metis technical note t2 - european commission · 2016. 12. 15. · 5 1 introduction metis is an...
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METIS Technical Notes October 2016
METIS Technical Note T2
METIS Power Market Models
2
Prepared by
Régis Bardet (Artelys)
Arthur Bossavy (Artelys)
Maxime Chammas (Artelys)
Laurent Fournié (Artelys)
Paul Khallouf (Artelys)
Bertrand Texier (Artelys)
Contact: [email protected]
This study was ordered and paid for by the European Commission, Directorate-General
for Energy, Contract no. ENER/C2/2014-639. The information and views set out in this
study are those of the author(s) and do not necessarily reflect the official opinion of the
Commission. The Commission does not guarantee the accuracy of the data included in
this study. Neither the Commission nor any person acting on the Commission’s behalf
may be held responsible for the use which may be made of the information contained
therein.
© European Union, October 2016
Reproduction is authorised provided the source is acknowledged.
More information on the European Union is available on the internet (http://europa.eu).
EUROPEAN COMMISSION
Directorate-General for Energy Directorate A — Energy Policy Unit A4 — Economic analysis and Financial instruments
Contact: Kostis Sakellaris
E-mail: [email protected]
European Commission B-1049 Brussels
Directorate C — Renewables, Research and Innovation, Energy Efficiency Unit C2 — New energy technologies, innovation and clean coal
Contact: Denos Remy
E-mail: [email protected]
3
Table of Contents
1 Introduction ................................................................................ 5
2 METIS main characteristics ............................................................ 6
2.1 Global description ...................................................................................... 6
2.2 Main characteristics of the power market module .......................................... 6
3 System model and reserve ............................................................ 9
3.1 Generation asset models ............................................................................ 9
3.1.1 Flexible hydro and thermal units ............................................................. 9
3.1.1.1 Cluster definition and main parameters ............................................... 9
3.1.1.2 Model description ............................................................................. 9
3.1.1.3 Flexible unit technical parameters .....................................................10
3.1.2 Non-dispatchable units .........................................................................11
3.2 Reserve supply models .............................................................................13
3.2.1 Reserve procurement methodology ........................................................13
3.2.2 Reserve procurement from variable RES .................................................16
4 Day-ahead and intraday markets ................................................. 17
4.1 General simulation process ........................................................................17
4.1.1 Modeling of market horizons .................................................................17
4.1.2 Modeling of system constraints ..............................................................19
4.1.3 Inclusion of forecast errors ...................................................................20
4.2 RES forecast error generation ....................................................................20
4.2.1 Methodology .......................................................................................21
4.2.2 Meteorological data used ......................................................................21
4.2.3 RES forecasts recalibration ....................................................................21
4.2.4 Forecast model performances ................................................................22
4.3 Demand forecast error generation ..............................................................24
4.3.1 Methodology .......................................................................................24
4.3.2 Data used for the simulation .................................................................25
4.3.3 Model calibration .................................................................................25
4.3.4 Model performances .............................................................................25
4.4 Outages ..................................................................................................26
4.4.1 Methodology .......................................................................................26
4.4.2 Data used for simulations .....................................................................27
4.5 Reserve sizing..........................................................................................27
4.5.1 Main assumptions ................................................................................27
4.5.2 Frequency Containment Reserve ............................................................28
4.5.3 Automatic Frequency Restoration Reserve (aFRR) and Manual Frequency Restoration Reserve (mFRR) ............................................................................29
4.5.4 Reserve sharing ...................................................................................36
4.6 Loss of load and replacement reserve .........................................................38
4
4.7 Bidding behavior ......................................................................................38
5 Balancing markets ...................................................................... 40
5.1 Inputs.................................................................................... 40
5.2 Outputs ................................................................................. 40
6 References ................................................................................ 42
7 Annex: demand and RES data generation...................................... 44
7.1 Global approach for climatic scenarios ........................................................44
7.2 Demand profiles .......................................................................................44
7.2.1 Temperature sensitivity and demand modeling ........................................44
7.2.2 Demand forecast errors generation ........................................................45
7.3 RES generation profiles .............................................................................45
7.3.1 Generation of solar and onshore wind power profiles ................................45
7.3.2 Hydro power modeling ..........................................................................49
7.3.3 Generation of solar and wind power forecasts ..........................................50
7.4 Generation units technical parameters ........................................................51
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1 INTRODUCTION
METIS is an on-going project1 initiated by DG ENER for the development of an energy
modelling software, with the aim to further support DG ENER’s evidence-based policy
making, especially in the areas of electricity and gas. The software is developed by Artelys
with the support of IAEW (RWTH Aachen University), ConGas and Frontier Economics as
part of Horizons 2020 and is closely followed by DG ENER. Two versions have been already
delivered at the DG ENER premises.
The intention is to provide DG ENER with an in-house tool that can quickly provide insights
and robust answers to complex economic and energy related questions, focusing on the
short-term operation of the energy system and markets. METIS was used, along with
PRIMES, in the impact assessment of the Market Design Initiative.
Figure 1 : Snapshot from METIS user interface screen
This document presents the main assumptions used to model power markets in METIS.
After a quick overview of METIS main characteristics in Section 2, Section 3 describes how
energy assets are modelled, with a particular focus on reserve procurement. Section 4
describes the main methodology used for day-ahead and intraday market modelling, then
Section 5 focuses on the balancing market.
1 http://ec.europa.eu/dgs/energy/tenders/doc/2014/2014s_152_272370_specifications.pdf
6
2 METIS MAIN CHARACTERISTICS
2.1 GLOBAL DESCRIPTION
METIS works complementary to long-term energy system models (like PRIMES from NTUA
and POTEnCIA from JRC). For instance, it can provide hourly results on the impact of higher
shares of variable renewables or additional infrastructure built.
More specifically, METIS is a modular energy modelling software covering with high
granularity (geographical, time) the whole European energy system for electricity, gas and
heat. Simulations adopt a MS-level spatial granularity and an hourly temporal resolution
(8760 consecutive time-steps per year). Uncertainties regarding demand and RES power
generation are captured thanks to weather scenarios taking the form of hourly time series
of wind, irradiance and temperature, which influence demand (through a thermal gradient),
as well as PV and wind generation. The historical spatial and temporal correlation between
temperature, wind and irradiance are preserved.
The Commission will be the owner of the final tool and will make efforts with the
Contractors to maximise transparency concerning the modelling techniques applied within,
with the final goal being to offer all relevant METIS modules and data as open-source, as
well as publish all produced material (from documentation to reports of studies performed
with METIS).
2.2 MAIN CHARACTERISTICS OF THE POWER MARKET MODULE
Calibrated Scenarios – METIS has been calibrated to a number of scenarios based either
on ENTSO-E TYNDP 2014 or PRIMES 2016 scenarios. METIS versions of PRIMES scenarios
include refinements on the time resolution (hourly) and unit representation (explicit
modelling of reserve supply at cluster and MS level). Data provided by the PRIMES
scenarios include: demand at MS-level, primary energy costs, CO2 costs, installed
capacities at MS-level, interconnection capacities.
Geographical scope – In addition to EU Member States, METIS scenarios include ENTSO-
E countries outside of EU (Switzerland, Bosnia, Serbia, Macedonia, Montenegro and
Norway) to model the impact of power imports and exports on the MS.
Market models – METIS market module replicates the market participants’ decision
process. For each day of the studied year, the generation plan (including both energy
generation and balancing reserve supply) is first optimised based on day-ahead demand
and RES generation forecasts. Market coupling is modelled via NTC constraints for
interconnectors. Then, the generation plan is updated during the day, taking into account
updated forecasts and asset technical constraints. Finally, imbalances are drawn to
simulate balancing energy procurement.
7
Figure 2 : Simulations follow day-ahead to real-time market decision process
Imbalances – Imbalances are the result of events that could not have been predicted
before gate closure. METIS includes a stochasticity module which simulates power plant
outages, demand and RES-e generation forecast errors from day-ahead to 1-hour ahead.
This module uses a detailed database of historical weather forecast errors (for 10 years at
hourly and sub-national granularity), provided by ECMWF, to capture the correlation
between MS forecast errors and consequently to assess the possible benefits of Imbalance
Netting. The stochasticity module also includes generation of random errors picked from
various probability distributions either set by the user or based on historical data.
Figure 3 : Example of wind power forecast errors for a given hour of the 10 years of data.
8
Reserve product definition – METIS simulates FCR, aFRR and mFRR reserves. The
product characteristics for each reserve (activation time, separation between upward and
downward offers, list of assets able to participate…) are inputs of the model. METIS also
includes a simplified representation of the use of Replacement Reserve during the intraday
timeframe.
Reserve dimensioning – The amount of reserves (FCR, aFRR, mFRR) that has to be
secured by TSOs can be either defined by METIS users or computed by the METIS
stochasticity module to assess the level of reserves that is required to ensure enough
balancing resources are available under a given probability. Hence, METIS stochasticity
module can take into account the statistical cancellation of imbalances between MS and
the potential benefits of regional cooperation for reserve dimensioning.
Balancing reserve procurement – Different market design options can be also
compared by the geographical area in which TSOs may procure the balancing reserves
they need. In case of regional cooperation for reserve procurement, interconnection
capacity has to be reserved for mutual assistance between MS, so that each MS can face
similar security of supply risks. Moreover, METIS users can choose whether demand
response and renewable energy systems are allowed to provide balancing services.
Balancing energy procurement – The procurement of balancing energy is optimised
following the same principles as described previously. In particular, METIS can be
configured to ban given types of assets, to select balancing energy products at national
level, to share unused balancing products with other MS, or to optimise balancing merit
order at a regional level.
9
3 SYSTEM MODEL AND RESERVE
This section describes the main assumptions used to model power generation technical
constraints. The optimal dispatch is simulated with hourly and country-level granularities,
using clusters to represent the diversity of generation unit performances within each
country. Reserve procurement is modelled explicitly for each cluster.
3.1 GENERATION ASSET MODELS
3.1.1 FLEXIBLE HYDRO AND THERMAL UNITS
3.1.1.1 CLUSTER DEFINITION AND MAIN PARAMETERS
Modelling dynamic constraints and binary states of units (to take into account starting
costs and dynamic constraints associated with thermal units) induces computational
difficulties, especially to simulate the operations of large energy systems like the European
one.
When an optimal dispatch on the whole of Europe has to be performed, relaxed (LP)
clustered unit commitment is an alternative modelling solution which allows to take into
account dynamic constraints and starting costs without having to include any binary
variables, hence avoiding excessively increasing the problem complexity. It consists of a
continuous representation, in which units with similar technical characteristics are bundled
together into clusters. A continuous variable represents the capacity of running units of
each cluster. The generation of a cluster is then bounded by its running capacity.
For each country, units using the same fuel (and same technology) and with similar building
date are bundled together into the same cluster. For each of the CCGT, Coal and lignite
fleets, three clusters are defined corresponding to units built after 2020, between 2000
and 2019, and before 19992.
3.1.1.2 MODEL DESCRIPTION
Asset parameters are, for each cluster 𝑖, at each time step 𝑡:
𝐶𝑖 : generation cost (€/MWh). Cost to generate 1MWh of electricity. This cost
includes variable OPEX, fuel and CO2 costs.
𝐶�̅� : running cost (€/MW/h). Additional hourly cost for running units (independent
of their load). Generation and running costs are computed using efficiency data at
Pmin and Pmax by type of unit (cf. Section 3.1.1.3), to represent the lower
efficiency of partially loaded units
𝛾𝑖: start-up cost (€/MW)
𝑃𝑚𝑎𝑥𝑖: Maximum generation (MW) (installed capacity of the cluster)
𝑃𝑚𝑖𝑛𝑖: Minimum stable generation (%), as a proportion of the running capacity
𝐴𝑣𝑎𝑖𝑙𝑖,𝑡: Availability (%), as a proportion of maximum generation
𝑇𝑂𝑓𝑓𝑖≥ 1: Minimum off-state duration (number of time steps)
The variables used to describe each cluster, at each time step 𝑡, are listed below:
Generation variable 𝑃𝑖,𝑡 ≥ 0
Running capacity variable �̅�𝑖,𝑡 ≥ 0
Difference variables:
o Positive part of difference in running capacity between t and t: 𝛿�̅�,𝑡+ ≥ 0
o Positive part of the difference in shutdown power between t-1 and t: 𝛿�̅�,𝑡− ≥ 0
2 Information on unit building dates before 2000 used by PRIMES was not provided. Therefore, the technical
characteristics of old units correspond to units built in 1990, which may overestimate the performance of very
old units still in operation in 2030.
