基于多群组均衡协同搜索的多目标优化发电调度

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Abstract This paper presents a novel equilibria-based multiple group synergistic searching (EMGSS) algorithm to cope with the highly constrained multi-objective generation dispatch (MOGD) with multiple contradictory objectives. As for the proposed algorithm, a synergistic evolutionary searching mechanism based on stochastic machine learning is developed to achieve the fitness assignment and strategic interaction among cooperative multi-groups. Furthermore, a novel equilibria-based hierarchical clustering is proposed to provide power dispatchers with a set of diversified optimum equilibria Pareto frontier (PF), and Nash equilibrium is used to extract the best decision solution from the resulting PF. The proposed EMGSS has been applied and tested over the IEEE 30-bus system and IEEE 118-bus system. Case studies have verified and confirmed the superiority of the algorithm to solve the multiobjective optimization problems with high-dimensional and large-scale objective functions.

Key words： Multiobjective generation dispatch equilibria-based hierarchical clustering synergistic evolutionary optimization optimum equilibria solution Pareto optimality

Received: 27 October 2013 Published: 30 November 2015

PACS:	TM732
	TM761

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	Articles by authors
	Zhou Bin
	Song Yan
	Li Jinming
	Yu Tao
	Wei Hua

Cite this article:

Zhou Bin,Song Yan,Li Jinming等. Multiobjective Optimal Generation Dispatch Using Equilibria-Based Multi-Group Synergistic Searching Algorithm[J]. Transactions of China Electrotechnical Society, 2015, 30(22): 181-189.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I22/181

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