The potential of Active Demand
February 26th 2015
Objectives and expected results in
terms of AD potential
 Core objectives
1. Identification and quantification of flexibilities based on
AD with industrial, commercial and residential consumers
2. Assessment of the quantitative impact of AD on system
performance and stability with focus on MV and LV grids
3. Assessment of economic benefits of AD for the key
stakeholders
 Expected results
– AD related flexibilities quantified
– AD impact on electricity system assessed
– Economic benefits of AD described
2
Interaction of Tasks
Coordinated assumptions
results
T 6.1
Residential and
C&I AD potentials
(RWE)
T 6.2
AD applied for system
services at MV/LV
(ERDF)
results
results
T 6.3
Economic benefits of
AD for stakeholders
(Comillas)
3
Coordinated assumptions I
scope
countries
•
•
•
•
segments
• for all countries: residential;
for Germany: also commercials and industry
horizon
France,
Germany,
Italy,
Spain
2020
scenarios
• baseline (low),
• optimistic (medium),
• technical potential (high)
calculated KPIs
•
•
•
•
•
energy efficiency [GWh]
savings achieved by energy efficiency [€]
CO2 reduction by energy efficiency [tons]
peak shaving potential [GW]
Impact on DSO CAPEX [€]
4
Coordinated assumptions II
AD-Programs and their Effectiveness
the considered AD-programs and their effectiveness are based on the
ADVANCED knowledge base:
Dynamic Pricing
programs
Feedback
programs
AD programs
Program Type
Effectiveness
Informative bill
5,68%
In-Home Display (IHD)
9,10%
Website
4,38%
Time of Use (no-automation)
5,16%
Time of Use (automated)
15,45%
Critical peak (no-automation)
16,33%
Critical peak (automated)
32,47%
Real-time pricing (no-automation)
10,19%
Real-time pricing (automated)
11,25%
5
Specific Assumptions for the calculation of
the residential potential
introduction of Smart
Meters will accelerate
AD programs
calculation of the KPI
“savings achieved by
energy efficiency [€]”
is based on official
price data published
by Eurostat (2014)
only total
consumption and
peak demand for
residential and C&I
consumers are
considered
IHD are considered as
‘direct feedback’, all
other feedback
programs as ‘indirect’
feedback programs
will not be combined
(because values for
the program type
effectiveness are not
available for all
combinations)
Assumptions for the
calculation of potential
the time horizon for
energy consumption
(peak and total) is
2020
the program type
effectiveness is
considered to be
similar in all
scenarios
dynamic pricing
programs cannot be
combined
program type
effectiveness is
based on the
ADVANCED
knowledgebase
program type
effectiveness
expresses the impact
of any program as a
percentage reduction
in (a) total energy
consumption or (b)
peak consumption
6
Methodology to Calculate the
AD Potential (residential)
Impact of nonquantitative
variables on
AD-programs
input from
each country
vary from
scenario
to scenario
x
AD-Program
Effectiveness
(Feedback and
DynamicPricing)
ADVANCED
Knowledge
base
x
Smart Meter
Rollout –
kW or kWh
covered by
Smart Meters
x
AD-Program
uptake rate
input from
each country
input from
each country
vary from
scenario
to scenario
vary from
scenario
to scenario
=
AD values for
the feedback
and dynamicpricing
programs
Calculation of
KPIs
input from
each country
results for each country
and each scenario
7
Impact of Non-Quantitative Variables
on AD-Programs
SCORE
placeholder - not in use
data privacy issues
comm capabilities
balancing regime and usage
of load profiles
automation technologies
FEEDBACK PROGRAM
communcation of daily-hourly
readings (meter -> DSO)
determination of the impact of non-quantitative variables (traffic light-system):
 statements must be tagged with a green or a red light for each AD program
 red and green lights indicate whether an important must-have condition is available
 only when all lights are green, the AD potential of a program type is calculated
 A red light indicates a show-stopper for this specific AD-program and indicates a
hurdle for the AD program
Time of Use (no-automation)
Time of Use (automated)
Critical peak (no-automation)
Critical peak (automated)
Real-time pricing (no-automation)
Real-time pricing (automated)
8
Country Specific Input Parameters of
the residential AD Potential Calculation
Total residential consumption in
2020 [TWh]
Peak load for the residential sector
in 2020 [GW]1
Consumer price for electricity in
2014 [€/kWh]
Carbon intensity of electricity
generation in 2020 [CO2(kg)/kWh]
Germany
Italy
France
Spain
127
79
168
83
40
16
43
16
0,292
0,23
0,159
0,209
0,576
0,386
0,0476
0,218
1Remarks:
•
•
In the German case approx. 14 GW of overnight heating system are already steered based on
temperature orientated profiles (excluded above).
