RESEARCH REPORT
NO D3.2-1
HELSINKI 2015
Jan Segerstam
Olli Huotari
Anna Seppänen
D3.2-1: Control mechanisms for
harnessing DR/DER potential
[D3.2-1]
[Jan Segerstam et al.]
CLIC INNOVATION OY
ETELÄRANTA 10
P.O. BOX 10
FI-00131 HELSINKI,
FINLAND
CLICINNOVATION.FI
ISBN XXX-XX-XXXX-X
ISSN XXXX-XXXX
11.09.2015
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CLIC Innovation
Research report
D3.2-1 Control mechanisms for harnessing DR/DER potential
11.09.2015
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Name of the report: Control mechanisms for harnessing DR/DER potential
Key words: Renewable energy, IoT, AMR, Flexibility, Combined resource
utilization,
Summary
Prosumers are in the center of the future energy system, re-shaping the way
we operate the energy system by allowing us to transform from generation following to load following system.
This deliverable concentrates on the demand response aspects of the
prosumer domain, by mapping the resources available at the prosumer premises and by studying different control mechanisms required to bring the
prosumer connected resources to the market.
The work done in this deliverable is used as basis for the conceptual work of a
flexibility gateway, which is envisioned to be a technical connection between
the untapped potential of prosumers and the future electricity market.
Helsinki, 31 October 2016
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CONTENTS
1
INTRODUCTION ...................................................................................... 7
1.1
PURPOSE AND TARGET GROUP............................................................. 7
1.2
PARTNER CONTRIBUTIONS AND LINKS TO INTERNATIONAL
COLLABORATION ............................................................................................ 7
1.3
RELATIONS TO OTHER ACTIVITIES IN THE PROJECT ................................ 8
1.4
DEFINITIONS AND ACRONYMS ............................................................... 9
2
FLEXIBILITY GATEWAY CONCEPT IN BRIEF..................................... 10
2.1.1
Flexibility Gateway ................................................................... 10
2.1.2
Two distinct control systems ..................................................... 11
3
DEFINITION OF DIFFERENT DR/DER RESOURCES .......................... 12
3.1
ENERGY LOADS ................................................................................. 12
3.2
RENEWABLE ENERGY GENERATION .................................................... 14
3.2.1
Renewable energy production .................................................. 14
3.3
ELECTRICAL ENERGY STORAGE SYSTEMS .......................................... 18
3.3.1
State of the Art and trends of Energy Storage technologies...... 18
3.3.2
What to take away from the EES systems? .............................. 21
3.4
HEAT RESOURCES ............................................................................. 21
4
RESOURCE CATEGORY MAP ............................................................. 23
4.1
COMBINING ELECTRICAL RESOURCES AND HEAT RESOURCES ............... 28
4.2
EXPANDED FLEXIBILITRESOURCE MAP ................................................ 28
5
CONTROL MECHANISMS FOR HARNESSING DR/DER POTENTIAL 31
5.1
CASE STUDIES .................................................................................. 31
5.1.1
AMR/AMI case ......................................................................... 32
5.1.2
IoT case ................................................................................... 33
5.1.3
Summary of the technologies ................................................... 35
6 MARKET MECHANISMS REQUIRED FOR VALUING DER
RESOURCES ................................................................................................ 36
6.1
CURRENT MARKET MECHANISMS ........................................................ 36
7 METHODS FOR MAXIMIZING OVERALL BENEFITS FROM DR AND
DER RESOURCES WITH COMBINED INFORMATION INTEGRATION ...... 39
7.1.1
Information sources related to flexibility .................................... 39
8
CONCLUSIONS ..................................................................................... 42
9
REFERENCES ....................................................................................... 43
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1 Introduction
This deliverable is part of the Work Package 3 which concentrates on flexibility
management in the FLEXe research program. The deliverable presents the
work done in task 3.2-1 which focused on different controllable energy resources and different control mechanisms to utilize said resources to provide
flexibility to different applications.
1.1 Purpose and target group
The purpose of this deliverable is to present the results achieved in the task
3.2-1 during the first and only funding period of the FLEXe project and the intended target group is all the stakeholder groups and research parties interested in the requirements for flexibility in the future energy systems.
This deliverable concentrates on the flexible energy resources, their characteristics and technical details, which are needed when designing the technical
concept for flexibility operator. This deliverable should be read together with
the deliverable 3.1-1, as the aforementioned deliverable presents the results
built on top of the results reached in this deliverable.
1.2 Partner contributions and links to international collaboration
Empower IM wrote chapters 1, 2, 3, 4, 5, 6, 7 and 8Error! Reference source
not found.. Empower IM also coordinated the work related to the writing of the
deliverable.
Fortum contributed 3.4 and 4.1 by providing information on consumer side
heating methods and district heating.
Horizon 2020 funded Project SENSIBLE, where Empower IM is a partner produced information and data on different electricity storage methods presented
in chapter 3.3.
ITEA3 Project SEAS produced information on field communication technologies presented in chapter 7. Empower IM is part of the ITEA SEAS project.
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1.3 Relations to other activities in the project
This deliverable is one of the subtasks of the task 3.2 and is therefore related
to six other subtasks under the task 3.2 structure. Table 1 below lists all the
other subtasks of the task 3.2.
Table 1. List of other deliverables under the task 3.2 in WP3 of FLEXe project.
No.
Deliverable name
D3.2-1
Control mechanisms for harnessing DR/DER potential
D3.2-2
Benefits of DSM measures in the future Finnish energy System
D3.2-3
Methods for monitoring DR/DER stresses on power components
D3.2-4
A general investigation about how to divide DR benefits between
DSO and markets
D3.2-5
Optimizing methods for local heat and power energy management
D3.2-6
Key elements and attributes affecting prosumers’ behavior
D3.2-7
Domestic space heating load management in Smart Grid
This deliverable is also linked in more detail to following deliverables:


D3.1-1 Report on alternative information platform and interface structures for operator-market participant coordination
D3.2-5 Optimizing methods for local heat and power energy management
This deliverable also had input from collaborating international research projects called Project SENSIBLE and project SEAS. The project SENSIBLE is a
research project which studies storage enabled sustainable energy for buildings and communities and has received funding from the European Union’s
Horizon research and innovation programme.
