Optimal Day-Ahead Bidding in the MIBEL's ... - heredia/files/EEM10_gnom.pdf · Introduction...

IntroductionOptimization Model

Case StudyConclusions

Optimal Day-Ahead Bidding in the MIBEL’s

Multimarket Energy Production System

C. Corchero, F.J. Heredia

Group of Numerical Optimization and Modelling - GNOM

Universitat Politecnica de Catalunya - UPC, Spain

http://gnom.upc.edu

Project DPI2008-02154, Ministry of Science and Innovation, Spain

June 25, 2010

C. Corchero, F.J. Heredia EEM10 - Madrid 1/22

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1 Introduction

Electricity Market

Physical Futures and Bilateral Contracts in the MIBEL

Market Sequence in the MIBEL

2 Optimization Model

Problem definition

Multistage stochastic program formulation

3 Case Study

Case Study characteristics

Results

4 Conclusions


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Electricity MarketPhysical Futures and Bilateral Contracts in the MIBELMarket Sequence in the MIBEL

Iberian Electricity Market: MIBEL

Medium / Long TermMedium / Long Term Day D-1Day D-1 Day DDay D

Bilateral Contracts

Derivatives MarketDay-Ahead

Market

Intradaily

Markets

Derivatives Market Bilateral Contracts Day-Ahead Market

Organized markets

Non organized markets

- Virtual Power Plants auctions (EPE)

- Distribution auctions (SD)

- International Capacity Interconnection auctions

- International Capacity Interconnection nomination

- National BC before the spot market

- International BC before the spot market

- National BC after the spot market

Physical Futures Contracts

Financial and Physical Settlement. Positions are

Day-Ahead Market


sent to OMEL’s Mercado Diario for physical delivery.

Financial Futures Contracts

OMIClear cash settles the differences between the

Spot Reference Price and the Final Settlement Price

Hourly action. The matching procedure takes place

24h before the delivery period.

Physical futures contracts are settled through a zero

price bid.

Organized markets











Day-Ahead Market









price bid.

Organized markets











Day-Ahead Market









price bid.


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Characteristics of Physical Futures and Bilateral Contracts

Base Load Futures Contract

A Base Load Futures Contract consists in a pair (LFC , λFC )

LFC : amount of energy (MWh) to be procured each interval of

the delivery period.

λFC : price of the contract (ce/MWh).

Bilateral Contracts

A Bilateral Contract consists in a pair (LBCt , λBC

t ) t ∈ T

LBCt : amount of energy (MWh) to be procured each interval t

of the delivery period.

λBCt : price of the contract (ce/MWh).


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Integration of the futures and bilateral contracts in the

DAM bid

The energies LFC and LBCt must be integrated in the MIBEL’s

DAM bid respecting the two following rules:

1 If generator i contributes with fitj MWh at period t to the

coverage of the FC j , then the energy fitj must be offered to

the pool for free (instrumental price bid).

2 If generator i contributes with bit MWh at period t to the

coverage of the pool of BCs, then the energy bit must be

excluded from the bid to the day-ahead market. Unit i can

offer its remaining production capacity P i − bit to the pool.


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Electricity Market Description

DAM

TechnicalRestrictions

AncillaryServices

IntradayMarkets

Real TimeManagement

Futurescontracts

Bilateral contracts

GenCoBids


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Characteristics of the sequence of markets in the MIBEL

Ancillary Services

Takes place after the DAM

The participants send bids to potentially increase or decrease

the matched energy of the matched units in the DAM.

If a bid is matched the unit must be available to increase or

decrease its generation level in a given interval.

Intraday Markets

It consists of 6 consecutive markets. It takes place just before

and during the delivery day.

It works exactly as the DAM, except the GenCo can

participate as a buying or selling agent.


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Integration of the market sequence in the DAM bid

The market sequence is integrated in the DAM bid model with the

following considerations:

1 A GenCo that participates in the Ancillary Services always bid

the AGC capacity of the unit and, the only decision to be

optimized is whether it participates or not.

2 In order to participate in the Ancillary Services the change in

the generation output of a unit between two successive

intervals must be within certain limits.

3 Just the first Intraday Market is considered.

4 In a specific interval, a unit can only participate as a selling or

buying agent, not both.


