Coordinated Fuzzy Constrained Optimal Power
Dispatch for Bilateral Contract, Balancing Electricity
and Ancillary Services Markets
Dr. Keerati Chayakulkheeree
Department of Electrical Engineering
Faculty of Engineering
Sripatum University
Supervisor
Assoc. Prof. Dr. Weerakorn Ongsakul
Energy Field of Study
School of Environment Resources and Developments
Asian Institute of Technology
1
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.
2
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.
3
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, EuroPES2002, 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) .
4
International Journal
5
International Conference
6
Overview
Outline of
ofthe
theResearch
Presentation
Introduction
Literature Reviews
Coordinated Constrained Optimal Power
Dispatch for Bilateral Contract, Balancing
Electricity and Ancillary Services Markets
Coordinated Fuzzy Constrained Optimal
Power Dispatch for Bilateral Contract,
Balancing Electricity and Ancillary
Services Markets
Conclusion
7
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
Consumers
Optional: Contract
Spot $$$
or CfD $$$
Regulated Electricity Delivery Co
Spot
DisCo (REDCo) SupplyCo
$$$ RetailCos
Spot $$$
Info
MWh
Power Pool/ISO
ISO/MO SA
Info
GridCo
MWh
Spot
$$$
GenCos
Contract
$$$
Traders
Contract $$$
[EPPO]
8
Introduction
•
•
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).
PowerGen 1
PowerGen 1
PowerGen 1
Existing and
new IPPs
GridCo
Bangkok and
vicinity areas
Areas outside
Bangkok and vicinity
DisCo
RetailCos
SupplyCo
Consumers
Independent Regulatory Body
Bilateral Contracts
ISO
DisCo
SupplyCo
9
[EPPO]
Introduction
• 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.
10
Introduction
• 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 nondispatchable load is imposed only on the generation side and
the load is supplied by BM.
11
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.
12
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
The problem formulation include three unbundled markets;
Bilateral Contract
Market (BCM)
Balancing
Market (BM)
Ancillary Services
Market (ASM)
13
Introduction
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.
14
Introduction
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.
15
Introduction
In particular, the objectives are as follow.
• 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
16
signal to consumers.
Introduction
Scope and limitation
The scope and limitations of the study are as follows:
•
•
•
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).
17
Introduction
Scope and limitation
• 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.
18
Introduction
Scope and limitation
• 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.
19
Introduction
Scope and limitation
• 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.
20
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.
21
Literature Review
optimal real power dispatch for electricity market
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]
22
Literature Review
Social Welfare Maximization in Electricity Market
Offer and bid in quadratic form
B( Pd )
Maximize
B (Pd )  C (Ps )
C ( PS )
B( Pd )  C ( PS )
or maximize
P
 ( D( Pd )  S ( Ps ))dP
0
D ( Pd )
where
[David 1998]
[Weber 1999]
D (Pd ) 
dB (Pd )
dPd
S (Ps ) 
dC (Ps )
dPs
S ( PS )
23
Literature Review
Social Welfare Maximization in Electricity Market
Offer and bid in linear form
Prices ($/MWh)
Quantities (MW)
NTD NDi
NG NS i
i 1 j 1
i 1 j 1
SW    Dij PDij    Sij PGij
[Numnonda & Annakkage 1999]
[Kumar & Sheble 1998]
24
Literature Review
Optimal real power dispatch for electricity market
Reference
Objective
Constraints
Offer and Bid
Optimization
Techniques
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 Social
Welfare
- Power balance
Quadratic bidding with
DSB
Newton method
OPF
Rau (1999)
Minimize Total
System 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 Total
System Cost
- Power balance
Linear bidding without
DSB
LaGrange
Relaxation with
Heuristic
Garng & Qing (2000)
Minimize Total System Cost
with Maximize the
Environment Index
- Power balance
- Ancillary services
requirement
- Environment index
Linear bidding without
DSB
Genetic Algorithm
Cheung et al (2000)
Minimize Total
System Cost
- Power balance
- Ancillary services
requirement
Multi blocks bidding
without DSB
Linear
25
Programming
Kumar and Sheble
(1998)
Literature Review
Optimal ancillary services and reactive power dispatch for ancillary services market
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 capacity
4. TMOR with the remaining capacity
5. 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
26
- Fixed AS requirement
Literature Review
Optimal ancillary services and reactive power dispatch for ancillary services market
• 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.
27
Literature Review
Optimal electricity spot pricing

