ABSTRACT This study presents a novel and robust multistage hybrid energy management algorithm for Home Energy Management Systems (HEMS), designed to optimize the integration of photovoltaic (PV) systems and energy storage systems (ESS) within home‐to‐grid (H2G) interactions. The proposed approach consists of two distinct stages: a global optimization stage and a local control stage. In the global stage, advanced optimization algorithms are utilized for the optimal scheduling of household appliances, aiming to minimize electricity costs and reduce the peak‐to‐average ratio (PAR). In the local stage, a rule‐based control strategy dynamically regulates ESS charging and discharging to ensure efficient utilization of surplus PV generation while minimizing grid dependency. Moreover, the study employs the Nondominated Sorting Genetic Algorithm II (NSGA‐II), which eliminates the need for predefined weights by generating not a single optimal solution but a comprehensive set of Pareto‐optimal solutions. The multistage structure demonstrates high effectiveness by intelligently managing the ESS, efficiently exploiting surplus PV energy, and significantly enhancing self‐consumption during high‐tariff periods. This coordinated control leads to electricity cost savings ranging from 38.53% to 61.38% and PAR reductions between 25.63% and 16.80% across various system configurations. The proposed framework generates 13, 14, and 5 Pareto‐optimal solutions for the grid‐only, grid + PV, and grid + PV + ESS scenarios, respectively, thereby offering a wide spectrum of trade‐off choices for decision‐makers. In contrast, traditional optimization methods such as particle swarm optimization (PSO), gray wolf optimization (GWO), and ant colony optimization (ACO) are limited to a single compromise solution due to their reliance on fixed weighting coefficients, restricting their ability to explore diverse objective trade‐offs.
Danish et al. (Sun,) studied this question.