Webinar: Electricity market designs for flexibility: from zonal to nodal architectures, findings from first market simulations

iea-isgan.org
ISGAN
International Smart Grid Action Network
ISGAN Academy Webinar
1
Electricity market designs for flexibility: from
zonal to nodal architectures, findings from
first market simulations
27. 05. 2021,
Online Webinar

ISGAN is the short name for the International Energy
Agency (IEA) Technology Collaboration Programme
(TCP) for a Co-operative Programme on Smart Grids
(ISGAN – International Smart Grids Action Network).
It is also an initiative of the Clean Energy Ministerial
(CEM) and was formally established at CEM2 in Abu
Dhabi, in 2011 as an Implementing Agreement under a
framework of the International Energy Agency (IEA).
The International Smart Grid Action Network (ISGAN)
creates a strategic platform to support high-level
government attention and action for the accelerated
development and deployment of smarter, cleaner
electricity grids around the world.
2
ISGAN in a Nutshell

ISGAN currently consists of 27 Contracting Parties.
Their nominated representatives form the Executive Committee headed by the Presidium,
assisted by two co-Secretariats and the Operating Agent of ISGAN.
ISGAN in a Nutshell
3
ISGAN Presidium
ISGAN Executive Committee
Co Secretariat Co Secretariat

Vision
ISGAN’s vision is to accelerate progress on key aspects of smart grid policy, technology, and investment
through voluntary participation by governments and their designees in specific projects and programs.
Its activities center foremost on those aspects of the smart grid where governments have regulatory
authority, expertise, convening power, or other leverage, focusing on five principal areas:
• Policy standards and regulation
• Finance and business models
• Technology system development
• Workforce skills and knowledge
• Users and consumers engagement
ISGAN facilitates dynamic knowledge sharing, technical assistance, peer review and, where
appropriate, project coordination among its Contracting Parties.
4

Value proposition
Webinars
Workshops
Conference
presentations
Policy briefs
Discussion
papers
Casebooks
Technology
briefs
Technical
papers
5

Annex 8 – ISGAN Academy
Offer the ISGAN community of high level engineers and decision makers a
means of rational and efficient continuous technical skills complement and
update in the field of smart grids
The Academy is proposed as a set of e-learning core modules
dealing with the entire value chain of smart grid
Fundamentals and further reading modules are also provided as appendices
Webinars organized every two months or co-hosted with the
Clean Energy Solutions Center
Operating Agent
Annex 8
ISGAN Academy
on Smart Grids
6
ISGAN Website: www.iea-isgan.org

Which are the most suitable market designs to capture the value of flexibility in power systems?
The European project OSMOSE has developed different models to assess the economic value of
different flexibility mixes in future power system scenarios. The webinar will introduce the zonal and
nodal market designs modelled in OSMOSE, present the first simulation results, and discuss the
preliminary findings of this ongoing work.
The speakers:
• Maxime Laasri, Research project manager, RTE
• Christoph Weber, Professor, University Duisburg-Essen
• Florian Boehnke, Scientifc Assistant, University Duisburg-Essen
• Sandrine Bortolotti, Research Engineer, RTE
Please, use the Q&A tool to pose questions, the speakers will answer at the end of the presentation.
The recording will be available through the ISGAN YouTube channel.
Electricity market designs for flexibility:
from zonal to nodal architectures, findings
from first market simulations
7

WEBINAR:
Electricity market designs for flexibility:
- From zonal to nodal architectures -
Findings from first market simulations
in the OSMOSE project

Agenda
27/05/2021 ISGAN | ELECTRICITY MARKET DESIGNS FOR FLEXIBILITY [OSMOSE WP2] 9
• Introduction to OSMOSE WP2
• PART 1: Zonal market architectures:
a) Study with RTE’s PROMETHEUS-ATLAS model
b) Study with Joint Market Model by UDE
• PART 2: Nodal market architectures:
b) Study with Joint Market Model by UDE
• Conclusions

Maxime Laasri, RTE
INTRODUCTION TO OSMOSE WP2

11
OSMOSE PROJECT: leveraging flexibilities
?
NEW FLEXIBILITY NEEDS
NEW
FLEXIBILITY
SOURCES
HOLISTIC APPROACH OF FLEXIBILITY
Flexibility is understood as a power system's ability to cope with variability and
uncertainty in demand, generation and grid, over different timescales.

