Multiobjective Optimal Generation Dispatch Using Equilibria-Based Multi-Group Synergistic Searching Algorithm
Zhou Bin1, Song Yan1, Li Jinming2, Yu Tao3, Wei Hua4
1. Hunan University Changsha 410082 China; 2. State Grid Hunan Electric Company Economic and Technical Institute Changsha 410004 China; 3. South China University of Technology Guangzhou 510640 China; 4. Guangxi University Nanning 530004 China
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.
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2015, Vol. 30 
