Integrated multi-objective energy management of a smart microgrid via coordinated smart energy hub and dynamic demand response under renewable uncertainty

With the help of a smart energy hub as well as a dynamic demand response (DR) program, this study suggests a comprehensive energy management strategy for an intelligent microgrid. The main goal is to reduce pollutants as well as total operating expenses while maintaining a reliable and adaptable energy supply in face of renewable energy uncertainty. A number of dispersed energy sources, including solar panels, WTs, micro turbines, solid oxide fuel cells, CHP units, as well as battery energy storage devices, are integrated into the suggested system. Demand-side flexibility is achieved through the integration of real-time DR systems and home charging for electric vehicles. Every component is represented using detailed physical and operational constraints. The smart energy hub serves as a central coordinator for balancing energy generation, conversion, storage and consumption, while facilitating market participation. Utilizing scenario-based modeling of solar and wind variability, the Non-dominated Sorting Genetic Algorithm II is employed to create and solve a nonlinear multi-objective optimization problem. The method is evaluated using two case studies, one with DR and one without. The findings demonstrate that incorporating DR significantly lowers emissions and operational costs by 9%, while increasing renewable energy production by up to 15%. Battery scheduling effectively correlates with price changes, whilst smart energy source dispatch decreases reliance on grid imports. Under high renewable integration, the results show that the proposed coordinated strategy improves microgrid flexibility, sustainability, and economic performance.