Optimal Integration for DGs with EVs planning in Distribution Networks with Hybrid MC-GA Technique using Realistic Load Models
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Abstract
With the growing integration of Distributed Generations (DGs) and Electric Vehicles (EVs) into distribution grids, the need for efficient and intelligent management strategies has become paramount. The need for power consumption is increasing every day in accordance with the standard demand pattern. As a result, either creating a lot of electricity or minimizing losses is crucial. In order to minimize real and reactive power losses from the system's point of view, this paper presents the optimal performance index-based size and location determination of DGs, EVs and their coordination in distribution networks using realistic load models (RLMs) such as RLM-1, RLM-2, RLM-3, RLM-4, and RLM-5, respectively. Improvement of the system power factor (SPF) is defined in this analysis as the power system's performance both with and without different kinds of DGs and DGs with EVs for various RLMs. PHEVs type of EVs used in this paper. The hybrid Monte Carlo-Genetic Algorithm (MC-GA) is the basis of the simulation technique that is being suggested. Networks with IEEE-16 and 37 buses in their distribution systems have been used to test the suggested methods. Comprehensive simulations on a test distribution network are used to show that the suggested method is effective. The system's performance can be optimized in terms of grid stability, efficiency, and reliability, according to the results. By stressing the significance of employing a hybrid MC-GA technique to meet realistic load circumstances, this research advances optimal DG and EV solutions.
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