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.
28
Literature Review
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.
29
Literature Review
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.
30
Literature Review
• 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]
31
Literature Review
• 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
32
constraint enforcement and control action.
Literature Review
• 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.
33
Literature Review
The fuzzy constraints in real power optimal dispatch
Proposed
[Guan et al 1995]
[Edwin Liu and Guan 1996]
Objective: Minimize Total Operating Cost
Objective: Maximize Social Welfare


Line Flow
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]

Line Flow
Ancillary Services
34
Requirement
Literature Review
• 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].
35
Literature Review
The fuzzy constraints in reactive power optimal dispatch
[Abdul-Rahman and Shahidehpour 1993]
[Tomsovic 1992]
Proposed
Objective: Minimize Total Reactive
Power Cost
Objective: Minimize Total Real Power Loss


Bus voltage magnitude
Bus voltage magnitude
36
Literature Review
Optimal power dispatch for bilateral contract and balancing electricity markets
Reference
Objective
Constraints
Offer
Ancillary
Services
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
- Power balance
- Crisp line flow
limit
None
(willingness to pay to
avoid deviation from
contract)
None
37
Literature Review
Optimal power dispatch for bilateral contract and balancing electricity markets
Reference
Objective
Constraints
Offer
Ancillary
Services
Kockar and
Galiana
(2002)
Minimize total
BCM and BM
cost
- Power balance
- Crisp line flow
limit
- 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
38
Literature Review
Conclusion



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.
39
Literature Review
 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 fuzzy40
constraints.
Bilateral Contract Amount
Offer price and
quantity in BM

Line Flow
Fuzzy Constraints
Bilateral Contract Market
Ancillary Services Market
Balancing Market
Coordinated
Fuzzy
Constrained
Optimal Power
Dispatch
Manual
operation
PGimax
high
PAGC
,i
Capable
for AGC
Manual
operation
low
PAGC
,i
PGimin
41
0
Coordinated Fuzzy Constrained Optimal
Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
(CFCOPD)
42
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
The problem formulation include three unbundled markets;
Bilateral Contract
Market (BCM)
Balancing
Market (BM)
Ancillary Services
Market (ASM)
43
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Coordinated fuzzy constrained optimal
power dispatch for bilateral contract,
balancing, and ancillary services markets
CFCOPD
Social Welfare Fuzzy
Maximization Subproblem
Mixed integer fuzzy
linear programming
PGi
PDi
AGC
TMSR
TMOR
Combined Reactive Power Cost and
Cost of Real Power Loss Fuzzy
Minimization Subproblem
Fuzzy linear
programming
|VGi |
Ti
44
Literature Review
Social Welfare Maximization in Electricity Market
Offer and bid in quadratic form
B( Pd )
Maximize
B (Pd )  C (Ps )
C ( PS )
B( Pd )  C ( PS )
or maximize
P
 ( D( Pd )  S ( Ps ))dP
0
D ( Pd )
where
[David 1998]
[Weber 1999]
D (Pd ) 
dB (Pd )
dPd
S (Ps ) 
dC (Ps )
dPs
S ( PS )
45
Literature Review
Social Welfare Maximization in Electricity Market
Offer and bid in linear form
Prices ($/MWh)
Quantities (MW)
NTD NDi
NG NS i
i 1 j 1
i 1 j 1
SW    Dij PDij    Sij PGij
[Numnonda & Annakkage 1999]
[Kumar & Sheble 1998]
46
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Social Welfare Fuzzy Maximization Subproblem
Maximize
 NS i

