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

3

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

4 Webinar Task 3.4 16/06/2020

Decarbonisation by 2050

Context

5 Webinar Task 3.4 16/06/2020

EU-SysFlex Project Structure

6 Webinar Task 3.4 16/06/2020

EU-SysFlex Project Structure

7 Webinar Task 3.4 16/06/2020

WP3 deliverables

8

https://eu-sysflex.com/documents/


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

Webinar Task 3.4- 16/06/20209

TASK 3.4Introduction and overview

Arne van Stiphout

10Webinar Task 3.4 16/06/2020

Task 3.4 in WP3

Webinar Task 3.4- 16/06/202011

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

Webinar Task 3.4- 16/06/202012

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

Webinar Task 3.4- 16/06/202013

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

Webinar Task 3.4- 16/06/202014

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

Webinar Task 3.4- 16/06/202015

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

Webinar Task 3.4- 16/06/202016

ENERGY & RESERVE MARKET INTEGRATION

Chapter 4 & Chapter 6

Arne van Stiphout

17Webinar Task 3.4 16/06/2020

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

Webinar Task 3.4- 16/06/202018

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

25Webinar Task 3.4 16/06/2020

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

33Webinar Task 3.4 16/06/2020

Context

• High shares of RES

• Service markets: maintain frequency stability

• Technology-neutrality: participation of RES in service markets

34

• Focus on market design and behaviour

Installed Capacity

Technical potential

Economic & Market potential


Methodology: EnergyVille Market Simulator

35 Webinar Task 3.4- 16/06/2020

Dynamic event calendar

Bid generator


36 Webinar Task 3.4- 16/06/2020


37 Webinar Task 3.4- 16/06/2020

Bid generator


38 Webinar Task 3.4- 16/06/2020

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

39 Webinar Task 3.4 16/06/2020

Results: Technologies perspectiveRenewable Ambition – high mFRR down demand

40


41


42


43

Results: Histogram of weighted average pricesRenewable Ambition – low mFRR demand

44 Webinar Task 3.4- 16/06/2020

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

Webinar Task 3.4- 16/06/202045

[email protected]

mailto:[email protected]

ENHANCING TSO-DSO INTEGRATION TO FACILITATE

MARKET ACCESS FOR DISTRIBUTED ENERGY

RESOURCESChapter 3

Danny Pudjianto, Goran Strbac

Contact: [email protected]

46Webinar Task 3.4 16/06/2020

Flexibility: focus on local or national level operation and infrastructure management ?

47

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.


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)

48

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


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

50

Analysis

INPUT DATA OUTPUT DATA


51

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


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

Webinar Task 3.4- 16/06/202053

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

Webinar Task 3.4- 16/06/202055

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

58

Where should we build the new PST investments to solve the cross-border problems?

??

??

?

59

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:

60

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)

61

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

62

Pre-selection of the optimal siting of PSTs Results: locations with top MI and CF values

Multiplier Indicator (MI) Congestion Factor (CF)

63

Pre-selection of the optimal siting of PSTs Results: locations with top MI and CF values

Multiplier Indicator (MI)

Congestion Factor (CF)

64

Pre-selection of the optimal siting of PSTs Selecting the best candidate location

Multiplier Indicator (MI) Congestion Factor (MI)

65

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

Webinar Task 3.4- 16/06/202066

$?$

$

$$

Who should pay for the PST investments?

Investment shares – introduction

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67

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

Webinar Task 3.4- 16/06/20206868

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

Webinar Task 3.4- 16/06/202069

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

Webinar Task 3.4 16/06/202071

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)

Webinar Task 3.4 16/06/202072

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?


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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

Webinar Task 3.4 16/06/202082

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?

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

impact analysis of market and regulatory options … · speakers •vito/energyville, belgium...

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