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Multi-Objective Optimal Siting and Sizing of Distributed Generators and Shunt Capacitors Considering the Effect of Voltage-Dependent Nonlinear Load Models

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Load modeling is essential to distribution system analysis, planning, and control. Therefore, in this work, effect of non-linear load models has been considered for the optimal site and size of… Click to show full abstract

Load modeling is essential to distribution system analysis, planning, and control. Therefore, in this work, effect of non-linear load models has been considered for the optimal site and size of DG and SC allocation. A new and efficient modified branch and bus ordering-based forward-backward load flow method has been applied to solve load flow problem in radial distribution system. Recently implemented several state-of-the-art evolutionary algorithms (EAs) are employed to solve optimal site and size of DG and SC allocation problems and it is shown that performance of multi-operator/multimethod is better than other algorithms that are based on a single operator and/or algorithm. Therefore, a new hybrid EA based on various state-of-the-art operators such as GA, DE, and PSO is designed and applied to solve optimal site and size of DG and SC allocation problems. Various technical objective functions (index of active and reactive power loss and voltage deviation index) are considered to show the impacts of non-linear load models. From the simulation results, it is shown that DG and SC allocation problem is multi-objective. Therefore, further weighted sum multi-objective technical, economic, and environmental functions are formulated to find the solution to DG and SC allocation problems. The gathered results demonstrate that the proposed methodology significantly minimizes the cost of energy supplied by grid, total operating cost, and active and reactive power losses. Consequently, it can be stated that the suggested methodology has considerable economic and technological benefits and may be used to address many optimization issues in various distribution networks.

Keywords: load models; multi objective; effect; methodology; allocation; load

Journal Title: IEEE Access
Year Published: 2023

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