Assessment of load management system for a grid connected renewable energy system coupled with battery for hospital load using hybrid firefly genetic algorithm

This study presents an advanced load and energy management system for a grid-connected hybrid energy system (HES) supplying hospital loads. The proposed configuration integrates solar, wind, battery storage, and grid power to ensure uninterrupted supply to critical, emergency, and non-critical hospital loads. To optimize energy dispatch under India's Time-of-Day (ToD) pricing scheme, a novel Hybrid Firefly Genetic Algorithm (HFAGA) is developed. The HFAGA optimizes the power transmission ratio, balancing battery charging, load supply, and grid interaction to maximize cost efficiency. Performance is benchmarked against the Ant Lion Optimization (ALO) algorithm and conventional load management strategies using MATLAB simulations with real-world weather and load profiles. Results demonstrate that HFAGA achieves a 30.4% daily cost reduction compared to conventional methods and outperforms ALO, which achieves a 27.6% reduction. Additionally, a 150 W experimental prototype using Delta HMI and a PIC microcontroller validates the system's operational effectiveness in real-time conditions. The proposed HFAGA-based system offers a robust, economically viable solution for energy management in critical healthcare infrastructure.