Next-generation power grids will likely enable concurrent service forresidences and plug-in electric vehicles (PEVs). While the residence powerdemand profile is known and thus can be considered inelastic, the PEVs' powerdemand is only known after random PEVs' arrivals. PEV charging scheduling aimsat minimizing the potential impact of the massive integration of PEVs intopower grids to save service costs to customers while power control aims atminimizing the cost of power generation subject to operating constraints andmeeting demand. The present paper develops a model predictive control (MPC)-based approach to address the joint PEV charging scheduling and power controlto minimize both PEV charging cost and energy generation cost in meeting bothresidence and PEV power demands. Unlike in related works, no assumptions aremade about the probability distribution of PEVs' arrivals, the known PEVs'future demand, or the unlimited charging capacity of PEVs. The proposedapproach is shown to achieve a globally optimal solution. Numerical results forIEEE benchmark power grids serving Tesla Model S PEVs show the merit of thisapproach.
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机译：下一代电网将可能为居民和插电式电动汽车（PEV）提供并发服务。虽然居住权需求曲线是已知的，因此可以认为是无弹性的，但仅在随机PEV到达后才知道PEV的功率需求。 PEV充电调度的目的是最大程度地减少将PEV大规模集成到电网中的潜在影响，从而为客户节省服务成本，而功率控制的目的是在运营限制和满足需求的情况下最大程度地降低发电成本。本文开发了一种基于模型预测控制（MPC）的方法来解决PEV联合充电调度和功率控制问题，以最大程度地降低PEV充电成本和发电成本，同时满足居民和PEV的电力需求。与相关工作不同，没有对电动汽车到达的概率分布，已知电动汽车的未来需求或电动汽车的无限制充电容量做出任何假设。所提出的方法显示出实现了全局最优的解决方案。服务于Tesla Model S PEV的IEEE基准电网的数值结果表明了该方法的优点。
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