Optimizing Grid-Connected Photovoltaic (PV) Battery Energy Storage through Multi-Objective Ant-Lion Optimization (MOALO)

As the demand for renewable energy continues to rise, it becomes crucial to discover effective ways to enhance grid-connected photovoltaic (PV) battery energy storage systems.The Institute of Petroleum Studies (IPS) complex at the University of Port Harcourt in Rivers State, Nigeria, embarked on a quest to determine the optimal approach for optimizing their PV battery energy storage system.This research aimed to fulfill this need by employing a diverse research methodology, incorporating the innovative MOALO theory.To begin with, the research gathered primary and secondary data to construct models for the power grid, solarPV, and battery.Furthermore, it meticulously analyzed the load profile of the IPS complex, at the University of Port Harcourt.Leveraging the power of the MOALO theory.The researchers accurately sized the system and evaluated the potential outcomes of simultaneously interconnecting all loads.To gauge the system's performance, there was a calculation of various parameters such as economics, random walk, boundary conditioning, entrapping ants, and ant trap development.Remarkably, the outcome showed that the fitness responses between the two trial runs, facilitated by the integration of MOALO, were strikingly similar, revealing a typical concaveconnected shape, which is characteristic of a multi-objective solver.The optimal multi-objective cost implication of the system was estimated to be around 4,300 USD, with a power mismatch performance of approximately -1.7819e+09.Based on these compelling findings, the study concluded that MOALO serves as an impressive optimization tool capable of minimizing power mismatches and optimizing costs.Moreover, it recommended the generation of excess power as a means to achieve sustainability.