OSMOSE PROJECT: key figures
 H2020 EU funded
 28M€ budget
 33 partners
 Leaders: RTE, REE,
TERNA, ELES, CEA, TUB
 2018 – 2022

OSMOSE PROJECT: project structure
WP = Work Package

WP2: market designs & regulations
*RT : Real Time ; DA : Day Ahead; ID : intra Day
orange is for CEGrid-JMM model from UDE, and blue is for the PROMETHEUS-ATLAS model from RTE
SIMULATIONS
 Explore and propose
some market-based
solutions for the
development of an optimal
mix of flexibility sources in
Europe
 Create advanced tools
and methodologies for
market design analysis
OBJECTIVES

Maxime Laasri, RTE
PART 1: ZONAL market studies:

Modeling a market environment in
WP1 Current Goals Achieved
Scenario
Antares is an open-source power system
simulator developed by RTE. It is
embedded into PROMETHEUS, and
available as a standalone application:
https://antares-simulator.org/
High marginal cost
scenario (80% net
load forecast
quantile of initial
study)
Low marginal cost
scenario (20% net
load forecast
quantile of initial
study)
Medium marginal
cost scenario (direct
output of initial
study)
Long-term vision
(marginal costs,
hydraulic reservoir
management…)
Day-Ahead
Orders
Clearing
Conversion
to ATLAS …
LONG-TERM PLANNING SHORT-TERM DECISIONS
ATLAS
ATLAS is an agent-based model implemented in
PROMETHEUS that can simulate complex market
environments. It can simulate market participants
formulating order books under uncertainty, perform
market coupling, and simulate each participant’s
portfolio optimization.
NB: In this study, market participants are all price
takers and submit orders based on their costs only.
They are two agents per country: one generation
company, and one energy supplier

Primary results of long-term planning simulations
• The study comprises
the same 33 countries
as in WP1, and the
same grid assumptions,
taken from e-Highway
• The number of thermal
clusters and the
stratification of their
variable costs has a
direct impact on the
system’s marginal cost
• NB: variable costs in
these first runs are
different from UDE’s
values. Final runs will
use the same values
WP1 Current
Goals Achieved
Scenario
109.8
109.9
110
110.1
110.2
12:00:00
AM
2:00:00
AM
4:00:00
AM
6:00:00
AM
8:00:00
AM
10:00:00
AM
12:00:00
PM
2:00:00
PM
4:00:00
PM
6:00:00
PM
8:00:00
PM
10:00:00
PM
12:00:00
AM
2:00:00
AM
4:00:00
AM
6:00:00
AM
8:00:00
AM
10:00:00
AM
12:00:00
PM
2:00:00
PM
4:00:00
PM
6:00:00
PM
8:00:00
PM
10:00:00
PM
FR
fr_mgcost
109.6
109.8
110
110.2
110.4
12:00:00
AM
2:00:00
AM
4:00:00
AM
6:00:00
AM
8:00:00
AM
10:00:00
AM
12:00:00
PM
2:00:00
PM
4:00:00
PM
6:00:00
PM
8:00:00
PM
10:00:00
PM
12:00:00
AM
2:00:00
AM
4:00:00
AM
6:00:00
AM
8:00:00
AM
10:00:00
AM
12:00:00
PM
2:00:00
PM
4:00:00
PM
6:00:00
PM
8:00:00
PM
10:00:00
PM
DE
de_mgcost
0
50
100
150
System marginal cost over one week
(€/MWh)
be medium de medium es medium fr medium

Short-term water values
Hourly water values over one year in FRANCE (€/MWh)
• Water values are close
to the system’s overall
marginal cost for
average storage levels
• We observe the
expected seasonality
for high and low storage
levels: water gains
value around the winter
but looses value in the
summer

