Demand response and renewable generation with optimization approach for energy management in smart grids

Rising electricity demand and the integration of renewable energy generation (REG) have led to increased variability in the daily load profile, posing significant energy management challenges. Smart grids (SGs), with their advanced demand response (DR) mechanisms and REG integration, offer promising solutions to these issues. This study presents a multi-objective optimization approach to schedule energy consumption for energy management in REG integrated SGs, targeting reducing operating expenses, decreasing emissions, and optimizing user comfort. A probability density function (PDF) is employed to model REG with greater accuracy. The proposed framework leverages the multi-objective fishswarm optimization algorithm (MOFSOA) to identify globally optimal solutions effectively. The findings indicate that MOFSOA with DR excels in simultaneously optimizing cost, emissions, and user comfort, outperforming solutions without DR and offering a robust strategy for sustainable and efficient energy management in SGs.