基于改进堆优化算法求解电动汽车并网动态经济调度  

Solving dynamic economic dispatch with electric vehiclebased on improved heap-based optimizer algorithm

作　　者：陈旭[1] 张智祥 Chen Xu;Zhang Zhixiang(School of Electrical&Information Engineering,Jiangsu University,Zhenjiang Jiangsu 212013,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《计算机应用研究》2024年第10期3032-3037,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(62303197)。

摘　　要：针对堆优化算法HBO处理含电动汽车的动态经济调度问题时存在收敛慢和精度低等问题,提出一种改进的堆优化算法RDHBO。首先,在RDHBO中引入了最优成员区域搜索和双种群交互策略。前者引导最优成员移动到更有希望的区域,提升了算法的收敛精度和收敛速度;后者充分利用被淘汰的劣势个体,丰富了种群的多样性,避免了算法陷入局部最优。然后,将RDHBO应用于四种充电场景的10机组电动汽车动态经济调度问题。仿真结果表明,与已有的代表性方法相比,RDHBO在产生低燃料成本和稳定性方面具有很强的竞争力。最后,对RDHBO的两种改进策略进行了消融实验,验证了两种改进策略的有效性。When dealing with dynamic economic dispatch problems involving electric vehicles(EVDED),HBO suffers from slow convergence and low accuracy.This paper proposed an improved heap-based optimizer algorithm called RDHBO.RDHBO adopted the optimal member region search and dual population interaction strategies.The former guided the optimal members to move to more promising regions,improving the convergence accuracy and convergence speed.The latter made full use of the eliminated inferior individuals,enriching the diversity of the population,avoiding the algorithm falling into the local optimum.It investigated a 10-unit EVDED problem with four charging scenarios.The simulation results show that RDHBO is highly compe-titive in generating low fuel cost and high stability compared with the existing representative methods.Finally,two improved strategies of RDHBO were subjected to ablation experiments,and the results show that combining the two strategies are effective.

关 键 词：动态经济调度 电动汽车 堆优化算法 区域搜索 双种群交互 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TM73[自动化与计算机技术—控制科学与工程]