Short-term water values
• Water values are close
to the system’s overall
marginal cost for
average storage levels
• We observe the
expected seasonality
for high and low storage
levels: water gains
value around the winter
but looses value in the
summer
Hourly water values over one year in ITALY (€/MWh)

Cleared quantities on 11/03 and 12/03 DA market
-20000
0
20000
40000
60000
80000
100000
120000
DE
total de_ccgt de_hard_coal de_lignite
de_ev de_w de_pv de_BioMass
de_Waste de_ror
-10000
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
FR
total fr_hydro fr_ccgt fr_nuclear fr_ev fr_w fr_pv fr_ror

Cleared quantities on 11/03 and 12/03 DA market
• High renewable penetration entails power stations being out-of-the-money, and opportunities for electric
vehicles charging
• CCGT provide flexibility to the market, with its own cost (see next slide)
-2000
0
2000
4000
6000
8000
10000
12000
14000
16000
BE
total be_ccgt be_nuclear be_ev
be_w be_pv be_BioMass be_ror
-10000
0
10000
20000
30000
40000
50000
ES
total es_hydro es_ccgt es_hard_coal
es_nuclear es_ev es_z_psp_gen es_w
es_pv es_BioMass es_Waste es_ror

Clearing prices on 11/03 and 12/03 DA market
0
20
40
60
80
100
120
140
160
ES
es_price es_mgcost
• As expected, day-ahead prices are more volatile
than the marginal cost due to the simulated market
environment (constrained market orders add
complexity and rigidity compared to a pure and
perfect competition-based system optimization)
• The depth of price drops induced by solar power
peaks can be radically different from one day to
the next, as illustrated for Spain
• Prices are also impacted by the level of
stratification in thermal units’ variable cost

Clearing prices on 11/03 and 12/03 DA market
• Day-ahead prices are sometimes continuously
higher than the marginal cost anticipated during
long-term planning (see France, Germany, and
Belgium, almost always in the same price group as
the grid is unconstrained on these days)
0
50
100
150
200
FR
fr_price fr_mgcost
0
50
100
150
200
BE
be_price be_mgcost
0
50
100
150
200
DE
de_price de_mgcost

b) Study with the Joint Market Model
Prof. Dr. Cristoph Weber, Florian Boehnke
PART 1: ZONAL market studies:

Agenda
• Introduction of the Joint Market Model (JMM)
• Preliminary Results
• Zonal Market Studies

Market Design Studies
Modelling Landscape – Joint Market Model (JMM)
• Modelling of power plant operation and
market outcomes (unit commitment)
• Starting point: with well-functioning
competition the market outcome corresponds
to the outcome of a central optimization
(fundamental model)
• Objective function minimizes variable
generation costs
• Two-stage optimization approach for
modelling of forecast errors and redispatch
• Modelling of different markets:
day-ahead, intraday, balancing, heating
markets
• Detailed formulation of technical restrictions
• Geoscope: Europe
26
27/05/2021 ISGAN | ELECTRICITY MARKET DESIGNS FOR FLEXIBILITY [OSMOSE WP2]
Geoscope
Rolling Planning Approach

Market Design Studies
Modelling Landscape – Joint Market Model (JMM)
27
Data Base Simulation Models
Market Model
Vertical Load Model
Grid Model
• Regional load & infeed
• Underlying conventional
generation
• Vertical Load
• Shadow prices
hydro power
plants
• Infeed run of river
• Average daily
prices
• Import/Export at
bordernodes
• PTDFs
• RAMs
• Price Zone
Configuration
CHP Model
Cluster Algorithm
• Power plant Data
• TimeseriesREN
• TimeseriesLoad
• Grid data
• Price Zone Config.
• Fuel & CO2 Prices
• Availabilities
• …
• Price Zone Configuration
• Power Plants
• Regional
Timeseries(Vertical
Load)
• Historical
Timeseries

