Electric vehicles (EVs) are considered to be a promising solution for current gas shortage and emission problems. To maximize the benefits of using EVs, regulated and optimized charging control needs to be provided by load aggregators for connected vehicles. An EV charging network is a typical cyberphysical system, which includes a power grid and a large number of EVs and aggregators that collect information and control the charging procedure. In this paper, we studied EV charging scheduling problems from a customer's perspective by jointly considering the aggregator's revenue and customers' demands and costs. We considered two charging scenarios: static and dynamic. In the static charging scenario, customers' charging demands are provided to the aggregator in advance; however, in the dynamic charging scenario, an EV may come and leave at any time, which is not known to the aggregator in advance. We present linear programming (LP)-based optimal schemes for the static problems and effective heuristic algorithms for the dynamic problems. The dynamic scenario is more realistic; however, the solutions to the static problems can be used to show potential revenue gains and cost savings that can be brought by regulated charging and, thus, can serve as a benchmark for performance evaluation. It has been shown by extensive simulation results based on real electricity price and load data that significant revenue gains and cost savings can be achieved by optimal charging scheduling compared with an unregulated baseline approach, and moreover, the proposed dynamic charging scheduling schemes provide close-to-optimal solutions.

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Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating real and/or reactive power at the connection point. Yet existing local autonomous control scheme of SIs is based on local information without coordination, which can lead to suboptimal performance. In this paper, a deep reinforcement learning (DRL) based algorithm is developed and implemented for coordinating multiple SIs. The reward scheme of the DRL is carefully designed to ensure voltage operation limits of the grid are met with more effective utilization of SI reactive power. The proposed DRL agent for voltage control can learn its policy through interaction with massive offline simulations, and adapts to load and solar variations. The performance of the DRL agent is compared against the local autonomous control on the IEEE 37 node system with thousands of scenarios. The results show a properly trained DRL agent can intelligently coordinate different SIs for maintaining grid voltage within allowable ranges, achieving reduction of PV production curtailment, and decreasing system losses.