IMPACT ANALYSIS OF MARKET AND REGULATORY OPTIONS … · Speakers •VITO/EnergyVille, Belgium...
Transcript of IMPACT ANALYSIS OF MARKET AND REGULATORY OPTIONS … · Speakers •VITO/EnergyVille, Belgium...
IMPACT ANALYSIS OF MARKET AND REGULATORY OPTIONS –
ADVANCED POWER SYSTEM AND MARKET MODELLING STUDIES
WEBINAR TASK 3.4
June 16th, 2020 This project has received funding from the European Union’s Horizon 2020 research and innovation programmeunder grant agreement No 773505.
Agenda
• Context and Overview of EU-SysFlex
• Introduction of WP3 and the Speakers
• Overview of Task 3.4
• Presentations of individual subtopics
• Conclusions
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https://eu-sysflex.com/documents/
Webinar Task 3.4 16/06/2020
Wind & Solar
Natural Gas
Coal
Other Renewables
Nuclear
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
2015 2020 2025 2030 2035 2040
Share of electricity by source European Union 2017 -2040Source IEA 2018
2017
Today
Future system will be heavily reliant on non synchronous sources of electricity
Context
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Decarbonisation by 2050
Context
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EU-SysFlex Project Structure
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EU-SysFlex Project Structure
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WP3 deliverables
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https://eu-sysflex.com/documents/
Webinar Task 3.4 16/06/2020
Speakers
• VITO/EnergyVille, Belgium• Gwen Willeghems, researcher energy markets
• Imperial College London, United Kingdom• Danny Pudjianto, research fellow• Dimitrios Papadaskalopoulos, research fellow
• National Centre for Nuclear Research, Poland• Marcin Jakubek, researcher • Endika Urresti-Padrón, lead engineer and researcher• Michał Kłos, researcher
• University College Dublin, Ireland• Ciara O’Dwyer, senior power systems researcher
• KU Leuven/EnergyVille, Belgium• Erik Delarue, assistant professor• Arne van Stiphout, post-doctoral researcher & task leader
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TASK 3.4Introduction and overview
Arne van Stiphout
10Webinar Task 3.4 16/06/2020
Task 3.4 in WP3
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Task 3.4 objective
Complement T3.2’s conceptual market designs by
advanced power system & market modelling
considering both the short-term (operational)
and the long-term (investment) impacts
on the pan-European power system
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Task 3.4 approach
• Highly renewable EU28 power system• “Energy Transition” (2030) 38% RE
• “Renewable Ambition” (2050) 48% RE
• Operational timeframe• Seconds, minutes, hours (+ LT effects)
• Power system reliability
• Advanced models• UC/ED, game-theoretic, agent-based, etc.
• Benchmark ideal, fully integrated market /regulatory setting with settings in whichdesign limitations are imposed
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0
200
400
600
800
1,000
1,200
1,400
ET-2030 RA-2050
Other RE
Solar
Wind
Hydro
Biomass
Other Th
Oil
Gas
Solids
Nuclear
[GW]
Task 3.4 focus
• Market and regulatory design• Integrated markets (simultaneous) vs. sequential markets
• Bias vs. neutrality in the ability for (new) technologies to participate
• Clearing frequency, temporal resolution & lead time for system services
Related work: Chapter 4, Chapter 5, Chapter 6, Chapter 9
• Market behavior• Market participant decision making in multi-service markets
• Potential for strategic behavior in system service provision
Related work: Chapter 9, Chapter 11
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Task 3.4 focus
• Geographical aspects• Locality of system services (incl. TSO-DSO coordination)
• Interconnections and cross-border exchange of flexibility products
• Cross-border coordination and congestion management
Related work: Chapter 3, Chapter 6, Chapter 7
• Investment effects• Long-term investment signals of short-term system services
• Cost/benefit analysis of cross-border coordination and investment
Related work: Chapter 8, Chapter 10
