1 Dr. Keerati Chayakulkheeree Department of Electrical Engineering Faculty of Engineering Sripatum University Supervisor Assoc. Prof. Dr. Weerakorn Ongsakul

1

Dr. Keerati ChayakulkheereeDepartment of Electrical Engineering

Faculty of EngineeringSripatum University

SupervisorAssoc. Prof. Dr. Weerakorn Ongsakul

Energy Field of StudySchool of Environment Resources and Developments

Asian Institute of Technology

Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral Contract, Balancing Electricity

and Ancillary Services Markets

2

This work has been supported in part by the Energy Conservation Promotion Fund of Energy Policy and

Planning Office of Thailand under contract 029/2545.

3

International Journals:

1. W. Ongsakul and K. Chayakulkheeree, Constrained Optimal Power Dispatch for Electricity and Ancillary Services Auctions, Journal of Electric Power System Research, Vol. 66, pp. 193-204, 2003.

2. K. Chayakulkheeree and W. Ongsakul, Fuzzy Constrained Optimal Power Dispatch for Competitive Electricity and Ancillary Services Markets, Electric Power Components and Systems Journal, 33, 4, April 2005.

3. W. Ongsakul and K. Chayakulkheeree, Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral Contract, Balancing Electricity and Ancillary Services Markets, IEEE Transaction on Power System, vol. 21, no. 2, May, 2006, pp. 593-604.

4. W. Ongsakul and K. Chayakulkheeree. Coordinated Constrained Optimal Power Dispatch for Bilateral Contract and Balancing Electricity Markets, International Energy Journal. , vol. 6, no. 1, part 2, Special Issue: Ancillary Services, ATC and Transmission Pricing, Optimization and AI Application, Power System Analysis, Power System Monitoring and Control, Power System Operation, June 2005, pp.2-1 - 2-18.

4

International Conference Proceedings:

1. W. Ongsakul, S. Chirarattananon, and K. Chayakulkheeree, Optimal Real Power Dispatching Algorithm for Auction Based Dispatch Problems, Proceedings of International Conference on Power Systems (ICPS), CIGRE, China, Sept. 3-5, 2001, 434-440 .

2. W. Ongsakul and K. Chayakulkheeree, Optimal Spinning Reserve Identification in Competitive Electricity Market by Adaptive Neuro-fuzzy Inference System, Euro-PES2002, The International Association of Science and Technology for Development (IASTED), Greece, June 25-28, 2002, 119-124 .

3. W. Ongsakul and K. Chayakulkheeree, Fuzzy Constrained Optimal Power Dispatch for Competitive Electricity and Ancillary Services Markets, The International Power Engineering Conference (IPEC2003), Singapore, Nov 27-29, 2003, 1004-1009 .

4. W. Ongsakul and K. Chayakulkheeree. Coordinated Constrained Optimal Power Dispatch for Bilateral Contract and Balancing Electricity Markets, International conference on Electric Supply Industry in Transition Issue and Prospect for Asia, AIT, Thailand, Jan 2004, (18-14)–(18-31) .

5

International Journal

6

International Conference

7

Outline of the Research

IntroductionLiterature ReviewsCoordinated Constrained Optimal Power Dispatch for Bilateral Contract, Balancing Electricity and Ancillary Services MarketsCoordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral Contract, Balancing Electricity and Ancillary Services MarketsConclusion

Overview of the Presentation

8

Introduction

• Privatizations of the Thai power sector started in the early 1990s with the objective of improve efficiency, lower

electricity price, and tackle financial debts. • It was initially focused chiefly on the generation sector.

• The earlier recommended future structure of Thai ESI would follow the full competitive model.

MWh

MWh

GridCo GridCo TradersTraders

MWh

Contract$$$

Info

Spot$$$

Contract $$$

Info

RetailCosRetailCosSpot$$$

Spot $$$

GenCosGenCos

ConsumersConsumers

ISO/MOISO/MOPower Pool/ISO

SASA

SupplyCoSupplyCoRegulated Electricity Delivery Co

(REDCo )

Spot $$$

Optional: Contractor CfD $$$

DisCoDisCo

[EPPO]

9

• Nevertheless, there was a concern that the proposed fully competitive market could result in highly volatile pool price since

the bilateral contracts are a small fraction of the total power trading..

• Accordingly, the Energy Policy and Planning Office of Thailand (EPPO) had proposed to restructure the Thai electricity supply

industry using the New Electricity Supply Arrangement (NESA).

[EPPO]

ISO

GridCo

Bilateral Contracts

RetailCosDisCo

SupplyCo Consumers

Independent Regulatory B

ody

Bangkok andvicinity areas

DisCo

SupplyCo

Areas outsideBangkok and vicinity

PowerGen 1 PowerGen 1 PowerGen 1 Existing andnew IPPs

Introduction

10

• In NESA, PowerGens/IPPs and consumers arrange physical electrical energy transactions with each other based on their own financial interests in BCM.

• Instead of letting the ISO know the prices of their contracts, participants must report the quantities of their bilateral contracts to the ISO before their actual dispatch time.

• In BM, ISO receives hourly electricity offers from PowerGens/IPPs and demand bids from dispatchable load consumers.

• Under NESA, most of power purchase transactions are in the form of bilateral agreements whereas a small power exchange (PX) will be used as a system balancing

mechanism.

Introduction

11

• In addition to NESA, in this dissertation, both generator and consumer agree to submit curtailment bids for their bilateral contract in order to receive the financial compensation for congestion management.

