Several studies have suggested that battery storage co-located with solar photovoltaics (PV) benefits electricity distributors in maintaining system voltages within acceptable limits. However, without careful coordination, these potential benefits might not be realized. In this paper we propose an optimization-based algorithm for the scheduling of residential battery storage co-located with solar PV, in the context of PV incentives such as feed-in tariffs. Our objective is to maximize the daily operational savings that accrue to customers, while penalizing large voltage swings stemming from reverse power flow and peak load. To achieve this objective we present a quadratic program (QP)-based algorithm. To complete our assessment of the customer benefit, the QP-based scheduling algorithm is applied to measured load and generation data from 145 residential customers located in an Australian distribution network. The results of this case study confirm the QP-based scheduling algorithm significantly penalizes reverse power flow and peak loads corresponding to peak time-of-use billing. In the context of feed-in tariffs, the majority of customers exhibited operational savings when QP energy-shifting.

Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.

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