S
P

OAGC

AGC

i
i
 ij Gij

j

1


NDi

SW    Dij PDij   OTMSRi  TMSRi  OTMOR i  TMORi 

iBD j 1
iBG DPLi
NDPLi
 CTB DPL P BCDPL 
BCNDPL 
CTBijNDPL PGij


ij
Gij
 j 1

j 1


Curtailment bids for
dispatchable demands
The load that can be response to the ISO dispatch
instruction in BM
Curtailment bids for nondispatchable demands
47
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Social Welfare Fuzzy Maximization Subproblem (cont.)
Subject to
NB
1. Power balance constraints
PGi  PDi 
V
i
V j y ij cos(ij  ij )
j 1
NB
QGi  Q Di  
V
i
V j y ij sin(ij  ij )
j 1
PGi 
NS i
P
Gij
~
 PGiBC
j 1
PDi 
NDi
P
Dij
BC
 PDi
j 1
PGiBC

DPLGi

j 1
BCDPL
PGij

DPLGi

j 1
BCDPL
PGij

NDPLGi

j 1
BCNDPL
PGij
BC
Di
P

NDPLGi

BCNDPL
PGij
j 1
DPLDi
  P
j 1
BCDPL
Dij
DPLGi
  P
j 1
BCDPL
Gji
NDPLDi
BCNDPL
  PDij
j 1
48
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Social Welfare Fuzzy Maximization Subproblem (cont.)
2. Fuzzy line flow limit constraints
~ max
fl  fl
PGi
~
DC flow sensitivity method
[Wood & Wollenberg 1996]
PDi
3. Fuzzy ancillary services requirement constraints
~
AGCR  % AGC  (  PDi  Ploss ) 
iBD
 AGC
iBG
~
TMSRR  %TMSR  (  PDi  Ploss ) 
iBD
~
TMORR  %TMOR  (  PDi  Ploss ) 
iBD
i
TMSR
iBG
i
TMOR
iBG
i
49
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Social Welfare Fuzzy Maximization Subproblem (cont.)
4. Generator maximum operating limit constraints
PGi  AGCi  TMSRi  TMORi  PGimax
0  PGi  PGimax  Z i
5. Generator minimum operating limit and
AGC regulating limit constraints [Rau 1999]
low
min
min
A i  (PAGC

P
)

Z

P
,i
Gi
i
Gi  PGi  0
0  AGC i 
AGC imax
PGi  AGCi  P
high
AGC,i
Ai  Z i  0
 Ai
 Ai
Zi = 1 Unit on
Zi = 0 Unit off
Ai = 1 AGC on
Ai = 0 AGC off
Manual
operation
PGimax
high
PAGC
,i
Capable
for AGC
Manual
operation
low
PAGC
,i
PGimin
0
50
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Social Welfare Fuzzy Maximization Subproblem (cont.)
6. TMSR and TMOR limit constraints
0  TMSR i  TMSR imax  Z i
PGimax
Operating
Range
min
min
 TMORi  PGi
 Zi  PGi
 Ui  0
0  TMORi  TMORimax Ui
Ui = 1 Unit supply TMOR
Ui = 0 Unit not supply TMOR
PGimin
Unit on
Supply
TMOR
Unit off
0
51
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Combined Reactive Power Cost and Cost of Real Power Loss Fuzzy Minimization Subproblem
Minimize
CQL 
 OQ
iBG
offer price
in the
leading
region
LG
Gi

 QGiLG  OQGiLD  QGiLD    Ploss
offer quantity
in the leading
region
offer price
in the
lagging
region
offer quantity
in the lagging
region
The linearized objective function is
Minimize TQC 
 OQ
iBG
LD
Gi

