Enhanced energy management in smart microgrids using hybrid optimization and demand response strategies - Lei Yang; Lile Wu; Genzhu Li (International Journal of Electrical Power & Energy Systems)
This paper presents a groundbreaking optimization model for efficient and resilient energy management in smart microgrids, particularly addressing challenges posed by decentralized renewable energy sources (RES) integration. The proposed model advances microgrid performance by minimizing operational costs, reducing pollution emissions, and maximizing RES utilization. A distinctive feature of this model is its hybrid strategy, which combines Demand Response Plans (DRPs) with an Incline Block Tariff (IBT). This approach engages industrial, commercial, and residential consumers to manage the inherent uncertainty and non-linearity of RES, thereby enhancing operational flexibility and reliability. In addition, a two-stage adjustable robust optimization model is introduced, supporting day-ahead planning that ensures secure and cost-effective energy management even under significant uncertainties in renewable energy production. The model is evaluated through two scenarios—operational cost and pollution emissions, and operational cost and availability—both with and without the DRPS and IBT scheme. Simulation results demonstrate that the model achieves substantial reductions in both costs and emissions while significantly improving system reliability and availability. The hybrid optimization model significantly enhances smart microgrid management. With the combined DRPS and IBT scheme, operational costs dropped by 23.21%, and emissions from wind and solar power decreased by 7.7% and 3.7%, respectively. This model improves cost-effectiveness and availability, optimizing renewable energy integration in microgrids.