Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm - Ghadi, Yazeed Yasin; Kotb, Hossam; Aboras, Kareem M.; Alqarni, Mohammed; Yousef, Amr; Dashtdar, Masoud; Alanazi, Abdulaziz (Frontiers in Energy Research, 2023)
A combined algorithm for reconfiguring and placement of Energy Storage Systems (ESS), Electric Vehicles (EVs), and Distributed Generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network's resilience during critical periods is investigated as well as improving the network's technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem's objective function have been examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a hybrid Genetic Algorithm (GA) and Shuffled Frog Leaping Algorithm (SFLA) algorithm are used. The proposed GA-SFLA algorithm has been used to solve the problem with changes in their structure and its combination in three stages. Finally, the proposed method has been implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto-front. Moreover, it has introduced a more optimized architecture with lower loss, lower operating cost, and greater reliability compared to other optimization algorithms.