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.

In this paper, we propose two decentralized Electric Vehicle (EV) charge scheduling schemes for shaping the load curve of residential communities connected to the electric grid. The first scheme is designed for Coordinated Valley-Filling (C-VF) of the load curve via only EV charging. The second scheme is designed for Coordinated Valley-Filling and Peak-Shaving (C-VF-PS) of the load curve via both EV charging and discharging. In both schemes, a set of grid-connected EVs referred to as an `EV Group' (EVG) coordinates their charge (and discharge) schedules by means of an iterative routine. Specifically, at each iteration of the respective routine, each EV in the EVG updates its charge (and discharge) schedule using a water-filling based algorithm that is specifically tailored for load curve shaping. To accommodate heterogeneous EV arrival times, which are often non-deterministic, each of C-VF and C-VF-PS is implemented in two methods, which differ in the way the EVG is formed. The first method requires all the grid-connected EVs to reschedule at designated time intervals, whereas the second method requires each EV to schedule only once, yielding lower computation and communication overheads. Numerical simulation results confirm that, compared to uncoordinated EV charging, C-VF and C-VF-PS reduce the load variance (flattens the load curve) by 47% and 65%, respectively. Furthermore, Method 2 is shown to be more effective than Method 1, in terms of computation and communication overheads.

Distributed-optimization-based approaches for electric vehicle (EV) charging coordination are becoming increasingly important to enable a massive-scale EV rollout without driving costly distribution network reinforcement. This article proposes an algorithm called Dis-Net-EVCD for distributedly coordinated, network-aware EV charging and discharging in unbalanced distribution grids, incorporating both EV customer economics (least cost charging) and distribution network awareness. The alternating direction method of multipliers underpins the development of Dis-Net-EVCD, wherein we seek to remove the need for central coordinating agents and allow EVs to iteratively determine their charge–discharge profiles locally via peer-to-peer communication. Numerical simulations carried out on the IEEE 13-node test feeder with 600 residential EVs demonstrate that EV customers implementing Dis-Net-EVCD yield a total operational cost reduction of 78% compared to uncoordinated EV charging, while conforming with the voltage regulatory requirements and fulfilling all of the EV charging demands ahead of their expected departure times. Moreover, Dis-Net-EVCD is shown to be approximately 60 times computationally faster than its centralized counterpart.

Increasing levels of distributed solar photovoltaic (PV) generation has created significant technical challenges in distribution networks, particularly regarding voltage regulation. Small-scale behind-the-meter battery storage is a key element in effectively overcoming such challenges, while offering financial benefits for customers. In this paper, we propose an optimization-based approach for dispatching power from residential-scale battery storage for grid support and customer savings, considering only local (behind-the-meter) measurements. The proposed approach takes into account the physics of the grid and both real and reactive power (coupled) for voltage regulation and grid loss reduction. The optimization-based approach, termed Local-Quadratic Program (L-QP), is formulated based on the linear power flow equations (LinDistFlow). The proposed L-QP is benchmarked against an approach in which real power is used to maximize the customer benefit, while reactive power is dedicated for voltage regulation. Numerical simulations with the IEEE 13 and IEEE 123 Node Test Feeders, which include realistic time-varying data for residential load and PV generation, demonstrate the technical advantages of the proposed approach.

We propose an innovative framework termed phasor-based control (PBC) to facilitate the integration of heterogeneous and intermittent distributed energy resources (DER) on the electric grid. PBC presents a unified approach that is agnostic to optimization criteria and to the particular characteristics of participating resources. It is enabled by synchronized, high-precision voltage phasor measurements that allow stating control objectives in grid-specific, rather than resource-specific, terms. We present qualitative justification and examine the general feasibility of this control approach, including the behavior of candidate control algorithms in simulation. Initial results suggest that PBC has significant potential to support stable and resilient grid operations in the presence of arbitrarily high penetrations of DER.