in exhibit xxx.
KPI 1
KPI 2
KPI 3
Weighted
priorities
KPIs weights
0,5
0,2
0,3
Measure 1
0,3
0,02
0,4
0,29
…
…
…
…
Measure 2
Measure 3
Measure …
1)
Following the same rules, a ranking of the costs is performed, and is used as qualitative
assessment of the costs.
2)
The final cost-efficiency ranking is given by the ratio
. It is important to
note the results provided here are qualitative, and should only be used to identify which
measure should be further investigate in more quantitative way. The graph provided here
below give an exemple of the results, where “quick wins” and ‘must do” can be easily
identified.
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1)
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
Balancing 3 phasesloading
Final score/cost
CVR
Reactive power
management
Network
reconfiguration
Distributed
Generation
Demand response
Increase of lines
capacities
High efficiency
transformers
1,00
2,00
3,00
4,00
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0,00
Increase of the
voltage level
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17.
ANNEX V- DETAILED SIMULATION RESULTS
17.1.1.1.
REVERSE POWER FLOWS APPEAR WHEN INCREASING DG PENETRATION
RATE
17.1.1.2.
IMPACT OF DG PENETRATION ON OPEX AND ON CAPEX
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17.1.1.3.
LOSSES COSTS IMPACT SIGNIFICANTLY THE SIZING OF THE
INFRASTRUCTURE (CAPEX)
17.1.1.4.
100% PV CASE VS DIFFERENT DG TYPE SHARE AMONG THE VOLTAGE
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17.1.1.5.
COST EVOLUTION WITH NIGHT/DAY TIME-OF-USE
17.1.1.6.
COST EVOLUTION WITH MIDDAY TIME-OF-USE
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
17.1.1.7.
COST EVOLUTION WITH MULTIPERIOD TIME-OF-USE
17.1.1.8.
LOSS REDUCTION THANKS TO A MULTIPERIOD TOU
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ASSESSMENT OF THE VALUE OF DEMAND RESPONSE, IN SUPPORT OF THE IMPLEMENTATION OF ARTICLE 15 OF THE
ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
LOW LEVEL OF SHIFTED ENERGY OVER THE YEAR PROVIDES ALREADY A
TOTEX DECREASE
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17.1.1.9.
17.1.1.10. GENERATION CURTAILMENT
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
17.1.1.11. DETAIL COST REDUCTION WITH A MIXED FLEXIBILITY SOLUTION
(CURTAILMENT + DEMAND RESPONSE)
OPEX
10%
8%
6%
4%
2%
0%
-2%
4%
OPEX decrease (%)*
CAPEX decrease (%)*
CAPEX
2%
0%
-2%
-4%
-6%
-8%
-10%
8%
6%
4%
2%
0%
North rural South_rural North_urban South_urban
100% DG penetration
200% DG penetration
300% DG penetration
17.1.1.12. PEAK REDUCTION ACHIEVED THANKS TO THE MIXED FLEXIBILITY
SOLUTION
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TOTEX decrease (%)*
TOTEX
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17.1.1.14. USE OF THE DEMAND RESPONSE BETWEEN THE VOLTAGE LEVEL
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17.1.1.13. USE OF DEMAND RESPONSE VS CURTAILMENT
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ENERGY EFFICIENCY DIRECTIVE (2012/27/EU)
17.1.1.16. REDUCTION OF LOSSES DEPENDING OF THE PRICE OF THE LOSSES
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17.1.1.15. TOTEX EVOLUTION FOR LOSSES COSTS AT 80€/MWH AND 120€/MWH
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17.1.1.17. COMPARISON OF THE TOTEX DECREASE BEWEEN THE BASE CASE AND
100% PV ONE
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A leading international engineering consultancy company, Tractebel Engineering is part of ENGIE*, an industrial group
with the financial strength to address the challenges of the future. With approximately 4,400 people in 33 countries, we
offer life-cycle engineering solutions for energy, water and infrastructure clients. Services include a full range of
engineering assignments: Architect Engineer, Owner’s Engineer and Consulting Engineer. Our customers are private and
public companies, as well as national and international institutions. *GDF SUEZ is now ENGIE
TRACTEBEL ENGINEERING S.A.
Avenue Ariane 7
1200 - Brussels - BELGIUM
www.tractebel-engineering-gdfsuez.com
Vincenzo GIORDANO
tel. +32 2 773 83 92
fax +32 2 773 88 90
[email protected]