The project SEAS is an ITEA3 research project which seeks to study Smart
Energy Aware Systems and tries to build a novel information model to enable
IoT solutions and traditional energy systems function together.
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1.4 Definitions and acronyms
AMI
Advanced Metering Infrastructure
AMR
Automatic Meter Reading
CEN
Comité Européen de Normalisation
CENELEC
Comité Européen de Normalisation Electrotechnique
DER
Distributed Energy Resource
DR
Demand Response
DSO
Distribution System Operator
DSM
Demand Side Management
EC
European Commission
EDM
Energy Data Management
EEA
European Environment Agency
EES
Electrical Energy Storage
ETSI
European Telecommunications Standard Institute
EV
Electrical Vehicle
HVAC
Heating, Ventilating, and Air Conditioning
IEA
International Energy Agency
IEC
International Electrotechnical Commission
IoT
Internet of Things
SEAS
Smart Energy Aware Systems
SENSIBLE
Storage Enabled Sustainable Energy
TSO
Transmission System Operator
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2 Flexibility gateway concept in brief
The flexibility resource study done in the task 3.2-1 is tightly related to the flexibility operator concept. The flexibility operator concept aims to be a technical
solution which can enable different market actors to utilize the flexible resources, such as demand response, in the future electricity grid. The flexibility
operator concept is presented in more detail in the deliverable 3.1-1, but below
is presented a short review of the concept on a high level.
2.1.1 Flexibility Gateway
The main idea of the flexibility gateway is to provide the resources easily and
effortlessly to the market participants. The market participants must be able to
comply to the electricity markets minimum requirements and to balance responsibility of the resources they wish to operate. Below in the Figure 1 is presented an overview of the flexibility operator concept.
IoT
#1.1
IoT system
#1
Portfolio
Renew
Local
manageme
community
able
nt
Flexibility Gateway
Portfolio
Renew
manageme
able
nt
Information
management
Charging
operator
IoT system
#2
Flexibility Gateway
information convergence
IoT
#1.2
IoT
#1.3
IoT
#1.4
IoT
#2.1
IoT
#2.2
IoT
#2.3
3rd party
services
DSO
AMR
#1
AMR
#2
AMR
#3
Nasdaq
Nordpool
Fingrid
reserves
Datahub
TSO
Grid
topology
Grid status
Wholesale
settlement
hub
DSO
Figure 1. Overall picture of the flexibility operator concept, which shows the links between different electricity market parties and systems.
Intention is that the flexibility gateway, as stated previously, will offer to its user
effortless operation of flexibility resources, but also enables optimum value extraction of the flexibility resources. Optimum value extraction might be
achieved by offering the resources to different market levels based on their ca-
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pability and usage or answering to the disturbance signals sent by the Distribution System Operator (DSO) or Transmission System Operator (TSO) in order
to keep the lights on.
So, in order to be able to design the technical architecture of the flexibility operator, a lot is needed to know about the surrounding framework where the
flexibility operator operates, such as:




What is the network topology like?
Where are the flexibility resources situated?
What are the electricity market specific requirements and constraints?
Or what are the flexibility resources and what are they capable of?
The deliverable 3.1-1 will discuss the different inputs from the electricity market framework more broadly, while in this deliverable we concentrate on the
highlighted areas of Figure 1 and the different information feeds needed for
optimal flexibility management.
2.1.2 Two distinct control systems
The Figure 1 shows two different systems attached to the flexibility operator,
yellow and blue, which are capable of controlling flexible resources. The blue
system description represents the Advanced Metering Interface (AMI) -system,
which is currently installed and working in all of Finland and is regulated by the
electricity market law and energy authority, while the yellow colored system architecture represents the sea of IoT-systems, which at the moment are not
regulated and as of today, still represent minor market share of the total “flexibility capable” –solutions on the market.
The reason these two architectures are studied in more accurate manner is
that the AMI-architecture now covers almost 100% of the Finnish electricity
metering and when looking at the IoT-systems, Forbes has reported in its article, which covers IoT-solution market adaptation and forecasts for growth, that
the installation of smart meters is projected to grow 251% from the levels of
2013 by year 2022, thus it seems appropriate to study the possibilities for future electricity market resources enabled by these new emerging connectivity
technologies. (Forbes, 2014)
While the AMI-system is completely regulated because of its nature, with the
exception of the technology used to read the meters, the spectrum of different
IoT-systems is plentiful. However, the IoT-systems are still somewhat novel
and this means that the industry is still undecided on the standards used for
communication. This topic will be revisited in greater detail on page 31.
The next chapter will discuss the different types of energy loads found at the
household level and their characteristics.
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3 Definition of different DR/DER resources
The aim of this chapter is to recognize and define on a conceptual level different Demand Response (DR) and Distributed Energy Resource (DER) types,
which are capable of delivering flexibility through a flexibility operator to market
parties who have balance responsibility of the said resources.
This chapter takes first a brief look at the flexibility operator concept and goes
over the main design principles of the concept and what information is expected from the resource side to successfully design the architecture for the
flexibility operator concept.
In the next chapter we go over different DR and DER types, simultaneously
casting a look at the major category types of said resources, such as energy
loads, energy generation and energy storage. The study is limited to household and small energy community level, as industrialized technology parks and
complexes are already starting to see solutions for flexibility resource utilization on different market levels by different service providers (Fingrid 2016).
The aim is to recognize the restrictions and capabilities of the resources and
possibly different relations to other supporting information sources and networks, which could add to the value of flexibility and its operation.
3.1 Energy loads
Flexibility resources at the customer premises are categorized into two groups,
consumption and generation, by the CEN-CENELEC-ETSI in their expert
group 1 reference architecture document. This document, however, expands
this grouping a bit by introducing storage (solutions) as its own group, by detaching battery technologies such as EV:s and home batteries from the generation category. This is done because from the flexibility’s point-of-view, some
of the resources can have a dual position when being utilized as a flexibility resource. (CEN-CENELEC-ETSI Smart Grid Coordination Group, 2012)
Thus we arrive to the following three categories:



Consumption
Generation
Storage
This chapter concentrates on the first category: consumption. Or as the title of
the chapter says the chapter will be looking into energy loads.
