净计量电价下电动汽车充电拖延策略的最优性研究
On the Optimality of Procrastination Policy for EV charging under Net Energy Metering
AI总结：
本文研究净计量电价下产消者电动汽车充电与屋顶太阳能、电池及柔性负荷的协同优化问题，证明拖延阈值策略最优，并提出短视策略，仿真显示其与理想上界的性能差距为0.5%-7.5%。
摘要：
我们考虑产消者（prosumer）在用户侧（behind-the-meter）进行电动汽车充电的问题，并与屋顶太阳能、电池储能以及诸如热水器和暖通空调（HVAC）等柔性用电负荷进行协同优化。在分时电价（time-of-use）净计量（net energy metering）电价机制下，考虑随机的太阳能发电和随机的电动汽车充电需求，我们构建了一个有限时域剩余最大化问题。我们证明，当电动汽车充电与柔性需求协同优化时，一种将电动汽车充电推迟到最晚可能时刻的拖延阈值策略（procrastination threshold policy）是最优的，且该策略的阈值可以很容易地离线计算。当电池储能也纳入协同优化时，我们证明在最优策略下，产消者的净用电量是用户侧可再生能源发电量的一个双阈值分段线性函数，并且拖延阈值策略仍然是最优的，尽管此时阈值无法轻易计算。我们提出了一种简单的短视（myopic）策略，并通过仿真证明，短视策略与一个理想上界（oracle upper bound）之间的性能差距约为0.5%至7.5%。
英文摘要：
We consider the problem of behind-the-meter EV charging by a prosumer, co-optimized with rooftop solar, electric battery, and flexible consumptions such as water heaters and HVAC. Under the time-of-use net energy metering tariff with the stochastic solar production and random EV charging demand, a finite-horizon surplus-maximization problem is formulated. We show that a procrastination threshold policy that delays EV charging to the last possible moment is optimal when EV charging is co-optimized with flexible demand, and the policy thresholds can be computed easily offline. When battery storage is part of the co-optimization, it is shown that the net consumption of the prosumer is a two-threshold piecewise linear function of the behind-the-meter renewable generation under the optimal policy, and the procrastination threshold policy remains optimal, although the thresholds cannot be computed easily. We propose a simple myopic solution and demonstrate in simulations that the performance gap between the myopic policy and an oracle upper bound appears to be 0.5-7.5%.
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