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Widespread utilization of plug-in electric vehicles (PEVs) would incur more uncertainties, and thus challenge the traditional planning strategies for distribution systems. In this paper, a scenario-based comprehensive expansion planning (SCEP) approach for distribution systems is presented to cope with future uncertainties. This approach can not only take into account the initial investment and operation costs but also consider the expected adaptation cost to other future scenarios, and in this way the flexibility of an initial planning scheme can be achieved. Future scenarios are identified in line with three uncertainties, namely the conventional load levels, penetration levels of PEVs and whether or not with successful implementation of a coordinated charging strategy. The charging profile for dump charging mode has been determined straightforwardly, while that for coordinated charging mode is obtained by solving the proposed day-ahead dispatching model (DDM). Furthermore, a priority-based real time dispatching strategy (PRTDS) is proposed to obtain direct charging instructions for PEVs, and thus to demonstrate the rationality of utilizing the charging profile acquired from DDM in distribution system expansion planning (DSEP). Finally, the performance of the proposed methodology is demonstrated by numerical studies. [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 progressive exhaustion of fossil energy and the enhanced awareness of environmental protection, more attention is being paid to electric vehicles (EVs). Inappropriate siting and sizing of EV charging stations could have negative effects on the development of EVs, the layout of the city traffic network, and the convenience of EVs' drivers, and lead to an increase in network losses and a degradation in voltage profiles at some nodes. Given this background, the optimal sites of EV charging stations are first identified by a two-step screening method with environmental factors and service radius of EV charging stations considered. Then, a mathematical model for the optimal sizing of EV charging stations is developed with the minimization of total cost associated with EV charging stations to be planned as the objective function and solved by a modified primal-dual interior point algorithm (MPDIPA). Finally, simulation results of the IEEE 123-node test feeder have demonstrated that the developed model and method cannot only attain the reasonable planning scheme of EV charging stations, but also reduce the network loss and improve the voltage profile. [ABSTRACT FROM AUTHOR]