ADVANCED METAHEURISTIC OPTIMIZATION OF RESIDENTIAL ENERGY MANAGEMENT USING GREY WOLF OPTIMIZER, RCF-GWO, AND HYBRID PSO-GWO FOR MULTI-OBJECTIVE SCHEDULING AND PERFORMANCE ENHANCEMENT
Nilesh Suresh Mahajan, Prashant J. Gaidhane
Abstract: The paper presents a comprehensive Home Energy Management System (HEMS) architecture which incorporates the more detailed operational models of non-interruptible appliances, interruptible appliances, thermal loads, photovoltaic (PV) generation, and battery energy storage. A new four-objective fitness criterion is proposed that optimizes all four variables, namely, electricity price, user dissatisfaction, household peak load, and battery cycling stress in order to allow grid-compatible and asset-conservative scheduling. The planning includes improved user-based dissatisfaction models including tolerance levels and thermal comfort limits in providing a better interpretation and realistic view of the residential behavioral preferences. In order to resolve the emerged multi-objective mixed-integer nonlinear optimization problem, three modern metaheuristic algorithms are tailored and tested the standard Grey Wolf Optimizer (GWO), a Rational Cooperative Foraging-based GWO (CFGWO/RCF-GWO) as well as a Hybrid PSO-GWO strategy. The process involves mathematical formulation of all algorithms, having Pareto-based selection and experimenting with realistic household loading process, outdoor temperature fluctuations and time of use tariffs. The results of simulations of three cases studies prove the superiority of the suggested improved algorithms. CFGWO is the best in terms of the total cost (46.96), the enhancement of the comfort level (0.388), and the percentage of PV used (75.2%), which are higher than normal GWO and Hybrid PSO-GWO. These findings confirm the efficiency of the suggested framework of modeling and explain the possibilities of further swarm-intelligence tools to cooperate and optimize residential energy scheduling, user comfort, and load management on the distribution level.
Keywords: GWO; PSO; CFGWO; PV; HEMS
DOI: 10.24874/PES08.03.015
Recieved: 09.06.2026 Revised: 10.08.2026 Accepted: 28.08.2026
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