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Task 3.4 output
• Chapter 3: Enhancing TSO-DSO integration to facilitate market access fordistributed energy resources
• Chapter 4: Interdependence of energy and reserve markets in high-RES systems
• Chapter 5: On the temporal granularity of joint energy-reserve markets in high-RES systems
• Chapter 6: Benefits of regional coordination in balancing capacity markets in futureEuropean power systems
• Chapter 7: Pre-selection of the optimal siting of phase-shifting transformers based on anoptimization problem solved within a coordinated cross-border congestionmanagement process
• Chapter 8: Defining TSO’s investment shares for PSTs used for coordinated redispatch
• Chapter 9: Increasing technology neutrality in service markets in power systems withhigh RES shares
• Chapter 10: Analysis of long-term investment signals provided by ancillary services markets
• Chapter 11: Impacts of flexibility and unit commitment characteristics on market power effects
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ENERGY & RESERVE MARKET INTEGRATION
Chapter 4 & Chapter 6
Arne van Stiphout
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Context
• Operational context• Within border integration
• Scheduling of energy and reserves are interdependent
• Cross-border integration
• Reduced reserve requirements (spatial smoothing)
• More efficient reserve allocation (spatial arbitrage)
• Increased opportunities for imbalance netting and reserve exchange in RT
• Institutional context• COMMISSION REGULATION (EU) 2017/2195 establishing a guideline of
electricity balancing (EBGL)
• All TSO’s proposal for a methodology for co-optimized allocation process of cross-zonal capacity for the exchange or sharing of reserves in accordance with Article 40 of the EBGL
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Within border integration
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ReserveSizing
EnergyDay-Ahead
ReserveAllocation
EnergyIntra-Day
ReserveActivation
Sequential
Joint
BeforeDay-Ahead
Day-Ahead Intra-Day Real Time
Chapter 4: Interdependence of energy and reserve markets in high-RES systems
Cross-border integration
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Historically Ongoing Planning stage Not yet planned
Sizing Uncoordinated Uncoordinated Uncoordinated Coordinated
Procurement* Uncoordinated Uncoordinated Coordinated Coordinated
Activation Uncoordinated Coordinated Coordinated Coordinated
Chapter 6: Benefits of regional coordination in balancing capacity markets in future European power systems
• IGCC• MARI• TERRE• PICASSO
• EBGL 2017• All TSO’s
proposal 2019
• Exception: Nordics
• Some bilateral reserve sharing (e.g., BE-FR)
“exchange” “sharing”*Need to allocate cross-zonal capacity!
Methodology
• Full UC/ED model (MUT/MDT, ramping, etc.)• Activation phase (base/medium UC fixed; base ED fixed)
• Sequential vs. joint day-ahead market clearing• DA-Energy does not anticipate DA-Reserve
• Base and medium units cannot start-up DAE>DAR
• Reserve allocation costs for load and RES• Cost for potential load curtailment (upward)
• Cost for potential RES curtailment (downward)
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EnergyDay-Ahead
ReserveAllocation
Day-Ahead
Case study
• Focus on CWE
• Full year in 15’ (365 x daily optimization)
• Trade-based network constraints• One node per zone
• Scenarios• Current System (2018)
• Energy Transition (2030)
• Renewable Ambition (2050)
• Additional information• 2018 time series load & RES
• Generation units
• NTCs from ENTSO-E’s TYNDP (2030)
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Results
1. Additional cost of sequentialenergy-reserve markets increases(more than linearly) withincreasing vRES
2. This increase in cost for reservescan be largely offset by furtheropening reserve marketsto renewables and load
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Conclusions
• Market integration can reduce cost of ensuring reliability• Within border integration (sequential > joint markets)
• Cross-border integration (requires harmonization!)