LD
 QGij
 OQGiLG  QGiLG    Ploss
52
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
Subject to
1. Power balance constraints
NB
PGi  PDi 
V
i
V j y ij cos(ij  ij )
j 1
NB
QGi  Q Di  
V
i
V j y ij sin(ij  ij )
j 1
2. Fuzzy bus voltage magnitude limit constraints
~
~
 Vi min   Vi   Vi max
~
53
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
3. Transformer tap-change limits
Ti min  Ti  Ti max
4. Generator reactive power limits constraints
0  QGiLD  Li  QGimax
OQGiLG , OQGiLD
0  QGiLG  (1  Li )  QGimin
QGi  Li  QGiLD  (1  Li )  QGiLG
Li  0,1
Li = 1 Lagging
Li = 0 Leading
QGimin
QGiLG
max
QGiLD QGi
54
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)
VGio  VGi and Ti o  Ti
Minimize combined reactive power cost and cost of real power loss by FLP
VGi  VGio   VGi and
Ti  Ti o  Ti
PGi
PDi
AGC
TMSR
TMOR
Solve power flow analysis (B)
Any lagging generator ( 0  QGi  QGimax ) violating zero low limit?
No
Yes
Change Li from 1 to 0
Any leading generator ( QGimin  QGi  0) violating zero high limit?
No
|VGi |
Ti
Yes
Change Li from 0 to 1
Add the fuzzy
voltage
constraints in
FLP problem
Yes
Yes
Any new bus voltage limit violation?
No
Computational Procedure
Does the combined reactive power cost and
cost of real power loss of the current power flow solution lower than that of the
previous power flow solution?
No
V Gi  V
Add the fuzzy
line flow
constraints in
MIFLP
problem
No
Yes
o
Gi
  VGi and Ti  Ti o  Ti
Any new line flow limit violation?
No
Does the current
power flow solution using MIFLP (A)
match the current power flow solution from combined reactive power cost and
cost of real power loss fuzzy minimization
subproblem (B)?
Yes
Compute loss sensitivity factor and spot price
STOP
55
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
The Fuzzy Constraints
The hyperbolic function is used to represent the nonlinear, Sshaped, membership function. The function can be expressed as,

ai  bi   1
1
μi ( x)   tanh   Bi x 
   i  
2
2   2

Very well satisfied
Strongly violated or Unsatisfied
56
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
The fuzzy membership functions in the social
welfare fuzzy maximization
57
Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing
Electricity and Ancillary Services Markets
The fuzzy membership functions in the
reactive power cost fuzzy minimization
i
1.0
0.5
 imin
 i  10
 imin
4
Bus voltage
magnitude
max

 i  10 4 imax
i
max
min
 imax
 imin  i  i
Bi x
i
1.0
Reactive power cost
0.5
i
i  
i
i
i
Bi x
58
Payment Schemes
CEP Scheme
The scheme maximize social welfare in BM as,
Maximize
SW 
NDi
 D P
iBD j 1
ij
Dij
NS i
  S ij PGij
iBG j 1
Subject to : Power balance constraints (1)
Line flow limit constraints (2)
Generator limit constraints Z i  PGimin  PGi  0
After obtain the BM dispatch solution, the scheme minimize
total ancillary services cost as,
Minimize ASC 
OAGC  AGC  OTMSR  TMSR  OTMOR  TMOR 
iBG
i
i
i
i
i
i
Subject to : Ancillary services requirement constraints (3)
Generator limit constraints (4- 6)
59
Payment Schemes
CEP Scheme
Spot Price
iCEP     L,i  QS ,i
Market Clearing Price
Network Quality of Supply
NC
Marginal Transmission Loss
 L,i    ( ITL )    (
dPloss
)
dPi
 QS ,i   l  a li
i 1
df l
ali 
dPi
Total AS payment
TASP   AGC  AGCR  TMSR  TMSRR  TMOR  TMORR
Reactive Power Spot Price
NB
dV j
dQloss
dP
 i  Q (1 
)   ( j ,l   j ,u )
  ( loss )
dQi
dQi
dQi
j 1
60
Payment Schemes
Payments under CEP scheme
 i ×QDiBC
 i ×QDi
BC
PDi
BM
TASP × NB
NDi
P
 iCEP ×
 PDi
Dij
j 1
i 1
Consumers
( L ,i   QS ,i ) ×P
j 1
TASP × NB
Power
Gens
 PDi
Consumers
BC
Di
NDi
 PDij
BCM
ISO
Transmission Charge
i 1
NSi
 iCEP × PGij
j 1
 i ×( Li ×QGiLD  (1  Li ) ×QGiLG )
Bilateral
Contract
Power
Gens
 AGC ×AGC i   TMSR ×TMSR i
  TMOR ×TMOR i
Payment for Electricity in BM at Bus i
Payment for Ancillary Services at Bus i
Payment for Reactive Power at Bus i
Payment for Incremental Transmission Loss at Bus i
ASM PowerGens
61
CEASP Scheme
Payment Schemes
The scheme maximize social welfare in BCM, BM, and ASM
simultaneously as,
Maximize
 NS i