In the French case some 3 GW of water heating systems are already steered out of the peak
today. In addition some 0,5 GW are moved out of the peak by critical peak pricing (excluded
above).
9
Calculation of KPIs
On the basis of this general information, four KPIs are calculated:




Energy efficiency thanks to feedback programs [GWh]
Peak shaving potential [GW]
Savings achieved [€]
CO2 reduction [tons]
energy efficiency thanks to feedback programs =
AD value for feedback programs (%) x yearly total consumption
peak shaving potential =
AD value for dynamic-pricing programs (%) x peak load
savings achieved through energy efficiency =
energy efficiency (%) x (average) price for electricity
CO2 reduction =
energy efficiency (%) x carbon intensity of electricity generation
10
Summary of Results for Demand Response:
(Additional) AD Potential
demand response
country
sector
baseline
scenario
optimistic
scenario
GW
GW
France
residential
0,14
0,28
Italy
residential
0,08
1,32
Spain
residential
0,2
0,75
Germany
overall
3,22
9,92
of which
industry
2,7
7,2
commercial
0,4
1,8
residential
0,12
0,92
3,64
12,27
Overall
potential
In all countries analyzed the AD potential increases
at least two-fold when major barriers are removed!
Remarks on additionality:
•
In the German case approx. 14 GW of overnight heating system are already steered based on temperature
orientated profiles (excluded above).
•
In the French case some 3 GW of water heating systems are already steered out of the peak today. In addition
some 0,5 GW are moved out of the peak by critical peak pricing (excluded above).
11
Summary of Results
Energy efficiency – “KPI view”
electricity savings1
optimistic
baseline scenario
scenario
m. €
m. €
CO2 reduction
optimistic
baseline scenario
scenario
thsd. tons
thsd. tons
country
sector
Germany
households
1.138
2.692
2.245
5.311
Italy
households
50
584
83
971
France
households
191
409
57
123
Spain
households
193
391
202
410
Removing major barriers to AD could also result in significant savings
for consumers and positive effects on the environment!
1Remark:
For the electricity savings only first round effects are calculated, i.e. losses in tax income etc.
have not been analysed.
12
AD applied for system services primarily
in LV-MV grids
System services and relevant products
(differentiation might be somewhat artificial as there is interaction!)
Reactive power/Voltage control
Frequency containment
reserves
Providing short-circuit power
Frequency restoration
reserve
Replacement
reserve
Black start and
islanding capabilities
Coordinated restart
of RES
Redispatch accord. to
voltage problems
Frequency
control
System
restoration
Shedding of load
accord. to voltage
problems
Voltage
control
System
operations
Redispatch/Congestion
management
Feed-in management
Controllable loads
Network/Grid analysis, monitoring
Planning of operations
13
Methodology:
Template of Flexibilty Requirements
Important dimensions of flexibility services
Player expressing the need
Timeframe for contracting
Delay before activation
Full activation time
Minimum and maximum capacity
Price of bid
Divisibility
Delivery period
Mode of activation
Delay between two activations
Measurement and communication requirements
Penalty requirements
Frequency of activation
Call method
Geographical criteria
14
Example: Optimization of distribution
network planning and construction (ENEL)
Description of service
Player expressing the need
DSO
Timeframe for contracting
Product is contracted years ahead (for an investment in a primary substation to
solve both peak demand and fault situations).
