A Hierarchical LP–PSO Optimization Framework for Energy Management in a Hybrid AC/DC Campus Microgrid

As renewable energy resources become more and more prevalent in campus distribution networks, there is a need for efficient energy management strategies that can coordinate photovoltaic (PV) generation, battery energy storage and power exchange with the utility grid. This is an optimization-based Energy Management System (EMS) for a hybrid AC/DC campus microgrid (AC/CM), which represents the operating characteristics of the Khwaja Fareed University of Engineering and Information Technology (KFUEIT), Rahim Yar Khan, Pakistan. The proposed model is developed using Linear Programming (LP) and Particle Swarm Optimization (PSO) to find an economically efficient operating schedule for the system while meeting power-balance, battery state-of-charge, inverter capacity, and grid operating constraints. LP is utilized to find a feasible scheduling solution, and PSO then guides a search within the feasible solution space to enhance the objective of the electricity purchasing cost. The framework is tested under clear-sky and cloudy-sky PV-generation scenarios through MATLAB R2026b/Simulink. The reported results show that use of PSO under clear-sky and cloudy-sky conditions reduced electricity cost by 15.32% and 14.61%, respectively, whereas under clear-sky and cloudy-sky conditions, respectively, the electricity cost reduction using LP was 8.35% and 9.52%. The results also show a decrease in battery cycling and grid-power variations, with PSO showing a better improvement than LP under the tested conditions. The reported PSO performance under the 40% reduction in solar irradiance is still about 92% of the clear-sky reference performance. The outcomes illustrate the value of optimization-based energy management in enhancing the economic and operational performance of the simulated KFUEIT campus microgrid, as renewable generation fluctuates.