• In BM, ISO receives hourly electricity offers from PowerGens/IPPs and demand bids from supply companies or dispatchable load consumers.

• The curtailment bids for dispatchable loads in BCM are submitted for point-to-point curtailment in which the loads can respond to the ISO dispatch instruction.

• On the other hand, the curtailment on the contract of non-dispatchable load is imposed only on the generation side and the load is supplied by BM.

Introduction

12

Introduction

• In ASM, the ancillary services offer prices and quantities are submitted by the PowerGens/IPPs. The selected ancillary services are AGC, TMSR, and TMOR. The AGC, TMSR, and TMOR are offered by the PowerGens/IPPs in $/MW and procured by ISO in hourly basis (Cheung et al., 2000; Rau, 1999).

• Moreover, the reactive power offer prices and quantities are submitted by the PowerGen/IPP. The reactive power is dispatched based on minimization of combined reactive power cost and cost of real power loss. The reactive power offer prices and quantities are offered by PowerGens/IPPs in $/MVAr.

13

The problem formulation include three unbundled markets;

Balancing Market (BM)

Bilateral Contract

Market (BCM)

Ancillary Services Market (ASM)

Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets

14

Motivation

• In the emerging deregulated power system, to alleviate network congestion in the bilateral contract market (BCM) when the supply in the balancing market (BM) are not enough, it may be necessary for the ISO to curtail some of the transactions for economical and security reasons.

• The crisp treatment of the constraints in the OPF problem could lead to over-conservative solutions.

• solving the ancillary services market (ASM) separately from the electricity market may not lead to the optimal social welfare since the ancillary services requirement are strongly related to electricity consumption.

• Moreover, consumers may not respond efficiently to the ancillary service prices if the spot prices, observed by consumers, include only marginal electricity price.

Introduction

15

Objective of the study

The main objective of the study is to develop an efficient optimal power dispatch algorithm for competitive electricity and ancillary services markets.

Introduction

16

• To simultaneously maximize the social welfare in electricity and ancillary services market and minimize the combined reactive power cost and cost of real power loss in ancillary services market subject to power balance, ancillary service requirements, and network constraints.

• To find the trade-off between operating reserves & network security and social welfare in competitive electricity and ancillary services markets by using mixed-integer fuzzy linear programming (MIFLP) and between the voltage security and combined reactive power cost and cost of real power loss in ancillary services market by using fuzzy linear programming (FLP).

• To propose electricity spot price including system marginal price and ancillary services marginal prices in order to send a sharper signal to consumers.

In particular, the objectives are as follow.

Introduction

17

Scope and limitation

• The proposed algorithm, developed in MATLAB m-file programming language requires the MATLAB optimization toolbox.

• Only single loading level is carried out for each test case and the ramp rate constraints are not considered in the study.

• The electricity offer price and quantity are monotonically increasing stair-case functions for both IEEE30 bus and Thai power 424 bus system. It is required that the PowerGens/IPPs submit the offered prices based on the typical plant incremental cost curves (Kumar and Sheblé, 1998; Huang and Zhao, 2000; Huang and Zhao, 1999; Barker Dunn and Rossi, 2001).

The scope and limitations of the study are as follows:

Introduction

18

• The ancillary services include only AGC, TMSR, and TMOR. The ancillary services which is usually procured by long-term contracts and allocated to the consumers proportionately to the MW consumed by each consumer.

• To test the proposed algorithm on the modified IEEE 30 bus test system, the ancillary services offer prices and quantities, reactive power offer prices and quantities, the demand bid price and quantities, and bilateral contract transactions of the modified IEEE 30 bus system are given.

• In the modified IEEE 30 bus system, the generator high capacity are given and the line 9-11 flow limit is reduced from the original IEEE 30 bus system.

Introduction


19

• The test Thai power 424 bus system is the reduced network of the Thai power system considering only EGAT 500 kV, 230 kV and 115 kV transmission system. The lower voltage transmission systems of PEA and MEA (115 kV, 69 kV and below) will be considered as lumped loads.

• In the simulation on Thai power 424 bus system, the generators offer prices and quantities are obtained by linearizing the fuel cost curve. Each generator fuel cost curve is linearized in to 5 segments of linear curve and used as the offer prices and quantities of the generator. In case the generator has bilateral contract with the consumer, the remaining amount of capacity will be used as the offer prices and quantities in BM.


Introduction

20

• The bilateral contract transactions, the ancillary services offer prices and quantities, and demand side bids of the Thai power 424 bus system are given.

• To illustrate the condition of Thai power 424 bus system under security line flow limit constraints, peak loading condition is used and three of four transmission lines from buses 51 to 52 sub-station are out of services.


Introduction

21

Literature Review

Optimal real power dispatch for electricity market

Optimal ancillary services and reactive power dispatch for ancillary services market

Optimal electricity spot pricing

Fuzzy constrained optimal real and reactive power dispatch

Optimal power dispatch for bilateral contract and balancing electricity markets.