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Problem definitionMultistage stochastic program formulation

Problem definition

The objective of the study is to decide the:

DAM instrumental price bid

optimal economic dispatch of the physical futures and

bilateral contract among units

maximizing the expected market sequence profits

taking into account the commitments deriving from futures

contracts and bilateral contracts, the technical production

constraints and the stochasticity of the electricity prices.


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Variables

First stage variables:

qit ≥ 0: Instrumental price offer bid in the DAM

fitj ≥ 0: Scheduled energy for futures contract j

bit ≥ 0: Scheduled energy for bilateral contracts

Second and third stage variables:

psit : Total generation

pM,sit : Matched energy in the DAM

r1sit , r2s

it , r3sit ∈ {0, 1}: Ancillary Services related variables

w sit ≥ 0, y s

it ≥ 0: Energy of the sell or purchase bid for the

Intraday Market

v sit ∈ {0, 1}: Intraday Market related binary variable


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Day-Ahead Market

DAM constraints

Matched energy bounded between the instrumental price offer and

the total available energy

pM,sit ≥ qit ∀i ∈ I ∀t ∈ T ∀s ∈ S

pM,sit ≤ P i − bit ∀i ∈ I ∀t ∈ T ∀s ∈ S

Limits to the instrumental price bid quantity

qit ≥ P i − bit ∀i ∈ I ∀t ∈ T

qit ≥ 0 ∀i ∈ I ∀t ∈ T

qit ≥∑

j |i∈Ujt

fitj ∀i ∈ I ∀t ∈ T


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Bilateral and Futures Contracts

Futures contracts constraints∑i∈Ujt

fitj = LFCj ∀j ∈ F ∀t ∈ T

Bilateral contracts constraints∑i∈I

bit = LBCt ∀t ∈ T

bit ≤ P i ∀i ∈ Ut ∀t ∈ T


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Market sequence (I/III)

Ancillary services constraints (I/II)

∆psit = ps

it − psi(t−1) ∀i ∈ I ∀t ∈ T \ {1} ∀s ∈ S

Ancillary services model:

Constant generation level: If ∆psit ∈ [−k, k]⇒ r1s

it = 1

Ramping up: If ∆psit ≥ k ⇒ r2s

it = 1

Ramping down: If ∆psit ≤ −k ⇒ r3s

it = 1


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Market sequence (II/III)

Ancillary services constraints (II/II)

(k −∆psit) ≥ MR(r1s

it − 1) ∀i ∈ I ∀t ∈ T ∀s ∈ S

(k + ∆psit) ≥ MR(r1s

it − 1) ∀i ∈ I ∀t ∈ T ∀s ∈ S

(∆psit − k) ≥ MR(r2s

it − 1) ∀i ∈ I ∀t ∈ T ∀s ∈ S

(∆psit + k) ≤ MR(1− r3s

it) ∀i ∈ I ∀t ∈ T ∀s ∈ S

r1sit + r2s

it + r3sit = 1 ∀i ∈ I ∀t ∈ T ∀s ∈ S


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Market sequence (III/III)

Intraday Market constraints

w sit ≤ M I v s

it ∀i ∈ I ∀t ∈ T ∀s ∈ S

y sit ≤ M I (1− v s

it) ∀i ∈ I ∀t ∈ T ∀s ∈ S

y sit ,w

sit ≥ 0 ∀i ∈ I ∀t ∈ T ∀s ∈ S


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Total generation and non-anticipativity constraints

Total generation constraints

psit = bit + pM,s

it − y sit + w s

it ∀t ∈ T ∀i ∈ Ut ∀s ∈ S

P i − gi r1sit ≤ ps

it ≤ P i − gi r1sit ∀t ∈ T ∀i ∈ Ut ∀s ∈ S

Non-anticipativity constraints

psit = ps

it ∀s, s : (λDs = λDs) ∀t ∈ T

r1sit = r1s

it ∀s, s : ((λDs , λRs) = (λDs , λRs)) ∀t ∈ T


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Objective function

maxp,q,f ,b

∑t∈T

∑i∈Ut

∑s∈S

Ps[λDs

t pM,sit + λRs

t r1sitgi+

+λIst y s

it − λIst w s

it − c li p

sit

]


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Case Study characteristicsResults

Case Study characteristics

Real data from the Spanish Market about the generation

company and the market prices.