10
Capacity of off-state units which could be started-up �̃�𝑖,𝑡 ≥ 0
The difference variable 𝛿�̅�,𝑡+ represents the capacity that has been started at time step t.
Start-up costs are associated with this variable.
As for 𝛿�̅�,𝑡− , it represents the capacity that has been shut down at time step t. It will be used
to determine power which could be started-up �̃�𝑖,𝑡. Indeed, �̃�𝑖,𝑡 is the capacity which is
turned off at time step t and which was shut down more than 𝑇𝑂𝑓𝑓𝑖 time steps before time
step t.
The objective is to minimise the total cost, which is the sum of: Generation costs: 𝐶𝑖 ⋅ 𝑃𝑖,𝑡
Running costs: 𝐶�̅� ⋅ �̅�𝑖,𝑡
Start-up costs: 𝛾𝑖 ⋅ 𝛿�̅�,𝑡+
The constraints are:
Balancing supply and demand
Generation bounded by running capacity: 𝑃𝑖,𝑡 ≤ �̅�𝑖,𝑡
Running capacity bounded by available installed capacity: �̅�𝑖,𝑡 ≤ 𝑃𝑚𝑎𝑥𝑖 ⋅ 𝐴𝑣𝑎𝑖𝑙𝑖,𝑡
Minimum stable generation constraint: 𝑃𝑖,𝑡 ≥ 𝑃𝑚𝑖𝑛𝑖 ⋅ �̅�𝑖,𝑡
Difference variables:
o 𝛿�̅�,𝑡+ ≥ �̅�𝑖,𝑡 − �̅�𝑖,𝑡−1
o 𝛿�̅�,𝑡− ≥ �̅�𝑖,𝑡−1 − �̅�𝑖,𝑡
o 𝛿�̅�,𝑡+ − 𝛿�̅�,𝑡
− = �̅�𝑖,𝑡 − �̅�𝑖,𝑡−1
Minimum off-state duration
�̃�𝑖,𝑡 = �̃�𝑖,𝑡−1 − 𝛿�̅�,𝑡+ + 𝛿�̅�,𝑡+1−𝑇𝑂𝑓𝑓𝑖
− + 𝑃𝑚𝑎𝑥𝑖 ⋅ (𝐴𝑣𝑎𝑖𝑙𝑖,𝑡 − 𝐴𝑣𝑎𝑖𝑙𝑖,𝑡−1)
3.1.1.3 FLEXIBLE UNIT TECHNICAL PARAMETERS
A literature review [3-16] resulted in the technical characterization of the different fleets
shown below and in Annex 7.4. Characteristics include minimum stable generation 𝑃𝑚𝑖𝑛
(in % of 𝑃𝑚𝑎𝑥), minimum off-state duration 𝑇𝑂𝑓𝑓 (also used as minimum start-up time, cf.
Section 4.1), maximum gradient, start-up costs.
Efficiency values at several operating points, and an average value were also provided by
[14]. Moreover, for each fleet, the article provides a function which adjusts efficiency to
the building year of the unit.
In the following Table, “oldest” corresponds to units built before 20003, “prevailing” before
2015 and “state of the art” after 2015.
Parameters \ Type of unit
Minimal generation level (% of
Pmax)
Positive load
gradient (% of
Pmax)
Negative load
gradient (% of
Pmax)
Starting cost
(€/MW)
Off-state minimal duration
(h)4
Efficiency (%)
@Pmin/@Pmax
OCGT - prevailing
50% 8%/min 8%/min 30 <1 27% / 36%
OCGT- state of the art
40% 12%/min 12%/min 21 <1 32% / 42%
Oil fired 50% 8%/min 8%/min 30 1 26% / 35%
CCGT - oldest 50% 2%/min 5%/min 45 2 40 / 49%
CCGT -
prevailing
50% 2%/min 5%/min 41 2 48% / 57%
3 Information on unit building dates before 2000 used by PRIMES was not provided. Therefore, the technical
characteristics of old units correspond to units built in 1990, which may overestimate the performance of very
old units still in operation in 2030. 4 As on-state minimum duration is 1 hour or less for most units, only the off-state minimum duration is modelled
in METIS.
11
CCGT – state of the art
40% 4%/min 5%/min 33 2 52% / 61%
Hard Coal Power Plant – prevailing
40% 2%/min 5%/min 65 6 36% / 42%
Hard Coal
Power Plant – state of the art
25% 4%/min 5%/min 50 4 41% / 46%
Lignite Power Plant – prevailing
50% 2%/min 5%/min 25 6 34% / 38%
Lignite Power
Plant – state of the art
50% 2%/min 5%/min 25 4 38% / 42%
Nuclear Power Plant
40% 5%/min 7% Rmax
5%/min 7% Rmax
24 No off-state
modelled
7,4€/MWh
Hydro turbine
(lakes and PHS)
60%5 Not
constrained
Not
constrained
0 <1 90%6
Biomass steam turbine
20% 4%/min 5%/min 36 1 33% / 36%
Table 1 - Technological data
Source : [3-16]
3.1.2 NON-DISPATCHABLE UNITS
Non-dispatchable units are modelled as a single asset by country and by type of
technology. Every asset is defined by a variable cost that depends on the technology, and
an availability time series. Depending on the market configuration, non-dispatchable units
may be curtailable and may be able to provide upwards and downwards reserves. Biomass
is modelled as a wood utility and is either must-run or flexible depending on the market
context.
PV Wind
onshore
Wind
offshore
Run-of-
the-
river
Waste Derived
gasses
Geothermal
Variable
cost
(€/MWh)
0 0.5 0.5 0 3.7 3.5 0.32
Availability Hourly time series Monthly
time
series
Fixed Fixed Fixed
Table 2 – Non-dispatchable unit parameters
Source : PRIMES
CHP units are not modelled per se7, but are included in the gas and biomass capacities.
Ten years of weather data have been used to build a database of hourly generation for PV,
onshore and offshore wind. The mean load factors by country for PV, onshore and offshore
wind are based on PRIMES EUCO27 data.
5 Even if hydro turbines have a very low technical Pmin, the efficiency of most hydro turbines decrease
significantly if part-loaded below 60%. As the detailed modelling of the efficiency curves is outside of the scope
of METIS, a minimum generation level of 60% is used. 6 For PHS, pumps are assumed to run at fixed speed and cannot provide balancing services. Pumps have an
efficiency of 90%, which leads to a total PHS efficiency of 81%. 7 METIS heat module is planned for 2017 and will include CHP units.
12
Table 3 - Wind onshore generation yearly full load hours (for the different years of weather data)
Table 4 - Wind offshore generation yearly full load hours (for the different years of weather data)
Zone year 2001
SC8
year 2002
SC9
year 2003
SC10
year 2004
SC1
year 2005
SC2
year 2006
SC3
year 2007
SC4
year 2008
SC5
year 2009
SC6
year 2010
SC7
AT 2 364h 2 271h 2 121h 2 251h 2 225h 2 154h 2 354h 2 273h 2 226h 2 254h
BA 2 409h 2 266h 2 142h 2 230h 2 042h 1 987h 2 138h 2 217h 2 151h 2 325h
BE 2 456h 2 513h 2 157h 2 401h 2 268h 2 483h 2 558h 2 520h 2 364h 2 165h
BG 2 827h 2 607h 2 459h 2 657h 2 647h 2 468h 2 610h 2 541h 2 361h 2 593h
CH 1 369h 1 277h 1 207h 1 284h 1 165h 1 250h 1 294h 1 303h 1 269h 1 260h
CZ 2 013h 2 118h 1 869h 2 161h 2 011h 1 951h 2 321h 2 116h 1 957h 1 945h
DE 1 612h 1 693h 1 455h 1 679h 1 550h 1 622h 1 832h 1 731h 1 580h 1 471h
DK 2 464h 2 642h 2 399h 2 697h 2 646h 2 494h 2 876h 2 791h 2 586h 2 487h
EE 2 171h 2 130h 2 225h 2 139h 2 265h 2 211h 2 350h 2 567h 2 015h 2 046h
ES 2 722h 2 629h 2 540h 2 417h 2 480h 2 470h 2 452h 2 521h 2 604h 2 704h
FI 2 638h 2 408h 2 690h 2 519h 2 773h 2 603h 2 754h 2 764h 2 447h 2 402h
FR 2 626h 2 652h 2 350h 2 473h 2 411h 2 496h 2 602h 2 541h 2 444h 2 427h
GR 2 970h 2 433h 2 882h 2 778h 2 757h 2 784h 2 720h 2 802h 2 730h 2 728h
HR 1 919h 1 778h 1 779h 1 765h 1 703h 1 653h 1 716h 1 776h 1 789h 1 795h
HU 2 031h 1 960h 1 808h 1 873h 1 859h 1 665h 1 882h 1 903h 1 781h 1 874h
IE 2 611h 3 022h 2 862h 2 973h 2 936h 2 908h 2 822h 3 095h 2 928h 2 340h
IT 2 241h 2 035h 2 097h 2 145h 2 045h 1 946h 2 120h 2 105h 2 203h 2 235h
LT 1 842h 2 002h 1 916h 1 875h 1 759h 1 758h 2 015h 2 093h 1 746h 1 783h
LU 1 815h 1 832h 1 581h 1 724h 1 571h 1 743h 1 853h 1 750h 1 654h 1 558h
LV 2 379h 2 510h 2 473h 2 424h 2 392h 2 383h 2 643h 2 800h 2 328h 2 300h
ME 2 436h 2 216h 2 281h 2 353h 2 128h 2 025h 2 185h 2 227h 2 209h 2 396h
MK 1 134h 1 013h 1 076h 1 165h 1 064h 938h 1 044h 1 073h 1 035h 1 163h
NL 2 514h 2 580h 2 251h 2 595h 2 505h 2 623h 2 786h 2 810h 2 558h 2 303h
NO 2 488h 2 446h 2 533h 2 667h 2 843h 2 684h 2 839h 2 681h 2 665h 2 321h
PL 2 100h 2 252h 2 045h 2 213h 2 038h 1 953h 2 359h 2 261h 1 986h 2 093h
PT 2 851h 2 755h 2 639h 2 429h 2 571h 2 532h 2 436h 2 592h 2 645h 2 848h
RO 2 684h 2 646h 2 471h 2 597h 2 502h 2 413h 2 634h 2 538h 2 305h 2 523h
RS 1 558h 1 522h 1 346h 1 501h 1 389h 1 262h 1 393h 1 464h 1 349h 1 550h
SE 2 678h 2 606h 2 708h 2 754h 2 788h 2 664h 2 918h 2 832h 2 608h 2 560h
SI 1 612h 1 424h 1 414h 1 371h 1 400h 1 402h 1 469h 1 480h 1 481h 1 498h
SK 1 439h 1 434h 1 315h 1 390h 1 351h 1 212h 1 430h 1 428h 1 280h 1 345h
UK 2 564h 2 694h 2 568h 2 766h 2 867h 2 730h 2 794h 2 965h 2 736h 2 319h
Zone year 2001
SC8
year 2002
SC9
year 2003
SC10
year 2004
SC1
year 2005
SC2
year 2006
SC3
year 2007
SC4
year 2008
SC5
year 2009
SC6
year 2010
SC7
BE 3 501h 3 505h 3 114h 3 342h 3 305h 3 535h 3 585h 3 630h 3 359h 3 158h
DE 3 187h 3 362h 3 028h 3 407h 3 412h 3 265h 3 640h 3 643h 3 351h 3 160h
DK 4 129h 4 355h 4 047h 4 389h 4 282h 4 145h 4 480h 4 379h 4 331h 4 190h
EE 2 131h 2 031h 2 157h 2 078h 2 182h 2 162h 2 309h 2 525h 1 946h 2 001h
ES 2 998h 2 946h 2 604h 2 641h 2 902h 2 759h 2 655h 2 509h 2 753h 3 052h
FI 2 990h 2 583h 2 952h 2 834h 3 018h 2 866h 3 022h 2 988h 2 711h 2 626h
FR 3 255h 3 357h 3 011h 3 051h 3 016h 3 151h 3 225h 3 228h 3 025h 3 002h
IE 3 037h 3 466h 3 320h 3 423h 3 462h 3 374h 3 262h 3 546h 3 435h 2 787h
IT 3 466h 3 067h 3 117h 3 182h 3 066h 2 827h 3 099h 3 176h 3 215h 3 473h
LV 3 001h 3 306h 3 071h 3 266h 3 076h 3 048h 3 332h 3 461h 3 064h 3 037h
NL 3 614h 3 578h 3 256h 3 601h 3 588h 3 699h 3 852h 3 920h 3 649h 3 355h
PL 2 989h 3 201h 2 970h 3 237h 2 942h 2 843h 3 327h 3 272h 2 936h 3 013h
PT 2 859h 2 936h 2 591h 2 480h 2 776h 2 690h 2 528h 2 447h 2 633h 2 977h
SE 2 813h 3 036h 2 848h 3 068h 2 939h 2 811h 3 195h 3 148h 2 952h 2 890h
UK 2 968h 3 064h 2 931h 3 038h 3 112h 3 103h 3 116h 3 322h 3 020h 2 722h
13
Table 5 - PV generation yearly full load hours (for the different years of weather data)
More details on the methodology are given in Section 7.
