While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent p...While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent power generation of renewable energy sources, such as Photovoltaic (PV) arrays, could introduce significant intermittency to a power grid. Meanwhile, uncontrolled PEV charging may cause load surge in a power grid. This paper studies the optimization of PEV charging/discharging scheduling to reduce customer cost and improve grid performance. Optimization algorithms are developed for three cases: 1) minimize cost, 2) minimize power deviation from a pre-defined power profile, and 3) combine objective functions in 1) and 2). A Microgrid with PV arrays, bi-directional PEV charging stations, and a commercial building is used in this study. The bi-directional power from/to PEVs provides the opportunity of using PEVs to reduce the intermittency of PV power generation and the peak load of the Microgrid. Simulation has been performed for all three cases and the simulation results show that the presented optimization algorithms can meet defined objectives.展开更多
可入网电动汽车(plug-in electric vehicle,PEV)近年来发展迅猛,考虑PEV与清洁能源大量并网后的差异性需求,构建兼顾输电网、配电网与用户利益的双层规划模型。上层输电网规划以系统运行成本最小、车主消费最少、弃风弃光惩罚最少、碳...可入网电动汽车(plug-in electric vehicle,PEV)近年来发展迅猛,考虑PEV与清洁能源大量并网后的差异性需求,构建兼顾输电网、配电网与用户利益的双层规划模型。上层输电网规划以系统运行成本最小、车主消费最少、弃风弃光惩罚最少、碳排放总量最小为目标,协同调度火电机组、新能源与PEV出力,实现PEV的时域调度;下层配电网模型以网损最小为目标,通过最优潮流计算实现PEV的空间调度。算例结果表明,对PEV进行有效调度并配合合理的电网规划方案,可降低电网运行成本,提高其对可再生能源的消纳能力。展开更多
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...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。展开更多
文摘While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent power generation of renewable energy sources, such as Photovoltaic (PV) arrays, could introduce significant intermittency to a power grid. Meanwhile, uncontrolled PEV charging may cause load surge in a power grid. This paper studies the optimization of PEV charging/discharging scheduling to reduce customer cost and improve grid performance. Optimization algorithms are developed for three cases: 1) minimize cost, 2) minimize power deviation from a pre-defined power profile, and 3) combine objective functions in 1) and 2). A Microgrid with PV arrays, bi-directional PEV charging stations, and a commercial building is used in this study. The bi-directional power from/to PEVs provides the opportunity of using PEVs to reduce the intermittency of PV power generation and the peak load of the Microgrid. Simulation has been performed for all three cases and the simulation results show that the presented optimization algorithms can meet defined objectives.
文摘可入网电动汽车(plug-in electric vehicle,PEV)近年来发展迅猛,考虑PEV与清洁能源大量并网后的差异性需求,构建兼顾输电网、配电网与用户利益的双层规划模型。上层输电网规划以系统运行成本最小、车主消费最少、弃风弃光惩罚最少、碳排放总量最小为目标,协同调度火电机组、新能源与PEV出力,实现PEV的时域调度;下层配电网模型以网损最小为目标,通过最优潮流计算实现PEV的空间调度。算例结果表明,对PEV进行有效调度并配合合理的电网规划方案,可降低电网运行成本,提高其对可再生能源的消纳能力。
文摘大规模的电动汽车(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。
