【摘要】 为实现光伏电站功率输出最大化以及功率输出与调频(FR)信号之间的功率偏差最小化,提出了一种新型光伏电站最优阵列重构(OAR)模型。为快速获取最优Pareto前沿,采用了一种寻优性能高效的多目标黑猩猩优化器(MOBO)。采用了一种多准则妥协解排序法(VIKOR)的决策方法,从所获取的Pareto前沿中确定最佳折衷解。为验证所提出的OAR多目标优化的有效性,在局部阴影条件(PSC)下,对10×10的网状结构(TCT) PV阵列进行了固定FR信号和时变FR信号的两个案例研究。仿真结果表明,与无优化相比,所提方法可以明显减小两个目标函数的功率偏差。特别是,时变FR信号下,所获得的功率偏差仅为无优化的51.10%和64.45%。更多还原

【Abstract】 A mathematical model of optimal array reconfiguration(OAR) is proposed for maximizing the power output and minimizing the power deviation between the power output and the frequency regulation(FR) signal of a photovoltaic power plant. The multi-objective bonobo optimizer(MOBO) is designed to rapidly obtain an optimal Pareto front because of its high optimization efficiency. The decision-making method called VIseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR) is employed to determine the best compromise solution from the obtained Pareto front. To verify the effectiveness of the proposed multi-objective optimization, two case studies with constant and varying FR signal are carried out on a 10×10 Total-Cross-Tied(TCT) PV array under partial shading conditions(PSC). The simulation results show that the proposed method can significantly reduce the power deviation of the two objective functions compared to that without optimization. Both power deviations obtained by the proposed method under varying FR signal are only 51.10% and 64.45% of that without optimization.更多还原