3.2 RESERVE SUPPLY MODELS
3.2.1 RESERVE PROCUREMENT METHODOLOGY
Unpredicted events, such as unplanned outages of power plants or forecast errors of load
or renewable energy generation, can result in imbalances of the power grid on different
time horizons. Different types of reserve, characterized by their activation delay, are
therefore procured in advance and then activated to restore balance on the power grid.
The Frequency Containment Reserve (FCR) aims at securing the grid’s security in case of
instantaneous power deviation (power plant outages, sharp load deviation, line section,
etc.). It is dimensioned by the maximum expected instantaneous power deviation and must
be available within 30 seconds (see Section 4.5).
The Automatic Frequency Restoration Reserve (aFRR) and the Manual Frequency
Restauration Reserve (mFRR) have different activation times, depending on countries - 5
and 15 minutes will be considered as standard for the aFRR and mFRR respectively. They
can be called upon to compensate load fluctuations or forecast errors.
Zone year 2001
SC8
year 2002
SC9
year 2003
SC10
year 2004
SC1
year 2005
SC2
year 2006
SC3
year 2007
SC4
year 2008
SC5
year 2009
SC6
year 2010
SC7
AT 1 102h 1 100h 1 231h 1 128h 1 150h 1 139h 1 144h 1 105h 1 114h 1 069h
BE 1 022h 1 038h 1 157h 1 073h 1 080h 1 062h 1 033h 1 023h 1 072h 1 065h
BG 1 327h 1 302h 1 353h 1 324h 1 271h 1 305h 1 343h 1 335h 1 302h 1 255h
CH 800h 785h 898h 850h 837h 844h 839h 807h 839h 782h
CZ 895h 934h 1 056h 971h 994h 987h 967h 950h 955h 923h
DE 919h 931h 1 058h 967h 987h 977h 938h 951h 969h 947h
DK 894h 916h 947h 915h 922h 918h 893h 930h 933h 905h
EE 821h 913h 824h 816h 866h 876h 840h 797h 825h 818h
ES 1 965h 1 936h 1 936h 1 973h 2 018h 1 955h 1 955h 1 911h 1 966h 1 900h
FI 733h 808h 745h 729h 768h 785h 734h 714h 757h 728h
FR 1 566h 1 554h 1 680h 1 621h 1 640h 1 605h 1 582h 1 551h 1 624h 1 582h
GR 1 635h 1 586h 1 603h 1 616h 1 594h 1 586h 1 623h 1 610h 1 563h 1 557h
HR 1 447h 1 409h 1 523h 1 398h 1 435h 1 438h 1 453h 1 429h 1 431h 1 363h
HU 880h 901h 974h 893h 909h 902h 929h 900h 914h 856h
IE 876h 843h 893h 873h 858h 865h 860h 831h 835h 893h
IT 1 428h 1 356h 1 458h 1 402h 1 410h 1 427h 1 435h 1 385h 1 398h 1 336h
LT 842h 895h 881h 855h 890h 888h 855h 833h 862h 846h
LU 862h 883h 986h 916h 916h 893h 873h 863h 902h 896h
LV 840h 898h 872h 851h 892h 896h 852h 829h 846h 838h
MK 1 298h 1 266h 1 307h 1 285h 1 283h 1 288h 1 300h 1 297h 1 244h 1 209h
NL 871h 872h 962h 898h 912h 901h 870h 881h 903h 895h
PL 803h 851h 921h 868h 897h 885h 858h 852h 867h 834h
PT 1 820h 1 792h 1 807h 1 876h 1 900h 1 837h 1 881h 1 829h 1 852h 1 800h
RO 1 333h 1 337h 1 402h 1 344h 1 302h 1 332h 1 385h 1 361h 1 366h 1 283h
RS 1 076h 1 088h 1 149h 1 081h 1 090h 1 092h 1 116h 1 109h 1 093h 1 028h
SE 837h 883h 873h 857h 873h 863h 840h 854h 852h 835h
SI 1 089h 1 064h 1 177h 1 056h 1 089h 1 083h 1 108h 1 061h 1 074h 1 018h
SK 869h 903h 981h 908h 919h 923h 928h 902h 916h 864h
UK 808h 796h 873h 801h 816h 827h 801h 798h 803h 811h
14
Figure 4 : Reserve types and usages
Depending on the chosen METIS market configuration, reserve procurement can be fixed
(in which case a share of base load unit capacity is dedicated to reserve supply) or
optimized jointly with day-ahead power dispatch8, as described below.
NOTATION:
The indexes i, j and t respectively refer to generation clusters, reserve types and
time steps.
The notation 𝑗′ ≤ 𝑗 is used to indicate that reserve 𝑗′ has a shorter activation delay
than reserve 𝑗.
�̅�𝑖,𝑡: running generation capacity of generation cluster 𝑖 at time step 𝑡.
𝑃𝑖,𝑡: generation variable of cluster 𝑖 at time step 𝑡.
𝑃𝑖,𝑡𝐼𝑁: for storage units, input generation variable of cluster 𝑖 at time step 𝑡.
𝑃𝑖,𝑡𝑂𝑈𝑇: for storage units, output generation variable of cluster 𝑖 at time step 𝑡.
𝑆𝑚𝑎𝑥𝑖: maximum storage level of storage unit 𝑖
𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝐿𝑒𝑣𝑒𝑙𝑖,𝑡: storage level of storage plant 𝑖, at the end of time step 𝑡 (hence,
takes into account production and consumption levels during time step 𝑡)
𝑖𝑛𝑓𝑙𝑜𝑤𝑖,𝑡: natural water inflow into hydro storage units at time step 𝑡.
𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝑈𝑃 : participation of generation cluster 𝑖 in the upward reserve 𝑗, at time step
𝑡
𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝐷𝑂𝑊𝑁: participation of generation cluster 𝑖 in the downward reserve 𝑗, at time
step 𝑡
𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑗,𝑡𝑈𝑃: upward reserve 𝑗 requirement at time step 𝑡
𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑗,𝑡𝐷𝑂𝑊𝑁: downward reserve 𝑗 requirement at time step 𝑡
Δ𝑇𝑗: activation delay characterizing reserve 𝑗
𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡𝑖𝑈𝑃: maximum generation increase rate per time unit (in % of running
capacity)
8 Joint optimal procurement of reserve and energy at day-ahead is a proxy to model the interlink between reserve
and power day-ahead markets, as operators take into account both opportunities to optimize their asset
management.
15
𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡𝑖𝐷𝑂𝑊𝑁: maximum generation decrease rate per time unit (in % of running
capacity)
𝑅𝑚𝑎𝑥 𝑖,𝑗𝑈𝑃: maximum acceptable share of running capacity to be allocated to upward
reserves9. The value is zero if the unit is banned from upward reserve procurement.
𝑅𝑚𝑎𝑥 𝑖,𝑗𝐷𝑂𝑊𝑁: maximum acceptable share of running capacity to be allocated to
downward reserves10. The value is zero if the unit is banned from downward reserve
procurement.
CONSTRAINTS:
Meeting reserves requirements at all times:
∀𝑗, 𝑡, ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝐷𝑂𝑊𝑁
𝑖
= 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑗,𝑡𝐷𝑂𝑊𝑁
∀𝑗, 𝑡, ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝑈𝑃
𝑖
= 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑅𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑗,𝑡𝑈𝑃
Maximal participation in the primary and secondary reserves:
A given unit can only allocate a part of its running capacity to reserves, since starting
up more capacity would take longer than the available delay. The following
constraints apply to all units for primary and secondary reserves:
𝑃𝑖,𝑡 + ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝑈𝑃
𝑗
≤ �̅�𝑖,𝑡
𝑃𝑚𝑖𝑛𝑖 ⋅ �̅�𝑖,𝑡 ≤ 𝑃𝑖,𝑡 − ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝐷𝑂𝑊𝑁
𝑗
∀ 𝑖, 𝑗, 𝑡, ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗′,𝑡𝑈𝑃
𝑗′≤𝑗
≤ �̅�𝑖,𝑡 ⋅ 𝑅𝑚𝑎𝑥 𝑖,𝑗𝑈𝑃
∀ 𝑖, 𝑗, 𝑡, ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗′,𝑡𝐷𝑂𝑊𝑁
𝑗′≤𝑗
≤ �̅�𝑖,𝑡 ⋅ 𝑅𝑚𝑎𝑥 𝑖,𝑗𝐷𝑂𝑊𝑁
Maximal participation in the tertiary reserve:
The tertiary reserve’s activation time may be long enough for peaking or hydro units
to start up and generate power within this delay11. The following equations would then
apply to such units only:
𝑃𝑖,𝑡 + ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝑈𝑃
𝑗
≤ 𝑃𝑚𝑎𝑥𝑖 ⋅ 𝐴𝑣𝑎𝑖𝑙𝑖,𝑡
0 ≤ 𝑃𝑖,𝑡 − ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝐷𝑂𝑊𝑁
𝑗
∀ 𝑖, 𝑗, 𝑡, ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗′,𝑡𝑈𝑃
𝑗′≤𝑗
≤ 𝑅𝑚𝑎𝑥𝑖,𝑗𝑈𝑃 ⋅ 𝑃𝑚𝑎𝑥𝑖 ⋅ 𝐴𝑣𝑎𝑖𝑙𝑖,𝑡
∀ 𝑖, 𝑗, 𝑡, ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗′,𝑡𝐷𝑂𝑊𝑁
𝑗′≤𝑗
≤ 𝑅𝑚𝑎𝑥𝑖,𝑗𝐷𝑂𝑊𝑁 ⋅ 𝑃𝑚𝑎𝑥𝑖 ⋅ 𝐴𝑣𝑎𝑖𝑙𝑖,𝑡
Other units (that is, units which cannot start up fast enough) are subject to the same
maximal participation constraints for tertiary reserve as for the primary and secondary
reserves.
Specific constraints for storage plants:
9 For most thermal units and for aFRR/mFRR, 𝑅𝑚𝑎𝑥 𝑖,𝑗
𝑈𝑃 = 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡𝑖𝑈𝑃 ⋅ Δ𝑇𝑗
10 For most thermal units and for aFRR/mFRR, 𝑅𝑚𝑎𝑥 𝑖,𝑗𝐷𝑂𝑊𝑁 = 𝑔𝑟𝑎𝑑𝑖𝑒𝑛𝑡𝑖
𝐷𝑂𝑊𝑁 ⋅ Δ𝑇𝑗
11 A penalty is added to units which supply tertiary reserve from standstill, to compensate start-up costs which
may occur if the unit is called for balancing services.