Zonal Market Design
Setup & Input Parameter
• Data input from WP1
• Scenario: “Current Goals”
• Scenario year: 2030
• Geoscope: 33 countries
• 2 case studies are part of this presentation:
• Reference Case (LP, no uncertainties, NTC)
• Uncertainties Case (LP, uncertainties for wind power generation, NTC)
28

Zonal Market Design
Input Parameter I
29
• Total Demand: 3.291 TWh
• Wind Power / PV / RoR / Total : 763 / 354 / 681 / 1798 TWh
• Max / Min Residual Load: 372 GW / 15 GW
0
100
200
300
400
500
600
1
245
489
733
977
1221
1465
1709
1953
2197
2441
2685
2929
3173
3417
3661
3905
4149
4393
4637
4881
5125
5369
5613
5857
6101
6345
6589
6833
7077
7321
7565
7809
8053
8297
8541
Load
[GW]
Time [h]
Load vs. ResidualLoad
Load Res_load
0
100
200
300
400
500
600
1
220
439
658
877
1096
1315
1534
1753
1972
2191
2410
2629
2848
3067
3286
3505
3724
3943
4162
4381
4600
4819
5038
5257
5476
5695
5914
6133
6352
6571
6790
7009
7228
7447
7666
7885
8104
8323
8542
Electricity
[GW]
Time [h]
Load and RenewableInfeed [MW]
Load Solar RoR Wind

Zonal Market Design
Input Parameter II
30
• Conventional capacities
see graph
• Carbon Price: 18 €/tCO2
• Fuel Prices (in €/MWh)
• Nat Gas: 24.0
• Hard Coal: 8.1
• Oil: 49.4
0
50
100
150
200
250
Installed Capacity [GW]

Zonal Market Design
Results I
• Preliminary results in the chart
• Day Ahead price range between
30 to 40 €/MWh
• Considerably lower levels only in
ES and PT
• Insufficient transmission capacity
• Uncertainty does not affect DA
prices (on average!)
• Stochastic uncertainty time series
• More volatile prices during the
year
31
0
5
10
15
20
25
30
35
40
45
AL AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK NL NO PL PT RO RS SE SI SK
[Euro/MWh]
Price
2030 - ref_vs_uncertainty 2030 - uncertainty_eval
Average Day Ahead Market Prices (Current Goals 2030)
0
20
40
60
80
100
AL AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE IT LT LU LV ME MK NL NO PL PT RO RS SE SI SK
[TWh]
Netposition
2030 - ref_vs_uncertainty
2030 - uncertainty_eval

Zonal Market Design
Results II
32
Day Ahead Price Comparison for Germany (reference - uncertainties) in 2030
• Volatile Day Ahead Prices when
considering uncertainties
• Deviations more dense in Q1 &
Q4 due to overall wind power
generation
• Multiple hours in Q2 & Q3 with
equal prices combined with strong
(arbitrary) peaks in price
difference
-15
-10
-5
0
5
10
15
20
1
200
399
598
797
996
1195
1394
1593
1792
1991
2190
2389
2588
2787
2986
3185
3384
3583
3782
3981
4180
4379
4578
4777
4976
5175
5374
5573
5772
5971
6170
6369
6568
6767
6966
7165
7364
7563
7762
7961
8160
8359
8558
Price
Difference
[Euro/MWh]
Day Ahead PriceComparison for Germany
(reference- uncertainties)
Price Deviation in €/MWh // 8760 hours

Zonal Market Design
Results III
33
• Initial update similar to
wind expectation of DA
UC
• Temporal correlation of
FC Errors
• Subsequent updates lead
to price differences
between DA and ID
market prices, pending on
the FC information
DA-price vs. ID last price update for first week in February 2030 (12:00h), Geoscope GER
35
40
45
50
55
60
65
1 13 25 37 49 61 73 85 97 109 121 133 145 157
Price
[Euro/MWh]
Hours [h]
DA Price Last ID Price