S
P

OAGC

AGC

i
i
 ij Gij

j

1


NDi

SW    Dij PDij   OTMSRi  TMSRi  OTMOR i  TMORi 

iBD j 1
iBG DPLi
NDPLi
 CTB DPL P BCDPL 
BCNDPL 
CTBijNDPL PGij


ij
Gij
 j 1

j 1


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)
62
Payment Schemes
CEASP Scheme
Spot price include marginal AS price
 iCEASP     L,i   QS ,i   AGC 
 TMSR 
dAGCR
(1  ITLi )
dPDi
dTMSRR
dTMORR
(1  ITLi )  TMOR 
(1  ITLi ).
dPDi
dPDi
Marginal AS price

AS
i
dAGCR
dTMSRR
  AGC 
 (1  ITL i )  TMSR 
 (1  ITLi )
dPDi
dPDi
 TMOR 
dTMORR
 (1  ITL i ).
dPDi
63
Payment Schemes
Payments under CEASP scheme
 i ×QDiBC
 i ×QDi
 iAS ×PDiBC

 NDi
DPL
BCDPL


CTB
P

ij
Gij

  PDij
j 1

 × j 1
  NDPLi

iBG
NDPL
BCNDPL

CTB
P

  PDi
ij
Gij

j
1

 iBD
DPLi
BM
Consumers
( L,i   QS ,i ) ×P
BC
Di
BCM
Consumers
NDi
iCEASP × PDij
j 1
Power
Gens
ISO
DPLi
BCDPL 
 CTBijDPL PGij
NSi
j 1
j 1
NDPLi
 iCEP ×  PGij
BCNDPL
 CTBijNDPL PGij
 AGC ×AGC i   TMSR ×TMSR i
  TMOR ×TMOR i
Bilateral
Contract
Power
Gens
j 1