16
Storage plants are subject to available energy constraints, in addition to generation
capacity constraints. The storage level of each storage unit is driven by the following
dynamics:
∀𝑖, 𝑡, 𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝐿𝑒𝑣𝑒𝑙𝑖,𝑡
= 𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝐿𝑒𝑣𝑒𝑙𝑖,𝑡−1 + 𝑖𝑛𝑓𝑙𝑜𝑤𝑖,𝑡 + 𝑖𝑛𝑝𝑢𝑡𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖 ⋅ 𝑃𝑖,𝑡𝐼𝑁 −
1
𝑜𝑢𝑡𝑝𝑢𝑡𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖
⋅ 𝑃𝑖,𝑡𝑂𝑈𝑇
Where
o 𝑃𝑖,𝑡𝐼𝑁 = 0 for hydro dams which cannot consume electricity to fill their storage
tanks. o 𝑖𝑛𝑓𝑙𝑜𝑤𝑖,𝑡 = 0 for pumped hydro storage units, which can only fill their reservoirs
by activating their pumps.
Such dynamics imply that a storage plant cannot produce more energy than what is stored
(since the storage level has to be positive at all times):
1
𝑜𝑢𝑡𝑝𝑢𝑡𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖
⋅ 𝑃𝑖,𝑡𝑂𝑈𝑇 ≤ 𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝐿𝑒𝑣𝑒𝑙𝑖,𝑡−1
Reserves participation must satisfy the following constraints:
∀𝑖, 𝑡,1
𝑜𝑢𝑡𝑝𝑢𝑡𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖
∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝑈𝑃
𝑗
≤ 𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝐿𝑒𝑣𝑒𝑙𝑖,𝑡
∀𝑖, 𝑡,1
𝑜𝑢𝑡𝑝𝑢𝑡𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝑖
∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝐷𝑂𝑊𝑁
𝑗
≤ (𝑆𝑚𝑎𝑥𝑖 − 𝑠𝑡𝑜𝑟𝑎𝑔𝑒𝐿𝑒𝑣𝑒𝑙𝑖,𝑡)
3.2.2 RESERVE PROCUREMENT FROM VARIABLE RES
Depending on the market configuration, variable renewable energy can participate in
reserve procurement. As variable RES and in particular wind energy have very high load
gradients and low minimum stable generation, the only constraints modelled are:
𝑃𝑖,𝑡 + ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝑈𝑃
𝑗
≤ 𝑃𝑚𝑎𝑥𝑖 ⋅ 𝐴𝑣𝑎𝑖𝑙𝑖,𝑡
0 ≤ 𝑃𝑖,𝑡 − ∑ 𝑟𝑒𝑠𝑒𝑟𝑣𝑒𝑖,𝑗,𝑡𝐷𝑂𝑊𝑁
𝑗
17
4 DAY-AHEAD AND INTRADAY MARKETS
This section presents the main METIS features when it comes to the simulation of day-
ahead and intraday markets.
4.1 GENERAL SIMULATION PROCESS
METIS simulates the successive clearing of short-term power markets, including day-
ahead, reserve procurement and intraday markets, using fundamental data on the power
systems (installed capacities, fuel costs) and market design rules such as priority dispatch,
banning or granularity of markets. The balancing market simulation is described in
Section 5. For intraday market simulation, METIS has a strong focus on the effect of
weather forecasts on the outcomes of these power markets: producers’ revenues, market
prices, net positions and flows.
An hourly time resolution is used in the simulations, which are generally run over a year.
Several realizations in terms of demand and RES profiles can be simulated, in order to
estimate the distribution of producers’ revenues.
4.1.1 MODELING OF MARKET HORIZONS
In order to model day-ahead and intraday markets, which have different timeframes and
are somehow intertwined together timewise, additional market-specific variables are
added, compared to the METIS system module. For each physical asset (production,
storage or transmission), the production is thus split into the sold/bought market volumes
on the day-ahead and intra-day markets. Similarly, demands are split into sold/bought
market volumes on the different market horizons. Hence, day-ahead decisions are not firm
and can be readjusted in intraday, according to new RES generation and demand forecasts.
Over the year, 8760 simulations are performed, hour by hour. For each simulation, the
optimization horizon is 48h. Market clearing constraints ensure that market decisions are
taken as soon as the considered market closes, with respect to the supply-demand
equilibrium. Thus, day-ahead sales are fixed every day at midday for the day to come,
starting at midnight. In the same way, intra-day sales are set every hour for the next hour.
NOTATION:
Index 𝑖 refers to a particular generation asset
𝑃𝑖 (𝑡) : Generation variable of cluster 𝑖 at time step t
18
12 AM 12 AM
12 AM 12 AM
𝑣𝑖𝐷𝐴(𝑡) : Volume sold on the day-ahead market by cluster 𝑖 at time step t
𝑣𝑖𝐼𝐷(𝑡) : Volume sold (can be negative) on the intra-day market by cluster 𝑖 at time
step t
𝐷(𝑡) : Demand at time step t
𝑑𝐷𝐴(𝑡), 𝑑𝐼𝐷(𝑡) : Demand on the day-ahead market and adjustment on intraday (can
be negative)
CONSTRAINTS:
Consistency between market horizons: 𝑃𝑖 (𝑡) = 𝑣𝑖
𝐷𝐴(𝑡) + 𝑣𝑖𝐼𝐷(𝑡)
𝐷(𝑡) = 𝑑𝐷𝐴(𝑡) + 𝑑𝐼𝐷(𝑡)
Market clearing constraints:
Equilibrium between demand and supply for each market: ∑ 𝑣𝑖
𝑋𝑋(𝑡) = 𝑖∈{𝑎𝑠𝑠𝑒𝑡𝑠} 𝑑𝑋𝑋(𝑡), for 𝑋𝑋 ∈ {𝐷𝐴, 𝐼𝐷}
For the sake of notation simplicity, imports, exports, spillage and loss of load are included
in {assets}.
NB: The dual variables (outputs of METIS embedded solver) associated with the above constraints represent the marginal cost of the market XX at the time step 𝑡.
Moreover, we assume that each production asset offers all it can (according to its forecast)
to the furthest-looking market available, that is day-ahead, then intraday. This adds the
following constraints:
From midnight the next day until the end of the optimization horizon, only day-
ahead is available:
𝑣𝑖𝐼𝐷(𝑡) = 0, with 𝑡 ∈ [12𝐴𝑀𝐷+1 ; 12𝐴𝑀𝐷+2]
𝑑𝐼𝐷(𝑡) = 0, with 𝑡 ∈ [12𝐴𝑀𝐷+1 ; 12𝐴𝑀𝐷+2]
In other words, intra-day variables are enforced to be zero while the day-ahead market is
still open. The graphs below summarize the market clearing constraints. For a simulation
at the time step t, the fixed variables are in orange, the free variables are in purple and
the free variables that are retained as inputs for next simulations are in green.
Additional market constraints can be added if needed
CLEARING
DA
𝒗𝒕+𝟏𝑫𝑨
+
𝒗𝒕+𝟏𝑰𝑫
𝒗𝒕𝑫𝑨
+
𝒗𝒕𝑰𝑫
CLEARING
ID
…
…
𝒗𝒕+𝟏𝟐𝑫𝑨
+
0
𝒗𝒕+𝟏𝟑𝑫𝑨
+
0
𝒗𝒕+𝑫𝑨
+
0
…
…
t =12 PM
CLEARING
ID
…
…
…
…
…
…
𝒗𝒕+𝟏𝑫𝑨
+
𝒗𝒕+𝟏𝑰𝑫
𝒗𝒕+𝟐𝑫𝑨
+
𝒗𝒕+𝟐𝑰𝑫
𝒗𝒕+𝟏𝟐𝑫𝑨
+
𝒗𝒕+𝟏𝟐𝑰𝑫
t >12 PM
19
Banning rules:
Some assets may be banned from participating to a given market XX. In such a case:
𝒗𝑖XX(𝑡) = 0
Interconnector capacity allocation for balancing reserve:
As described in Section 4.5.4, a share of interconnection capacity can be allocated for
regional reserve sharing. In such cases, the allocated capacity cannot be changed during
intraday:
𝒗𝑖ID(𝑡) ≤ 𝑁𝑇𝐶 − 𝒗𝑖
DA,reserved(𝑡) Other examples of use are given in the section on market distortion.
4.1.2 MODELING OF SYSTEM CONSTRAINTS
In addition to modeling constraints between market timeframes, METIS power market
module ensures that the system module constraints (see Section 3) are enforced.
In addition to this, a link is made between the short-term (METIS power market module)
and the mid-term (METIS system module) to ensure consistency in the results. This is what
generally producers would do: calibrate their mid-term decisions such as mid-term hydro
levels and pass on this information to the shorter-term decision making models (intraday
decisions).
Mid-term hydro storage constraints12
Storages units have a limited energy volume that can be injected in the network in a given
time range. In the case of hydraulic dams, this limit is typically annual and given by the
total water inflow over the year. It usually prevents storage plants from constantly
generating power at full capacity. As a consequence, the water stored in dams has to be
saved when it is not most needed to produce electricity during more demanding periods.
Such an economic-based management, applied to hydro dams at different time scales –
from weekly to inter-seasonal, has to be enforced in METIS. It is done in the system module
by setting a ”guide” curve13 which defines, on a weekly basis, the minimal allowed storage
level. The storage level yearly time series resulting from METIS system module therefore
takes into account both long-term water management (by satisfying the weekly “guide”
curve) and mid-term management (through the hourly optimization).
This system-module storage level time series is then given as an input to METIS power
market module which derives from it the long/mid-term water management information
that must constrain short-term decisions. To do so, the storage level at the end of each
optimization horizon (i.e. 48 hours) in METIS power market module must be greater than
the storage level resulting from METIS system module at the same time step.
For 𝑖 in {storage assets}, the constraint for the simulation at the hour h is14:
𝑺𝒊𝒔𝒚𝒔𝒕𝒆𝒎𝑴𝒐𝒅𝒖𝒍𝒆
(𝒉 + 𝟒𝟖) ≤ 𝑆𝑖(ℎ + 48)
12 More information on hydro modelling is provided in Annex 7.3.2 13 This curve, based on historical data, actually takes into account non-economic considerations, such as tourism,
that affect water management. 14 A variant could be to remove the upper bound of this constraint to take into account the fact that the system
risks are asymmetrical (risk of loss of load if storage level is too low vs risk of underused storage)
20
Where 𝑺𝒊𝒔𝒚𝒔𝒕𝒆𝒎𝑴𝒐𝒅𝒖𝒍𝒆
(𝒉 + 𝟒𝟖) is the storage level at time step ℎ + 48 that comes out of the
system module run. It is therefore a fixed bound in the power market run, where 𝑆𝑖(ℎ + 48) is the storage level variable at time step ℎ + 48
Start-up delays for thermal assets
METIS market module also takes into account the fact that starting a hard coal power plant
must be notified 6 hours in advance whereas only 2 hours are needed for a CCGT plant (cf.
Section 3.1.1.3 for more details on the unit technical parameters). At each simulation hour ℎ, the running capacity �̅�𝑐𝑜𝑎𝑙(ℎ + 6) is an output of the optimization problem that will be
retained as input for the following simulations. In the same way, �̅�𝐶𝐶𝐺𝑇(ℎ + 2) is also fixed
at the outcome of the simulation at h.
�̅�𝑐𝑜𝑎𝑙(𝑡) = �̅�𝒄𝒐𝒂𝒍(𝒕) , 𝑡 ∈ [ℎ, ℎ + 5]
�̅�𝐶𝐶𝐺𝑇(𝑡) = �̅�𝑪𝑪𝑮𝑻(𝒕) , 𝑡 ∈ [ℎ, ℎ + 1]
Blue variables are outputs of a previous optimization.
4.1.3 INCLUSION OF FORECAST ERRORS
The METIS power market module replicates a natural decision process in terms of decisions
on the market horizon and in terms of progressive acquisition of more accurate forecasts.
Forecast values for demand and RES productions get more and more accurate as we get
closer to real-time. Put differently, the forecast for the next hour has a higher quality than
the one for the day to come.
So, at each time step, demand time series are updated using the best forecast available
(see Sections 4.2 and 4.3) For instance:
Consequently, day-ahead decisions are taken using a day-ahead forecast for the demand.
