Fuzzy logic-based energy management in smart grids for renewable integration

Abstract

This study explores the creation and execution of energy management methods using fuzzy logic in smart grids, with the goal of effectively incorporating renewable energy sources. The research employs empirical data that includes information on renewable energy production, changes in energy use, the current state of battery storage, and control measures taken. The data analysis demonstrates significant variations in renewable energy sources, namely solar energy ranging from 350 kW to 410 kW, wind energy changing from 180 kW to 220 kW, and hydro energy varied from 120 kW to 150 kW. The energy consumption in different sectors exhibits varied patterns. Residential consumption ranges from 250 kW to 275 kW, industrial demand increases from 300 kW to 330 kW, and commercial consumption fluctuates from 200 kW to 225 kW. The battery storage status shows changes, with Battery 1 seeing an increase from 150 kWh to 165 kWh, Battery 2 fluctuating between 180 kWh and 195 kWh, and Battery 3 maintaining a stable range of 200 kWh to 215 kWh. The use of control actions based on fuzzy logic demonstrates flexibility, where Control Action 1 ranges from 0.6 to 0.8, Control Action 2 fluctuates from 0.5 to 0.7, and Control Action 3 varies from 0.6 to 0.9. The study highlights the flexibility and quick response of the energy management system based on fuzzy logic. It can adjust control actions in real-time to accommodate changes in renewable energy generation, consumption patterns, and battery storage. This indicates its potential to optimize energy flow and ensure grid stability in smart grids, facilitating the efficient integration of renewable energy.