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Li, Ang (2013) CONTROL SYSTEM MODEL FOR ANALYSIS OF ELECTRICITY MARKET BIDDING PROCESS. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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This dissertation proposes a closed-loop control system model to facilitate mathematical analysis and promote operational efficiency of the dynamic bidding process. Electricity market deregulation has brought an innovation of the market structure and changed the electric power production from the old monopolistic way to a competitive market environment. Electricity is treated as a commodity and being traded among the market participants. The analysis of electricity market behavior becomes increasingly important and challenging. This dissertation develops a control-theoretic model to analyze and predict electricity market behavior. The model is based on the perspective of the power generation side (GENCOS) and ISO. The purpose is to achieve a rational profit maximizing behavior for GENCOS during the day-ahead bidding process and to improve the wholesale market efficiency. The control-theoretic model uses the game theory embedded with the learning ability as the major bidding strategy, which allows GENCOS to adjust their next-day bidding in the form of supply function equilibrium (SFE) through market observations. Recursive least square (RLS) method based on two ARMA models is introduced for demand and price forecasting in order to maximize the GENCO’s profit. This method is implemented into the bidding strategy of SFE with learning process. In order to better capture the demand and price dynamics beforehand, this dissertation also introduces an adaptive multiresolution prediction algorithm. This algorithm establishes a systematic structure to hierarchically decompose the original demand and price data into subtasks with different time frames, within which the data are able to be trained separately and efficiently. The real market data from New York Independent System Operator and PJM interconnection are used to demonstrate the effectiveness of the proposed model and training algorithm.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMao, Zhi-Hongzhm4@pitt.eduZHM4
Committee CoChairReed, Gregorygfr3@pitt.eduGFR3
Committee MemberChaparro, Luis F.lfch@pitt.eduLFCH
Committee MemberLi, Ching-Chungccl@pitt.eduCCL
Committee MemberSedic, Ervinesejdic@pitt.eduESEJDIC
Committee MemberSun, Minguidrsun@pitt.eduDRSUN
Committee MemberGemmell,
Date: 31 January 2013
Date Type: Publication
Defense Date: 15 November 2012
Approval Date: 31 January 2013
Submission Date: 26 November 2012
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 104
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Electricity market, supply function equilibrium, recursive least square, multiresolution prediction algorithm
Date Deposited: 31 Jan 2013 21:37
Last Modified: 15 Nov 2016 14:07


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