Zonal Market Design
Results IV
• Positive deviation (dispatched quantity > planned
quantity)
• Depending on the lead time conv. power plants used for
ramping needs
• Most flexibility provided by Hydro Power
• Negative Deviation (dispatched quantity < planned
quantity)
• Energy is stored (Pump / Electric (DSM))
• Downregulation of conv. Power plants
34
0
100
200
300
400
500
600
700
800
900
[TWh]
Generation per Technology
Generation per Technology
Cumulated Deviation per Technology (1 year)
-10
-5
0
5
10
15
HYDRO PUMP NUCLEAR NAT GAS WASTE LIGNITE ELECTRIC COAL
Cumulated
Deviation
[
10^6
MWh]
Diagrammtitel
Neg. Deviation Pos. Deviation

PART 2: NODAL market studies:
Sandrine Bortolotti, RTE

Study perimeter
• Small region of central France simulated with a nodal
market
• The rest of France is modeled with consistent electric
regions
• The rest of Europe is modeled on a country scale
• RES forecast data come from historical measurements
(which preserve spatial correlations between forecasts),
and were scaled to match 2030 levels
• Selection of one interesting week to simulate at the
beginning of November

National load forecasts

Regional load forecasts (05_fr)

Substation load forecasts (VICHYP3)

Primary results on historical data analysis: RMSE on
load forecasts
2 takeaways:
• The geographical scope has a huge influence
on the magnitude of the forecast errors
• Regarding load forecast, aggregating some
substations reduce drastically the magnitude
of the errors.

Primary results on historical data analysis
2 takeaways:
• The geographical scope has a huge influence
on the magnitude of the forecast errors
• The uncertainty only decrease significantly a
few hours before real time

b) Study with the Join Market Model
Prof. Dr. Cristoph Weber, Florian Boehnke
PART 2: NODAL market studies:

Nodal Market Design
Methodology
• Nodal market design applies prices for electricity consumed or generated at a nodal level
• Market design to fully consider physical grid restrictions (congestion management)
(Zonal Markets NTC  Zonal Markets FBMC  Nodal Markets)
• Prices at adjacent nodes are equal in case of sufficient transfer capacities
• Implemented in many U.S. markets, e.g. California (CAISO), Texas (ERCOT)
• Main inputs
• Grid ENTSOE TYNDP (without coordinates)
• Zonal market design inputs (country level)  Nodalizing input parameters (nodal level)
43

Nodal Market Design
Nodalizing Wind Power
• Nodalizing Wind on NUTS3 level (county)
• Estimating capacities from different data sources for
current regional assets
• Creation of infeed timeseries
• Weather information from Cosmo-EU
• Multiplied by turbines’ power curves
• Scaling to 2030 infeed timeseries
• Wind turbines are assigned to nodes by Voronoi-
areas
44
Voronoi Areas with Nodes

Nodal Market Design
Preliminary results
• Based on German case study
• Not based on TYNDP Grid
• Average day-ahead market prices for January 2030
• Published in MS8
45

CONCLUSIONS
Maxime Laasri, RTE

Key messages
47
• The investigation of different market designs in a
European context is necessary for ensuring the
viability of any potential prospective energy mix
• Forecast uncertainties are a key element to be
reflected in market design studies, as they
impact how market opportunities are leveraged by
market participants and how power system
operators respond subsequently
• Nodal market designs are challenging to model
and simulate in practice

Upcoming activities
48
• A second webinar on WP2 will be organised in Fall, with focus on modeling
• Three webinars jointly organised with the EU-SYSFLEX project on 15-16-17 June
14h00 CET:
 High RES scenarios : from adequacy to stability challenges and new solutions
 IT challenges to activate and monitor flexibilities, Wednesday
 Value and demonstrations of flexibility provision by distributed sources

Thanks for your attention
Maxime Laasri, RTE: maxime.laasri@rte-france.com
Sandrine Bortolotti, RTE: sandrine.bortolotti@rte-france.com
Prof. Dr. Cristoph Weber: christoph.weber@uni-due.de
Florian Boehnke: florian.boehnke@uni-due.de
49
The OSMOSE project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 773406

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