• Alternative providers to play an increasing role in reserve provision• Can partially offset cost increase of reliability in high-RES systems
• Act sooner rather than later
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TEMPORAL GRANULARITYOF ENERGY-RESERVE MARKETS
Chapter 5
Erik Delarue
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Research question
• Benefits of reducing the temporal granularity of reserve markets?• Frequency of reserve sizing and allocation (unit level)
• Resolution (block length) of reserve sizing and allocation
• Taking into account wind power uncertainty
• Impact in terms of• Total operating system cost
• Scarcity on the reserve markets (as a measure of liquidity)
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Reservesizing
EnergyDay-Ahead
Reserveallocation(unit level)
EnergyIntra-Day
Reserveactivation
Reserveprocurement
(portfolio)
Joint clearing
Research question
1. Reserve sizing frequency (RSF)
2. Reserve sizing resolution (RSR)
3. Reserve procurement frequency (RPF)
4. Reserve procurement contract duration (RPCD)
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Methodology & Case study
• Model• Mixed-integer UC/ED model
• Rolling horizon optimization
• Frequency and resolution of reservesizing and allocation can be adjusted
• Case study• Focus on Belgium
• Full year in 15’ (365 x daily optimization)
• EU-SysFlex Scenarios
• Energy Transition (2030)
• Renewable Ambition (2050)
• Additional information
• 2018 time series load & RES
• Generation units
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Methodology: reserve allocation & activation
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Start of look-ahead period
Joint energy-reserve market clearing/procurement frequency
ED evaluation represents balancing
• Historical wind power forecast data for Belgium from January 2016 to December 2018
• Forecast errors sorted by measured power and forecast lead time
Wind powerforecast updates
Methodology & Case study
• Different temporal parameter sets between 2 “extremes”
• Reserve sizing frequency (RSF) > How regularly are reserves sized?
• Reserve sizing resolution (RSR) > How long are the sizing blocks?
• Reserve procurement frequency (RPF) > How regularly are reserves procured?
• Reserve procurement contract duration (RPCD) > What is the reserve product resolution?
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Results: Impact on total operating cost
• Total cost-efficiency gains of 1.8% (ET) and 1.5% (RA)• Largest cost savings more frequent reserve sizing (RSF)
• Main driver: reduced wind power uncertainty (with shorter forecast lead times)
• Procurement and activation of less reserve capacity
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Energy Transition Renewable Ambition
Conclusions
• RSF + RSR. More frequent reserve sizing with a higher reserve resolution resulted in total cost-savings of 0.75-1.10%
• RPF + RPCD. Reducing the reserve procurement contract duration and procuring more frequently yielded cost savings of 0.75%• Also facilitates the integration of vRES in reserve markets
• Implementation. More frequent sizing and procurement could pose challenges related to market operation• Debatable whether they weigh up to the considerable benefits
• Policy implication. Implement shorter term, higher resolutionreserve markets• Supports recommendations put forward in Article 32 of Commission
Regulation (EU) 2017/2195: procurement on a short-term basis to the extent possible and where economically efficient
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INCREASING TECHNOLOGY-NEUTRALITY
IN SERVICE MARKETS IN POWER SYSTEMS
WITH HIGH RES SHARESChapter 9
Gwen Willeghems, Hanspeter Höschle, Yuting Mou, Carlo MannaVITO/EnergyVille
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Context
• High shares of RES
• Service markets: maintain frequency stability
• Technology-neutrality: participation of RES in service markets
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• Focus on market design and behaviour
Installed Capacity
Technical potential
Economic & Market potential
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Methodology: EnergyVille Market Simulator
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Dynamic event calendar
Bid generator
Methodology: EnergyVille Market Simulator
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Methodology: EnergyVille Market Simulator
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Bid generator
Methodology: EnergyVille Market Simulator
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Dynamic event calendar
Case study
• Shorten the mFRR procurement cycle →impact on RES participation in mFRR and DAM
• Geographical area: Belgium
• Stylized versions of the Belgian DAM and mFRR contracting
• Frequency of mFRR procurement: daily, weekly, and monthly
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Results: Technologies perspectiveRenewable Ambition – high mFRR down demand
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Results: Technologies perspectiveRenewable Ambition – high mFRR down demand
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Results: Technologies perspectiveRenewable Ambition – high mFRR down demand
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Results: Technologies perspectiveRenewable Ambition – high mFRR down demand
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Results: Histogram of weighted average pricesRenewable Ambition – low mFRR demand
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Conclusions
• From technical potential to market potential: Product and market design should allow translating technical potential into market potential and economic value
• Variable RES require substantial changes to the market design (intermediate solutions are not sufficient)
• Obstacles for delivering technical potential to a serviceQuestion to ask: why technologies cannot or do not want to offer?
• Future work: ➢Daily procurement in blocks of 1 or 2 hours
➢Costs associated with participation in service markets due to loss of green energy certificates
• Market simulator allows for more insights on how specific market and product characteristics drive market participant behaviour
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ENHANCING TSO-DSO INTEGRATION TO FACILITATE
MARKET ACCESS FOR DISTRIBUTED ENERGY
RESOURCESChapter 3
Danny Pudjianto, Goran Strbac
Contact: [email protected]
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Flexibility: focus on local or national level operation and infrastructure management ?