A report published by the Finnish universities Tampere University of Technology (TUT), Lappeenranta University of Technology (LUT) and Tampere university of applied sciences (TAMK) in 2014 did a summary of different electrical
loads and their potential in Finland during a research program called “DR
Pooli”. The results of the electrical load study are summarized below in Table
2. (Järventausta, et al., 2015)
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Table 2. List of existing electrical loads in Finland and their utilizable potential in load control.
Amount of power
consumption [MW]
Available
flexibility
[MW]
Control
type
Electric heaters in cars
1100
500
x
Geothermal
heat pump
250
?
x
Air source
heat pump
400
?
x
Electric heaters
5000
1800
x
Hot water
heater
1500
1200
x
Electrical
sauna stove
9000
450
x
Load type
[on/off]
Control
type
[adjustable]
x
As can be seen from the table, there are significant amounts of different electricity loads capable of providing DR in Finland. Interestingly, from the viewpoint of flexibility, not that many of the existing energy load resources are capable of adjustable control commands, while all of the listed load types are capable of simple on/off control commands. This might set certain challenges on
how to operate the resources when participating to different electricity markets
and set limitation to end-point control device capabilities.
Another issue which can be pointed out from the amount of flexible loads available is that they are going to have an effect when changing load state on distribution and transmission grid operation. This creates a need to communicate
with the transmission (TSO) and distribution (DSO) system operators.
While both the TSO and DSO would probably benefit from more detailed information exchange, it is the DSOs who’ll have to face the challenges of DR activation in their networks. To circumvent the potential harm from uncontrolled
DR activations in their networks following information sources have been suggested as solutions:


Grid topology information
Network status information
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By sharing the above mentioned status information with the DR users or activators, the grid can pre-emptively avoid any harmful situations in its distribution grid. This concludes the outlook on distributed loads in Finland and in the
next chapter we will take a look at the different distributed renewable energy
production methods.
3.2 Renewable energy generation
This chapter takes a look at the renewable electricity generation resources,
which are in an important part, when it comes to forecasting flexibility needs
for optimal flexibility resource utilization against the current market or current
grid status.
The chapter casts a look on major renewable energy generation resources
and their growth when moving towards the 2020 EC targets to give the reader
an idea of what is the outlook for the renewable energy generation domain.
3.2.1 Renewable energy production
European Commissions continuing work on climate change and the recent ratification of Paris Agreement, which is a global agreement on climate change,
are guiding politics and markets to grow the share of the renewable energy
production in Europe and across the world.
European Environment Agency (EEA) forecasts in its Renewable Energy in
Europe 2016 report, that renewables continue their steady growth towards
2020 targets. Below in Figure 2 a bar graph illustrates the growth trend of the
renewable energy sources in Europe, estimated by the EEA. (European
Environment Agency, 2016)
Figure 2. Graph illustrating renewables growth in Europe from 2005 to 2020, by EEA.
The above presented bar graph can be divided into growth trends by the different types of renewable energy generation and below is presented the three
major energy generation forms by Nordic relevance: Solar, Wind and Hydro.
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3.2.1.1 Solar power generation
Solar power generation is growing faster than anticipated by the EEA as a renewable energy production method. The growth is mainly accelerated by German and Italian solar power projects in the EU. In Figure 3 the trend described
above can be seen clearly and the growth has already surpassed the estimates set for the year 2020. (European Environment Agency, 2016)
Figure 3. Solar power growth in the EU, forecasted by the EEA.
While the increase in solar power generation is significant when measured
against the historical levels, the overall effect of the growth is typically dwarfed
by the growth of wind power generation. However, it could be argued that the
solar generation is having a great impact on the stability of the electricity system, because of the way how it is implemented to the power grid. (Fraunhofer
ISE, 2016)
Solar might not have that big effect on the transmission grids due to its quite
diminutive amount present in the systems, it does have an effect on the usability of the distribution grids. This is because most solar power is installed in decentralized and uniform manner to the network, thus the generation spikes create local problems mainly in the distribution networks. (Fraunhofer ISE, 2016)
Taking into account the growth of solar power generation in the distribution
networks is one of the functions which are important when considering where
to focus the efforts of flexibility resources.
3.2.1.2 Wind power generation
Wind generation is probably the most prominent contributor in the growth of renewable energy generation methods. EEA has forecasted steady growth for
the wind power generation when approaching the year 2020 goals and figure 4
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shows that the growth of wind power generation is on track when compared to
the forecasts. (European Environment Agency, 2016)
Figure 4. Illustration of the forecasted growth of wind power generation by the EEA.
Also when comparing the different renewable energy generation methods,
wind power generation is perhaps most receptive to the intermittency challenge in its power generation. This however, can be and in many cases has
been solved by building sophisticated forecasting models, which give fairly
good forecasts on the power generation potential of the wind turbines. (Fares,
2015)
3.2.1.3 Hydro power generation
Hydro power generation is also one of the three most reputed renewable energy generation methods, and probably one of the most controllable solutions.
Hydropower generation differs greatly from the two other methods presented
above, since it is renewable energy generation method which can provide the
needed flexibility to the system at a moment’s notice. However, when looking
at the growth forecasts of EEA for hydropower generation in the EU the growth
is stagnant and barely growing. The hydropower generation growth forecast is
presented in Figure 5 below.
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Figure 5. Illustration of the growth of hydro power generation in the EU by the EEA.
The stagnation on the hydropower growth is often explained by the environmental and social impacts, and some of the stagnation can be explained by
the fact that most of the natural resources have been already built full of hydropower and the growth which is seen in the EEA forecasts comes from turbine
upgrades or rainfall variation. (European Environment Agency, 2016)
(International Energy Agency, 2010)
The stagnation of hydropower projects also is a clear signal that the amount of
generation flexibility from this resource is limited and other resources are
needed to provide the flexibility to the markets and networks to solve the challenges caused by intermittent production methodologies.