Delay before activation
Full activation time
Minimum and maximum
capacity
Day-ahead in case of peak demand and real-time in case of emergency.
Real time (0-15 minutes).
Some MVA for a primary substation.
Price of bid
Fixed price for capacity reservation and a variable price for energy
Divisibility
Yes.
Several hours, several days each year (can be variable each year – supposed to
increase during the period), for 2 or 3 years.
Delivery period
Mode of activation
Delay between two
activations
Measurement and
communication
requirements
Penalty requirements
Frequency of activation
Call method
Geographical criteria
Could be activated by signal or an interface between DSO and aggregators.
Depending on the use case.
N.A.
AD must be guaranteed and penalty requirements are correlated to the risk and
costs of disruption of power supply.
Always available (in the periods defined in the contract).
Call for tender.
15
Yes.
Results on Flexibility requirements (1/2)
Major AD usages – system operator‘s point of view
• Deferral of investments on the network
(e.g. deferred substation upgrades or deferred new lines)
• Power Flow control and network congestion solutions
• Voltage control and reactive power compensation
• Emergency situations
• Network restoration or system restoration (black-start)
• Islanded operation
Also, the contribution of domestic customers and small or
medium commercial and industrial customers to active power
reserves and frequency control (TSO) will be essential.
16
Results on Flexibility requirements (2/2)
Important dimensions
of flexibility services
Possible realizations
Mode of activation
• automatically and in certain cases reacting upon a local
measurements or
• centralized signal sent by an operator and / or directly
managed by the network operator for emergency
situations.
Timeframe for
contracting
Dependent on market design and technical problem
to be solved (DSOs tend to favor longer contracts)
Capacity (Max. or Min.)
Driven by local characteristics (topology of the network,
load characteristics), i.e. some MVA for a primary
substation, some 100 kVA for a MV feeder
Penalties requirements
might be necessary and will be proportional to the risk and
cost of not provided service or energy ( aggregation)
Mode of Activation
(reliability)
has a high importance, automatic systems could be
required
17
Other important issues for AD-use
by system operators
 Integrate AD in a “merit order” of other relevant solutions
(e.g. traditional network expansion, wide-area management,
OLTCs etc.),
 Clarify the frontier between technical minimum requirements
and market-based products,
 Take into account the geographical dimension of network and
operators’ expectations,
 Optimize coordination between AD Operators, TSOs and
DSOs:
– to assess and prevent undesirable side effects of AD on distribution networks ;
– to get the best value from flexibilities (and have the same products compete both in
national / local mechanisms).
 Insert AD-use in different timeframes and business cases.
18
Lessons learned from the process
 The views of the DSOs might differ depending on national context:
– main drivers for network investments
(renewable energy sources development or peak load increase),
– regulatory context (i.e. existence of aggregators or not, level of smart
meters roll-out).
 Investment criteria are complex and DSOs needs are local; i.e.
several considerations have to be made, regarding:
– the context (e.g. demand / generation increase) and
– existing network configurations
 Technical, economical and regulatory changes are needed in
particular for the DSOs’ AD use.
– They are currently studied in different pilots in Europe.
19
Economic benefits of AD for stakeholders
Methodology
 Benefits for distribution networks (and users):
Select location
of consumers,
generators,
substations
(map)
Inputs for scenarios:
-AD program:
consumption profiles
-Participation rate
-Concentration of
responsive consumers
Build initial
network
Planning
criteria
Initial network
Network
expansion
Assess
invest.
Costs
(€/yr,
%,
€/cons)
Transformer
substations
MV Feeders
LV Feeders
Reinforced network
20
Methodology: additional scenario definition
Boundary condition
Scenarios
Country
Spain, Italy, Germany & France
Macroeconomic and
regulatory environment:
Uptake rate x SM rollout
• Business as Usual (baseline)
• Optimistic
• Technical Potential
AD Program
• Feedback
• Dynamic pricing
Location of responsive
demand
• Dispersed
• Concentrated
Network type
• Urban
• Rural
Example of the analysis of one case study / scenario:
What would be the benefits of feedback programs for an urban network in Spain,
with the participation rate that corresponds to the optimistic scenario in this country,
if the participative consumers are randomly dispersed in the network?
