基于衍生搜索政治优化算法解决含可再生能源的多区域经济调度问题

Derivative Search-based Political Optimization Algorithm for Multi-area Economic Dispatch Considering Renewable Energy

随着可再生能源并入多区域电力系统,其不确定性大大增加了电力系统多区域经济调度的复杂度。如何高效求解含有风力和太阳能的多区域经济调度(multi-areaeconomic dispatch containing wind and solar energy,MAEDWS)问题面临着严峻的挑战。针对现有优化算法在处理MAEDWS问题时存在收敛速度慢和求解精度低等不足,该文提出一种基于衍生搜索的政治优化(derivative search-based political optimizer,DSPO)算法。在政治优化算法的基础上,引入首脑引领策略和衍生搜索机制。前者引领候选解前往更有希望的区域,加快收敛速度;后者在区域获胜者周围衍生邻域解,丰富多样性。该文将DSPO算法和其他6种代表性算法应用于MAEDWS问题,并进行对比分析。收敛曲线和性能指标的结果表明DSPO算法在收敛效率、求解精确度、稳定性方面取得了整体最优。

With the integration of renewable energy sources into the multi-area power system,their uncertainty has greatly increased the complexity of the multi-area economic dispatching.It is a great challenge how to efficiently solve the multi-area economic dispatching containing the wind and solar energy(MAEDWS).In this paper,a novel Derivative Search-based Political Optimization(DSPO)algorithm is proposed to address the shortcomings of slow convergence speed,low solution accuracy,and so on in the existing optimization algorithms in solving the MAEDWS problems.A leader guide strategy and a derivative search mechanism are introduced into the political optimization algorithm.The former leads the candidate solutions to the more promising regions to speed up the convergence,while the latter derives some neighborhood solutions around the regional winners in the partisan election stage to enrich the diversity of the sollutions.The DSPO algorithm is applied to the MAEDWS problem and compared with the other six well-regarded algorithms applied to it respectively.The simulation results show that the DSPO algorithm achieves the overall best performance in terms of the convergence efficiency,the solution accuracy,and the stability.