Optimized rule-based energy management for AC/DC hybrid microgrids using price-based demand response

• A novel rule-based EMS integrates real-time forecasts and price signals with adaptive thresholds fine-tuned using PSO optimization. • The system achieves 64.97% annual cost savings and full peak-hour grid independence through strategic PV-BESS coordination. • Introduces three intelligent battery states that dynamically respond to pricing and generation, enabling high operational flexibility. • Demonstrates real-world applicability via a seasonally tested, grid-connected hybrid microgrid at an educational institution. • PSO-tuned EMS delivers faster, scalable, and computationally efficient performance over GA, validating suitability for near real-time control. The increasing integration of renewable energy sources (RESs) and battery energy storage systems (BESSs) into hybrid AC/DC microgrids offers opportunities for cost reduction and flexibility but poses challenges in control. This paper proposes a PSO-tuned rule-based energy management system (EMS) that coordinates photovoltaic (PV) generation, BESS, and the utility grid under dynamic pricing. The framework integrates price-based demand response (DR), adaptive battery operation rules, and real-time forecasts to minimize energy consumption cost (ECC). Compared with Genetic Algorithms, PSO achieves faster convergence and higher computational efficiency. A case study at an educational institution demonstrates significant seasonal ECC reductions—39.4% in autumn, 76.5% in winter, 65.0% in summer, and 79.5% in spring—resulting in annual savings of 64.97% (from INR 3.40 million to INR 1.19 million). The EMS ensures intelligent load shifting, optimal battery utilization, and zero grid import during peak tariffs while enabling surplus PV injection. Results confirm the proposed approach as a scalable, efficient, and practical solution for reducing costs, improving renewable self-consumption, and enhancing resilience in next-generation hybrid microgrids.