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Increase in resources of flexibility at the local level - this needs a stronger planning and control coordination between national and local infrastructure management objectives i.e. a whole-system approach is required, to manage the synergies and conflicts across different applications/objectives.
Optimal solution:
Whole-System approach
National
services
Local
services
* The reference system was without distributed flexibility.
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Topic 1: Enhancing TSO-DSO integration to facilitate DER access to energy and ancillary service markets
• Objective:❑To demonstrate and analyse the performance of different market-based TSO-
DSO integration approaches (centralised [whole-system] and decentralised markets via VPP concept)
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Potential future model using TSO-DSO interface
Centralised market
Decentralised markets- Local (DSO as an
aggregator [VPP]) and national markets
Large-scale SCOPF (coordinated energy and ancillary services) for the whole-system
Multi-stage optimisation:- Aggregation (VPP) for distribution- SCOPF for transmission
1. VPP characteristics 2. Application of VPP to solve
transmission problems with focus on the balancing and flow management of transmission;
3. Impact of distribution network control optimization
4. Performance of the decentralised compared to centralised approaches and identify potential synergy and conflicts between DSO and TSO’s actions (balancing local and national objectives).
Study
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Decentralised and multi-stage energy and ancillary service markets
WP 3 Workshop, 21-22 May 2019, Paris49
Transmission model
Virtual Power Plant
National energy and ancillary service markets
TSO
Local energy and ancillary service markets
DSOZ
DSOA
Large generators
/storage
DER
Aggregator DER
DER
Aggregator DER
Interconnectors
Distribution models
Local energy and ancillary service markets
Virtual Power Plant Approach
• Characteristics of VPP• PQ capability curve
• Reserves
• Cost function
• Merit order dispatch
• Factors affecting the VPP characteristics• Demand
• DG availability
• Local constraints
• Faults
• Network control settings
• Local market power assessment
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Analysis
INPUT DATA OUTPUT DATA
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Impact of optimising transformers and reactive compensator
Smart distribution controlNon-smart distribution control• Voltage optimisation by
adjusting transformer settings and the use of distributed reactive compensator enhance VPP’s PQ capability.
• The role of DSO to optimise local network devices is important.
• Commercial framework to reward active use of distribution network to support transmission needed
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Transmission services from DER
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- The value of reactive power servicesis location-specific.
- Increased availability of DER servicesis beneficial for the TSO since itprovides alternative solutions andDER may be spread in the locationswhere the services are needed.
- Improve market competition forproviding the services which willeventually reduce the cost.
Key findings: comparison between incremental and whole-system approach
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Incremental approach Whole-system approach
Practical approach as the problems are decomposed to
less complex problems and solved incrementally.
More complex and computationally intensive
It requires more market and control procedures and
therefore, tends to require more time for operational
planning. This increases the risk as the system changes
dynamically. Operating near real-time (15 min – 1 h) can
reduce the uncertainty.
A simpler process but the system is much
complex.
Maybe suboptimal but the use of smart control may
provide “corrective” actions.
Optimal from the system perspective but not
necessarily optimal from TSO or DSO’s individual
perspective
It can trigger conflicts i.e.
- Active constraints in the transmission system or
- access restriction to the DER capacity resources
which incurs a higher cost to the other party
Maximise the synergy and access of DER capacity
to both transmission and distribution services
The cost of using DER can be allocated more easily since
the volume needed by each party is clearly identified.
Cost allocation between transmission services
and DSO services requires decomposition of the
benefits (more complex)
CROSS-BORDER
COORDINATIONChapter 7 & Chapter 8
WP3 Fast track workshop - 21/03/2018 54
Endika Urresti Padrón – [email protected] Jakubek – [email protected]ł Kłos – [email protected]
Cross border congestion management
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Cross-border coordination of Remedial Actions
-50MW
50MW
-100MW
100MW
Current procedure – Manually Coordinated RA Near future – Optimized Coordinated RA
Cost sharing framework
PST investments – cross border congestion management
• PST actions are quite effective to relieve congestions at the borders
• Traditionally, the TSOs investments are focused on the redispatch cost reduction at the TSO level (national level)
• In the future, the TSO should think broader as system-wide (coordinated RA paradigm)
? ?
