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In this paper, a new model for distribution network rearrangement (DNR) with regard to plug-in electric vehicles (PEVs) is presented. The proposed model is a random model (random programming) in which scenarios corresponding to vehicle uncertainty are simulated by the Monte Carlo method. The distribution system was also mathematically modeled by an approximate linear model. This way of modeling helps us to derive a new design based on linear integer programming. The electricity market is considered to be the day-ahead electricity market. Uncertainties regarding the price of electricity as well as the load of the distribution network are also considered. The simulation results on a 24-bus distribution system showed that the proposed model has the required efficiency. Uncertainties related to PEVs are modeled by random programming using Monte Carlo Simulation (MCS). The DNR problem is formulated as a MILP problem. This problem is then solved by the Banders decomposition (BD) algorithm. Numerical results also showed that if the PEV is charged intelligently in the DNR issue, then the cost of purchasing electricity from above will be lower than in the case where the PEV is not controlled in a controlled way. We also see a different switching strategy in these two cases, which indicates that considering the electric vehicle in the DNR problem is inevitable. In addition, a method for rearranging distribution networks to transfer load from one feeder to another was presented. It has been observed that this reduces the load of high-capacity feeders well and causes the construction of new feeders to be delayed. It was also observed that rearrangement of the distribution network leads to a reduction in network losses. Also, with the help of rearrangement, the amount of interrupted load can be significantly reduced.

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Rearrangement of Electrical Distribution Networks With Optimal Coordination of Grid-Connected Hybrid Electric Vehicles and Wind Power Generation Sources