Start-up decisions for coal and CCGT clusters are respectively taken using the h-6 and h-
2 forecasts.
4.2 RES FORECAST ERROR GENERATION
The METIS market module is able to assess the interplay between RES forecast errors
evolution and short-term markets (day-ahead and intraday). Since METIS in particular
focuses on regional cooperation, the RES generation forecast errors conserve the observed
spatial and temporal correlations.
METIS uses historical data of weather forecast (one value by hour, zone and horizon) to
generate demand and RES forecast. However, METIS market module also includes features
to generate stochastic events for a given day, in order to study a particular situation under
various forecast errors and imbalances.
D(s) =
𝑫 ∗∗∗(𝒔) , 𝑠 𝜖 [𝑡 + 1, 𝑡 + 2]
𝑫 ∗∗(𝒔) , 𝑠 𝜖 [𝑡 + 3, 𝑡 + 6]
𝑫∗(𝑠) , 𝑠 > 𝑡 + 6
𝑫(𝑠) , 𝑠 = 𝑡
𝐷∗∗∗(𝑡 + 2)
𝐷∗∗(𝑡 + 6) 𝐷∗(𝑡 + 24)
𝐷(𝑡)
21
4.2.1 METHODOLOGY
RES generation data are computed using a power conversion model which estimates wind
power and PV generation with an hourly time step, based on meteorological inputs (wind
speed and solar irradiation). This model has been developed by IAEW and has been
calibrated so that the capacity factors match data provided by PRIMES for 2030 (see
Section 7.3.1 for further details).
When it comes to simulate RES production forecasts, one basically uses the same power
conversion model with meteorological forecasts as inputs. To that purpose, we use
historical Numerical Weather Predictions (NWP)15 provided by the European Center for
Medium-range Weather Forecasts (ECMWF). As a final step to the simulation of the
forecasting process, RES forecasts are statistically recalibrated using (simulations of)
production realizations so as to ensure forecasts to be unbiased and with state-of-the art
performance.
To simulate intra-day operations, hourly update of forecasts is derived from the most up-
to-date NWP and current (i.e. present) production estimate. In between NWP updates,
such a procedure must ensure improved RES forecasts performance in the first forecast
hours.
RES forecast errors are finally generated by computing the difference between RES
production realizations and forecasts simulations.
4.2.2 METEOROLOGICAL DATA USED
4.2.2.1 For the simulation of production realizations
RES production realizations have been simulated at
hourly granularity from the interpolation of ERA-Interim
reanalysis data16 over the period 2001-2010. Yearly full
load hours for both PV and onshore wind production
simulations for the considered countries are given in
Section 3.1.
For further details on the related simulation methodology,
data used and simulation results, we refer to 7.3.1. Those
simulations are now identified to real production
measurements.
4.2.2.2 For the simulation of production forecasts
We use ECMWF forecasts that have been derived from the
High RESolution17 (HRES) global model at the same
spatial resolution than the ERA-Interim data (0.75° in
longitude and latitude). Those forecasts cover a 20 year-
long period between 1994 and 2014, but only the 2001-
2010 period associated to production realizations is kept
so as to generate forecast errors. Forecasts have been
derived twice a day at 00h UTC and 12h UTC for 48h
ahead. Initially available at a 3h temporal resolution for
the first 24h ahead and at a 6h temporal resolution for
the next 24h, they have been interpolated using cubic
splines before spatial aggregation and power conversion.
4.2.3 RES FORECASTS RECALIBRATION
To get state-of-the art forecasts performance, RES
production forecasts derived from NWP require statistical
recalibration. Such a recalibration allows to correct
15 Numerical Weather Predictions uses state-of-the-art mathematical models of the atmosphere and oceans
to predict the weather based on previous weather conditions. Hindcasts provided by ECMWF are based on a
unique model and used historical weather to compute historical predictions. Therefore, the forecast performances
are constant for the 10 years of weather predictions. 16http://www.ecmwf.int/en/research/climate-reanalysis/era-interim 17 http://www.ecmwf.int/en/forecasts/documentation-and-support/medium-range-forecasts
Step 1: Power conversion
aggregate weather forecasts for each
zone
use power conversion model to get
production forecasts
Step 2: Adaptive statistical recalibration
t
forecastsobservations
GW
22
production forecasts for bias introduced by meteorological forecasts or by approximations
in power conversion modeling and temporal interpolation. The full description of the
recalibration procedure can be found in Section 7.3.3.1.
The next figure shows three time series on the same graph: realizations (simulations),
forecasts made at midnight before recalibration and forecasts made at midnight after
recalibration. These are time series for photovoltaic generation in Germany during the first
week of 2006. For all following graphs, PV and wind generation are expressed as a ratio of
installed capacity.
Figure 5 - Power generation given by simulations, forecasts before and after recalibration.
Simulations and forecasts shown here are for the first few days of 2006 in Germany.
During the afternoon, forecasts are overestimating power generation before recalibration.
It is noticeable that recalibration removes this bias.
Next figure shows the boxplot of forecast error with prediction horizon for PV generation
in Germany for the midnight run.
Figure 6 - Forecast error boxplot before and after recalibration.
The graph before recalibration shows that after 24 hours of time horizon, forecasts are
slightly out of phase, mostly due to interpolation with a lower temporal resolution. It also
shows that even for the first 24 hours of prediction, bias is not zero. Recalibration corrects
both effects, as one can notice in the recalibrated forecasts boxplots. Thus, recalibration
removes bias and corrects approximations due to interpolation.
4.2.4 FORECAST MODEL PERFORMANCES
The figure below shows the evolution of final error standard deviation for the midnight
forecast run with time horizon. There is one curve for each European country.
23
Figure 7 - Standard deviation of the final error for the midnight forecasting run for PV and Wind power.
For each country, the error standard deviation tends to increase with time horizon, as
expected.
The following table gives the error standard deviation for each country, for the midnight
run and a time horizon of 1, 10 and 15 hours, expressed as a percentage of installed
capacity.
PV Wind
Country Standard deviation (%) Standard deviation (%)
h = 118 h = 10 h = 15 h = 1 h = 10 h = 15 AT 0 1,3 2,6 1,8 3,5 6,4 BE 0 1,4 3,2 2,0 3,9 6,4 BG 0 1,3 2,2 2,2 4,3 8,5 CH 0 1,0 2,2 1,3 2,8 5,0 CZ 0 1,2 2,4 1,7 3,5 5,6 DE 0 1,0 1,9 1,1 2,3 3,4 DK 0 1,0 2,2 2,0 3,5 5,2 EE 0 1,1 1,9 2,0 3,9 5,0 ES 0 0,9 2,1 1,2 3,0 4,0 FI 0 0,8 1,3 1,7 3,9 4,4 FR 0 1,0 2,2 1,2 2,6 4,0 GB 0 0,9 1,9 1,6 3,3 4,3 GR 0 1,3 2,0 1,8 4,6 6,6 HR 0 1,9 3,0 1,8 4,3 5,8 HU 0 1,0 2,0 1,9 3,8 6,5 IE 0 1,2 3,0 2,3 4,3 5,8 IT 0 0,8 1,8 1,2 2,7 4,6 LT 0 1,3 1,9 1,7 3,5 4,5 LU 0 1,4 3,1 1,8 3,5 5,4 LV 0 1,1 1,8 2,1 4,0 5,5 MK 0 1,5 2,6 1,5 2,8 4,9 NL 0 1,1 2,6 2,0 3,9 6,1 NO 1,4 4,4 5,5 PL 0 0,9 1,6 1,4 3,2 5,0 PT 0 1,2 2,9 1,9 4,9 7,2 RO 0 1,2 2,0 1,8 3,8 6,6 RS 0 1,1 2,1 1,7 3,1 5,8 SE 0 0,6 1,3 1,5 3,3 3,9 SI 0 1,3 2,8 1,9 3,8 5,6 SK 0 1,1 2,1 1,4 2,8 4,5
Table 6 - Standard deviation of PV and Wind power forecast errors for the midnight run at 1h, 10h and 15h time horizons.
Additional verification statistics were investigated to ensure forecasts were well calibrated
and with satisfying performance depending on relevant parameters (hour of the day for
demand and PV production, production level for wind power, etc.). One can see for instance
in Figure 8 (left panel) the increased uncertainty associated to forecasts of wind power
production at high production level. Another interesting aspect was to look at performance
improvement due to spatial smoothing of errors (Figure 8 right panel).
18 For PV midnight h-1 forecasts, the standard deviation is null as there is no PV generation during night.
24
Figure 8 : Forecast errors distribution depending on production level for the aggregated European
wind power production, one hour ahead (left panel). Forecasts performance improvement due to spatial smoothing of errors for wind power production. Here performances are measured using the
Mean Absolute Percentage Error (MAPE) criterion.
4.3 DEMAND FORECAST ERROR GENERATION
As for RES generation, the stochasticity module generates forecast errors for power
demand at several short-term horizons (up to 24 hours). These forecast errors are then
used in the market simulations.
4.3.1 METHODOLOGY
METIS database includes 50 years of power demand hourly time series. These data have
been computed using:
Hourly demand time series for one year, published by ENTSO-E. These time series
include evolutions of the structure of the power demand, as estimated by ENTSO-E
in its V1 and V3 2030 scenarios19.
50 years of daily mean temperature data.
The designed model generates hourly demand time series from daily temperature data
based on:
1. a thermosensitive component which estimates the daily mean demand level from
the daily mean temperature using a statistical model,
2. a non-thermosensitive component representing the hourly variability of the demand
residuals (i.e. the difference between the hourly demand and the first component,
the latter being constant over a day).
19 In the absence of demand historical hourly data, ENTSO-E v1 hourly profiles are used.
25
For more information about this model, we refer
to 7.2.
To generate demand forecast errors, we
simulate errors in forecasting both
components of the demand generation
model. The global error is then computed from
the sum of the first and second component
forecast errors.
The forecast of the first component is basically
obtained by feeding the associated statistical
(piecewise linear) model with daily mean of
temperature forecasts provided by ECMWF.
To simulate the forecasting error of the non-
thermosensitive component, we use a
statistical ARMA model fitted to real forecasting
error data provided by the ENTSOE20. Such a
model allows to capture the temporal
correlation but neglect the spatial correlation in
the non-thermosensitive part of forecast errors
from different countries.
4.3.2 DATA USED FOR THE SIMULATION
4.3.2.1 Thermosensitive component of
demand forecasts
As for the RES forecast errors generation, we use ECMWF temperature forecasts21 to
produce forecasts of the demand’s thermosensitive component.
4.3.2.2 Non thermosensitive error component
We use historical day-ahead forecasting error data provided by the ENTSOE. For the
calibration of the dedicated ARMA model, we used data from a country whose electrical
demand has low thermosensitivity.
4.3.3 MODEL CALIBRATION
Details about the related procedures can be found in 7.2.2.
4.3.4 MODEL PERFORMANCES
Demand forecasts update has been simulated through scaling of day-ahead forecast errors.
Scaling factors have been determined by linear interpolation of MAPE (Mean Absolute
Percentage Error) performances observed for different prediction horizons. We used both
performance results observed in ENTSOE historical error data (for h = 24) and in the
literature22 (for h = 1), to compute these factors. A summary of the model performances
that can be observed across prediction horizons is given in the table below.
Country Standard deviation (%)
h = 1 h = 13 h = 23
AT 0,4 2,4 4,1
BE 0,4 1,1 1,6
20 https://transparency.entsoe.eu/ 21 http://www.ecmwf.int/en/forecasts/documentation-and-support/medium-range-forecasts 22 “A comparison of univariate methods for forecasting electricity demand up to a day-head”, Taylor et al.,
International Journal of Forecasting, 2006, vol. 22(1): p.1-16.
