基于改进NGA的风光互补发电容量优化

Capacity Optimization of Wind/PV Hybrid Power Generation Based on Improved NGA

基于风光互补发电系统,为了确保负载端的可靠性和稳定性,使建造成本最小;同时,考虑到风光互补发电系统是多目标且复杂的非线性优化问题,很难建立与之对应的数学模型。引入了Pareto最优概念,提出一种改进小生境遗传算法(NGA)来处理问题,通过加入竞标赛选择算子,设计了动态的交叉和变异概率函数,融入了自适应策略,旨在提高算法的收敛速度,全局寻优能力。算例表明,采用改进的NGA算法比未改进的算法在进行系统优化时,收敛速度提高了60.8%,优化后的系统负荷缺电率(LPSP)减少了26.6%,供电概率过剩率(SPSS)减少了138%,证明了算法可行性。

This paper is based on the Wind/PV hybrid power generation system,and proposes a method in order to ensure the reliability and stability of the load end and minimize the construction cost;at the same time,considering that the Wind/PV power generation system is a multi-objective and complex nonlinear optimization problem,it is difficult to establish a corresponding mathematical model.This paper introduces the concept of Pareto optimality,and proposes an improved niche genetic algorithm(NGA)to deal with the problem.By adding the selection operator of the bidding competition,the dynamic crossover and mutation probability functions are designed,and the adaptive strategy is integrated to improve algorithm convergence speed and global optimization ability.The calculation example shows that when the improved NGA algorithm is used to optimize the system,the convergence speed is increased by 60.8%;loss of power supply probability(LPSP)is reduced by 26.6%,and surplus of power supply(SPSS)is reduced by 138%,which proves the feasibility of the algorithm.