3.2.1.4 Biogas for power generation
Biogas is the fourth and last renewable energy source we are taking a look
into in this chapter. Biogas in electricity generation is very controllable and favorable as it is based on the principle of burning the gas in an engine or a turbine solution, which allows the user to run the resource when its most optimal.
(European Environment Agency, 2016)
The EEA outlook for growth has noticed that the growth of biogas in electricity
generation has grown faster than expected. The forecasted growth rate of biogas based electricity generation can be seen in Figure 6 below. (European
Environment Agency, 2016)
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Figure 6.Biogas growth in the EU, forecasted by the EEA.
One of the explaining factors for the unexpected growth of biogas in electricity
generation is that it can easily supplement the power generation of other energy resources, which are affected by intermittency challenges. Biogas electricity generation is in its own way a renewable solution for traditional electricity
generation domain.
This concludes the look on renewable energy generation methods and in next
chapter we take a look at the electrical energy storage systems.
3.3 Electrical Energy Storage Systems
Electrical Energy Storage (EES) Systems offer an interesting component to
the list which is energy resources for flexibility. The field for experimental and
proven electricity storage methodologies is quite vast and in this chapter we
take a brief look at the different solutions and some market estimates and feasibilities of the EES field.
This chapter goes over the state of the art and the vision for electrical energy
storage systems and paints a brief overlook of what can be expected from the
EES systems in the future.
3.3.1 State of the Art and trends of Energy Storage technologies
At the current state of development, the electrical energy storage systems can
be classified into 5 different categories. These categories are: mechanical,
electrochemical, electrical, chemical and thermal according to IEC whitepaper
on electrical energy storage systems. The classifications for the energy storage systems and their subsystems can be found in Figure 2 below.
(International Electrotechnical Commission, 2011)
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Electrical Energy Storage Systems
Mechanical
Pumped
hydro PHS
Compress
ed air CAES
Electrochemical
Flywheel FES
Secondary
batteries
Flow
batteries
Electrical
Doublelayer
Capacitor
- DLC
Supercon
ducting
magnetic
coil SMES
Chemi
cal
Therm
al
Hydrogen
Sensible
heat
storage
Figure 7. Electrical energy storage systems classified based on energy form (International
Electrotechnical Commission, 2011)
The most well-known classes from the above presented classification may be
the Electrochemical and the mechanical main classes, which include such
EES systems as Li-ion batteries, pumped hydro and flywheels. Of course the
EES systems also differ from different technological choices by the size of the
solutions. For example, at the moment Pumped Hydro Systems are mainly in
the 1GW range when measured by the nominal power, whereas currently mature applications of Li-ion solutions are in the 1W-1kW range.
Below in Figure 3 is and illustration of the different EES systems in their different states of maturity on a state of the art and nominal power scale. One curiosity, which shows from the figure is the development of Li-ion technology. As
the technology becomes more developed, the greater the size of the solution.
For example, the Figure 3 shows that Li-ion portable (meaning cell phones) as
a mature technology, whereas Li-ion mobile (car batteries) has just emerged
from development, while Li-ion stationary (centralized battery systems) is approaching maturity closely behind.
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Figure 8. Maturity and state of the art for electrical energy storage systems as defined by the
IEC. (International Electrotechnical Commission, 2011)
It should be noted that the above graph does not take into account the cost of
the storage solutions presented here, as it only states maturity and nominal
power scale. Thus, while a storage methodology might be mature from the development viewpoint, does not mean that the solution is applied in masses.
Another look at the development of different storage technologies can be seen
in Figure 9 below.
Figure 9. Vision for EES future potential, research needs and feasibility. (International
Electrotechnical Commission, 2011)
The usability of the storage technology also depends greatly on the use case
of the technology in case. For example, in Figure 9 can be seen that Li-ion solutions are feasible in electric vehicles, as proven by EV makers such as
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Tesla, but when looking at different use cases for the Li-ion technology, the
status of the technology changes from feasible to in development.
(International Electrotechnical Commission, 2011)
3.3.2 What to take away from the EES systems?
As it stands today, it is hard to forecast the development of EES systems, but
what is certain is that there are already feasible technologies available on the
storage market and many are utilized today, for example, pumped hydro storage (PHS) is in certain parts of the world very favorable because the electricity
prices are quite high.
Another thing to take away from the above chapters is that the intended application of the EES system is also important when making technology choices.
For example, continuing with the PHS it only excels in gigawatt range and thus
trying to build solutions utilizing PHS technology for home energy management purposes is fruitless.
EES systems however provide an interesting opportunity for creating flexibility
to the needs of electricity grids and markets as they enable timely adjustable
dispatch of resources in a market which is soon to be filled with different intermittent energy production solutions.
Next chapter takes a look at heat resources, which depending the situation
can have an effect on how the flexibility resources are run.
3.4 Heat resources
Usually when resources are mapped the studies only concentrate on either
electricity resources or heat resources. In this deliverable we look at both resource types as we are mapping potential resources for flexibility utilization.
Now, in general heat resources can be divided into three different groups at
end-user level, which are as follows:



Hot water reserves
Electric heaters (incl. HVAC and Heat pumps)
District heating/cooling
The categories listed above are quite broad, but they differ from each other in
distinctive ways to justify them as separate categories. Here, many of these resources have double roles as they act as a source of heat and also as a consumption capable device, with the distinction of district heating which can act
also as a heat storage.
The ability of heat resources to act in different roles is an important factor
when building schemes for resource optimization for flexibility. More information of local heat optimization can be found from deliverable 3.2-5, which
was authored by Fortum.
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The next chapter concentrates on the resource category map, which will be
built based on the findings of different resource types found in chapter 3.
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4 Resource category map
Before diving into the specific groupings of different flexibility resource categories let us take a brief look at the flexibility resource characterizations made by
the CEN-CENELEC-ETSI Smart Grid Coordination Group below in Figure 10.
Figure 10. Flexibility characterization groups made by the CEN-CENELEC-ETSI Smart Grid Coordination Group, 2014.