Step 1: Thermosensitive error computation
use power conversion model to get daily mean demand forecasts error
aggregate temperature data to daily resolution
and recalibrate forecasts
cooling heating
Step 2: Non-thermosensitive error simulation
Simulate hourly non-thermosensitive forecast errors from ARMA modeling. Sum
both error components and recalibrate using historical errors.
t 0
GW
26
BG 0,4 1,4 2,2
CH 0,3 1,1 1,8
CZ 0,1 0,9 1,5
DE 0,4 1,7 2,7
DK 0,4 0,5 0,6
EE 0,4 1,2 1,8
ES 0,4 0,7 0,9
FI 0,3 1,1 1,8
FR 0,3 0,8 1,1
GB 0,3 1,1 1,7
GR 0,3 1,1 1,8
HR 0,4 0,9 1,3
HU 0,2 0,9 1,5
IE 0,3 1,1 1,7
IT 0,4 0,9 1,3
LT 0,3 1,0 1,7
LU 0,4 1,7 2,8
LV 0,3 1,0 1,6
MK 0,4 1,4 2,3
NL 0,4 1,2 1,8
NO 0,3 1,0 1,6
PL 0,4 0,9 1,4
PT 0,4 1,1 1,7
RO 0,4 0,8 1,1
RS 0,4 1,0 1,5
SE 0,3 1,0 1,6
SI 0,4 2,1 3,5
SK 0,5 1,3 2,0 Table 7 : Standard deviation of demand forecast errors for prediction horizons
h=1, h=13 and h=23
4.4 OUTAGES
4.4.1 METHODOLOGY
The availability of production clusters incorporates stochastic simulation of unplanned
outages.
For each cluster unit, a time series describing the unit’s availability (or non-availability) is
generated from the concatenation of consecutive episodes with random durations sampled
from truncated exponential distributions23.
For each cluster, hourly lost capacity due to units’ outages is computed from the sum of
units’ availability, considering a number of units derived from the cluster’s hourly running
capacity.
23 Exponential distribution is a usual hypothesis found in the literature to model unit outage duration distribution,
see for instance «System availability with non-exponentially distributed outages”, Cao et al., IEEE Transactions
on Reliability, 2002, vol. 51(2), p.193-198. doi: 10.1109/TR.2002.1011525.
27
4.4.2 DATA USED FOR SIMULATIONS
Annual mean outage durations were based on a literature survey. Using the annual mean
number of outages computed from historical data provided by RTE (Réseau de Transport
d’Electricité), we derived the mean duration of a single outage. Minimum and maximum
outage durations were also derived from RTE historical data. All these parameters used for
simulation are given in the table below.
Type de cluster
Mean annual outage
duration (h)
Mean outage duration (h)
Minimum outage
duration (h)
Maximum outage
duration (h)
Coal fleet 490 233.38 0 6517
Lignite fleet 190 233.38 0 6517
Oil fleet 290 61.28 0.3 3022
OCGT 330 151.17 0.2667 8088
CCGT 330 151.17 0.2667 8088
Nuclear fleet 50 64.59 0 2931.5
Table 8 : Annual outage duration along with parameters of the duration distribution for one outage are given for each considered technology.
4.5 RESERVE SIZING
4.5.1 MAIN ASSUMPTIONS
One important hypothesis of the model is that the intraday energy market is liquid and
that, as such, the variation in net demand or forecast errors over one hour is met by the
offers done on the market. In reality, TSOs use Replacement Reserves (RR, not explicitly
modelled here, but a proxy is used as described in Section 4.6) to make sure that enough
capacity is available and running (or ready to be running) for the next 1 to 4 hours. Hence,
only variations/events occurring in a time horizon smaller than 1 hour are taken into
account and used for the sizing of the FCR, aFRR and mFRR (except for some market design
options where the impact of “bad” forecasts on FRR sizing is investigated and forecasts
with horizon longer than one hour are used).
Step 1: Unit outages time series simulation
t
MW Pm
Lost capacity
Outage and non-outage consecutive episode durations are simulated from
truncated exponential distributions
0
Cluster characteristics: • Installed capacity • Hourly running capacity
Step 2: Aggregating to cluster level
t
MW
Hourly computations using a number of units derived from the running capacity
28
Besides, METIS uses an hourly granularity by default. As a consequence, 15 or 30 minutes
intraday gateways are not modelled and all variations occurring inside the hour have to be
dealt with by the FRR.
Finally, FCR and aFRR are simulated as a single synchronized reserve and the specific
constraints of FCR are not integrated by default. FCR and aFRR sizing are added to define
the required synchronized reserve.
The main evolution in FRR needs that is to be assessed when comparing to today’s situation
is the growing share of renewables in the production mix. The immediate impact will be
that both empirical and deterministic methods (see 4.5.3) which are currently used in some
countries will prove to be insufficient in the near/longer term, when renewables account
for an important part of the hourly/daily electricity production. Reserve sizing is thus bound
to evolve towards a more probabilistic approach.
In order to compute the FRR sizing following a probabilistic methodology, a TSO point of
view is used. It means a forecast state of the system, with a 5min granularity, is compared
to an actual state of the system, also with a 5min granularity. Reserves (aFRR and mFRR)
are called upon to take care of the resulting imbalances (difference between what was
forecast by the TSO and what actually happened). aFRR and mFRR sizings are computed
based on the 0.1% and 99.9% centiles of imbalances.
The whole simulation process and FRR sizing is explained in more details in the following
parts.
4.5.2 FREQUENCY CONTAINMENT RESERVE
FCR is shared between ENTSO-E continental members with a total sizing of 3GW which is
split among MS proportionally to their annual power generation.
FCR sizing for each Member State is assumed to follow the same rule up to 2030.
The FCR values used in METIS are presented below (FCR is assumed to be symmetrical for
each country):
Tableau 1 – FCR sizing by member state
Country FCR
(MW) Country
FCR
(MW) Country
FCR
(MW) Country
FCR
(MW)
AT 65 EE 45 IT 535 PL 171
BA 14 ES 421 LT 57 PT 51
BE 100 FI 931 LU 6 RO 57
BG 44 FR 650 LV 42 RS 46
CH 71 GB 900 ME 25 SE 644
CY GR 60 MK 9 SI 16
CZ 75 HR 10 MT SK 29
DE 583 HU 75 NL 102
DK 50 IE 90 NO 352
29
4.5.3 AUTOMATIC FREQUENCY RESTORATION RESERVE (AFRR) AND MANUAL
FREQUENCY RESTORATION RESERVE (MFRR)
4.5.3.1 Units participating to the reserve
Only synchronized units can participate in the aFRR because the Full Activation Time (FAT),
i.e. the time required for the reserve to be fully activated, is too low for the non-
synchronized units to start-up.
30
FAT varies a lot between Member States as can be seen on the following figure:
Figure 9 : Diversity of aFRR products across continental Europe
By default, the FAT chosen for the aFRR in METIS is 5 minutes.
As for the mFRR, because its FAT is set to 15 minutes, assets which can start-up in less
than 15 minutes (OCGT and hydro power plants) can also participate, even if they were
not running at the beginning of the event.
4.5.3.2 Sizing approach
Three approaches are described in the ENTSO-E Operation handbook for aFRR and mFRR
reserves sizing, referred to as empiric, probabilistic or deterministic [18]
Empirical approach (currently used in France in case of low demand gradient. A
margin, computed as the 5-min forecast gradient of the demand, is used whenever
the demand gradient is high).
Variable hourly sizing, based on the maximum anticipated demand level D
(expressed in MW).
𝑎𝐹𝑅𝑅 𝑟𝑒𝑞𝑢𝑖𝑟𝑒𝑚𝑒𝑛𝑡𝑠 = √10 𝐷 + 22500 − 150
Probabilistic approach (currently used in Germany).
Based on load fluctuations standard deviation, RES generation forecasts and outage
statistics, this methodology consists in applying convolution techniques to Normal
probability distributions, in order to get the maximum upward / downward balancing
requirements for a given probability.
It results in hourly reserve requirements that could be aggregated to get a fixed-
valued sizing over longer time-spans.
31
The following figure illustrates the probabilistic reserve sizing with a 99% probability
(i.e. that 99% of the time there is no reserve shortage):
Figure 10 : Probabilistic reserve sizing illustration
Source: ENTSO-E’s Supporting Document for the Network Code on Load-Frequency Control and Reserves
Deterministic approach (currently used in Ireland and United-Kingdom)
Consists in setting reserves’ size to the value of the biggest expected generation
incident. It is mentioned as “Dimensioning incident” on the previous illustration.
Both empirical and probabilistic approaches can be implemented in METIS. The following
paragraphs will provide a more detailed description of the way the probabilistic approach
is done for the FRR reserves.
4.5.3.3 Probabilistic approach
A TSO’s point of view was adopted for the market simulations: imbalances (difference
between the forecast and the actual states of the system) with a 5min granularity are
computed, and it is then assumed that these imbalances must be dealt with by the aFRR
and mFRR. In our approach, FRR reserves must be able to cope with imbalances 99.9% of
the time.
Imbalances are the results of variable RES units, typically wind and solar power plants, as
well as forecast errors of the non-flexible loads. In order to simulate a 5min system from
data with 1h granularity as it is usually the case in METIS, additional data was needed.
32
The “actual” state of the system was thus simulated using real 5min data of demand and
wind for UK in 2015 extracted from Gridwatch, and 15min PV production data from a
German TSO, 50Hertz, which was linearized to go down to the 5min granularity. These
datasets were used to compute sub-hourly patterns (series of twelve 5-min data-points)
for classes of generation/demand level and hourly gradient. These patterns are then
applied to other countries for hours with similar gradient and level characteristics.
In order to model the imbalances that will trigger the call to aFRR and mFRR, the actual
state of the system is compared to forecasts. Hence, h-1 forecasts of demand and PV
generation (30min for wind24) have been collected from METIS forecast database and
linearized in order to get 5min data.
Subtracting the actual values from the forecasts gives access to the imbalance levels with
a 5min granularity and for 10 years of weather data. These imbalances are finally scaled
(using the square root of the mean demand or installed capacities) to mimic how
imbalances evolve depending on demand and RES integration scenarios.
The imbalance generation process can be summed up as follows:
FRR activation is modelled using the following process
For deviations that are not too large compared to the aFRR sizing (imbalances <
aFRR sizing * 0.9), only aFRR is triggered
For large deviations, aFRR is automatically activated the first 5 minutes, then
replaced by mFRR. mFRR activation ends when imbalances come back below a
24 The use of 30min forecasts for wind resulted from comparisons between modelled imbalances, using today
wind and PV installed capacities, and historical values published by ENTSO-E.
33
second threshold (imbalances < aFRR sizing * 0.5 ) thanks to updated forecasts
and intraday market.
Therefore, as commonly practiced by TSO, aFRR is dimensioned to compensate for the
variation of imbalances during a 5min interval25, excluding outages, while the mFRR is
sized to cope with total imbalances26 including outages (see Section 4.4 for more details
about the generation of outages).
Depending on the studied market designs, the imbalances used for the stochastic approach
have been computed on groups of countries (for regional cooperation within ROCs) or
aggregated in time (if the reserve sizing is constant over time, over a year for instance).
So the reserve can be sized for each country separately or for a group of country and can
be fixed over the year or change depending on the time of the day.
Model validation
The way the FRR reserves are calculated in METIS in order to take into account the
demand/RES variations is similar to what is done by various TSOs around the world:
In Belgium, Elia used a similar methodology in 2013 in order to assess the need for
ancillary services in the country in 2018 and based its calculation on the convolution
of different events. See [19]
In France, in case of high demand gradient, RTE bases its calculation of the aFRR
on the 5-minute gradient. See [20]
In the US, the Eastern Wind Integration and Transmission Study (EWITS) aimed at
assessing the impact of wind power on the need for reserves. The forecast error
and the resulting standard deviation were assumed to be dependent upon the
production level. See [21]
The following graphs show the distribution of the simulated imbalances (blue), which thus
corresponds to the FRR calls, with actual data taken from the ENTSOE website (orange).
The graphs show the results for Denmark. One can see that the model follows the historical
outcome quite closely:
25 The 0.1% and 99.9% percentiles of imbalance variations are used to compute the downward and upward aFRR
size. 26 The 0.1% and 99.9% percentiles of total imbalances are used to compute downward and upward FRR sizes.
mFRR sizes are then calculated by substracting aFRR to FRR sizes
34
35
The following table shows the comparison between the actual 2015 aFRR and mFRR sizes
and the ones simulated with METIS (the figures displayed are the sum of the national sizing
of each country).
