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Online optimal power management considering electric vehicles, load curtailment and grid trade in a microgrid energy market

机译:在微电网能源市场中考虑电动汽车,削减负荷和电网交易的在线最佳功率管理

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This paper proposes an online optimal power management problem for microgrid energy market considering electric vehicle (EV) charging/discharging, load curtailment and grid power transactions. Fuel and emission costs of conventional sources, hourly profits, incentives for load curtailment, revenue/cost of grid power trade and charging/discharging price of electric vehicles (EV) are considered by the microgrid central controller (MGCC) for the dispatch. A realistic model for EV is considered with its state of charge (SOC), age/life, charging/discharging rate limits, trip period and number of switching. Two different objectives of the MGCC viz. operational cost minimization and overall profit maximization are compared in the CIGRE LV benchmark microgrid in terms of revenue, expense, node voltage, curtailed load, EV power, grid trade and overall execution time. Particle swarm optimization with time varying acceleration co-efficient (PSO-TVAC) and modified backward forward sweep (BFS) based optimal power flow (OPF) method is used to optimize the benefits.
机译:本文提出了一种针对微电网能源市场的在线最优电源管理问题,该问题考虑了电动汽车(EV)的充电/放电,负载削减和电网电力交易。微电网中央控制器(MGCC)会考虑常规能源的燃料和排放成本,小时利润,削减负荷的激励措施,电网贸易的收入/成本以及电动汽车的充电/放电价格(EV),以进行调度。考虑电动汽车的现实模型及其充电状态(SOC),寿命/寿命,充电/放电速率限制,跳闸时间和开关次数。 MGCC的两个不同目标。在CIGRE LV基准微电网中,从收入,费用,节点电压,削减的负载,EV电源,电网交易和总体执行时间方面比较了运营成本最小化和总体利润最大化。使用具有时变加速度系数(PSO-TVAC)和基于改进的后向前向扫描(BFS)的最优功率流(OPF)方法的粒子群优化来优化收益。

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