A novel framework for optimizing residential load response planning with consideration of user satisfaction

This study presents an optimization framework for residential energy management that integrates photovoltaic (PV) systems, battery storage, and demand response strategies. The primary objective is to minimize electricity costs while ensuring efficient use of renewable energy resources. The proposed method utilizes the Meerkat Optimization Algorithm (MOA), which is compared against other optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning-Based Optimization (TLBO). The results show that the proposed MOA achieves significant cost reductions. For example, under Time-of-Use (TOU) tariffs, the total electricity cost is reduced by 14% compared to the base case, while under Real-Time Pricing (RTP), the reduction is 16%. The optimized system also yields a 5 kW PV system and a 10 kWh battery, compared to 3 kW PV and 6 kWh battery in the GA and PSO cases. Additionally, the MOA provides a more computationally efficient solution, with a calculation time of 73 s, compared to 91 s for GA and 102 s for PSO. This study demonstrates the effectiveness of the MOA in optimizing residential energy systems, providing a robust solution for reducing electricity costs while integrating renewable energy sources. The approach is generalizable to other energy management applications and can be adapted for various regions and household configurations.