GW 2015 – historical data 2015 - METIS
aFRR
Upwards 8.6 9.9
Downwards 7.2 8.8
mFRR
Upwards 19.1 15.1
Downwards 16.6 11.7
Total 51.5 44.2
aFRR values are a bit overestimated, but the difference is smaller than 20% of the historical
value. mFRR sizes are much smaller in METIS: while historical and simulated imbalances
are consistent, several countries currently use a deterministic approach for reserve sizing
which may overestimate the reserve needs.
The table below shows the evolution of the aFRR and mFRR sizes as calculated with METIS,
between 2015 and 2030. Total FRR sizes will increase by 20% in 2030, mostly due to
higher shares of wind energy.
GW 2015 2030
aFRR
Upwards 9.9 10.5
Downwards 8.8 10.0
mFRR
Upwards 15.1 17.4
Downwards 11.7 15.6
Total 44.2 53.5
Increase +21%
The assessed impact of wind power capacity on the reserve needs is consistent with
available publications on the subject. It was found that 1MW of additional wind power
increases the aFRR size by 4.3kW. A study from NREL found around 3.5kW additional
regulating reserve per MW of wind power [21].
36
Figure 11 : Linear regression of additional aFRR needs (2030 compared to 2015) and additional
installed wind power capacity, for the 30 countries
4.5.4 RESERVE SHARING
With regional cooperation, countries can share their imbalance risks to decrease global
reserve sizing requirements by pooling part of it. Indeed, for a given level of security of
supply, the total regional reserve requirement is lower.
METIS implements the following methodology to simulate reserve sharing:
First the stochastic approach is used to assess the size of the reserve for each
country
Then the stochastic approach is used to compute the regional reserve sizing, by
calculating the imbalances over the whole region (i.e. by adding the imbalances of
each individual country within the region) and calculating the aFRR and mFRR sizes
based on those regional imbalances
The regional reserve is assigned to each country in proportion of their individual
levels of electricity demand
Finally, reserve procurement is optimized so that:
o Each country procures an amount of reserve at least equal to its share of
the regional reserve sizing
o Local reserve procurement plus cross-border capacity reservation27 is equal
to national reserve sizing. Hence, each country can face its own imbalances
with locally procured reserve and imports.
This method guarantees that the level of security of supply is similar for both national and
regional reserve sizing.
27 Reserved cross-border capacity cannot be used for day-ahead and intraday exchanges and are kept for the
balancing market. Hence, cross-border capacity reservation is computed as the optimal trade-off between
interconnection use for arbitrages and reserve sharing.
37
The following map defines regions used in METIS.
38
4.6 LOSS OF LOAD AND REPLACEMENT RESERVE
Unplanned events such as a reduction of wind or PV generation, an increase of demand or
a producing power plant outage, might lead to loss of load in the model, when the available
capacities are not sufficient to face the mismatch between supply and demand. In real
markets, Replacement Reserve (capacity which can start in a few hours) is procured at
day-ahead (or before) and allows to avoid such loss of load. As Replacement Reserve is
not modelled in METIS, periods with consecutive hours of loss of load can happen.
In order to compare fairly the different policy options, a proxy has been developed to count
loss of load. Instead of counting the loss of load at a price of 15k€/MWh, the cost of a
corresponding replacement reserve is computed ex-post for each country. This cost is
computed as:
Investment cost of peak units (60 k€/MW/yr) to cover most of the loss of load (all
but 3 hours)
Production cost of peak units at 180€/MWh (variable cost of oil fleets, including CO2
emissions) to cover most of the loss of load (all but three hours)
VoLL (15k€/MWh) for the remaining three hours of loss of load.
The computation process is described below.
4.7 BIDDING BEHAVIOR
METIS is able to simulate the impact of several bidding behaviors, including scarcity pricing,
on market players revenues and on marginal costs.
Marginal Cost Bidding
Technology bids according to actual production costs
No kind of mark-up
Energy only market with perfect competition
Lost Load
Time
Input loss of load curve
Peak three hours of lost load
Load covered by the replacement reserve
Replacement reserve capacity
1
2
3
4
1
2
3 4
3 hours
39
Competitive Bidding
Mark-up depending on utilization of cluster’s capacity
Stepwise mark-up with growing utilization
Overall bid never exceeds marginal costs of next
technology cluster
NB: Scarcity pricing is a particular case of competitive
bidding. It occurs when the most expensive technology
is being used.
Oligopoly Bidding
Technology with highest costs needed for load coverage
adds mark-up
Mark-up based on market share and portfolio
Increase to production costs of next technology with
different operator
Fixed Costs Bidding
Each bid includes fixed costs
(OPEX and/or CAPEX)
Mark-up depends on type and age of technology
Mark-up is limited to next technology
cluster’s bid
Most of the parameters used to simulate the effect of bidding behaviors come from the
system module (in the case of markups depending on how far the next generation in the
merit order is, for instance).
Yet, METIS needs the user to input some additional parameters, like:
The level of price caps
In the case of oligopoly bidding, the ownership distribution of each cluster among
operators.
NB: The model simulates the effect of bidding behaviors on prices, focusing on the marginal
unit, which is the one that ultimately fixes the price. Currently, the model does not consider
the possible impact on volumes and flows.
Load
Nuclear Lignite Coal
CCGT OCGT Oil
Volume
Price
Volume
Price
A A B
Load
Load
Nuclear Lignite Coal
CCGT OCGT Oil
Volume
Price
40
5 BALANCING MARKETS
5.1 INPUTS
The balancing market simulation is computed ex-post on a given year of weather data.
Hence, it takes as input for each hour of the year:
The set of units which procured reserve, as a result of the day-ahead market
simulation (cf Section 3.2).
For each unit, input parameters (pmax, pmin, cluster characteristics) and output
results from the day-ahead model and reserve procurement (maximum
downward/upward variation).
For each unit, variable costs (fuel costs or “water value” for hydro storage).
Technology Variable cost
Hydro Day-ahead water value (dual value
associated to the storage constraint)
Industrial demand
response 225€/MWh
Other demand
response Day-ahead price
Other fleets Day-ahead production cost
Planned power exchanges for NTCs.
Balancing market configuration. Balancing services can be procured either on a
national basis or with regional cooperation (including imbalance netting). Additional
interconnection capacity, or on the contrary penalty to use interconnectors, can be
added for balancing exchanges.
The activation cost of balancing energy is assumed to have two components: a fixed
activation cost plus the variable cost. The same is valid for downwards reserves: fixed
activation minus variable cost (saved fuel costs or water value). The fixed activation cost
has been estimated by comparing historical balancing costs to the costs of electricity. This
analysis suggests producers add a mark-up of around 8€/MWh to their variable cost.
Competitive pressure would likely drive this mark-up down. This effect has not been
modelled.
5.2 OUTPUTS
METIS balancing market module computes:
Imbalances for each country, with a 5 minute granularity, aFRR and mFRR calls on
a national basis (cf Section 4.5.3)
Optimal dispatch of aFRR and mFRR balancing products, using a national or regional
merit order. The merit order is deducted from total activation or deactivation costs,
which is composed of a participation cost (constant for all fleets) and a variable cost
(dependent of the technology):
o Activation cost for upward reserve: Participation cost + Variable cost
o Deactivation cost for downward reserve: Participation cost – Variable cost
41
Therefore, expensive fleets are called first for downward reserve, while cheap fleets
are called first for upward reserve. However, imbalance netting (which consist in
the cancellation of opposite reserve demand) is prioritized if sufficient
interconnection capacity is available.
Under regional cooperation, balancing exchanges are constrained by
interconnection capacity. For a given type of balancing product (aFRR or mFRR),
balancing activations with opposite direction are cancelled, if the interconnection
capacity allows to do it.
Statistics are gathered on balancing costs, interconnection use and number of time
steps for which balancing activation exceeds reserve size.
The impact of balancing market on the following intraday gateway is not modelled.
42
6 REFERENCES
[1] Heterogeneous Unit Clustering for Efficient Operational Flexibility Modeling for Strategic
Models, Bryan S. Palmintier and Mort D. Webster, Massachusetts Institute of Technology,
Engineering Systems Division, ESD Working Papers Series, January 2013
[2] Dynamic Constraints for Aggregated Units: Formulation and Application, Nicolas
Langrené, Wim van Ackooij, and Frédéric Bréant, IEEE TRANSACTIONS ON POWER
SYSTEMS, VOL. 26, NO. 3, AUGUST 2011
[3] Kraftwerksliste der Bundesnetzagentur, Online verfügbar unter
www.bundesnetzagentur.de/cln_1912/DE/_Sachgebiete/ElektrizitaetundGas/Unternehme
n_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/kraftwerk
sliste.html
(Abgerufen am 12. August 2013), Bonn, 2013
[4] BoAplus, Online verfügbar unter
http://www.rwe.com/web/cms/de_/1101724/boaplus/
Abgerufen am 4. August 2013), Essen, 2013
[5] Moderne Braunkohlekraftwerke - ein flexibler Baustein für die Energiewende,
Energiewirtschaftliche Tagesfragen (63. Jg, 2013), Heft 1/2, Essen, 2013
[6] Bestmarken in puncto Wirkungsgrad und Flexibilität, BWK - Das Energie-Fachmagazin
9-2011, Springer-VDI-Verlag GmbH & Co. KG, Düsseldorf, 2011
[7] Betriebsflexibilität von GuD-Kraftwerken: Ein Schlüssel zur optimierten Netzeinbindung
erneuerbarer Energieerzeugung, BWK - Das Energie-Fachmagazin 04/2011, Springer-VDI-
Verlag GmbH & Co. KG, Düsseldorf, 2010
[8] Kernkraft und erneuerbare Energien – Technische Flexibilität zum Ausgleich zufälliger
Einspeisung,
VGB PowerTech 1-2 (2011), Hrsg. VGB PowerTech, Essen, 2011
[9] Verträglichkeit von erneuerbaren Energien und Kernenergie im Erzeugungsportfolio,
Online verfügbar unter http://www.ier.uni-
stuttgart.de/publikationen/_pb_pdf/Hundt_EEKE_Langfassung.pdf
(Abgerufen am 4. August 2013), Stuttgart, 2009
[10] Auswirkungen von fluktuierender Windenergieeinspeisung auf das regelund
thermodynamische Betriebsverhalten konventioneller Kraftwerke in Deutschland -
Bestandsaufnahme und Ableitung zukünftiger Anforderungen, VGB PowerTech u.
Universität Rostock, Rostock, 2009
[11] Eurelectic
[12] Blum, R. Christensen, T. High Flexibility Power Plants – 25 Years of Danish Experience
(2013)
[13] Feldmüller, A. (2013) Wie flexible ist des heutige konventionelle Kraftwerkspark aus
Herstellersicht ? (Abgerufen am 12. August 2013), Bonn, 2013
[14] Competition in Electricity Generation in Germany and Neighboring Countries from a
System Dynamics Perspective, Grobbel C., European University Studies. Series V,
Economics and Management, Frankfurt am Main, 1999
[15] IEA 2014. Thermal Power Plant Economics and Variable Renewable Energies. A Model-
based Case Study for Germany. International Energy Agency, Paris, 2014
43
[16] Deutsches Institut fur Wirtschaftsforschung – Current and Perspective Costs of
Electricity Generation until 2050. DIW, Berlin, 2013
[17] ENTSO-E TYNDP 2014: https://www.entsoe.eu/major-projects/ten-year-network-
development-plan/tyndp-2014/Pages/default.aspx
[18] Operation Handbook, P1 - Policy 1: Load-Frequency Control and Performance [C]:
https://www.entsoe.eu/fileadmin/user_upload/_library/publications/entsoe/Operation_Ha
ndbook/Policy_1_final.pdf
[19] Elia, Evolution of ancillary services needs to balance the Belgian control area
towards 2018 (2013)
[20] RTE, Documentation technique de reference, 2013
[21] NREL, Operating reserves and wind power integration: An international comparison
(2010)
[22] European Climate Assessment & Dataset project: http://eca.knmi.nl/
[24] J. H. Friedman, « Multivariate Adaptive Regression Splines », Annals of
Statistics, vol. 19, no 1, 1991
[25] https://cran.r-project.org/web/packages/earth/earth.pdf
[26] https://onlinecourses.science.psu.edu/stat510/node/64
44
7 ANNEX: DEMAND AND RES DATA GENERATION
7.1 GLOBAL APPROACH FOR CLIMATIC SCENARIOS
To assess the benefits of regional cooperation, it is crucial to use consistent weather data
through Europe. For this reason, correlated RES generation data were integrated in METIS,
as represented in Figure 12.
