Optimizing Smart Home Microgrid Through Demand Response Based on Mandani Fuzzy Inference System
Abstract
This study presents the development and implementation of a demand response-based energy management strategy using a Mamdani Fuzzy Inference System (FIS) within a smart home microgrid powered by hybrid renewable energy sources in Garoua, Cameroon. The system integrates photovoltaic (PV), wind turbine (WT), and battery storage components, whose optimal configuration was determined using Particle Swarm Optimization (PSO) to minimize the Levelized Cost of Energy (LCOE). A fuzzy logic controller dynamically manages energy flow and prioritizes controllable household loads based on five real-time inputs: PV output, WT output, battery state of charge (SoC), electricity consumption, and time of day. Simulation results demonstrate a reduction in energy consumption by 8.675%, enhanced utilization of renewable resources, and 15.0676% energy export to the grid, offering economic opportunities through feed-in tariffs or net metering. The proposed FIS-based demand response strategy effectively flattens load peaks, fills valleys, and improves the overall efficiency, flexibility, and resilience of the microgrid without compromising user comfort.
Keywords
Smart home microgrid, Demand response, Fuzzy inference system, Energy optimization, Renewable energy integration