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The spatial-temporal distribution of electric vehicle (EV) charging loads can be improved by implementing optimized charging scheduling, and by the joint planning of a distribution network (DN) and a charging network of EVs. In this paper, such a joint planning scheme was studied and modeled as two-level optimization model. In the upper level of the optimization, a joint planning model of a DN and a charging network of EVs with distributed photovoltaic (PV) systems and/or energy storage (ES) units is constructed to minimize the total cost. Then the optimized results of the DN and the EV charging network construction plan are used as the inputs of the lower-level operation model. In the lower-level operation optimization, an optimal charging scheduling approach is proposed to minimize the sum of the DN's operation cost and the traffic congestion cost. The spatial-temporal distribution of the charging demand, under an optimized charging strategy, is used as feedback for the upper-level optimization model, which effectively can reduce the scale of the DN and the charging network construction. The proposed joint planning model was demonstrated through applications in a city in China. [ABSTRACT FROM AUTHOR]

An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method. [ABSTRACT FROM PUBLISHER]

With the ever-increasing penetration of electric vehicles (EVs), extreme fast charging stations (XFCSs) are being widely deployed, wherein battery energy storages (BESs) are also installed for reducing the peak charging power. However, integrating the XFCS with a high-capacity power converter into the power distribution network (PDN) is difficult and uneconomical due to the restrictions regarding urban planning and high investment in PDN expansion. Considering the fluctuation in the EV charging demand and the limited capacity of the power converter, a collaborative policy for real-time EV charging power allocation and BES discharging power control is proposed based on Markov Decision Process (MDP), which is solved by the constraint deep deterministic policy gradient (CDDPG). The proposed model makes it possible to integrate the XFCS with reduced capacity power converter into the PDN with a minimal negative impact on the quality of service (QoS) of EV owners. Finally, the experimental evaluation with real-word data sets demonstrates that the proposed approach is more effective than benchmark methods in dynamically allocating charging power for XFCS. • The charging process and satisfaction for extreme fast charging are analyzed. • An MDP-based power allocation policy is proposed to enhance charging satisfaction. • A constraint DDPG algorithm is proposed to avoid excessive extra service time. • A cooperation policy for XFCS and battery energy storages is proposed. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management" by R. Li, Q. Wu and S. S. Oren in the January 2014 issue.