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Participation of distributed energy resources in the load restoration procedure, known as intentional islanding, can significantly improve the distribution system reliability. Distribution system reconfiguration can effectively alter islanding procedure and thus provide an opportunity to supply more demanded energy and reduce distribution system losses. In addition, high-impact events such as hurricanes and earthquake may complicate the procedure of load restoration, due to disconnection of the distribution system from the upstream grid or concurrent component outages. This paper presents a two-level method for intentional islanding of a reconfigurable distribution system, considering high impact events. In the first level, optimal islands are selected according to the graph model of the distribution system. In the second level, an optimal power flow (OPF) problem is solved to meet the operation constraints of the islands by reactive power control and demand side management. The proposed problem in the first level is solved by a combination of depth first search and particle swarm optimization methods. The OPF problem in the second level is solved in DIgSILENT software. The proposed method is implemented in the IEEE 69-bus test system, and the results show the validity and effectiveness of the proposed algorithm.

Due to increased energy consumption in upcoming years, the power system needs to be expanded to meet suitable technical conditions. The primary requirement is to gain accurate information about consumption growth in the planning horizon, which can be obtained via forecast studies. Since renewable sources can grow beside the demand, the accurate prediction should consider simultaneous changes in supply and demand in the future. In this paper, a reliability-constrained transmission expansion planning (RCTEP) is proposed. It simultaneously is based on the load forecasting and renewable sources production, named the net power demand forecasting technique (NPDFT). NPDFT consists of a time series-based logistic method, which forecasts loads at planning years. RES generation forecasting forecasts the following year’s generation by an estimated coefficient. RCTEP minimizes the summation of the planning, operation, and reliability cost so that it is limited to the AC optimal power flow equations, planning constraints, and reliability limitations for N – 1 contingency. Then, the stochastic programming based on the Monte Carlo Simulation and the simultaneous backward approach models the uncertainties of the load, RES power, and availability of network equipment. This problem is solved by the hybrid algorithm of grey wolf optimization and training and learning optimization algorithm to achieve the securable optimal solution with a low standard deviation. Generally, this paper contributes to predicting the net power demand, simultaneous modeling of operation, reliability, and economic indices, besides using hybrid algorithms to solve the defined problem. Finally, this strategy is implemented on the 3-bus, 30-bus, and 118-bus transmission networks in MATLAB software. The numerical results confirm the capabilities of the proposed method in improving network operation and reliability indices. Higher reliability can be found for the network by defining a desirable penalty price. Also, operation indices, such as voltage profile and power loss, increase more than 10% under these conditions.