Multi-Objective Optimal Power Flow Based on  Evolutionary Algorithm and Bargaining Game

doi:10.3969/j.issn.1000-7229.2015.05.004

Abstract

Abstract:

To solve the multi-objective optimal power flow （OPF） problem, genetic algorithm （GA） was used to find the Pareto front, and fully reflect the interaction and deviation internal relation of different optimal objective functions. On this basis, Nash Bargaining Game was used to get the global optimal solution. This paper discussed the multi-objective OPF problem with considering the minimum generation cost （or coal consumption） and the minimum system loss simultaneously. This paper firstly verified that the problem could satisfy the Nash Bargaining axiom, secondly obtained the Pareto front by strong Pareto evolution algorithm 2 （SPEA2）, which could ensure faster convergence rate and more uniform Pareto front, and then used Nash Bargaining to find the optimal solution and solve the possible contradiction between the different objective function. Case study on IEEE 14-bus system verified the effectiveness of the proposed algorithm.

Key words: multi-objective, optimal power flow （OPF）, Nash Bargaining Game, genetic algorithm

CLC Number:

TM 73

FU Yanlan, ZHAO Xuelin, LIU Kaicheng, HE Guangyu. Multi-Objective Optimal Power Flow Based on Evolutionary Algorithm and Bargaining Game[J]. ELECTRIC POWER CONSTRUCTION, 2015, 36(5): 20-24.

References

［1］袁贵川，王建全,韩帧祥.电力市场下的最优潮流［J］.电网技术, 2004,28（5）:13-17.
Yuan Guichuan, Wang Jianquan, Han Zhenxiang. Optimal power flow under electricity market［J］.Power System Technology,2004,28（5）:13-17.
［2］王彬, 何光宇, 卢建刚, 等. 考虑电网运行状态不确定性的最优潮流研究［J］. 电力建设, 2014，35（10）: 1-6.
Wang Bin, He Guangyu, Lu Jiangang， et al. Optimal Power Flow with Considering Operation State Uncertainty of Power System［J］. Electric Power Construction, 2014, 35（10）: 1-6.
［3］ Wei H, Sasaki H, Kubokawa J. An interior point nonlinear programming for optimal power flow problems with a novel data structure［J］. IEEE Transactions on Power Systems, 1997, 13（3）:870-877.
［4］ Yan Wei, Yu Juan, Yu D C, et al. A new optimal reactive power flow model in rectangular form and its solution by predictor corrector primal dual interior point method［J］. IEEE Transactions on Power Systems, 2006,21（1）:61-67.
［5］刘明波，段晓军，赵艳. 多目标最优潮流问题的模糊建模及内点解法［J］.电力系统自动化，1999，23（14）：37-40.
Liu Mingbo，Duan Xiaojun，Zhao Yan. Fuzzy modeling and interior point algorithm of multi-objective OPF problem［J］. Automation of Electric Power Systems，1999，23（14）：37-40.
［6］卓峻峰,赵冬梅.基于混沌搜索的多目标模糊优化潮流算法［J］.电网技术,2003,27（2）:41-44.
Zhuo Junfeng, Zhao Dongmei. A chaos optimization based algorithm for multi-objective fuzzy optimal power flow［J］. Power System Technology, 2003,27（2）:41-44.
［7］唐忠, 蔡智慧, 黎文华. 基于免疫禁忌混合算法的多目标最优潮流计算［J］. 高电压技术, 2008, 34（9）: 1954-1958.
Tang Zhong, Cai Zhihui, Li Wenhua. Multi-objective optimal power flow calculation based on immune/Tabu search hybrid algorithm ［J］. High Voltage Engineering, 2008, 34（9）: 1954-1958.
［8］卫志农, 向育鹏, 孙国强, 等. 计及碳排放含有碳捕集电厂电网的多目标动态最优潮流［J］. 电网技术, 2012, 36（12）: 11-17.
Wei Zhinong, Xiang Yupeng, Sun Guoqiang, et al. Carbon emission-considered multi-objective dynamic optimal power flow of power system containing carbon-capture plant［J］.Power System Technology,2012,36（12）:11-17.
［9］叶承晋, 黄民翔. 考虑暂态稳定性的多目标最优潮流［J］. 中国电机工程学报, 2013, 33（10）: 137-144.
Ye Chengjin, Huang Minxiang. Multi-objective optimal power flow considering transient stability［J］.Proceedings of the CSEE,2013, 33（10）:137-144.
［10］ Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II［J］. IEEE Transactions on Evolutionary Computation, 2002, 6（2）: 182-197.
［11］ Mendoza F, Bernal-Agustin J L, Dominguez-Navarro J A. NSGA and SPEA applied to multiobjective design of power distribution systems［J］. IEEE Transactions on Power Systems, 2006, 21（4）: 1938-1945.
［12］ Zitzler E，Laumanns M，Thiele L. SPEA2: Improving the strength Pareto evolutionary algorithm［R］, 2001.
［13］张伯明, 陈寿孙. 高等电力网络分析［M］. 北京：清华大学出版社，1996.

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Multi-Objective Optimal Power Flow Based on Evolutionary Algorithm and Bargaining Game

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