J Shanghai Jiaotong Univ Sci

Journal of shanghai Jiaotong University (Science) ›› 2015, Vol. 20 ›› Issue (2): 218-223.doi: 10.1007/s12204-015-1610-2

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Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands

Improved Real-Coded Genetic Algorithm Solution for Unit Commitment Problem Considering Energy Saving and Emission Reduction Demands

PAN Qian1*(潘谦), HE Xing1 (何星), CAI Yun-ze1 (蔡云泽),WANG Zhi-hua2 (王治华), SU Fan2 (苏凡)   

  1. (1. Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China; 2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200240, China)
  2. (1. Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China; 2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200240, China)
  • Published:2015-04-02
  • Contact: PAN Qian(潘谦) E-mail:miracle4501@sjtu.edu.cn

Abstract: Unit commitment (UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm (IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.

Key words: genetic algorithm (GA)| unit commitment (UC)| improved real-number encoding

摘要： Unit commitment (UC), as a typical optimization problem in electric power system, faces new challenges as energy saving and emission reduction get more and more important in the way to a more environmentally friendly society. To meet these challenges, we propose a UC model considering energy saving and emission reduction. By using real-number coding method, swap-window and hill-climbing operators, we present an improved real-coded genetic algorithm (IRGA) for UC. Compared with other algorithms approach to the proposed UC problem, the IRGA solution shows an improvement in effectiveness and computational time.

关键词: genetic algorithm (GA)| unit commitment (UC)| improved real-number encoding

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