采用改进共生生物搜索算法的大规模电动汽车接入微电网协调优化研究

Research on Coordination and Optimization of Large Scale Electric Vehicles Connected to Microgrid Using Improved Symbiotic Organisms Search Algorithm

针对大规模电动汽车接入微电网运行问题,首先分析包含电动汽车的微电网架构、运行模式、输出特性和数学模型等,并利用蒙特卡罗方法建立电动汽车无序充电负荷模型,建立以微电网综合成本和峰谷差最小的目标函数。其次,为了求解该复杂、高维、非线性模型,且为提高标准共生生物搜索算法的寻优性能,提出改进型共生生物的搜索算法。该算法在标准SOS算法的生物种群初始化阶段采用准反射学习机制;在互利共生搜索阶段采用改进受益因子策略;在偏利共生搜索阶段采用收缩随机数产生因子区间策略。最后,通过电动汽车无序充电和协同优化运行场景实验,对微电网综合运行成本和峰谷差进行对比验证,结果表明,本文所提方法的科学性和有效性。

Aiming at the optimization and coordination operation of electric vehicles connected to microgrid,the architecture,operation mode,output characteristics and mathematical model of microgrid including electric vehicles are analyzed,and the disorderly charging load model of electric vehicles is established by using Monte Carlo method.An objective function is established to minimize the comprehensive cost and peak-valley difference of microgrid.To solve the complex,high-dimensional and model and enhance the performance of original symbiotic organisms search algorithm(SOS),a novel improved symbiotic organisms search algorithm algorithm(ImSOS)is proposed.In ImSOS,a quasi-reflection-based learning scheme is employed in the population initialization step of original SOS.Moreover,the strategy of the modifications of benefit factors is used in the mutualism phase of SOS.A strategy of narrowing the search range of randomly generated coefficients is adopted in the commensalism phase of SOS.Finally,by setting up scenarios of disordered charging and coordinated optimized operation of electric vehicles,the output comprehensive operating cost and peak-valley difference in microgrid are compared.The result presents the scientificity and effectiveness of the method proposed in this paper.