The flexibility resources are characterized based on their controllability and can
be divided into 5 different categories based on these characterizations. Figure
10 shows that the characterizations vary from uncontrollable to freely controllable resources and in between two ends there are characterizations with different
limitations for the resources.
When building resource pools there are obviously flexibility characteristics which
are preferred over the other types, but in the big picture all of the above presented characterizations are going to be present in the flexible energy system.
It is therefore wise to take into account the characterization types of different
flexibility resources when designing systems, algorithms and flexibility portfolios.
Now, keeping the above in mind the flexibility resources can be divided into
three different categories based on their physical properties, which were briefly
introduced in the previous chapter. The categories for energy resources are:
Energy load, Energy storage, and Energy production. These categories were
formed by the actual different physical properties which each of the resources
have, such as consuming electricity, storing electricity and generating electricity.
Diving further into the categories, the characterizations of CEN-CENELEC-ETSI
Smart Grid Coordination group have a larger impact on the sub-categories of
the local flexibility resource category groups. The focus in the grouping to follow
is to find the most usable and most relevant resources for the different applications stated before.
The first category presented here is EnergyLoads category, because it is the
most common of the categories. The EnergyLoads category consists of such
resource types which are readily available and installed at most of the consumption sites all over Europe. The electricity loads category can contain all the different electricity consuming devices at consumption site level, but in the context
of flexible energy systems, only significant energy loads are considered.
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This means that in Finland most of the relevant controllable loads reside in heating, be it electrical or based on hot water boiler technology. Also air-conditioning
and cooling done by HVAC systems and lightning solutions at office building
level are significant electricity loads and easily suitable for remote or local control to deliver flexibility when taking into account the characterization groups of
CEN-CENELEC-ETSI.
Also special production processes with special facilities in the industrial domain
with different industrial applications are more than capable of delivering flexibility
in different market based situations as demand response. For example, an industrial grade application of energyload demand response can be found in pulp
and paper mill industry, where the processes are energy intensive and can be
stopped for demand response activities. (Kumpulainen, et al., 2015)
Now, moving on these different resources can be organized into groups which
can be illustrated in following manner:
Figure 11. Illustration of EnergyLoad group, which contains the most significant electrical loads
available for flexibility.
This first group shows us the EnergyLoad category and the main sub-categories
of controllable energy loads by relevance. The main requirement for this group
was that the devices or device categories are capable of only consuming electricity. The three first groups fall under the category of shiftable flexibility resources in the CEN-CENELEC-ETSI characterization group, while the last category is reserved for different electrical device groupings and special industry
processes, which can be located on the same characterization grouping at either
of the categories. The three first electrical devices listed in this listing can also
be seen as heat resources, which is important and we will get back to the matter
in the following chapter.
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Moving on to the second group, EnergyStorage, which consists of energy storage devices, such as electrical batteries and their applications such as electrical
vehicles. This leads to the formation of the following group in Figure 12:
Figure 12. Illustration of EnergyStorage group, which contains the most prominent electricity
storage methodologies from flexibility's viewpoint.
The EnergyStorage group contains the main applications of electrical storage
methodologies of said batteries and their applications. The group contains energy storage technologies only capable of storing electricity, because the main
criteria for this group was in the analysis phase that the group has to be able to
generate, store and consume energy.
These characteristics of EnergyStorage group are very important in when designing for example, systems or portfolio management, since they introduce
complexity to the design by having more than one natural ability to participate
into the system flexibility management. And even though the category is called
energy storage, it does not list the devices in the first group, which were capable
of storing heat, because we are looking at the categories by their primary function in this phase.
The third group contains different prominent locally installable energy production
methods. While this group concentrates mainly on the new and rising renewable
energy production methods, such as photovoltaics and wind power generation,
it does also take into account different micro combined heat and power plants
(µCHP), which may have a significant role in the future to provide local flexibility.
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The different generation methods of the energy production category are presented in Figure 13 below.
Figure 13. Illustration of EnergyProduction group, which contains electricity and heat generation
units meant for small local applications.
Depending on the use case of the resource presented in the energy production
group the form of flexibility can vary greatly and be any of the characteristics
presented by the CEN-CENELEC-ETSI. For example, photovoltaics or wind
power solutions can be completely uncontrollable, whereas µCHP can be
solely utilized for providing flexibility at local sites.
Combining the above presented three energy categorization groups together
form a map that illustrates the whole field of flexibility resources available for
resource utilization. Below in Figure 14 is presented an illustration of all of the
local flexibility resources in one map, which still shows the three different energy resource categories in their own groups.
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Figure 14. Flexibility resource map which maps different types of local flexibility resources by
their attributes.
The resource map drawn only by the resource types can already give a reminder to the readers that the flexibility resources have different properties,
such as consumption, generation and storage, which illustrates the possibilities of flexibility utilization in demand response schemes.
The local flexibility resource map will be taken one step further in the next
chapter in order to map out different characteristics of the resources, which we
already glimpsed briefly in this chapter and to show how the resources are
linked to each other and to see what kind of relations they have.
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4.1 Combining electrical resources and heat resources
When diving deeper into the local flexibility resource map with the help of the
CEN-CENELEC-ETSI characterisation groups we can start seeing links and relations between different resources. The EnergyStorage category is in the focal
point of attention due to its nature of having multiple properties in regards handling electricity as found out in the previous chapter.
The EnergyStorage category has the following properties: Consume energy,
store/keep energy and produce energy. These properties are somewhat overlapping with the other categories, but not identical in capability. This is because
the main function of EnergyStorage sub-category is, well, storing energy, but as
a side product the EnergyStorage sub-category gains consuming and production properties in the form of recharging and depleting of battery charge when
operating different tasks. However, the EnergyStorage type resources have
couple additional limitations compared over to the other sub-categories.
The EnergyStorage resources have resource size limitation, as in the size of the
battery and the battery depletion rate, which depletes the production potential
of the EnergyStorage over time. However, by taking into account the triple nature of EnergyStorage resources the FlexibilityResource can be seen, depending on the observer’s viewpoint, as more complex entity or as an entity that offers more options and utilization possibilities for different use cases.
