基于免疫二进制萤火虫算法的主动配电网低碳目标网架规划

Active Distribution System Planning for Low-Carbon Objective Using Immune Binary Firefly Algorithm

主动配电网(ADS)网架规划是一个非常复杂的大规模组合优化问题.萤火虫算法(FA)是一种新型的智能优化算法,全局搜索能力强、算法结构简单,而且收敛速度快.在此基础上借鉴生物免疫机制,群体更新时考虑抗体浓度,进一步提高种群的多样性和算法全局寻优能力,提出了一种免疫二进制萤火虫算法(IBFA)进行主动配电网网架规划.以线路投资、运行维护、网损和碳排放环境成本最小为目标,考虑分布式电源(DG)与柔性负荷(FL),建立了主动配电网网架规划模型.通过与二进制粒子群优化(BPSO)算法对比求解IEEE-14节点算例,验证了免疫萤火虫算法在全局寻优能力和收敛性方面的优越性,同时证明分布式电源和柔性负荷是抑制碳排放、提高系统整体效益的有效方式.

Active distribution system(ADS)planning is a complicated process of large-scale combinatorial optimization.Firefly algorithm(FA)is a novel intelligent optimization algorithm with global search capability,simple structureand high convergence speed.Considering updating antibody population to increase diversity and global optimizationability and based on biological immune system,this paper proposed an immune binary firefly algorithm(IBFA)to solve the active distribution planning problem.To achieve the minimization of total cost including theline investment,operation and maintenance,net loss,and the environmental cost of CO2emission from electricitygeneration,we established ADS planning model taking distributed generation(DG)and flexible load(FL)into account.Compared with binary particle swarm optimization(BPSO)algorithm to solve the IEEE-14node system case,IBFA is verified to be superior in global search ability and convergence.Meanwhile,it is proved that DG and FL aretwo effective ways to reduce CO2emission and improve system benefit.