??
?
$$$
$$
Where should we build the new PST investments to solve the cross-border
problems?
Who should pay for the PST investments?
“Pre-selection of the optimal siting of phase shifting
transformers based on an optimization problem solved within a
coordinated cross-border congestion management process”
“Defining TSOs’ investment shares for PSTs
used for coordinated redispatch”
Pre-selection of the optimal siting of PSTs
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Where should we build the new PST investments to solve the cross-border problems?
??
??
?
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Pre-selection of the optimal siting of PSTsHow current PSTs work
Total cost [€] Total volume of redispatching [MW]
With the use of (current) PSTs 12 579 3 573
Without the use of PSTs 134 023 16 435
Congestions on CBCOs perborders
(The width of the orange line isproportional to the averagecongestion severity)
Results of Cross-Border Congestion Management tool:
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Pre-selection of the optimal siting of PSTs as a decision support methodology
• thousands of branches in the system, each can be a candidate for potential PST location
• Traditional methods: computationally demanding (verification the investment potential on expanded grid with new market solution)
Aim of the pre-selection method:
• Limit the list of candidates for PST location using the current gridstate, current market and Cross-Border Congestion Management solutions
• Sort-list of candidates from pre-selection: input to more computationally demanding method(s)
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Pre-selection of the optimal siting of PSTsTwo pre-selection methods
Marginal influence of phase shift over (any) branch on RD costs
Multiplier Indicator (MI)
Sensitivity of power flows over congested lines to change in phase shift over (any) branch
Congestion Factor (CF)
Dual variable in Congestion Management problem, associated with constraint on constant phase
shift over the branch
PSDF (Phase Shift Distribution Factor) x congestion
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Pre-selection of the optimal siting of PSTs Results: locations with top MI and CF values
Multiplier Indicator (MI) Congestion Factor (CF)
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Pre-selection of the optimal siting of PSTs Results: locations with top MI and CF values
Multiplier Indicator (MI)
Congestion Factor (CF)
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Pre-selection of the optimal siting of PSTs Selecting the best candidate location
Multiplier Indicator (MI) Congestion Factor (MI)
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Pre-selection of the optimal siting of PSTs Results: Congestion Management costs with a new PSTs
Total cost [€] Total volume of redispatching [MW]
With existing PSTs 12 579 3 573
With existing PSTs + best candidate 394 197
The lowest Congestion Management cost across the list of 13 candidate locations
with top MI & CF values
Investment shares – motivation
• Investment shares are derived from the calculation of zonal savings possible due to installation of new PST devices
• Savings are assessed according to new approach developed in EU – cost sharing of remedial actions
• The intention is to introduce „polluter pays” principle and differenciate between flow types of unequal priority
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$?$
$
$$
Who should pay for the PST investments?
Investment shares – introduction
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Which network elementsgenerate costs?
Who is responsible for power flow components?
What is the zonal share for each congested element?
What is the optimalsolution? What is the cost?
How to divide the cost intonetwork elements?
If combined, who pays and how much?
cost-related pathcauser-related path
Who is a beneficiary (whosaves and how much)?
Supplementary questions:
implemented within SysFlex
conceptualized within SysFlex
Investment shares – case study
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scenarios reference
(R)
new PST:
DE-AT (DA)
new PST:
PL-CZ (PC)cost of redispatch
[EUR]12 579 394 9 407
savings compared
to R [EUR]- 12 185 3 172
Two locations of PSTs are considered:• German-Austrian (DA)• Polish-Czech (PC)
Both locations bring savings. Zonal savings indicate investment shares.
𝑠𝑧𝑏𝑒𝑓𝑜𝑟𝑒→𝑎𝑓𝑡𝑒𝑟
=𝐶𝑧𝑏𝑒𝑓𝑜𝑟𝑒
− 𝐶𝑧𝑎𝑓𝑡𝑒𝑟
σ𝑧(𝐶𝑧𝑏𝑒𝑓𝑜𝑟𝑒
− 𝐶𝑧𝑎𝑓𝑡𝑒𝑟
).
investment share of zone 𝑧
zonal savings on RA due to the investment
sum of the zonalsavings
Investment shares – results
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Which network elements generate costs?
