Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, faces many problems such as increasing losses, voltage deviation, line overloads, etc. The optimization of real and reactive due to installation energy resources at appropriate buses can minimize losses improve profile, especially for congested networks. As result, optimal distributed generation allocation (ODGA) problem considered proper tool processes planning operation systems grid changes expeditiously based on type penetration level renewable sources (RESs). This paper modifies AO using quasi-oppositional-based learning operator address this reduce burden primary grid, making resilient. To demonstrate effectiveness MAO, authors first test algorithm performance twenty-three competitions evolutionary computation benchmark functions, considering different dimensions. In addition, modified Aquila optimizer (MAO) applied tackle problem. proposed ODGA methodology presented in multi-objective function comprises decreasing loss total deviation distribution while keeping operating security restrictions mind. Many publications investigated effect number DGs, whereas others found out influence DG types. Here, examines effects types capacities units same time. approach tested IEEE 33-bus cases with several multiple types, including multi-objectives. obtained simulation results are compared optimizer, particle swarm algorithm, trader-inspired algorithm. According comparison, suggested provides superior solution faster convergence DNs.