22

Supply Cost Minimization - Without Demand Side Bidding (DSB)

- [Rau, 1999] - [Huang & Zhao 2000] - [Zhang et al, 2000] - [Cheung et al 2001]

Social Welfare Maximization - With DSB

- [David 1998] - [Weber 1999] - [Numnonda & Annakkage 1999] - [Kumar & Sheble 1998]

Literature Review

optimal real power dispatch for electricity market

23[David 1998][Weber 1999]

P

sd dPPSPD0

))()((

d

dd dP

PdBPD

)()(

s

ss dP

PdCPS

)()(

or maximize

where)( dPD

)( SPS

)()( sd PCPB

Maximize)( SPC

)()( Sd PCPB

)( dPBOffer and bid in quadratic form

Social Welfare Maximization in Electricity Market

Literature Review

24


NG

i

NS

jGijij

NTD

i

ND

jDijij

ii

PSPDSW1 11 1

Quantities (MW)Prices ($/MWh)

[Numnonda & Annakkage 1999][Kumar & Sheble 1998]

Offer and bid in linear form

Literature Review

25

Reference Objective Constraints Offer and BidOptimization Techniques

Kumar and Sheble (1998)

Maximize Generators Profits with the Fixed Forward Market

- Power balance- Ramp rate- Forward contract

Multi blocks bidding with DSB

Linear programming

David (1998) Maximize Social Welfare

- Power balance in bilateral contract, multilateral contract and real time market- Line flow limit

Quadratic bidding with demand elasticity

OPF

Weber (1999)Maximize SocialWelfare

- Power balanceQuadratic bidding with DSB

Newton method OPF

Rau (1999)Minimize TotalSystem Cost

- Power balance- Ancillary services requirement - AGC supply condition

Single block bidding without DSB

Mixed Integer Linear Programming(NAG software)

Numnonda & Annakkage (1999)

Maximize Social Welfare- Power balance- Line flow limit

Multi blocks bidding with DSB

Genetic Algorithm

Dekrajandpetch et al (1999)

Minimize TotalSystem Cost

- Power balanceLinear bidding without DSB

LaGrange Relaxation with Heuristic

Garng & Qing (2000)Minimize Total System Cost with Maximize theEnvironment Index

- Power balance- Ancillary services requirement- Environment index

Linear bidding without DSB

Genetic Algorithm

Cheung et al (2000)Minimize TotalSystem Cost

- Power balance- Ancillary services requirement

Multi blocks bidding without DSB

Linear Programming

Literature Review

Optimal real power dispatch for electricity market

26


Reference Process Offer Method

Rau (1999)

1. Electricity dispatch (single block bid without DSB)2. AGC, TMSR, TMOR dispatch with the AGC supply constraint.3. TMNSR

AGC, TMSR, TMOR, TMNSR are offered in terms of $/MWh

- Mixed integer Linear Programming (NAG – commercial software) - Fixed AS requirement

Garng and Qing (2000)

1. Electricity dispatch (linear bid without DSB)2. AGC, TMSR and TMOR dispatch(includes environment aspect)

AGC, TMSR, TMOR are offered in terms of $/MWh

- GA- Fixed AS requirement

Cheung et al (2000)

1. Electricity dispatch (multi block bid without DSB)2. AGC auction with the AGC supply constraint.3. TMSR with the remaining capacity4. TMOR with the remaining capacity5. TMNSR with the remaining capacity

- AGC is offered in term of $/h- TMSR, TMOR, TMNSR are offered in terms $/MWh- Loss opportunity cost payment

- Hybrid dispatch (LP)- Fixed AS requirement

Fynn et al (2001)1. Electricity dispatch (linear bid + start up cost without DSB)2. Spinning reserve dispatch

Spinning reserve in linear function + FOR

- Recurrent neuron network solve the augmented LaGrange- Fixed AS requirement

Literature Review

27

• The optimal reactive power dispatch was generally used to improve the voltage profile and minimize system loss (Deep and Shahidehpour, 1990; Abdul-Rahman and Shahidehpour, 1993; Tomsovic, 1992).

• However, in competitive electricity market, the reactive power was treated as one of the commodities in the ancillary services market and Some optimal reactive power scheduling in electricity market has been proposed (Bhattachaya and Zhong, 200; Gil et al., 2000; Dai et al., 2001).

• However, cost of real power loss due to reactive power dispatch and soft characteristics of bus voltage limits were not included.


Literature Review

28

Optimal electricity spot pricing

Literature Review

The spot pricing is an important tool in the implementation of deregulation. Schwepp et al (1987) proposed the original spot pricing concept.

The two important components in the spot price called incremental transmission loss and the transmission line constraint relaxation could be calculated with the power system sensitivity techniques (Wood and Wollenberg 1989).

Several methods have been proposed to obtain optimal electricity spot prices (Baughman et al, 1997; El-Keib & Ma, 1997; Gil et al, 2000; Xie et al, 2000) by including constraints terms into the spot price.

However, consumers may not respond efficiently to the ancillary service prices if the spot prices, observed by consumers, include only marginal electricity price.

29

Fuzzy constrained optimal real and reactive power dispatch

The fuzzy set theory is a natural and appropriate tool to represent inexact relation. Based on the fuzzy set theory, an OPF problem can be modified to include fuzzy constraints and fuzzy objective function.

Guan et al (1995) applied a fuzzy set method taking into account the fuzzy nature of the line flow constraints in OPF. Edwin Liu and Guan (1996) applied a fuzzy set method to efficiently model the fuzzy line flow limits and control action curtailment in OPF. However, the developed OPFs were applied to the centralized dispatch in the vertically integrated ESI structure.

The voltage control problem consisted of fuzzy voltage constraints (Tomsovic, 1992). The fuzzy voltage constraints have been applied to the real power loss minimization problem in (Abdul-Rahman and Shahideshpour, 1993; Tomsovic, 1992). However, the methods were aimed at the centralized voltage control in the vertically integrated ESI structure.