What is the zonal share for each congested element?
Zonal savings
Zonal investment shares
cost-related path
causer-related path
Investment shares – conclusions
• New investment sharing key developed for new reality of optimized and globally coordinated remedial actions
• Flow decomposition shows that the beneficiary zones can be well distanced from the location of new investment
• The method can be used not only for the PSTs, but any other network elements of inter-zonal impact.
• The method is independent from the ultimate solution for cost-sharing – any concept can be adopted
• The method is based on the concept of cost-sharing (CACM art. 74) and extends its utilization
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Analysis of long-term investment signals provided by ancillary services markets
University College Dublin
Damian Flynn
Ciara O’Dwyer
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Introduction
• … new system services for large-scale RES integration
• Impact of flexibility service requirements on long-term power system investments?
• Appropriate flexibility and complexity details for investment models?
• Impact on operating costs and curtailment?
• Test system: Ireland, based on EirGrid’s Steady Evolution and Low Carbon Living scenarios (Network Sensitivities 1 &3 from Work Package 2)
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Backbone
*
* Common input data can be used for both an investment model and a scheduling model
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• Unit Constraints• Min. gen. levels and min.
uptime and shut down hours
• Inertial floor of 17.5 GWs enforced
Methodology• Investment options
• OCGT / CCGT / grid-scale batteries
• Representative weeks selected
• Renewables (wind, PV, biomass, hydro) pre-selected
• Objective Function• Fuel and carbon costs, start-up costs and variable O&M
• Investment model considers capital equipment costs
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Steady Evolution (~ 50% variable renewables)
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Costs? Competitors? Requirements?
Steady Evolution (~ 50% variable renewables)
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Costs? Competitors? Requirements?
Steady Evolution (~ 50% variable renewables)
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Costs? Competitors? Requirements?
Steady Evolution (~ 50% variable renewables)
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Low Carbon Living (> 60% variable renewables)
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Costs? Competitors? Requirements?
Low Carbon Living (> 60% variable renewables)
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Costs? Competitors? Requirements?
Low Carbon Living (> 60% variable renewables)
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Conclusions
• Operational detail in investment models• Temporal resolution & selection of representative periods
• Inclusion of reserves (system specific)
• Clear long-term signals for investors
• Sub-optimal investments• Increased operating costs, RES curtailment and CO2 emissions
• Insufficient flexibility at certain times
• Financial gaps for investors
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Impacts of flexibility and unit
commitment characteristics on
market power effects
Dimitrios Papadaskalopoulos, Yujian Ye, Goran Strbac
Control and Power group, Imperial College London
EU SYSFLEX: Webinar for Task 3.4
16/06/2020
Motivation: Market power effects
• Deregulated electricity sector: introduction of market competition
• Imperfect competition: European markets are still characterized by a small
number of large players, exhibiting strategic behavior and exercising market
power > increased price levels and social welfare loss
• Need for fundamental change in employed market models:
➢Moving away from centralized welfare-maximization models assuming
perfectly competitive behaviour…
➢…to models capturing the strategic, price-making behaviour of multiple
self-interested players (maximizing profit) and identifying the market
outcomes emerging from their interactions
• Important limitations of previous work in the SYSFLEX context:
➢Capturing time-coupling operation characteristics of market participants >
impact of flexible demand and energy storage on market efficiency?
➢Capturing the non-convex unit commitment (UC) characteristics of the
generation side > market power effects under complex bidding?
Lower Level (LL) problem:
Market clearing process
Max Social welfare
subject to:
• System constraints
• Individual players’ constraints
Upper Level (UL) problem:
Bidding decisions of strategic player
Max Profit of strategic player
subject to:
• Strategic player’s constraints
Prices/dispatch Strategic action
MPEC problem:
Bidding decisions of strategic player
Max Profit of strategic player
subject to:
• Strategic player’s constraints
• LL-equivalent KKT optimality
conditions
Lower Level (LL) problem:
Market clearing process
Max Social welfare
subject to:
• System constraints
• Individual players’ constraints
Bi-level problem:
Possible only if LL problem is continuous
and convex > neglect physical
characteristics associated with binary UC
decisions (fixed / start-up / shut-down
costs, minimum-up / down times)
Multi-period equilibrium programming model (MPEPM) :
Individual player’s decision making
Proposed analytical approach to address fundamental limitation
Need to minimize duality gap
Y. Ye, D.
Papadaskalopoulos, J.
