计及电动汽车和可再生能源不确定因素的多目标分布式电源优化配置  

Multi-Objective Optimal Allocation of Distributed Generation Considering Uncertainties of Plug-in Electric Vehicles and Renewable Energy Sources

作　　者：彭显刚[1] 林利祥 刘艺[1] 林卓琼 

机构地区：[1]广东工业大学自动化学院,广东省广州市510006

出　　处：《电网技术》2015年第8期2188-2194,共7页Power System Technology

基　　金：广东省自然科学基金项目(10151009001000045)

摘　　要：大规模电动汽车(plug-in electric vehicle,PEV)和风光等可再生能源发电并网使配电网分布式电源(distributed generation,DG)定容选址需考虑更多的不确定因素,为此,利用机会约束规划方法建立了以环境效益、供电可靠性、DG总费用和有功损耗最优为目标的DG优化配置模型,并提出蒙特卡洛模拟嵌入改进量子粒子群(improved quantum particle swarm optimization algorithm-Monte Carlo simulation,IQPSO-MCS)的方法进行求解。在优化配置中考虑了风电、光伏、微型燃气轮机3种DG的选址和定容;并针对输出功率不确定的风力发电、光伏发电和电动汽车建立了概率模型,利用蒙特卡洛模拟法将随机性问题转化为确定性问题,实现含不确定因素的配电网随机潮流计算;最后由带自适应变异机制的IQPSO算法全局寻优得到最优配置方案。以IEEE 33节点测试配电系统为例,验证了所提模型和方法的有效性和实用性。As large-scale of plug-in electric vehicles（PEV） and renewable energy sources such as wind and solar energy integrated into power grid, distributed generation planning needs to consider more uncertainties. A multi-objective optimal DG allocation model considering environmental benefit, power supply reliability, DG＇s total cost and network loss is established under chance constrained programming（CCP） framework, and a Monte Carlo simulation-embedded Improved Quantum Particle Swarm Optimization Algorithm（IQPSO-MCS）-based approach is proposed to obtain optimal solution. Siting and sizing of three DG types, i.e. wind power, photovoltaic and micro-turbine, are reviewed. Uncertainties relating to DG and PEV are represented with probability models, MCS method is used to convert stochastic problem to deterministic and calculate the probabilistic load flow. Finally, the optimal allocation scheme is obtained with IQPSO algorithm with self-adaptive mutation mechanism. Feasibility and effectiveness of the proposed model and method are verified with simulation results for IEEE 33-bus test system.

关 键 词：分布式电源 电动汽车 不确定性 多目标规划 改进量子粒子群 

分 类 号：TM721[电气工程—电力系统及自动化]