Market Based Congestion Management in the Distribution System Under Electric Vehicle Integration

With the goal of reducing distribution network congestion caused by electric vehicle (EV) charging planned for a day-ahead basis, an economically efficient distributed optimization based dynamic tariff (DDT) is presented. All aggregators engage in the congestion control technique that adopts a decomposition-based optimization method. As a result, as compared to the traditional approach of day-ahead dynamic tariff, this method gives more predictability and clarity. In DDT approach aggregators provide their complete accumulated demand response (DR) and must be consider it during the operation. The DSO estimates congestion in days ahead and releases the DDT before to the settlement of the day-ahead market, using iterative interactions between the DSO and aggregators congestion control framework. As a result, the aggregators optimize their energy purchase portfolio considering expected price and publicly available DDT. The Roy Billinton Test System (RBTS four-feeder)'s network is being employed to perform case studies to illustrate the efficiency of the established approach towards preventing distribution network congestion caused by EV charging. The case study results show that the DDT technique, when compared to alternative decomposition-based approaches such as the multiagent system method, may minimize total energy usage and power losses costs.