Stochastic Optimization of a Multi-Carrier Energy System with the Participation of Renewable Energy Sources and Integrated Demand Response Programs

In modern engineering, optimizing energy hub-based microgrids that incorporate renewable energy resources to meet both electrical and thermal demands presents a significant challenge. This study focuses on the stochastic optimization of a multi-carrier energy microgrid, integrating renewable energy sources to enhance efficiency and reduce operational costs. A demand response program is employed to optimize the allocation of costs and improve the load profiles for both electricity and thermal energy. To address the uncertainty of renewable resources, a scenario-based planning approach is implemented to reduce the impact of variability. The model schedules energy production and consumption for a 24-hour period, with objective functions targeting energy purchase costs, fuel costs, profits from energy sales, and greenhouse gas emission reduction. The proposed methodology is tested on a sample microgrid system using Python solvers for optimization. Results, analyzed under various scenarios, show a significant reduction in costs when compared to conventional systems. Specifically, the total cost for meeting electrical and thermal demands through the traditional electricity and gas network is 279,910 cents, while the optimized system reduces the cost to 164,682 cents, yielding a savings of approximately 41%. These findings highlight the effectiveness of the proposed optimization model in reducing both costs and environmental impact.