21
Country-specific results: Spain
 Network reinforcements mainly driven by load growth
– Great potential to defer network investments both with Feedback
and Dynamic Pricing
 Significant contribution of LV and MV/LV to overall savings
Concerns / Expectations:
• Load growth
• Very small penetration of
solar PV at low voltage
levels
% total investment without AD
Baseline
Optimistic
Tec. Pot.
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Dynamic Pricing
Rural
Feedback
Dynamic Pricing
Feedback
Urban
22
Country-specific results: Italy
 Moderate reinforcement needs: limited benefits of AD
 Example of highest impact on urban network
– Most of the required investments can be avoided with sufficient
number of comparatively small modifications in the loads
 Example of some investments needed in the rural area for
new connections that cannot be avoided with AD
Concerns / Expectations:
• Moderate load increase
• Significant volume of new
Solar PV at low voltage
levels, especially rural
% total investment without AD
Baseline
Optimistic
Tec. Pot.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Dynamic Pricing
Rural
Feedback
Dynamic Pricing
Feedback
Urban
23
Country-specific results: Germany
 Network reinforcements driven by massive integration of
Solar PV in certain rural areas
– Moderate reduction of investments with Dynamic Pricing
 No reinforcements in urban networks: no impact of AD
Concerns / Expectations:
• Stagnation of load
• New PV in urban areas
limited by space
• Massive integration of
solar PV in some rural
areas
% total investment without AD
Baseline
20%
Optimistic
Tec. Pot.
17.9%
15%
9.2%
10%
5%
1.7%
0%
2.2%
2.2%
0.0%
Dynamic Pricing
Feedback
Rural
24
Country-specific results: France
 Exemplary MV networks:
– Actions taken at LV prove to be relevant and positive for both LV
and MV investments
 Example of AD being helpful to avoid load-driven
reinforcements in highly constrained networks
Optimistic
DP
Avoided
Avoided
Avoided
FB
Avoided
Avoided
Avoided
DP
Required
Avoided
Avoided
FB
Required
Required
Avoided
Concerns / Expectations:
• Moderate load increase
• Concern about
constrained networks
• Limited penetration of PV
Urban
Rural
Technical
Baseline
Potential
Table: Ability of AD to avoid the single reinforcements required in the
urban and rural networks analysed for France (required or avoided
reinforcement)
25
Economic impact of AD on distribution
network investments: main conclusions
Expansion
drivers
• + High potential to defer investments driven by load growth
• ± Dyn. Pricing limited potential for integration of new PV
Network
typology
• + Urban networks with high utilization rates
• - Rural areas with dispersed loads and low power intensity
Level of
constraints
• + AD could lessen overloads in highly constrained networks
• - Small impact on networks designed with ample capacity
Location of AD
consumers
• Makes a difference for low participation rates
• + Concentrated & under control unless
• - Connections are dispersed and uniformly distributed
26
Regulatory barriers and
recommendations
Barriers
Recommendations
DSO lacks incentives and tools to integrate AD into
efficient investment strategies
Review remuneration to incentivize long-term
efficient investments and innovation
Network tariffs: conflicting components, flat and
volumetric tariffs do not send the right signal
Cost-reflective tariffs to incentivize an efficient
consumption for the system as a whole
Roles of DSOs, retailers and emerging actors
remain to be (re) defined
DSO entitled the choice to count on new forms of
AD to alleviate congestions and operate networks.
Decide on issues related to access to metering
data, billing and direct commercial contact.
