Heuristic-based optimization for smart home energy management with renewable integration and energy storage systems

Abstract

The rapid growth of energy demand in urban areas emphasizes the necessity of efficient demand-side management (DSM) strategies in smart homes. This study presents a heuristic-based optimization framework for appliance scheduling in smart home environments that incorporates renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The objective is to minimize the electricity cost and the peak-to-average ratio (PAR) while satisfying the user comfort constraints. Three optimization techniques, genetic algorithm (GA), binary particle swarm optimization (BPSO), and wind-driven optimization (WDO), are implemented and evaluated in three scenarios: without renewable integration, with RSERs, and with both RSERs and ESSs. Simulation results show that BPSO has the lowest electricity cost and carbon emissions, while WDO has a faster convergence speed and competitive performance in PAR reduction. RSER and ESS integration has a major positive impact on energy efficiency, grid dependence, and sustainability. The results give useful insights to choose appropriate optimization methods and contribute to the development of effective, sustainable, and user-centric smart home energy management systems.