Expanding the same kind of examination to the rest of the FlexibilityResource
categories reveals that similar multi-purpose resources are also in the other categories, such as the heat resources in the EnergyLoad resource category and
the µCHP generation units in the EnergyProduction resource category.
4.2 Expanded FlexibilitResource map
Taking the above mentioned into consideration when defining the FlexibilityResource map produces a new map, which is presented in Figure 15 below.
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Figure 15. Local flexibility resource map with role classifications and optimization opportunities
in a multi-resource location.
The difference to the previous version of the ResourceMap can be seen in the
now visible “duality” connection lines, which are there to present that some of
the resources carry out two different roles in the real world, and thus the flexibility from these resources should be valued differently because of this dual role
of the said resources.
For example, there are two kinds of dual role resources on the map. The first
type is the electricity storage resources, which can act as electricity production
and consumption devices, thus being capable of providing flexibility in either
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ramp up or ramp down situations. Whereas the heat resources with dual role
are capable of providing electricity consumption with time shifting capabilities or
replacing electric heating with its heat reservoir capabilities.
The map regarding flexibility resources isn’t definitive as it generalizes quite a
lot different resources for flexibility, but it provides a starting point when designing services or strategies regarding local flexibility resources. Additional research is needed to find how different combinations of resources affect each
other, when utilized simultaneously and when building applications, it is imperative to understand how different resources can actually be utilized as was seen
in the case of different EES systems.
In the next chapter we take a look at the control mechanisms for harnessing
DR/DER potential by taking a look into few case studies of the most prevalent
technologies available.
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5 Control mechanisms for harnessing DR/DER
potential
This chapter takes a look at the two major control mechanisms used for controlling DR/DER resources at the end user level. Basically the field can be divided at the moment to AMR/AMI-systems and IoT-systems.
In the following chapter we take a look at two case studies which were built
around these two technologies.
5.1 Case studies
Here we take a closer look at the two different control mechanisms. The first
and foremost technology of the two are the AMR/AMI-systems which stands for
Automatic Meter Reading or Advanced Metering Infrastructure. The AMR/AMI
offers in many cases the possibility to connect controllable loads to the AMRmeters themselves via a controllable relay and as of today is the most available
method for controlling loads in Finland.
The second control mechanism related to flexible electricity loads is made up of
the Internet of Things based systems (IoT). These systems vary from vendor to
vendor and the most common denominator for these types of control devices is
that they are controlled over the IP-protocol, hence the name IoT and their fairly
inexpensive cost ratio. The two different technologies are illustrated in Figure 16
below.
Contro
llableD
evice
Meteri
ngPoin
t
Resourc
eManag
er
Energy
Storag
e
AMI
slave
Datahub
Contro
llableD
evice
Supplier (BR)
Resourc
eManag
er
Meteri
ngPoin
t
DSO
AMI
master
Service
provider for
connectivity /
flexibility
operation
Energy
Storag
e
AMI
slave
Energy
Community
Service
Provider (BR)
Joint balance
settlement
(JBS)
TSO
Charging
operator (BR)
Contro
llableD
evice
Markets
Meteri
ngPoin
t
Energy
Storag
e
3rd party (BR)
Ancill
ary
Econ
omic
New market actors & roles
Existing regulated operations
Markets
Regulated operations
Market operations
Free market operations
IoT solutions
Figure 16. Illustration of the future of electricity markets with regulated activities on the right side
of the illustration and open market activities on the left side of the illustration.
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The presentation of the two technologies of connectivity solutions can be seen
quite clearly in the figure above. The open market activities are presented on
the left side of the figure with dotted lines where the IoT-solutions are shown
with the yellow colour, whereas the AMR/AMI-based solutions are presented on
the right side of the figure with solid lines representing regulated energy market
activities.
Besides the connectivity methods mentioned earlier in the chapters the other
main difference between these two control methodologies is that at least in Finland the AMR/AMI-technologies are installed, owned and maintained by the
Finnish DSOs and therefore regulated by the Finnish electricity market law,
whereas the IoT-solutions are seen as open market solutions for controlling flexibility and therefore accessible, at least in principle, to almost anybody in the
electricity market stakeholder field.
Next we take a look at the capabilities of the AMR/AMI system in a case study,
which studied resource activation times over the AMR/AMI architecture.
5.1.1 AMR/AMI case
The AMI/AMR-case set the baseline for the study. The AMR/AMI-case concentrated on the current dominant technology by utilising some of the latest
AMR-meters in a real-world test environment which had produced data worth
of one year of connect and disconnect commands with the full current active
AMR/AMI-communication process. This meant that the meters got their activation signal from an external business system which then sent the commands to
the AMR/AMI meter reading system, which is usually integral part of the
AMI/AMR system architecture.
The AMI/AMR communication process consists of the following flow presented
in Figure 17 below.
Slave
meter
External Business
System sending a
control command
request e.g. EDM
Meter reading
system. Differs from
manufacturer to
manufactorer.
Master meter
Slave
meter
Slave
meter
Figure 17. Process chain for AMR/AMI control chain.
In the baseline case, the control command was generated in an external system, such as Energy Data Management System (EDM) and was then sent to
the meter reading system for processing. The meter reading system then
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pushed the message to the master meter, which handed the control command
to a slave meter under its command. Then the slave meter sent an
acknowledge receipt back to the external business system following the same
chain.
In the baseline case we followed over the period of one years’ time the leadthrough times from the above process and managed to get a set of over 5000
control command actions during the time period. Below is a graph which presents the lead-through times against the amount of actions performed.
Lead-through times for AMI based DR
3500
3000
2500
2000
1500
1000
500
0
Figure 18. illustration of AMR/AMI lead-through times. On the x-axle are the lead-through times
and on the y-axle are the number of actions performed in the time indicated on the x-axle.
As can be seen from the Figure 18 the lead-through times tend to concentrate
on the 1 minute 30 second mark, and the largest bulk of actions performed is
done by the 5 minute mark. The actual lead-through time for the system was 1
minute 20 seconds. This performance is perfectly acceptable on day-ahead and
intraday electricity markets and in portfolio balance management activities, but
these lead-through times rule off basically all of the ancillary markets with stricter
activation times, such as frequency reserves markets.
