Cost Minimization in Residential Hybrid Energy Systems Using Advanced Scheduling Algorithms

This study proposes an advanced optimization methodology for managing a grid-connected residential hybrid thermal and electrical energy system, incorporating a combined heat and power (CHP) fuel cell and a battery-based energy storage system (ESS). A predictive scheduling framework is designed to optimize the operational plan for distributed energy resources (DER) over a 24-hour period. The main objective is to reduce the operational expenses of a smart home by strategically allocating resources while considering dynamic electricity tariffs and the efficiency of the ESS. To achieve this, an enhanced Adaptive Gravitational Search Algorithm (AGSA) is employed. The research also includes a comparative evaluation of the AGSA against conventional Gravitational Search Algorithm (GSA) and Harmony Search Algorithm (HSA) methods. This comparison underscores the AGSA's superior performance in optimizing residential energy systems. The findings offer significant insights into the application of optimization algorithms for improving cost-efficiency and energy management in modern smart homes.