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Dr. Keerati Chayakulkheeree Department of Electrical Engineering Faculty of Engineering Sripatum University

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

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Dr. Keerati Chayakulkheeree Department of Electrical Engineering Faculty of Engineering Sripatum University

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  1. 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

  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: • 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. • 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. • 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. • 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: • 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 . • 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 . • 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 . • 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. Overview of the Presentation Outline of the Research 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

  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. [EPPO]

  9. 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). [EPPO]

  10. 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.

  11. 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 non-dispatchable load is imposed only on the generation side and the load is supplied by BM.

  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. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets Bilateral Contract Market (BCM) Balancing Market (BM) Ancillary Services Market (ASM) The problem formulation include three unbundled markets;

  14. 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.

  15. 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.

  16. 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 signal to consumers.

  17. 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).

  18. 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.

  19. 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.

  20. 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.

  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. Skip to conclusion

  22. 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]

  23. Literature Review Maximize or maximize where Social Welfare Maximization in Electricity Market Offer and bid in quadratic form [David 1998] [Weber 1999]

  24. Literature Review Prices ($/MWh) Quantities (MW) Social Welfare Maximization in Electricity Market Offer and bid in linear form [Numnonda & Annakkage 1999] [Kumar & Sheble 1998]

  25. Literature Review Optimal real power dispatch for electricity market

  26. Literature Review Optimal ancillary services and reactive power dispatch for ancillary services market

  27. 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.

  28. 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.

  29. 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.

  30. 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.

  31. 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]

  32. 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 constraint enforcement and control action.

  33. 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.

  34. Literature Review The fuzzy constraints in real power optimal dispatch Proposed [Guan et al 1995] [Edwin Liu and Guan 1996] Objective: Maximize Social Welfare Objective: Minimize Total Operating Cost Line Flow 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] Ancillary Services Requirement

  35. 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].

  36. Literature Review Proposed Objective: Minimize Total Reactive Power Cost Bus voltage magnitude The fuzzy constraints in reactive power optimal dispatch [Abdul-Rahman and Shahidehpour 1993] [Tomsovic 1992] Objective: Minimize Total Real Power Loss Bus voltage magnitude

  37. Literature Review Optimal power dispatch for bilateral contract and balancing electricity markets

  38. Literature Review Optimal power dispatch for bilateral contract and balancing electricity markets

  39. 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.

  40. 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 fuzzy constraints.

  41. Coordinated Fuzzy Constrained Optimal Power Dispatch Offer price and quantity in BM Bilateral Contract Amount Line Flow Fuzzy Constraints Bilateral Contract Market Ancillary Services Market Balancing Market Manual operation Capable for AGC Manual operation 0

  42. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets (CFCOPD)

  43. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets Bilateral Contract Market (BCM) Balancing Market (BM) Ancillary Services Market (ASM) The problem formulation include three unbundled markets;

  44. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets Social Welfare Fuzzy Maximization Subproblem Combined Reactive Power Cost and Cost of Real Power Loss Fuzzy Minimization Subproblem Coordinated fuzzy constrained optimal power dispatch for bilateral contract, balancing, and ancillary services markets CFCOPD • PGi • PDi • AGC • TMSR • TMOR Fuzzy linear programming • |VGi | • Ti Mixed integer fuzzy linear programming

  45. Literature Review Maximize or maximize where Social Welfare Maximization in Electricity Market Offer and bid in quadratic form [David 1998] [Weber 1999]

  46. Literature Review Prices ($/MWh) Quantities (MW) Social Welfare Maximization in Electricity Market Offer and bid in linear form [Numnonda & Annakkage 1999] [Kumar & Sheble 1998]

  47. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets Social Welfare Fuzzy Maximization Subproblem Maximize Curtailment bids for dispatchable demands Curtailment bids for non-dispatchable demands The load that can be response to the ISO dispatch instruction in BM

  48. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets Social Welfare Fuzzy Maximization Subproblem (cont.) Subject to 1. Power balance constraints ~

  49. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets 3. Fuzzy ancillary services requirement constraints Social Welfare Fuzzy Maximization Subproblem (cont.) 2. Fuzzy line flow limit constraints ~ DC flow sensitivity method [Wood & Wollenberg 1996]

  50. Coordinated Fuzzy Constrained Optimal Power Dispatch for Bilateral, Balancing Electricity and Ancillary Services Markets 5. Generator minimum operating limit and AGC regulating limit constraints [Rau 1999] Manual operation Capable for AGC Manual operation 0 Social Welfare Fuzzy Maximization Subproblem (cont.) 4. Generator maximum operating limit constraints Zi = 1 Unit on Zi = 0 Unit off Ai = 1 AGC on Ai = 0 AGC off

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