5.1.2 IoT case
In the IoT-technology case purpose built IoT-devices were installed at different
consumption sites where various loads were connected to the devices via building automation system interface. The devices used in this simulation were purpose built with a really fast lead-through times in mind utilizing fast wired internet
connections to achieve stabile connection and fast response times.
The communication process in the case of the IoT devices is somewhat similar
with the baseline case done on AMR/AMI-technology. The differences in this
case were in the selection of systems, which were chosen real-time execution
in mind. This meant that the EDM system was replaced with the EMS, an Energy
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Management System, which was connected to the IoT-gateway which was responsible for controlling the IoT-devices. A process chain of the above is presented below in Figure 19.
IoTdevice
External Business
System sending a
control command
request e.g. EMS
Building
automation
system
IoT-Gateway
IoTdevice
Building
automation
system
IoTdevice
Building
automation
system
Figure 19. Illustration of an IoT-technology based control command chain.
While the control command chain/process is equal in process steps compared
to the AMR/AMI-process chain, it should be noted that a lot of the components
in the IoT-process chain were selected with fast lead-through times in mind,
going all the way from the external business system which was in charge of
sending control commands to the IoT-devices which were modified for the task
of metering and controlling in near real-time.
In Figure 20 are listed the test results from one test run as a snapshot of the
capabilities of purpose built IoT-devices. The lead-through time for the whole
IoT-execution chain can be seen varying between 1.2 and 3.6 seconds. This
gives us an average of 2.11 seconds for the lead through times.
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Lead-through for IoT based DR
10.
9.
8.
7.
6.
5.
4.
3.
2.
1.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
Figure 20. Snapshot of the lead-through times in seconds based on activation tests performed
during one test session.
5.1.3 Summary of the technologies
Before we set to compare the lead-through times between the two technologies,
the assumption was that the IoT-solution would be faster when compared to the
AMR/AMI-solution. The tests have proved the hypothesis, which is mostly
thanks to the real-time processing capabilities of the purpose built IoT-systems
and the difference between wireless and wired communication technologies.
The IoT-based systems consistently achieve lead-through times of 2.11 seconds on average, whereas the average lead-through time for an AMR/AMI
based system was 1 minute 20 seconds. By only comparing the lead-through
times the IoT-based system is clearly a more capable system if the comparison
is based on activation time. However, when taking into account other properties
of both of the technology paths the differences become more understandable.
Both of the system types have their usages, the AMR/AMI-system is good in
providing metering information while also being capable of providing demand
response with simple ON/OFF-controls with considerable sized electrical
loads. Whereas the IoT-system is good providing real-time control capabilities
and depending on the system, also different types of control commands.
In the next chapter we take a look at different market mechanisms which allow
the usage of the control mechanisms presented in this chapter.
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6 Market mechanisms required for valuing DER
resources
Market information is crucial part of valuing the flexibility resources one has.
Market information provides the current value of your flexibility compared to
other resources and also acts as signal for deploying the flexibility resources at
a right moment.
Without market mechanism valuing the flexibility resources becomes tricky as
one can often either undervalue or overvalue their resources, which in turn in
the long run creates sustainability problems for business run on providing flexibility.
The market mechanisms vary from country to country, but a principal division
between two market types can be made easily. The markets can be divided
into Economic markets and Ancillary markets. The difference between these is
that in Economic markets the purpose is to trade flexibility between market
parties to their various needs. In Nordic countries these products are traded in
the Elspot and Elbas. In ancillary markets the resources are typically sold to
the market organizer, for example, in Finland the national TSO Fingrid has
several ancillary markets which serve the purpose of “keeping the lights on”.
These different markets value flexibility differently based on the capabilities of
the flexibility resources and based on the time left before the delivery hour.
Typically, the value of flexibility can be seen rising the closer we get to the delivery hour, examples of this kind of behavior can be found from the Nordpool,
the Fingrid markets, and with some Northern American markets which also
deal with similar market structure.
Of course, the valuing of flexibility isn’t as straight forward as bidding all the resource at the last moment and often the best result can be achieved by looking
at the different market levels as an entirety. In the next chapter we have listed
the different market levels in Finland with different flexibility related variables.
6.1 Current market mechanisms
The current market mechanisms in Finland consist of 2 Nord Pool operated
markets, the Elspot and Elbas, and of 4 different Fingrid operated ancillary
markets. The markets are listed in more detail in Table 3 and in Table 4. The
tables consist of information on what is the minimum bid size, the minimum activation time for the resources and median prices for the markets, when the
data was available. The Table 3 also includes a column for a new and upcoming Fingrid market called FRR-M capacity, which acts on a capacity market
principle, meaning that the bidders to the market are selling capacity instead of
deliveries. (Nord Pool, 2016) (Fingrid, 2016)
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Table 3. Listing of current electricity market mechanisms for trading flexibility part 1/2. (Nord
Pool 2016, Fingrid 2016)
El ba s
(i ntra -da y)
El s pot
Type
Frequency Res tora tion
Res erve - Ma nua l
( FRR-M )
FRR-M ca pa ci ty*
Up
Down
Up
Down
0,1MW
0,1MW
5MW
5MW
?
?
Activa tion time
12h
1h
15min
15min
?
?
Activa tion frequency
NA
NA
Several times in 24 h
?
?
Up to seller
Same day remaining hours?
Same day remaining hours?
?
?
Previous day, latest 13:00
Same day,
latest 1h before delivery hour
Same day, latest 45 min.
before delivery hour
(Alustavat tarjoukset
?
?
Previous day, ~ 14:00
(during the day)
???
?
?
If bid is accepted
If bid is accepted
mi ni mum bi d
Tra de pl a nni ng
Bi ddi ng cl os i ng
Bi ds a ccepted
i nforma tion
Control execution
Ka . Tuntihi nta 2015,
€/MW
29,65
Vuos i ma rkki na hi nta
(2016) €/MW,h
NA
NA
Ma rkki na n yl l ä pi täjä
Nord Pool
Nord Pool
From flexibility operation
Automatic activation is coming perspective, the resources are
locked if they are sold to here
35,59
24,47
?