Literature Review

30

Optimal power dispatch for bilateral contract and balancing electricity markets

To consider the bilateral contract market (BCM) in the balancing electricity market (BM) dispatch,

David (1998) minimized total BM cost and total deviation of transaction from the contract subject to power balance and line flow limits. The electricity offers in BM were in quadratic functions and the demand elasticity were present.

Galiana and Illic (1998) and Fang and David (1999) proposed the optimal dispatch minimizing total deviation of transaction from the contract subject to power balance and line flow limit.

In (Kockar and Galiana, 2002), the price-based curtailment on BCM has been proposed. The objective was to minimize total BCM and BM cost subject to power balance, crisp line flow limit. In their model, quadratic electricity offer using the units heat rates was used in BM and linear bid was used for bilateral contract curtailment bid.

The coordination of TMSR with BCM and BM dispatch has been proposed by Wang et.al. (2002). The linear electricity offer in BM (Single block), linear demand side bid, and linear bilateral contract curtailment bid were used. The TMSR was offered in $/MWh.

Literature Review

31

•To alleviate network congestion in the bilateral contract market (BCM) when the supply in the balancing market (BM)

are not enough, it might be necessary for the ISO to curtail some of the transactions for economical and security reasons.

•Some curtailment strategies aim to minimize deviations from transaction requests made by market participants in bilateral and multilateral contract markets. [David1998][Galiana &

Illice 1999][Fang & David 1999]

•To coordinate the bilateral contract market with pool dispatch, the congestion was managed in the economical

manner using either BM or the bilateral contract curtailment bids. [Kockar & Galiana 2002][Wang et al 2002]

Literature Review

32

• To incorporate line limit constraint, an optimal dispatch problem can be formulated as an extended problem in the

optimal power flow (OPF) which involves the determination of the instantaneous optimal steady state of an electric power

system.• However, a serious drawback in OPF is the crisp treatment of

the constraints. Constraint limits are given fixed values that have to be met at all times. Crisp treatment of the constraints in the OPF problem usually leads to over-conservative solutions

[Guan et al 1995]. • From a practical point of view, an OPF does not need to find a

rigid minimum/maximum solution. Certain trade-off among objective function and constraints would be desirable than rigid

constraints. • Realistic OPF solutions require special attention to the

constraint enforcement and control action.

Literature Review

33

• The fuzzy set theory is a natural and appropriate tool to represent inexact relation. Based on the fuzzy set theory, an OPF problem can be modified to include fuzzy constraints

and fuzzy objective functions. • These developments have made it possible to overcome some

of the limitations of the conventional OPF [Guan et al 1995], [Edwin Liu and Guan 1996].

• However, the developed fuzzy constrained OPFs were applied to the centralized dispatch in the vertically integrated ESI

structure.

Literature Review

34

[Guan et al 1995][Edwin Liu and Guan 1996]

Line Flow

The fuzzy constraints in real power optimal dispatch

Proposed

Line Flow

Ancillary Services Requirement

Objective: Minimize Total Operating Cost

Objective: Maximize Social Welfare

It is quit obvious that linear membership function will not always be adequate for fuzzy constraints

representations. Quite often S-shaped membership functions have been suggested in the research field of fuzzy mathematical programming. [Zimmermann

1991], [Leberling 1981], [Werners 1984]

Literature Review

35

• The optimal reactive power dispatch is generally to improve the voltage profile and minimize system loss. However, in

competitive electricity market, the reactive power is usually one of the commodities in ancillary services market and the practical

voltage control problem consists of fuzzy voltage constraints. • Some optimal reactive power scheduling in electricity market has been proposed in [Bhattacharya and Zhong 2001], [Gil et al

2000], [Dai et al 2001]. • However, the practical voltage control problem consists of fuzzy voltage constraints [Tomsovic 1992]. Therefore, fuzzy optimization has been applied in the reactive power control

problem [Abdul-Rahman and Shahidehpour 1993], [Tomsovic 1992]. Nonetheless, the reactive power is not treated as an

ancillary service in [Abdul-Rahman and Shahidehpour 1993], [Tomsovic 1992].

Literature Review

36

[Abdul-Rahman and Shahidehpour 1993][Tomsovic 1992]

Proposed

Bus voltage magnitude

Objective: Minimize Total Reactive Power Cost

The fuzzy constraints in reactive power optimal dispatch

Objective: Minimize Total Real Power Loss

Bus voltage magnitude

Literature Review

37


Literature Review

Reference Objective Constraints OfferAncillaryServices

David (1998)

Minimize total RBM cost and Minimize total deviation of transaction from the contract

- Power balance - Crisp line flow limit

Quadratic electricity offer in RBM (model with demand elasticity)

None

Galiana and Illic (1998)

Minimize total deviation of transaction from the contract

- Power balance- Crisp line flow limit

None None

Fang and David (1999)

Minimize total deviation of transaction from the contract


None (willingness to pay to avoid deviation from contract)

None

38

Reference Objective Constraints OfferAncillaryServices

Kockar and Galiana (2002)

Minimize total BCM and BM cost


- Quadratic electricity offer in BM(ISO must known the unit actual heat rates)- Bilateral Contract Curtailment bid

None

Wang et.al. (2002)

Minimize total BCM and BM cost

- Power balance- Crisp line flow limit- TMSR requirements

- Linear electricity offer in BM (Single block)- Linear DSB - TMSR offered in $/MWh- Bilateral Contract Curtailment bid

- TMSR- Replacement reserve

Proposed Problem Formulation

Social welfare fuzzy maximization and combined reactive power cost and cost of real power loss fuzzy minimization

-Power balance-Fuzzy line flow-Fuzzy bus voltage-Fuzzy AGC, TMSR, TMOR requirements

-Incremental stair-case function electricity offer in BM-Decremented stair-case function DSB-TMSR, TMOR, AGC offered in $/MWh-Reactive power offered in $/MVArh-Bilateral Contract Curtailment bid

-TMSR-TMOR-AGC-Reactive power


Literature Review

39

Literature Review

The literature review indicates that there is growing interest to develop more suitable models for optimal real and reactive power dispatch for competitive electricity markets. Linear, non-linear optimization and artificial intelligent techniques has been applied to different models of electricity markets.