10 thermal generation units from a Spanish GenCo with daily

bidding in the Spanish Market.

Market prices from January 1rst , 2008 to January 1rst , 2009.

Scenario set built from the reduction of available historical

data.

Model implemented and solved with AMPL/CPLEX


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Results: energy committed to futures contracts

2 4 6 8 10 12 14 16 18 20 22 240

100

200

Yea

rly c

ontr

act

(MW

h)

unit 1unit 2unit 3

2 4 6 8 10 12 14 16 18 20 22 240

100

200

Mon

thly

con

trac

t (M

Wh)

unit 2unit 4unit 5unit 6

2 4 6 8 10 12 14 16 18 20 22 240

100

200

Hour

Wee

kly

cont

ract

(M

Wh)

unit 3unit 4unit 7unit 8unit 9

(a)

Multimarket

2 4 6 8 10 12 14 16 18 20 22 240

100

200

Yea

rly c

ontr

act

(MW

h)

2 4 6 8 10 12 14 16 18 20 22 240

100

200

Mon

thly

con

trac

t (

MW

h)

2 4 6 8 10 12 14 16 18 20 22 240

100

200

Hour

Wee

kly

cont

ract

(M

Wh)

unit 3unit 4unit 7unit 8unit 9

unit 2unit 4unit 5unit 6

unit 1unit 2unit 3

(b)

DAM


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Results: bilateral contracts settlement

2 4 6 8 10 12 14 16 18 20 22 240

350

2 4 6 8 10 12 14 16 18 20 22 240

560

2 4 6 8 10 12 14 16 18 20 22 240

285

2 4 6 8 10 12 14 16 18 20 22 240

370

2 4 6 8 10 12 14 16 18 20 22 240

65

2 4 6 8 10 12 14 16 18 20 22 240

165

2 4 6 8 10 12 14 16 18 20 22 240

365

2 4 6 8 10 12 14 16 18 20 22 240

350

2 4 6 8 10 12 14 16 18 20 22 240

310

2 4 6 8 10 12 14 16 18 20 22 240

310

Hour

Unit 10

Unit 9

Unit 8

Unit 6

Unit 5

Unit 4

Unit 2

Unit 1

(a)

Unit 3

Unit 7

Energy(MWh)

Multimarket

2 4 6 8 10 12 14 16 18 20 22 240

350

2 4 6 8 10 12 14 16 18 20 22 240

560

2 4 6 8 10 12 14 16 18 20 22 240

285

2 4 6 8 10 12 14 16 18 20 22 240

370

2 4 6 8 10 12 14 16 18 20 22 240

65

2 4 6 8 10 12 14 16 18 20 22 240

165

2 4 6 8 10 12 14 16 18 20 22 240

365

2 4 6 8 10 12 14 16 18 20 22 240

350

2 4 6 8 10 12 14 16 18 20 22 240

3100

2 4 6 8 10 12 14 16 18 20 22 240

310 Unit 10

Unit 9

Unit 8

Unit 7

Unit 6

Unit 5

Unit 4

Unit 3

Unit 2

Unit 1

Hour

(b)

Energy(MWh)

DAM


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Conclusions

It has been developed a model for the optimal DAM bid with

Futures and Bilateral Contracts taking into account the

Ancillary Services and the first Intraday Market.

The optimal solution determines the optimal instrumental

price bidding and the optimal economic dispatch of the BCs

and the FCs.

The numerical experiments show how the Ancillary Services

and the Intraday Market affect both the optimal bid to the

DAM and the optimal allocation of the energy of the Bilateral

and Futures Contracts among the generation units.


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Optimal Day-Ahead Bidding in the MIBEL’s

Multimarket Energy Production System

C. Corchero, F.J. Heredia

Group of Numerical Optimization and Modelling - GNOM

Universitat Politecnica de Catalunya - UPC, Spain

http://gnom.upc.edu

Project DPI2008-02154, Ministry of Science and Innovation, Spain

June 25, 2010


gnom.upc.edu

Optimal Day-Ahead Bidding in the MIBEL's ... - heredia/files/EEM10_gnom.pdf · Introduction...

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