i
×( Li ×QGiLG  (1  Li ) ×QGiLD )
Payment for Electricity in BM at Busi
Payment for Ancillary Services at Busi
Payment for Bilateral Contract Curtailment at Busi
Payment for Reactive Power at Busi
Payment for Incremental Transmission Loss at Busi
ASM
PowerGens
64
Numerical Results
Experimentations
1. Competitive Electricity Price Scheme (CEP)
2. Competitive Electricity and Ancillary Services
Prices Scheme (CEASP)
3. CEASP Scheme with Bilateral Contract Curtailment
Bids
4. Fuzzy Constrained CEASP Scheme with Bilateral
Contract Curtailment Bids
Test Systems
1. Modified IEEE 30 Bus System
2. Thai Power 424 Bus System
65
Numerical Results
Numerical Results on IEEE 30 bus system
66
Numerical Results
IEEE 30 bus system
• Voltage Constraint: +5%, -10%
• Ancillary services requirement:
• AGC
3%
• TMSR
5%
• TMOR
5%
67
Bilateral contract quantities and curtailment bids
Numerical Results
IEEE 30 bus system
AS offer prices and quantities in ASM
Electricity offer prices and quantities in BM
68
IEEE 30 bus system
Numerical Results
Electricity bid prices and quantities in BM
69
Numerical Results
IEEE 30 bus system
Dispatch results in BM
70
Numerical Results
IEEE 30 bus system
Electricity spot prices in BM
71
Numerical Results
IEEE 30 bus system
Item
Social Welfare in BM, ASM, and ASM ($/hr)
Social Welfare in BM ($/hr)
Social Welfare in BM and ASM ($/hr)
Electricity Market Clearing Price in BM ($/MWhr)
Total Real Power Dispatch (MW)
Electricity Market Clearing Quantity in BM (MW)
Total Electricity Generation in BCM (MW)
Real Power Loss
Total Payment of ISO ($/hr)
Payment to PowerGens/IPPsfor Electricity in BM ($/hr)
Payment to PowerGens/IPPsfor AS in ASM ($/hr)
Payment for Bilateral Contract Curtailment ($/hr)
Total Consumer Payment ($/hr)
Payment of Consumer in BM for Electricity in BM ($/hr)
Payment of Consumer in BM for AS ($/hr)
Payment of Consumer in BM with Bilateral Contract
Curtailment ($/hr)
Payment of Consumer in BCM for AS ($/hr)
Payment of Consumer in BCM for incremental transmission
loss ($/hr)
ISO Surplus ($/hr)
Average Electricity Price in BM ($/MWhr)
Total Reactive Power Cost
Payment to PowerGens/IPPsfor Reactive Power ($/hr)
Payment of consumers for Reactive Power ($/hr)
Average Reactive Power Price ($/MVARh)
50.000
261.000
3.218
1009.360
980.002
764.270
245.090
0.000
1209.109
1113.849
795.082
40.270
49.458
260.996
3.191
765.903
39.251
Fuzzy Constrained
CEASP Scheme
with Bilateral
Contract
Curtailment
238.919
455.442
456.698
236.820
247.651
11.670
314.568
51.624
51.815
258.798
259.577
3.185
3.176
835.701
588.802
591.922
244.623
235.047
13.530
8.732
947.958
612.818
616.716
40.960
39.375
0.000
209.842
0.000
206.791
13.530
204.990
8.732
196.942
123.643
62.652
46.762
46.818
CEP Scheme
220.880
CEASP Scheme
without Bilateral
Contract
Curtailment
221.463
439.970
220.880
15.400
314.218
11.670
313.607
846.955
735.356
244.646
0.000
960.019
133.847
16.280
369.250
483.142
689.856
4.841
223.290
440.109
221.463
15.220
313.645
199.749
16.707
371.238
CEASP Scheme
with Bilateral
Contract
Curtailment
113.064
12.926
360.927
481.280
688.099
4.834
112.258
12.831
363.078
481.793
682.848
4.773
483.501
72 685.224
4.771
Numerical Results
Thai power 424 bus system
424 Buses
744 Transmission lines
and Transformers
146 Generators
73
Numerical Results
Thai power system
The Offer Price
The offer price of each
GenCo is obtained by
linearized generation cost
74
Numerical Results
Thai power system
The Offer Price
Bilateral Contract Amount
Offer price and quantity in BM
5 segments is used in
the simulation
75
Numerical Results
Thai power system
Price (Baht/MWh)
Aggregate Supply Curve of Thai Power System
76
Numerical Results
Thai power system
• 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.
77
Numerical Results
Thai power system
3 of 4 lines out of service
Line overloaded
78
Numerical Results
Thai power system
Fuzzy Constrained
CEASP Scheme
Item
CEP Scheme
with Bilateral
Contract
Curtailment
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
406546.068
Electricity Market Clearing Price in BM (THB /MWhr)
1341.665
1288.581
1235.497
896.866
Total 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
475.254
Total 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
747.185
Total 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
998536.956
ISO Surplus (THB /hr)
1213760.473
1161012.284
1112817.938
797249.664
Average Electricity Price in BM (THB /MWhr)
1519.656
1460.466
1399.932
1023.720
Total 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
966751.955
79
Average Reactive Power Price (THB /MVARh)
271.607
267.596
264.986
248.511
CEASP Scheme
without Bilateral
Contract
Curtailment
CEASP Scheme
with Bilateral
Contract
Curtailment
Conclusion
Summary
 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.
80
Conclusion



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
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market participants.
Thank you
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