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作 者:彭显刚[1] 林利祥 刘艺[1] 王星华[1] 孟安波[1]
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:《中国电机工程学报》2015年第16期4077-4085,共9页Proceedings of the CSEE
基 金:广东省自然科学基金项目(10151009001000045)~~
摘 要:大规模的电动汽车(plug-in electric vehicle,PEV)和风力、太阳能等可再生能源(renewable energy sources,RES)发电并网使未来智能配电网规划需考虑更多不确定因素。在考虑PEV充电随机性和RES出力间歇性的基础上,利用机会约束规划法建立了计及环境成本、DG总费用和有功损耗的多目标分布式电源优化配置模型,并提出一种考虑随机变量相关性的拉丁超立方采样蒙特卡洛模拟嵌入纵横交叉算法(crisscross optimization algorithm-correlation Latin hypercube sampling Monte Carlo simulation,CSO-CLMCS)的方法对优化模型进行求解。该方法首先根据PEV和RES的概率模型及随机变量间的相关性,利用CLMCS概率潮流计算方法计算配电网概率潮流,并根据概率潮流结果检验约束条件及计算目标函数值,最后由CSO算法进行全局寻优得到最优配置方案。采用实际算例进行仿真,结果验证了所提模型和方法的可行性和有效性。As large-scale of plug-in electric vehicles(PEV) and renewable energy sources(RES) such as wind and solar energy integrated into power grid, the smart distribution network planning inevitably needs to consider more uncertainties. Given this background, under the chance constrained programming framework, a multi-objective optimal distributed generation planning model was established considering the randomness and correlation of RES generation and PEV, in which the environmental cost, DG's total cost and network loss were taken as the objectives. A correlation Latin hypercube sampling Monte Carlo simulation(CLMCS) embedded crisscross optimization algorithm(CSO)-based approach(CSO-CLMCS) was proposed to solve the optimization model. The method firstly used CLMCS to calculate the probabilistic load flow(PLF) according to the probability model of random variables. Then the constraint conditions were checked and the objective function value was obtained using the result of PLF. Finally, the optimal planning scheme was obtained by a new smart search algorithm, i.e. CSO. The simulation was conducted on an actual distribution network. The results validate the feasibility and effectiveness of the proposed model and method.
关 键 词:分布式电源 电动汽车 多目标规划 蒙特卡洛模拟 纵横交叉算法
分 类 号:TM71[电气工程—电力系统及自动化]
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