Modeling and optimization of hybridized solar PV–CHP–biomass energy system considering impact of battery energy storage system using firefly algorithm

The growing demand for electricity, heating, and cooling, coupled with rising fuel costs and environmental concerns, has intensified interest in hybrid and multi-energy systems capable of integrating renewable and conventional energy sources. This research work presents a hybrid solar photovoltaic–Combined Heat and Power–biomass (PV–CHP–biomass) energy system integrated with a battery energy storage system for cost-optimal energy management in rural and semi-industrial applications. A Firefly Algorithm (FA)-based stochastic optimization framework is developed to address photovoltaic intermittency using real solar irradiance data and time-of-use electricity tariffs. The proposed hybrid system integrates a 3.5 kW photovoltaic array, 700 kW combined heat and power (CHP) unit, 1000 kW boiler, 500 kWh battery energy storage system, and thermal storage facilities to simultaneously satisfy electricity, heating, and cooling demands of an abattoir facility. Simulation results obtained using Python and the Pymoo library demonstrate that the proposed intelligent scheduling strategy significantly improves system operational efficiency and economic performance. Comparative scenario analysis indicates that operational costs can be reduced by approximately 92.97% when BESS integration is implemented under hybrid operating conditions involving the grid, PV, CHP, biomass, and boiler systems. Furthermore, the proposed framework effectively minimizes grid dependency, improves storage utilization, and enhances operational flexibility under varying fuel price conditions. The findings confirm that the proposed hybridized energy framework provides a practical and economically viable approach for sustainable rural energy management.