A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm

An implicit reserve constraint unit commitment (IRCUC) model is presented in this paper. Different from the traditional unit commitment (UC) model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical...

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Main Authors: Wei Han, Hong-hua Wang, Xin-song Zhang, Ling Chen
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/912825
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spelling doaj-d8d76eb64e2d429292fff49a9bc746ee2020-11-24T23:05:57ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/912825912825A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm AlgorithmWei Han0Hong-hua Wang1Xin-song Zhang2Ling Chen3College of Energy & Electrical Engineering, Hohai University, Nanjing 211100, ChinaCollege of Energy & Electrical Engineering, Hohai University, Nanjing 211100, ChinaCollege of Energy & Electrical Engineering, Hohai University, Nanjing 211100, ChinaCollege of Energy & Electrical Engineering, Hohai University, Nanjing 211100, ChinaAn implicit reserve constraint unit commitment (IRCUC) model is presented in this paper. Different from the traditional unit commitment (UC) model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarm algorithm (AFSA) is employed to solve this proposed model. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from 10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions.http://dx.doi.org/10.1155/2013/912825
collection DOAJ
language English
format Article
sources DOAJ
author Wei Han
Hong-hua Wang
Xin-song Zhang
Ling Chen
spellingShingle Wei Han
Hong-hua Wang
Xin-song Zhang
Ling Chen
A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm
Mathematical Problems in Engineering
author_facet Wei Han
Hong-hua Wang
Xin-song Zhang
Ling Chen
author_sort Wei Han
title A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm
title_short A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm
title_full A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm
title_fullStr A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm
title_full_unstemmed A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm
title_sort unit commitment model with implicit reserve constraint based on an improved artificial fish swarm algorithm
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description An implicit reserve constraint unit commitment (IRCUC) model is presented in this paper. Different from the traditional unit commitment (UC) model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarm algorithm (AFSA) is employed to solve this proposed model. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from 10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions.
url http://dx.doi.org/10.1155/2013/912825
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