Some lack of standardization in AMI and ICT, SM
functionalities & home appliances
Harmonization at EU & implementation at MS
Consumer protection sometimes missing,
consumers not engaged
Data protection, rights to be informed and
provided tools to understand complex AD
27
The potential of AD
AD potential in Europe is
substantial and could be
increased by removing
major barriers
A good coordination
between AD operators,
TSOs and DSOs is needed
to get the most from
flexibilities (enable national
/ local mechanisms)
Key Messages
AD has highest potential
if network expansion is
driven by load growth and
is used in highly utilised
urban networks which are
already constrained
Regulatory barriers are
substantial (e.g. DSO
remuneration, network tariff
design, standardization etc.)
but can be overcome
28
Thank you!
Results of D6.1 - results (energy)
9%
electrical energy
baseline scenario
optimistic scenario
7,26%
technical potential
6%
kWh
covered
uptake
rate
kWh
covered
uptake
rate
kWh
covered
uptake
rate
Germany
45%
100%
100%
100%
100%
100%
Italy
100%
100%
100%
100%
100%
100%
France
80%
20%
81%
40%
100%
100%
Spain
79%
25%
80%
50%
100%
100%
3,19%
3,07%
3%
1,53%
0,71%
0,27%
2,28%
1,12%
0%
Germany
Italy
France
baseline scenario
Spain
optimistic scenario
kWh covered by feedback program (%)
baseline scenario
Germany
Italy
France
FB 0
55
0
20
FB 1
30
97
FB 2
15
FB 3
optimistic scenario
Spain
Germany
Italy
France
21
0
0
19
7
71,1
40
65
3
1
2,37
50
0
0
72
5,53
FB 4
0
0
0
FB 5
0
0
FB 6
0
FB 7
0
technical potential
Spain
Germany
Italy
France
Spain
20
0
0
0
0
3
72
0
0
0
0
35
5
2,4
100
100
100
100
10
0
73
5,6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
Results of D6.1 – results (power)
9%
electrical power
baseline scenario
optimistic scenario
8,25%
technical potential
6%
kW
covered
uptake
rate
kW
covered
weighted
potential
kW
covered
uptake
rate
Germany
20%
30%
25%
2,30%
100%
100%
Italy
100%
10%
100%
8,25%
100%
100%
France
80%
100%
81%
0,65%
100%
100%
Spain
90%
13%
95%
4,60%
100%
100%
4,60%
2,30%
3%
0,31%
0,65%
0,32%
0,52%
1,24%
0%
Germany
Italy
France
baseline scenario
Spain
optimistic scenario
kW covered by dynamic pricing program (%)
baseline scenario
Germany
Italy
France
DP 0
80
0
53
DP 1
20
100
DP 2
0
DP 3
optimistic scenario
Spain
Germany
Italy
France
10
75
70
52
9
1,08
2,5
30
0
34
2,52
7,5
0
0
2
0
DP 4
0
0
2
DP 5
0
0
DP 6
0
0
technical potential
Spain
Germany
Italy
France
Spain
5
0
0
0
0
9
9,31
0
0
0
0
0
34
27,93
0
0
0
0
2,5
0
2
0
0
0
0
0
0
12,5
0
3
0
100
100
100
100
0
60,48
0
0
0
31,77
0
0
0
0
0
25,92
0
0
0
25,99
0
0
0
0
31
Results of D6.1 –
(Additional) AD Potential
electrical power1
country
sector
baseline scenario
electrical energy
optimistic
scenario
optimistic
scenario
baseline scenario
GW
%
GW
%
GWh
%
GWh
%
Germany
households
0,12
0,31
0,92
2,30
3.898
3,07
9.220
7,26
Italy
households
0,08
0,52
1,32
8,25
216
0,27
2.516
3,19
France
households
0,14
0,32
0,28
0,65
1.199
0,71
2.574
1,53
Spain
households
0,20
1,24
0,75
4,60
928
1,12
1.879
2,28
In all countries surveyed the AD potential increases
at least two-fold when major barriers are removed!
1Remarks
•
•
on additionality:
In the German case approx. 14 GW of overnight heating system are already steered based on
temperature orientated profiles (excluded above).
In the French case some 3 GW of water heating systems are already steered out of the peak
today. In addition some 0,5 GW are moved out of the peak by critical peak pricing (excluded
above).
32