Optimal energy management and emission reduction for a grid integrated industrial microgrid using improved osprey optimization algorithm

Grid integration of renewable energy resources in industry necessitates the need for effective energy management in the modern power system to ensure reliability, efficiency and sustainability. In this article, a comprehensive framework for energy management is proposed for a photovoltaic, wind, battery and grid integrated microgrid, using an Improved Osprey Optimization Algorithm (IOOA) to optimize the system parameters. A quantitative model is formulated to mathematically represent the microgrid, and a logic driven energy management system is proposed to coordinate power exchange and ensure system stability. An optimization problem is formulated for the microgrid to reduce the total generation expenses and emission levels and is solved using IOOA by integrating with the energy management system. The effectiveness of the proposed method is assessed through a comparative analysis with well-established metaheuristics algorithms, namely conventional Osprey Optimization Algorithm, Marine Predators Optimization Algorithm, and Particle Swarm Optimization Algorithm under MATLAB environment. The optimization results indicate that IOOA achieves 17.94%, 38.46% and 47.54% reduction in generation cost and 0.40%, 0.83% and 0.97% reduction in emission compared to the conventional OOA, MPA and PSO respectively. This highlights the effectiveness of the proposed IOOA in optimizing both economic and environmental objectives within the industrial microgrid system that contribute to the advancement of renewable energy integration. Additionally, the sensitivity analysis reveals that variations in solar irradiation, wind speed and load demand will have a significant effect on the system performance.