Many improvements on optimal power dispatch for electricity markets has been done by coordinating electricity with ancillary services.

However, most of the previous methods are based on the objective of minimizing total operating cost without demand side bids. In this dissertation, demand side bidding is included by using the objective of social welfare maximization in the coordinated constrained optimal power dispatch for BCM, BM and ASM.

Conclusion

40

Several optimal spot pricing were proposed by including constraints terms. However, the marginal prices of ancillary services were not considered.

This research proposes a sharper spot price signal including marginal electricity and marginal ancillary services prices and additional reactive power spot price.

It has also shown that the fuzzy constrained OPF can provide a better solution than that of crisp constrained OPF. However, the fuzzy constrained OPFs were applied to the centralized dispatch in the vertically integrated ESI structure and merely either the line flow and transformer loading limits or bus voltage limits were treated as fuzzy constraints.

In this research, the fuzzy constrained optimal power dispatch is used to maximize the social welfare in electricity market and minimize combined reactive power cost and cost of real power loss in the electricity market by treating the AGC, spinning and operating reserves requirements, line flow and transformer loading limits, and voltage limits as fuzzy constraints.

Literature Review

41

Line FlowFuzzy Constraints

Capable for AGC

minGiP

Manual operation

Manual operation

0

maxGiP

lowiAGCP ,

highiAGCP ,

Bilateral Contract Amount Offer price and quantity in BM

Balancing Market

Bilateral Contract Market

Ancillary Services Market

Coordinated Fuzzy

Constrained Optimal Power

Dispatch

42

Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing

Electricity and Ancillary Services Markets

(CFCOPD)

43

The problem formulation include three unbundled markets;

Balancing Market (BM)

Bilateral Contract

Market (BCM)

Ancillary Services Market (ASM)


44

Coordinated fuzzy constrained optimal power dispatch for bilateral contract,

balancing, and ancillary services markets

CFCOPD

Social Welfare Fuzzy Maximization Subproblem

Combined Reactive Power Cost and Cost of Real Power Loss Fuzzy

Minimization Subproblem

PGi

PDi

AGCTMSR TMOR

Mixed integer fuzzylinear programming

|VGi |

Ti

Fuzzy linear programming


45[David 1998][Weber 1999]

P

sd dPPSPD0

))()((

d

dd dP

PdBPD

)()(

s

ss dP

PdCPS

)()(

or maximize

where)( dPD

)( SPS

)()( sd PCPB

Maximize)( SPC

)()( Sd PCPB

)( dPBOffer and bid in quadratic form


Literature Review

46


NG

i

NS

jGijij

NTD

i

ND

jDijij

ii

PSPDSW1 11 1

Quantities (MW)Prices ($/MWh)

[Numnonda & Annakkage 1999][Kumar & Sheble 1998]

Offer and bid in linear form

Literature Review

47

Curtailment bids for dispatchable demands

Curtailment bids for non-dispatchable demands

Social Welfare Fuzzy Maximization Subproblem

The load that can be response to the ISO dispatch instruction in BM

Maximize


BDi BGi NDPLi

j

BCNDPLGij

NDPLij

DPLi

j

BCDPLGij

DPLij

iiii

NS

jiiGijij

ND

jDijij

PCTBPCTB

TMOROTMORTMSROTMSR

AGCOAGCPS

PDSW

i

i

11

1

1

48

Social Welfare Fuzzy Maximization Subproblem (cont.)

Subject to

NB

jijijijjiDiGi yVVPP

1

)cos(

NB

jijijijjiDiGi yVVQQ

1

)sin(

BCGi

NS

jGijGi PPP

i

1

BCDi

ND

jDijDi PPP

i

1

1. Power balance constraints

~


iii NDPLD

j

BCNDPLDij

DPLG

j

BCDPLGji

DPLD

j

BCDPLDij

BCDi PPPP

111

iiii NDPLG

j

BCNDPLGij

NDPLG

j

BCNDPLGij

DPLG

j

BCDPLGij

DPLG

j

BCDPLGij

BCGi PPPPP

1111

49

max~ll ff

2. Fuzzy line flow limit constraints

~

GiP

DiP DC flow sensitivity method[Wood & Wollenberg 1996]