?
NA
NA
?
?
Fingrid
Fingrid
Table 4. Listing of current electricity market mechanisms for trading flexibility part 2/2.
Frequncy Contai nment Res erve - Norma l
(FCR-N)
Frequency Res tora tion
Res erve - Automa tic
(FRR-A)
Frequency Contai nment
Res erve - Di s turba nce
(FCR-D)
Up
Down
Up
Down
Up
Down*
mi ni mum bi d
0,1MW
0,1MW
5MW
5MW
1MW
?
Activa tion time
3min**
3min**
2min
2min
0-30sec***
?
Type
Fingrid will announce
beforehand the hours when
needed (Fingrid ei tee
Continuously
Activa tion frequency
Power plants few times a year,
disconnectable loads rarely
Tra de pl a nni ng
Next day hours, usually planned after getting Spot results
and between 18.00. Situation might change even after 18.00
Next day hours
Bi ddi ng cl os i ng
Previous day,
latest 18:00 (yearly contract),
latest 18:30 (hourly contract)
Previous day,
latest 18:00 (yearly contract),
latest 18:30 (hourly contract)
Previous day,
latest 22:00
Previous day,
latest 22:00
Bi ds a ccepted
i nforma tion
Control execution
Frequency needs to be metered at all times and control of
DR needs to be changed accordingly.
Fingrid automatically adjusts
Phone call
Ka . Tuntihi nta 2015,
€/MW
23,24
23,24
4,44
3,56
16,13
?
Vuos i ma rkki na hi nta
(2016) €/MW,h
17,42
17,42
NA
NA
4,5
?
Ma rkki na n yl l ä pi täjä
Fingrid
Fingrid
Fingrid
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The several different market layers create a possibility for optimal resource utilization and discovering of the true value of the flexibility resource, although it
might be quite challenging for some market parties to participate and follow all
the market levels simultaneously. Also the fact that the markets are fragmented can be seen as an argument against the current market mechanism
which consists of several layers between two different market operators creating unnecessary obscurity.
This concludes the chapter on market mechanisms. In the next chapter we
take a look at the entirety of information sources, which have an effect on flexibility operation, and can provide additional value to the flexibility operation.
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7 Methods for maximizing overall benefits from
DR and DER resources with combined information integration
As stated in the previous chapter, this chapter concentrates on building the big
picture of resource optimization. In this chapter we take a look into the different
information sources, which are needed to utilize flexibility in such manner that
does not harm the energy system and is beneficial for all the parties engaged
to the system.
First we look at the different input sources and then we build connections between these information sources to find the information sharing processes
needed for finding the optimal value of flexibility in the energy system.
7.1.1 Information sources related to flexibility
Taking a look at the previous chapters we can start to see an ecosystem of information sources which each other creates additional value for flexibility. As a
quick recap the information sources fall into following categories:





Electricity Grid information, incl. topology and status
Flexibility resource information
Market information, prices and volumes
Forecasts
District heating information
To extract the optimal value of the information resources listed above when
planning flexibility optimization, the information sources need to be combined
to discover new information, which helps the flexibility owners to value their resources properly. A schematic of how information sources could be combined
is presented in Figure 21 below.
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Electricity Grid
operational status
information
Electricity Grid topology
information
FlexibilityResources,
resource type
information, metered
information, Datahub
Expanded flexibility
information, flexibility
database with flexibility
forecasts
District heating price and
availability
Weather forecasting
Market information
Figure 21. Information sources for optimal DR&DER optimization
As can be seen form the Figure 21 the different information sources are combined together to create new knowledge of flexibility, which can be used to optimize the utilization of flexibility or discover the value of flexibility in different
market and network operational situations.
Let us expand a little bit on few of the information sources in the information
source schematic. First, the market information source follows the same thinking presented in chapter 6, where market information builds from all the different market levels available in current markets, but it also supports the idea that
the market information can be something completely different in the future.
The main idea however with the market information is to act as an activation
signal for resource utilization and thus support market driven future in energy
domain. By following this thinking the district heating information component
belongs to the same category with the market information, but is separated because of its supplementing nature. The district heating network can help to optimize the overall utilization of flexibility resources by offering alternative “running schemes”, but isn’t as critical for flexibility optimization as the market information component.
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Another important information source which has not been discussed that
widely in the document is the Datahub, which at least in the Nordic markets
provides the basic information management processes and information related
to end-user consumption and in Finland basic flexibility information. The Datahub is a significant market enabler as it provides all the basic data to all the
players in the energy market domain. It is still uncertain of what kind of flexibility information will be present in the datahub, but to extract the most value out
of the DR resources, there should be an information source, which provides information related to the usability of the flexibility resources as was found in
chapter 5.
The third critical information component is formed from the electricity grid information blocks. These blocks set the baseline for the flexibility utilization in the
real world. The grid topology information block gives the geographical usage
information for the flexibility which is important for the sake of having stabile
electricity grid, whereas the electricity grid operational status is the signal
which acts as a signal for reliability based flexibility actions. It should also be
noted that the grid information sources create the basis for a healthy market
for flexibility as the grid is the market enabler in real world.
The next chapter is the final chapter of this deliverable and it presents the conclusions of this deliverable.
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8 Conclusions
The challenges brought forward by the intermittent renewable energy production methods need flexibility solutions to avoid potentially harmful situations for
example in the distribution grids.
Also, valuing the flexibility resources correctly by their real market value can
be challenging, when the markets are so fragmented and the information
needed for valuing flexibility is distributed across different market actors service providers.
To make sense of the market for flexibility and to extract the maximum value
out of the flexibility resources, while at the same time optimizing the usage of
said resources can be challenging in the current market environment. To
achieve optimum utilization of flexibility resources, the market party has to
have deep knowledge of the different market levels, flexibility resource types
and other market affecting technologies. Also building a solution which can
take all this into account can be very costly.
To answer the challenges found during the study Empower IM has created in
other task a flexibility gateway concept, which could solve most of the problems related to optimal utilization of one’s flexibility resources. The Flexibility
Gateway concept is presented in the Deliverable D3.1-1.
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9 References
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