Kazempour and G.
Strbac, “Incorporating
Non-Convex Operating
Characteristics into Bi-
Level Optimization
Electricity Market Models,”
IEEE Transactions on
Power Systems, 2019.
Relaxation of
UC variables
1
2
4
5
6
7
10
13
3
8
11
14
9
12
1516
Scotland
England
• Pool-based, joint energy and reserve
market with complex bidding
• Day-ahead horizon / hourly steps
• Network: DC power flow model
• Producers: strategic behavior
expressed through economic
withholding variable
• Flexible demand: energy-neutral
time-shifting flexibility
• Energy storage: strategic behavior
expressed through capacity
withholding variable
• Different scenarios examined
regarding the congestion of the
network and the location of flexible
demand and energy storage
Test system and main assumptions
North: low cost
generation and
small demand
South: high cost generation and
large demand
Impact of demand flexibility (α)
Without network congestion
15
20
25
30
35
40
45
50
55
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Syst
em d
eman
d (
GW
)
Time (h)
α = 0% α = 2% α = 4% α = 6% α = 8% α = 10%
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Mar
ket
pri
ce i
ncr
ease
(£/M
Wh)
Time (h)
α = 0% α = 2% α = 4% α = 6% α = 8% α = 10%
With network congestion (α=10%)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
U U-DS-
SC
U-DS-
EN
U-DS-
SC&EN
C C-DS-
SC
C-DS-
EN
C-DS-
SC&EN
Gen
erat
ion p
rofi
t in
crea
se
Scotland England-12%
-10%
-8%
-6%
-4%
-2%
0%
U
U-DS-
SC
U-DS-
EN
U-DS-
SC&EN C
C-DS-
SC
C-DS-
EN
C-DS-
SC&EN
So
cial
wel
fare
lo
ss
(mil. £)Generation profit Demand profit Storage
profit
Social
welfareScotland England Scotland England
No storage
(uncongested)45.87 40.71 41.48 259.84 \ 387.90
No storage 37.52 56.09 51.72 226.48 \ 377.99
Distributed storage
(Scotland)35.73 59.89 56.18 222.36 1.17 383.90
Distributed storage
(England)44.76 32.83 42.86 263.87 1.35 386.45
Large-scale storage
(Scotland)36.86 57.67 53.55 224.16 1.50 380.22
Large-scale storage
(England)43.13 39.98 49.06 244.97 1.81 383.64
Impact of energy storage (9.5GW, 19GWh)
Value of factoring UC characteristics in strategic bidding
State-of-the-art
(UC-agnostic)
Proposed
(UC-inclusive)
Optimal
(enumeration)
Bidding decision 1.1318 1.2305 1.2368
Profit (£) 1,484,327 1,627,065 1,662,579
State-of-the-art
(UC-agnostic)
Proposed
(UC-inclusive)
Optimal
(enumeration)
Bidding decision 1.1904 1.1226 1.1147
Profit (£) 412,241 455,145 455,668
Producer 4
Producer 5
Conclusions
• Market power effects are time-dependent and location-dependent (in cases
with network congestion)
• Ability to capture UC characteristics in market clearing leads to more
profitable bidding decisions for strategic players and reveals new forms of
strategic behavior > need for regulatory attention
• Flexible demand and energy storage reduce the extent of market power
exercised by large producers, despite the fact that the overall energy
consumption is not reduced
• Under network congestion, flexible demand and energy storage deteriorates
the market power potential of local producers and improves the market
outcome for the local consumers > higher market efficiency benefits when
located in areas with more expensive generation and higher demand
• Large energy storage owners can exercise market power to their own
benefit, creating a synergy with co-located producers
TASK 3.4Common conclusions
Arne van Stiphout
Webinar Task 3.4- 16/06/2020
1. Innovative market and product designs
Innovative designs can facilitate renewable integration
• Close to real time system service markets• Reduces flexibility need: better forecasts, knowledge of networks, etc.
