Summary
A smart distribution system should be able to restore interrupted customers as quickly as possible after outages. By optimal allocation of switching and protective devices, it is possible to enhance the reliability and increase the restoration capacity of the loads after an outage. In this paper, by taking into account uncertainty in the load data, a novel practical method for the simultaneous planning of optimum location of switching devices including tie‐lines and remote‐control switches (RCSs), fault indicators, and cut‐out fuses is proposed. In this paper, a method for minimization of the costs associated with equipment investment and interruption cost has been proposed. Practical parameters such as geographical position and conditions of the understudy network and feeders, and the configuration and possible constraints of the real network from the experts' viewpoint, have been considered in this study. The actual characteristics of the network are extracted by utilizing the analytical hierarchical process (AHP), the geographic information system (GIS) data, and the event recording software. To optimize the problem, the multi‐objective nondominated sorting genetic algorithm II (NSGAII) is employed. The efficacy of the proposed method is proved through simulation of a real medium‐voltage (MV) feeder.
The ever-increasing need for more reliable power supply as well as cost-e ective and environmentally friendly utilization of distributed energy resources will result in the formation of Multiple Micro-Grids (MMGs) in the near future of distribution system. To reach this prospect, coordination among MMGs is necessary. Accordingly, this paper proposes a new non-hierarchical multi-level architecture for the optimal scheduling of Active Distribution Network (ADN) with MMGs. The proposed model is a decentralized decision making algorithm to optimally coordinate the mutual interaction between local optimization problems of ADN and MMGs. A non-hierarchical Analytical Target Cascading (ATC) method is presented to solve the local optimization problems in parallel. Also, the underlying risks of energy trading caused by renewable generation uncertainty are re ected in both the objective functions and the constraints of local optimization problem. The numerical results of modi ed IEEE 33-bus distribution test system containing two microgrids demonstrate the e ectiveness and merits of the proposed model.
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