风光互补独立供电系统的多目标优化设计

在风光互补独立供电系统的设计中,如何配置风力发电机、太阳能电池板和蓄电池,在满足负荷需求的前提下,使风能和太阳能资源得到充分利用,负荷的供电可靠性较高,而系统成本最小,这是一个多目标优化设计问题。本文首先建立了风力发电和光伏发电的天气模型,提出了衡量供电可靠性的失负荷概率LOLP、衡量清洁能源浪费的失能量概率LOEP和系统成本等指标,并采用蒙特卡罗仿真进行计算。为了求解这一多目标优化问题,本文提出了一种混沌自适应进化算法(CSEA),新算法的混沌初始种群算子提高了初代种群的多样性,分组选择策略保证了各代中一定数量的劣势个体能参与进化,自适应遗传算子增加了劣势个体的交叉和变异概率,从而避免算法早熟,增强了算法的全局搜索能力。算例表明,CSEA算法比传统单目标遗传算法的结果更加接近实际运行的Pareto优化前端,综合效果更优。另外,与纯光伏、纯风力发电方式比较发现,风光互补供电方式更为合理。可见,采用CSEA算法进行风光互补独立供电系统的优化设计,对于提高供电可靠性,降低成本,减少能源浪费具有非常重要的意义。

Multi-Objective Optimal Sizing of Stand-Alone Hybrid Wind/PV System

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.