Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

N.M. Pindoriya; Sri Niwas Singh; Jacob Østergaard

doi:10.1109/ISAP.2009.5352896

Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

N.M. Pindoriya, Sri Niwas Singh, Jacob Østergaard

Indian Institute of Technology Kanpur

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.

Original language	English
Title of host publication	ISAP'09
Publisher	IEEE
Publication date	2009
ISBN (Print)	978-1-4244-5097-8
DOIs	https://doi.org/10.1109/ISAP.2009.5352896
Publication status	Published - 2009
Event	International Conference on Intelligent System Applications to Power Systems - Curitiba, Brazil Duration: 1 Jan 2009 → … Conference number: 15

Conference

Conference	International Conference on Intelligent System Applications to Power Systems
Number	15
City	Curitiba, Brazil
Period	01/01/2009 → …

Bibliographical note

Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Keywords

Hybrid particle swarm optimization,
Day-ahead self-scheduling
Electricity market.
LMP forecast,

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title = "Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO",

abstract = "This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.",

keywords = "Hybrid particle swarm optimization,, Day-ahead self-scheduling, Electricity market., LMP forecast,",

author = "N.M. Pindoriya and Singh, {Sri Niwas} and Jacob {\O}stergaard",

note = "Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; International Conference on Intelligent System Applications to Power Systems ; Conference date: 01-01-2009",

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AU - Pindoriya, N.M.

AU - Singh, Sri Niwas

AU - Østergaard, Jacob

N1 - Conference code: 15

PY - 2009

Y1 - 2009

N2 - This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.

AB - This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.

KW - Hybrid particle swarm optimization,

KW - Day-ahead self-scheduling

KW - Electricity market.

KW - LMP forecast,

U2 - 10.1109/ISAP.2009.5352896

DO - 10.1109/ISAP.2009.5352896

M3 - Article in proceedings

SN - 978-1-4244-5097-8

BT - ISAP'09

PB - IEEE

T2 - International Conference on Intelligent System Applications to Power Systems

Y2 - 1 January 2009

ER -

Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

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Keywords

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