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| Multi-Objective Optimal Sizing of Stand-Alone Hybrid Wind/PV System |
| Song Honglei, Wu Junyong, Ji Luyu, Gao Lizhi, Liu Yinlei, Huang Pengzhou |
| Beijing Jiao Tong University Beijing 100044 China |
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Abstract In optimal sizing of stand-alone hybrid wind/PV system, how to make more full use of wind and solar energy source, and how to configure PV modules, wind generators and batteries based on the load demand with higher reliability and lower system cost is a multi-objective optimization problem in nature. In this paper, the wind generators and PV Systems are modeling at first. Loss of load probability (LOLP) and loss of energy probability (LOEP) indices which reflect the reliability level and the lost of energy are proposed and calculated through Monte Carlo simulation. To solve the multi-objective optimization problem, a so called chaos self-adaptive evolutionary algorithm (CSEA) multi-objective evolutionary algorithm is proposed in detail. Simulation results show that, The chaotic initial generation helps to improve the initial diversity, the Grouping selection strategy and self-adaptive genetic operator help to avoid pre-maturity and enhance the global searching ability of the algorithm. Comparison with the single-objective optimization show that CSEA outperforms GA in terms of diversity preservation and in converging closer to the pareto-optimal frontier in one-run-time. Stand-alone hybrid wind/PV system is more reasonable comparing with PV system or wind farm in power system. Application of CSEA on the Optimal Sizing of Stand-alone hybrid wind/PV system can improve system reliability, reduce the cost and energy waste, which has great significance.
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Received: 09 October 2009
Published: 07 March 2014
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[1] Mukund R. Patel. 风能与太阳能发电系统——设计、分析与运用[M]. 2版. 北京: 机械工业出版社, 2009.
[2] 刘山凤, 龙江, 方韬. 风光互补新能源成新趋势[J]. 电气技术, 2008(12): 1-2.
[3] 崔逊学. 多目标进化算法及其应用[M]. 北京: 国防工业出版社, 2006.
[4] 郑锁珍. 风光互补发电是最合理的独立电源系统[J].机械管理开发, 2008(6): 1-2.
[5] Shahirinia A H, Tafreshi S M M, Hajizadeh Gastaj A, et al. Optimal sizing of hybrid power system using genetic algorithm[C]. 2005 International Conference on Future Power Systems, 2005: 2-5.
[6] Xu Daming, Kang Longyun, Chang Liuchen, et al. Optimal sizing of standalone hybrid wind/PV power systems using genetic algorithms[C]. 2005 Canadian Conference on Electrical and Computer Engineering, 2005: 1722-1725.
[7] Knowles J, Corne D. The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization[C]. Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, Nanjing, 1999: 98-105.
[8] 徐大明, 康龙云, 曹秉刚.基于NSGA-II的风光互补独立供电系统多目标优化[J]. 太阳能学报, 2006, 27(6): 1-3.
[9] Srinivas N, Deb K. Multiobjective function optimization using nondominated sorting genetic algorithms[J]. IEEE Transactions on Evolutionary Computation, 1995, 2(3): 221-248.
[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): 182-186.
[11] 王志良, 邱林, 付强, 等. 混沌优化算法在非线性约束规划问题中的应用[J]. 华北水利水电学院学报, 2002, 23(2): 1-3.
[12] 张成, 李影, 邢伟. 基于适应度分组的进化策略[J].系统仿真学报, 2007, 19(21): 5081-5082.
[13] 刘勇, 张晓红. 遗传算法的多目标优化资源选择算法[J]. 火力与指挥控制, 2008, 33(2):3-4.
[14] IEEE Guide for Array and Battery Sizing in Stand-
Alone Photovoltaic Systems[S]. IEEE Standards Coordinating Committee 21, New York, USA, 2008.
[15] 沈辉, 曾祖勤. 太阳能光伏发电技术[M]. 北京: 化学工业出版社, 2004.
[16] Huang Pengzhou, Wu Junuong, Liu XiaoMin. Multi-
objective sizing optimization of standalone photovoltaic system[C]. The 1st International Confer- ence on Sustainable Power Generation and Supply, 2009, Nanjing, China, 2009: 2-4. |
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2011, Vol. 26 
