A genetic algorithm based method for bidding strategy coordination in energy and spinning reserve markets |
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| Affiliation: | 1. Key Laboratory of Cryogenics, Technical Institute of Physics and Chemistry, CAS, Beijing 100190, China;2. Beijing Key Laboratory of Thermal Science and Technology, Technical Institute of Physics and Chemistry, CAS, Beijing 100190, China;3. University of Chinese Academy of Sciences, Beijing 100049, China;1. Department of Operations Research and Business Informatics, Saarland University, Germany;2. Department of Finance and Tax, University of Cape Town, South Africa;1. Key Laboratory of Molecular Medicine, Ministry of Education, Department of Biochemistry and Molecular Biology, Institutes of Medical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China;2. State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200032, China;3. Children’s Hospital, Fudan University, Shanghai 200032, China;1. School of Mechanical Engineering, Chonnam National University, Gwangju, Republic of Korea;2. Pacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, United States;1. The Guangxi Key Laboratory of Multimedia Communication and Network Technology, Guangxi University, Nanning 530004, China;2. School of Electrical Engineering, Guangxi University, Nanning 530004, China;3. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China;4. School of Electric Engineering and Telecommunications, University of New South Wales, Sydney, Australia;1. University of Mannheim, P.O. Box 10 34 62, DE-68131 Mannheim, Germany;2. University of Engineering and Technology, Peshawar, Pakistan;3. Saarland University, Saarbrücken, Germany;4. University of Cape Town, Cape Town, South Africa |
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| Abstract: | The problem of building optimally coordinated bidding strategies for competitive suppliers in energy and spinning reserve markets is addressed based on the Monte Carlo simulation and a refined genetic algorithm (RGA). It is assumed that each supplier bids a linear energy supply function and a linear spinning reserve supply function into the energy and spinning reserve markets, respectively, and the two markets are dispatched separately to minimize customer payments. Each supplier chooses the coefficients in the linear energy and spinning reserve supply functions to maximize total benefits, subject to expectations about how rival suppliers will bid. A stochastic optimization model is first developed to describe this problem and a Monte Carlo and genetic algorithm based method is then presented to solve it. A numerical example is utilized to illustrate the essential features of the method. |
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