Figure 12: Correlated RES generation in METIS: for each year of weather data, one corresponding
scenario is built.
The following paragraphs describe the methodology which was used to build the correlated
demand time series and RES generation.
7.2 DEMAND PROFILES
7.2.1 TEMPERATURE SENSITIVITY AND DEMAND MODELING
The objective is to generate fifty hourly scenarios of demand for each country by means of
a statistical model fitted to the following data sources: - historical daily temperature data from years 1965 to 2014 for all countries from the
European Climate Assessment & Dataset project (ECA), see [22].
- hourly demand data projections for 2030 provided by ENTSO-E TYNDP 201428
visions 1 and 3, see [17].
In this regard, each demand scenario is modelled as the sum of a thermo-sensitive
component and the non-thermo-sensitive one. The thermo-sensitive component is
computed by using a piecewise linear model. This model is set up with one threshold and
two slopes29 and calibrated by getting recourse to a Multivariate Adaptive Regression
Splines method30 that involves the computation of temperature gradients (MW of demand
increase per °C increase) for each country.
As depicted Figure 13 for Spain, the temperature scenarios of each country drive its
thermo-sensitive demand scenarios by using the country temperature gradients. Then,
thermo-sensitive and non-thermo-sensitive demand scenarios are added so as to complete
the generation of the country demand scenarios.
28 Data is given as hourly time series for one year and average seasonal temperatures. 29 The use of two slopes - one slope associated to low temperatures and one slope associated to high temperatures allows for
applying the same approach for each country, with the same number of parameters, although three slopes could have been
used for countries with both heating and cooling gradients.
30 See [24] for the method and [25] for its R implementation.
45
Figure 13 : Two gradients and one threshold accounting for heating and cooling effects on Spain demand
7.2.2 DEMAND FORECAST ERRORS GENERATION
Here we provide additional material on how the demand forecast errors generation model
described in Section 4.3 has been calibrated.
7.2.2.1 Recalibration of temperature forecasts
We either observed a somewhat constant temperature forecasts bias, or a bias with annual
seasonality. Thus, we used a linear model to recalibrate temperature forecasts with
parameters estimated on a monthly basis.
7.2.2.2 Calibration of the ARMA model on the non-thermosensitive error component
Historical day-ahead forecast errors are sometimes biased31. To be consistent with the rest
of the methodology, we centered the error time series by computing its difference with the
daily mean error at hourly granularity.
To choose an appropriate ARMA model to fit to the data, we looked at the autocorrelation
(ACF) and partial autocorrelation (PACF) functions. The former has a shape that tapers to
0 while the second shows non-null values at specific time lags, which indicates32(see [26])
an auto-regressive AR process. Focusing on the non-null coefficients of the PACF function,
while trying to keep the model’s order reasonable, we chose an AR(24) model. The
coefficients of the fitted model are given in the following table:
Table 9 : AR(24) coefficients estimation from maximum likelihood fit to the centered day-ahead
demand forecast error time series of the Dutch national electric demand.
Lag (h)
1 2 3 4 5 6 7 8 9 10 11 12
Coef .55 .12 -.03 -.06 -.04 -.06 -.03 -.03 -.03 -.04 -.05 -.02
Lag (h)
13 14 15 16 17 18 19 20 21 22 23 24
Coef -.01 -.06 -.03 .00 .00 -.02 -.01 -.03 .00 .02 .04 .04
7.3 RES GENERATION PROFILES
7.3.1 GENERATION OF SOLAR AND ONSHORE WIND POWER PROFILES
Generation of ten historical yearly profiles for wind power and solar power has been
performed by a model developed by IAEW. The model uses historical meteorological data,
units’ power curves and historical generation data as input parameters to determine RES
generation profiles and calibrate the results for each region in the models scope.
31 This may come from the use of an asymmetric cost function undertaken by the related operational forecasting
system. 32 https://onlinecourses.science.psu.edu/stat510/node/64
46
The methodology is depicted in Figure 14.
Figure 14: Methodology
Input Data
Meteorological Data
The delivered time series of renewable feed-ins are based on fundamental wind, solar and
temperature time series for 10 years (2001 to 2010) on a detailed regional level derived
from the ERA-Interim data provided by Meteo Group Germany GmbH. From ERA-Interim’s
model, values for wind speed (m/s), global irradiation (W/m2) and temperature (°C) are
derived for every third hour and interpolated to hourly values by Meteo Group. The regional
resolution of the data is one hourly input series (wind, solar, temperature) on a 0.75°
(longitude) times 0.75° (latitude) grid model, which ensures an adequate modeling
accuracy. The regional resolution is shown in Figure 15, in which each blue dot represents
one data point.
Input data
meteorological data
units’ power curve
historical data
Model
aggregate meteorological data for each country
use historical data for back testing and calibrating model
Results
load factor time series for each
country
47
Figure 15: Regional resolution of meteorological data
Historical Data
To generate realistic time series, a calibration of the models is inevitable. Therefore
information regarding the yearly full load hours for wind and PV generation in each country
is necessary. To derive the yearly number of full load hours the installed capacities of wind
and PV generation as well as the yearly energy production have been investigated for each
country.
In case of unavailable data the full load hours were derived based on the data of a
neighboring country. As the availability for data regarding installed wind generation
capacities and generated energy is satisfying in almost every country it is rather low for
information regarding PV power. Only for a few countries reasonable full load hours could
be derived from historical published data. For the other country data from the Photovoltaic
Geographical Information System was used instead.
Model
In first step the high-resolution meteorological data are aggregated for each country and
NUTS2 region. The aggregation is thereby based on the regional distribution of wind and
PV capacities. The required distribution of wind and PV generation capacities is extracted
from different databases and is aggregated at high voltage network nodes. In countries
with no available information a uniform distribution is assumed.
Each high voltage network node gets the nearest meteorological data point assigned to
and the data is weighted with the installed capacity at the network node. Thereby the wind-
speed is weighted by the installed wind generation capacity whereas global irradiation and
temperature are weighted with the installed PV generation capacity. The weighted time
series for all nodes in each region are aggregated and divided by the overall installed wind
respectively PV capacities. Subsequently, it is necessary to calibrate the generation models
for each country by scaling the meteorological data accordingly. The process of calibration
is display in Figure 16.
48
Figure 16: Model calibration
The meteorological data is fed into generation models for PV and wind generation. The
resulting load factor time series are compared with the historical full load hours for the
specific country and the deviation between load factor time series and the historic full load
hours in each year i is to be minimized by scaling the meteorological data accordingly. In
this minimization the yearly deviation between time series full load hours (FLH) and
historical data is weighted with the installed capacity (IC) in the specific year according to
formula 1.
min ∑(
10
𝑖=1
𝐹𝐿𝐻𝑖,𝑡𝑖𝑚𝑒 𝑠𝑒𝑟𝑖𝑒𝑠 − 𝐹𝐿𝐻𝑖,ℎ𝑖𝑠𝑡𝑜𝑟𝑖𝑐𝑎𝑙 𝑑𝑎𝑡𝑎) ∙ 𝐼𝐶𝑖 (1)
The scaling factors are chosen independently for wind speed and global irradiation and are
individual for each country.
Calibration to PRIMES load factors
In order to generate RES generation profiles for the METIS EuCo27 2030 scenario, the
installed capacities and full load hours for each country from PRIMES were used. From
these data each NUTS2 region was assigned a share of the country’s installed generation
capacities for PV, onshore wind and offshore wind (if applicable) according to the region’s
average global irradiation and wind speed in comparison to the countries average global
irradiation and wind speed, respectively. The model was then calibrated by minimizing the
deviation between time series full load hours and PRIMES full load hours in 2030.
The resulting full load hours for both wind and PV for exemplary countries are shown in
Figure 17.
aggregated meteorological data
scaling of meteorological data
generation model load factor time series
historical full load hours
49
Figure 17 - Wind and PV full load hours per year
Whereas the PV full load hours per year are not changing significantly from one year to the
next, the resulting full load hours from wind generation vary considerably.
7.3.2 HYDRO POWER MODELING
Run-of-river power plants, inter-seasonal storage dams/reservoirs and pumped hydro
storage units are modelled separately.
Run-of- river power plants are represented similarly to other RES plants, which means that
their generation at all times is bounded by a maximal load factor time series. Pumped
hydraulic storage is modelled as a storage module with a global efficiency rate of 81%.
Inter-seasonal hydro storage is modelled as reservoirs with water inflows time series and
minimum water level at the end of each week in a system-module run. These minimum
values, called “guide curves”, are based on historical values to replicate the standard
management of inter-seasonal hydro storage33.
To implement hydro power modelling, national data – for run-of-river units’ generation as
well as minimal allowed storage level and water inflows time series – has been derived
from power generation and storage level history.
33 The computation of guide curves requires a stochastic optimization of reservoir management to face
uncertainties on water inflows and future load, which is out of the scope of METIS.
50
Figure 18: Yearly storage in France
Figure 19: Example of French hydro management (in blue): PHS and reservoir are used when the
French demand (red curve) and exports are high, while the minimum water level avoids to use all reservoir water before the winter period.
7.3.3 GENERATION OF SOLAR AND WIND POWER FORECASTS
Here we give supplementary material on how solar and wind power forecasts generated
from the model described in Section 4.2 were recalibrated.
7.3.3.1 Recalibration model
RES production forecasts derived from meteorological forecasts used as input to IAEW
power conversion model require additional statistical recalibration for at least three
reasons:
to incorporate actual production estimate as additional information for forecasts
actualization in between NWP actualizations,
to remove potential bias that may lie in meteorological forecasts or may be caused
by improper power conversion modeling,
0
0,5
1
1,5
2
2,5
3
3,5
Ener
gy v
olu
me
(TW
h)
Minimal storage level allowed Actual storage level
51
to correct approximations due to interpolation of meteorological forecasts available
with sparse temporal resolution (6 hours) at horizons further than 24 hours ahead.
To deal with these limitations, we consider a statistical recalibration model that re-estimate
RES production forecasts from initial forecasts, using actual production estimate as
additional input. The considered model can be written as:
𝑌(𝑧, 𝑟𝑖 + ℎ) = 𝑎𝑧,𝑟𝑖,ℎ�̂� (𝑧, 𝑟𝑖 , ℎ) + 𝑏𝑧,𝑟𝑖,ℎ𝑌(𝑧, 𝑟𝑖) + 𝑐𝑧,𝑟𝑖,ℎ + 휀(𝑧, 𝑟𝑖 , ℎ),
Where 𝑌 is the production simulation, �̂� the production forecast derived from
meteorological forecasts power conversion, 𝑧 the considered zone, 𝑟 the hour of day
forecasts’ actualization is derived (i.e. 𝑟 = 0, … , 23 h UTC), ℎ the forecast horizon (in hour),
휀 the modeling error and 𝑖 the sample day.
Parameters of the model are estimated by a least-squares approach. Normalized
production is constrained so as to stay bounded34. To bring additional flexibility to the
model, parameters are adaptively estimated using a 3 months long moving time window
for statistical learning, with parameters’ estimation actualized every week. This must help
capture long term variations associated to the forecasting process, such as climatic
variations or variations in NWP models’ parameterization.
7.4 GENERATION UNITS TECHNICAL PARAMETERS
Different sources were used to set generation units technical parameters used in our
model, among which the following tables:
Source: The Danish Experience with Integrating Variable Renewable Energy, Agora Energiewende
34 Between 0 and 1 for wind power and between 0 and a maximum production value defined for each hour of day
for PV generation. Such a value is adaptively estimated using 1 month long moving time window and over the
10 years/scenarios generated from IEAW reanalysis data.
52
Source : IAEW