• Reduces flexibility cost: conventional resources + alternative resources
• New technologies are key flexibility enablers and providers• Enablers: smart tech. to coordinate use of distributed resources
• Providers: variable renewables, demand response, storage
• Coordination, vertical and horizontal, is crucial• TSO-DSO: manage conflicts local vs. national optimization priorities
• TSO-TSO: reserve exchange/sharing, congestion management
Webinar Task 3.4- 16/06/202093
2. Design and implementation challenges
• Dealing with more complex market operations• More dynamic, more (types of) participants, more service integration
• Cross-border integration: similar challenges + harmonization
• Figuring out how to share costs and benefits• Operation: vertical (TSO-DSO) and horizontal (TSO-TSO)
• Investment: “shared” assets, investments in other control areas
• Addressing new potential market power effects• Demand response and storage can decrease market power effects
• Accounting for technical constraints in market power models
• Providing sufficiently stable investment signals• Delivering adequate remuneration and considering market saturation
Webinar Task 3.4- 16/06/202094
3. Act sooner rather than later
Impacts increase as renewable shares increase
• Partial design improvements are worthwhile• Moving to real-time: weekly > daily > hourly > quarterly
• Market integration: services (e.g., day-ahead mFRR and energy),cross-border (exchange vs. sharing of reserves)
• The importance of preparing for the mid-term• Long-term: energy market signals could drive flexibility investment
• Mid-term: potentially insufficient returns vs. increased flexibility need
Webinar Task 3.4- 16/06/202095
This project has received funding from the European Union’s Horizon 2020 research andinnovation program under grant agreement No 773505.
Thank You!
Questions?
ANNEX
Tuning penalty constant W
Producer 4
Producer 5
W Bidding decision DG DG_min UL profit Real profit
1 1.1836 238,587 209,676 1,819,014 1,477,990
10 1.2139 187,686 175,442 1,766,701 1,513,320
100 1.2214 153,115 144,338 1,673,052 1,588,290
10,000 1.2305 129,270 129,270 1,626,409 1,627,065
100,000 1.6400 87,514 87,514 630,992 630,636
1,000,000 1.7110 73,080 73,080 435,956 436,991
W Bidding decision DG DG_min UL profit Real profit
1 1.0877 138,976 122,135 539,353 380,443
10 1.2154 95,617 89,379 493,397 390,240
100 1.1704 86,822 81,845 469,792 425,373
10,000 1.1226 74,840 74,840 451,269 455,145
100,000 1.2030 42,293 42,293 419,362 427,683
1,000,000 1.2879 28,311 28,311 335,921 334,065
Validating accuracy of proposed approach
(in terms of market clearing solution)
Comparison between:
a) Market clearing solution of proposed strategic bidding model
b) Market clearing solution of original UC problem with bids given
by proposed model
Strategy of
player 1
Initialisation (r = 0)
Player 1: MILP Player 2: MILP Player N: MILP
Strategy of
player 2
……
Strategy of
player N
Strategies
converged ?
Yes
No
Store as NE
r = r + 1
Multi-period equilibrium programming model (MPEPM):
Finding Nash Equilibria (NE)
New forms of strategic behaviour
t=1 t=2 t=3 t=4 t=5 t=6
Dispatch (MW) 650.4 650.4 650.4 650.4 650.4 650.4
Revenue (£) 26470.27 27187.62 27187.62 25752.93 25035.59 26470.27
Variable cost (£) 54980.05 54980.05 54980.05 54980.05 54980.05 54980.05
Other cost (£) 9900 9900 9900 9900 9900 9900
Profit £) -38409.8 -37692.4 -37692.4 -39127.1 -39844.5 -38409.8
Producer 5: Revealing actual fixed cost
Producer 5: Misreporting higher fixed cost > 5.24% profit increase
t=1 t=2 t=3 t=4 t=5 t=6
Dispatch (MW) 650.4 0 0 0 0 0
Revenue (£) 26470.27 0 0 0 0 0
Variable cost (£) 54980.05 0 0 0 0 0
Other cost (£) 9900 12000 0 0 0 0
Profit £) -38409.8 -12000 0 0 0 0