Development and Application of a novel Improved Hybrid Multi Objective Black Widow Optimization Algorithm for optimization of a utility network connected hybrid system in view of Cost of Electrical Energy, Dependability, impact on atmosphere

Grid-connected hybrid systems functioning assessment is mostly based on the system’s cost, dependability, and ability to reduce infrared absorption gas (IRAG). A framework comprising a wind turbine (WTU) system, battery set, and a solar photovoltaic (PV) array coupled to a utility network has been modelled as well as subjected to multi-goal optimization. Here, the main target is the optimum decrease in the cost of electrical energy (COE) as well as loss of power supply probability (LPSP), along with incrementing renewable energy factor (REF) and achieving optimum IRAG. The hybrid framework’s yielded power was estimated using mathematical models of components. The outcomes demonstrated each system component’s contribution to the overall energy used to meet the electrical demand. The novelty of the paper lies in the development and application of a new hybrid algorithm which is named an improved hybrid multi-objective black widow optimization algorithm (IHMOBWOA) for the optimization of the system. The link between the suggested hybrid network and the grid was examined in terms of the system’s capacity to sell or purchase electricity from the suggested network. To examine this relationship, three scenarios were put forth. Here, the multi-objective (MO) based black widow optimization algorithm (MOBWOA), MO-based mantis search algorithm (MOMSA), and MO-based coati optimization algorithm (MOCOA) are all contrasted with the suggested technique for assessing the performance of the suggested methodology.