3. Fuzzy ancillary services requirement constraints

BGi

iBDi

lossDi AGCPPAGCAGCR ~)(%

BGi

iBDi

lossDi TMSRPPTMSRTMSRR ~)(%

BGi

iBDi

lossDi TMORPPTMORTMORR ~)(%

50

maxGiiiiGi PTMORTMSRAGCP

iGiGi ZPP max0

4. Generator maximum operating limit constraints

0)( minmin, GiGiiGi

lowiAGCi PPZPPA

iii AAGCAGC max0

ihigh

iAGCiGi APAGCP ,

0 ii ZA

Zi = 1 Unit onZi = 0 Unit offAi = 1 AGC onAi = 0 AGC off

5. Generator minimum operating limit and AGC regulating limit constraints [Rau 1999]

Capable for AGC

minGiP

Manual operation

Manual operation

0

maxGiP

lowiAGCP ,

highiAGCP ,



51

iii ZTMSRTMSR max0

0minmin iGiiGii UPZPTMOR

iii UTMORTMOR max0

6. TMSR and TMOR limit constraints

Ui = 1 Unit supply TMORUi = 0 Unit not supply TMOR

minGiP

0

maxGiP

Operating Range

Supply TMOR

Unit off

Unit on



52

Combined Reactive Power Cost and Cost of Real Power Loss Fuzzy Minimization Subproblem

Minimize

lossBGi

LGGi

LGGi

LDGij

LDGi PQOQQOQTQC

The linearized objective function is

Minimize

offer quantity in the leading

region

offer quantity in the lagging

region

offer price in the

leading region

offer price in the

lagging region


lossBGi

LDGi

LDGi

LGGi

LGGi PQOQQOQCQL

53

2. Fuzzy bus voltage magnitude limit constraints

Subject to

NB

jijijijjiDiGi yVVPP

1

)cos(

NB

jijijijjiDiGi yVVQQ

1

)sin(

1. Power balance constraints

~

maxmin ~~iii VVV


54

maxminiii TTT

max0 GiiLDGi QLQ

min)1(0 GiiLGGi QLQ

LGGii

LDGiiGi QLQLQ )1(

3. Transformer tap-change limits

1,0 iL

4. Generator reactive power limits constraints

Li = 1 LaggingLi = 0 Leading

LDGiQLG

GiQ maxGiQmin

GiQ

LDGi

LGGi OQOQ ,


55

Initialize real power loss from power flow analysis

Solve MIFLP for real power scheduling and ancillary services

Solve power flow analysis with the real power scheduling output from MIFLP (A)

Compute loss sensitivity factor and spot price

STOP

Any new line flow limit violation?

Does the currentpower flow solution using MIFLP (A)

match the current power flow solution from combined reactive power cost andcost of real power loss fuzzy minimization

subproblem (B)?

Add the fuzzyline flow

constraints inMIFLPproblem

No

No

Yes

Yes

and

and

Minimize combined reactive power cost and cost of real power loss by FLP

Solve power flow analysis (B)

Does the combined reactive power cost andcost of real power loss of the current power flow solution lower than that of the

previous power flow solution?

Any lagging generator ( ) violating zero low limit?

Any leading generator ( ) violating zero high limit?

Change Li from 1 to 0

Change Li from 0 to 1

Any new bus voltage limit violation?Add the fuzzy

voltageconstraints inFLP problem

No

Yes

No

No

Yes

Yes

Yes

No

Gio

GiGi VVV io

ii TTT

Gio

Gi VV io

i TT

max0 GiGi QQ

0min GiGi QQ

andGio

GiGi VVV io

ii TTT

Computational Procedure

PGi

PDi

AGCTMSR TMOR

|VGi |

Ti

56

The Fuzzy Constraints

2

1

2tanh

2

1)(μ

iii

i

baxBx

i

The hyperbolic function is used to represent the nonlinear, S-shaped, membership function. The function can be expressed as,

Very well satisfied

Strongly violated or Unsatisfied


57

The fuzzy membership functions in the social welfare fuzzy maximization


58

xBi

5.0

maxi

maxi

i0.1

Bus voltagemagnitude

max

max410

i

ii

minimin

i

min

min410

i

ii

xBi

5.0

i i

i0.1 Reactive power cost

i

ii

The fuzzy membership functions in the reactive power cost fuzzy minimization


59

CEP Scheme

Maximize

Minimize

The scheme maximize social welfare in BM as,

Subject to : Power balance constraints (1) Line flow limit constraints (2) Generator limit constraints

After obtain the BM dispatch solution, the scheme minimize total ancillary services cost as,

Subject to : Ancillary services requirement constraints (3) Generator limit constraints (4- 6)

0min GiGii PPZ

Payment Schemes

BGi

NS

jGijij

BDi

ND

jDijij

ii

PSPDSW11

BGi

iiiiii TMOROTMORTMSROTMSRAGCOAGCASC

60

iQSiLCEPi ,,

)()(i

lossL,i dP

dPITL

NC

ililiQS a

1,

i

lli dP

dfa

CEP Scheme

TMORRTMSRRAGCRTASP TMORTMSRAGC

Spot Price

Market Clearing Price

Marginal Transmission Loss

Network Quality of Supply

Total AS payment

)()()1(1

,,i

lossNB

j i

jujlj

i

lossQi dQ

dP

dQ

dV

dQ

dQ

Reactive Power Spot Price

Payment Schemes

61

Payments under CEP scheme

ISO

Consumers

NB

iDi

NDi

jDij

P

P

TASP

1

1

iTMOR

iTMSRiAGC

TMOR

TMSRAGC

NDi

j

DijCEPi P

1

Consumers

NB

iDi

BCDi

P

PTASP

1

NSi

jGij

CEPi P

1

Transmission Charge BilateralContract

BM BCM

ASM

PowerGens

PowerGens

PowerGens

Payment for Electricity in BM at Bus i

Payment for Ancillary Services at Bus i

Payment for Incremental Transmission Loss at Bus i

BCDiiQSiL P )( ,,

Payment for Reactive Power at Bus i

))1(( LGGii

LDGiii QLQL

Dii QBC

Dii Q

Payment Schemes

62

CEASP Scheme

Maximize

Subject to : Power balance constraints (1) Line flow limit constraints (2) Ancillary services requirement constraints (3) Generator limit constraints (4) Generator minimum and AGC regulating limit constraints (5) TMSR and TMOR limit constraints (6)

