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A fully decentralized controlled (FDC) electric vehicle (EV) charger, where its design is intended to mitigate the charging impact on power grids, is proposed in this paper. The proposed FDC EV charger uses the parameters measured from the power grid, EV charger and EV battery pack to adjust the charging current. Fuzzy logic control is used in this paper to integrate these measured parameters into the proposed FDC EV charger. As the proposed charger can be realized without a centralized control center and information exchanges between EV chargers, an FDC scheme for EV charger can therefore be effectively achieved. A prototype of the proposed FDC EV charger with a rated power of 500 W is implemented. Experimental results verify that the proposed charger can automatically adjust the charging current according to variations in the power grid and EV. Simulations analyze the impact of 100 EVs with a rated power of 7 kW charged simultaneously. Simulation results show that the EV charging impact on power grids can be effectively mitigated by the proposed FDC EV chargers. [ABSTRACT FROM PUBLISHER]

Utilizing battery storage systems (BSSs) can reduce the intermittent output of PV generation systems (PVGSs) and make them dispatchable. The aim of this paper is to design an optimal charging/discharging scheduling for BSSs such that the line loss of distribution systems interconnected with sizeable PVGSs can be minimized. A mathematical model for BSSs which can be used to simulate the charging procedures such as the commonly-used constant current to constant voltage (CC-CV) charging method, the discharging procedures and the state of charge (SOC) is proposed first. The minimum line loss problem considering the intermittent output of PVGSs and the scheduling of BSSs is then formulated based on the BSS mathematical model. The optimal charging/discharging scheduling of BSSs can then be obtained by a genetic algorithm (GA) based method. Test results demonstrate the validity of the proposed mathematical model and optimal charging/discharging scheduling for BSSs. [ABSTRACT FROM PUBLISHER]

This paper uses a charging selection concept for plug-in electric vehicles (PEVs) to maximize user convenience levels while meeting predefined circuit-level demand limits. The optimal PEV-charging selection problem requires an exhaustive search for all possible combinations of PEVs in a power system, which cannot be solved for the practical number of PEVs. Inspired by the efficiency of the convex relaxation optimization tool in finding close-to-optimal results in huge search spaces, this paper proposes the application of the convex relaxation optimization method to solve the PEV-charging selection problem. Compared with the results of the uncontrolled case, the simulated results indicate that the proposed PEV-charging selection algorithm only slightly reduces user convenience levels, but significantly mitigates the impact of the PEV-charging on the power system. We also develop a distributed optimization algorithm to solve the PEV-charging selection problem in a decentralized manner, i.e., the binary charging decisions (charged or not charged) are made locally by each vehicle. Using the proposed distributed optimization algorithm, each vehicle is only required to report its power demand rather than report several of its private user state information, mitigating the security problems inherent in such problem. The proposed decentralized algorithm only requires low-speed communication capability, making it suitable for real-time implementation. [ABSTRACT FROM PUBLISHER]