The scheme maximize social welfare in BCM, BM, and ASM simultaneously as,

Payment Schemes

BDi BGi NDPLi

j

BCNDPLGij

NDPLij

DPLi

j

BCDPLGij

DPLij

iiii

NS

jiiGijij

ND

jDijij

PCTBPCTB

TMOROTMORTMSROTMSR

AGCOAGCPS

PDSW

i

i

11

1

1

63

).1()1(

)1(,,

iDi

TMORiDi

TMSR

iDi

AGCiQSiLCEASPi

ITLdP

dTMORRITL

dP

dTMSRR

ITLdP

dAGCR

).1(

)1()1(

iDi

TMOR

iDi

TMSRiDi

AGCASi

ITLdP

dTMORR

ITLdP

dTMSRRITL

dP

dAGCR

CEASP Scheme

Spot price include marginal AS price

Marginal AS price

Payment Schemes

64

Payments under CEASP scheme Payment Schemes

ISO

Consumers

PowerGens

Consumers

PowerGens

NDi

jDij

CEASPi P

1

NSi

jGij

CEPi P

1

BCDi

ASi P

BDiDi

NDi

jDij

BGiNDPLi

j

BCNDPLGij

NDPLij

DPLi

j

BCDPLGij

DPLij

P

P

PCTB

PCTB1

1

1

BilateralContract

NDPLi

j

BCNDPLGij

NDPLij

DPLi

j

BCDPLGij

DPLij

PCTB

PCTB

1

1

Payment for Electricity in BM at Bus i

Payment for Ancillary Services at Bus i

Payment for Bilateral Contract Curtailment at Bus i

BM BCM

ASM

iTMOR

iTMSRiAGC

TMOR

TMSRAGC

PowerGens

Payment for Incremental Transmission Loss at Bus i

BCDiiQSiL P )( ,,

Dii Q

Payment for Reactive Power at Bus i

BC

Dii Q

))1(( LDGii

LGGiii QLQL

65

Experimentations

1. Competitive Electricity Price Scheme (CEP)2. Competitive Electricity and Ancillary Services

Prices Scheme (CEASP)3. CEASP Scheme with Bilateral Contract Curtailment

Bids4. Fuzzy Constrained CEASP Scheme with Bilateral

Contract Curtailment Bids

1. Modified IEEE 30 Bus System2. Thai Power 424 Bus System

Test Systems

Numerical Results

66

Numerical Results on IEEE 30 bus system

Numerical Results

67

IEEE 30 bus system

• Voltage Constraint: +5%, -10%• Ancillary services requirement:

• AGC 3%• TMSR 5%• TMOR 5%

Numerical Results

68

Bilateral contract quantities and curtailment bids

Electricity offer prices and quantities in BM

AS offer prices and quantities in ASM

IEEE 30 bus systemNumerical Results

69

Electricity bid prices and quantities in BM

IEEE 30 bus systemNumerical Results

70

Numerical Results

Dispatch results in BM

IEEE 30 bus system

71

Numerical Results

Electricity spot prices in BM

IEEE 30 bus system

72

IEEE 30 bus system

Numerical Results

Item CEP Scheme

CEASP Scheme without Bilateral

Contract Curtailment

CEASP Scheme with Bilateral


Fuzzy Constrained CEASP Scheme with Bilateral


Social Welfare in BM, ASM, and ASM ($/hr) 220.880 221.463 223.290 238.919 Social Welfare in BM ($/hr) 439.970 440.109 455.442 456.698 Social Welfare in BM and ASM ($/hr) 220.880 221.463 236.820 247.651Electricity Market Clearing Price in BM ($/MWhr) 15.400 15.220 11.670 11.670Total Real Power Dispatch (MW) 314.218 313.645 313.607 314.568 Electricity Market Clearing Quantity in BM (MW) 50.000 49.458 51.624 51.815 Total Electricity Generation in BCM (MW) 261.000 260.996 258.798 259.577 Real Power Loss 3.218 3.191 3.185 3.176Total Payment of ISO ($/hr) 1009.360 980.002 846.955 835.701 Payment to PowerGens/IPPsfor Electricity in BM ($/hr) 764.270 735.356 588.802 591.922 Payment to PowerGens/IPPsfor AS in ASM ($/hr) 245.090 244.646 244.623 235.047 Payment for Bilateral Contract Curtailment ($/hr) 0.000 0.000 13.530 8.732Total Consumer Payment ($/hr) 1209.109 1113.849 960.019 947.958 Payment of Consumer in BM for Electricity in BM ($/hr) 795.082 765.903 612.818 616.716 Payment of Consumer in BM for AS ($/hr) 40.270 39.251 40.960 39.375 Payment of Consumer in BM with Bilateral Contract Curtailment ($/hr) 0.000 0.000 13.530 8.732 Payment of Consumer in BCM for AS ($/hr) 209.842 206.791 204.990 196.942 Payment of Consumer in BCM for incremental transmission loss ($/hr) 123.643 62.652 46.762 46.818ISO Surplus ($/hr) 199.749 133.847 113.064 112.258Average Electricity Price in BM ($/MWhr) 16.707 16.280 12.926 12.831Total Reactive Power Cost 371.238 369.250 360.927 363.078 Payment to PowerGens/IPPsfor Reactive Power ($/hr) 483.142 481.280 481.793 483.501 Payment of consumers for Reactive Power ($/hr) 689.856 688.099 682.848 685.224Average Reactive Power Price ($/MVARh) 4.841 4.834 4.773 4.771

73

Thai power 424 bus system

424 Buses744 Transmission lines and Transformers146 Generators

Numerical Results

74

The Offer Price

The offer price of each GenCo is obtained by

linearized generation cost

Numerical Results

Thai power system

75

Bilateral Contract Amount

Offer price and quantity in BM

The Offer Price

5 segments is used in the simulation

Numerical Results

Thai power system

76

Aggregate Supply Curve of Thai Power System

Numerical Results

Thai power system

Pri

ce

(Bah

t/M

Wh)

77

• Voltage Constraint: +5%, -10%• Ancillary services requirements [Chao and Wilson 2001] :

• AGC 3%• TMSR 5%• TMOR 5%

• The 16 GW loading condition with 3 of 4 lines from bus 51 to bus 52 out of services is presented.

Numerical Results

Thai power system

78

3 of 4 lines out of service

Line overloaded

Numerical Results

Thai power system

79

Numerical Results

Item CEP Scheme

CEASP Scheme without Bilateral


CEASP Scheme with Bilateral


Fuzzy Constrained CEASP Scheme with Bilateral


Social Welfare in BM, ASM, and ASM (THB/hr) 336815.628 342012.919 343208.857 405798.883 Social Welfare in BM (THB /hr) 729605.482 732750.982 734424.957 738333.539 Social Welfare in BM and ASM (THB /hr) 336815.628 342012.919 343733.489 406546.068Electricity Market Clearing Price in BM (THB /MWhr) 1341.665 1288.581 1235.497 896.866Total Real Power Dispatch (MW) 16148.852 16099.242 16098.124 16097.343 Electricity Market Clearing Quantity in BM (MW) 1423.940 1376.740 1376.740 1376.740 Total Electricity Generation in BCM (MW) 14247.147 14247.147 14246.089 14245.349 Real Power Loss 477.765 475.355 475.295 475.254Total Payment of ISO (THB /hr) 3199116.842 3030463.394 2930244.426 2230073.242 Payment to PowerGens/IPPs for Electricity in BM (THB /hr) 2531224.561 2364622.904 2263925.899 1637306.844 Payment to PowerGens/IPPs for AS in ASM (THB /hr) 667892.281 665840.489 665793.894 592019.213 Payment for Bilateral Contract Curtailment (THB /hr) 0.000 0.000 524.633 747.185Total Consumer Payment (THB /hr) 4412877.314 4191475.677 4043062.364 3027322.906 Payment of Consumer in BM for Electricity in BM (THB /hr) 2100375.582 1949204.939 1865353.403 1354114.922 Payment of Consumer in BM for AS (THB /hr) 63523.870 61476.850 61464.313 54534.779 Payment of Consumer in BM with Bilateral Contract Curtailment (THB /hr) 0.000 0.000 524.633 747.185 Payment of Consumer in BCM for AS (THB /hr) 636974.432 636822.993 636651.129 564854.284 Payment of Consumer in BCM for incremental transmission loss (THB /hr) 1548479.561 1482494.044 1417604.573 998536.956ISO Surplus (THB /hr) 1213760.473 1161012.284 1112817.938 797249.664Average Electricity Price in BM (THB /MWhr) 1519.656 1460.466 1399.932 1023.720Total Reactive Power Cost 1027858.810 994947.277 969639.128 808768.592 Payment to PowerGens/IPPs for Reactive Power (THB /hr) 231473.622 214757.082 217258.141 234080.585 Payment of consumers for Reactive Power (THB /hr) 1062737.229 1041026.722 1030855.468 966751.955Average Reactive Power Price (THB /MVARh) 271.607 267.596 264.986 248.511

Thai power system

80

Conclusion

The dissertation proposes a coordinated fuzzy constrained optimal power dispatch for bilateral contract, balancing electricity, and ancillary services markets.

The results show that the CFCOPD algorithm is efficiently and effectively maximizing the social welfare by MIFLP and minimizing the combined reactive power cost and cost of real power loss by FLP in the bilateral contract, balancing electricity, and ancillary services markets leading to a lower average electricity price to the consumer.

Summary

81

The CFCOPD could trade off between the system security and social welfare in the social fuzzy maximization subproblem and between bus voltage magnitudes and combined reactive power cost and cost of real power loss in the combined reactive power cost and cost of real power loss fuzzy minimization subproblem.

The dispatch results show that the social welfare of the fuzzy constrained CEASP scheme with bilateral contract curtailment bids is higher than those of CEP scheme, CEASP scheme, and CEASP scheme with bilateral contract curtailment bids, leading to a lower average electricity price.

The proposed CFCOPD is potentially applicable to the proposed NESA for Thailand due to a higher social welfare, lower electricity price, and sharper price signal sent to all market participants.

Conclusion

82

Thank you

Documents

1 Dr. Keerati Chayakulkheeree Department of Electrical Engineering Faculty of Engineering Sripatum University Supervisor Assoc. Prof. Dr. Weerakorn Ongsakul