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

<title>Abstract</title> In contemporary engineering, devising an optimal plan for an energy hub-based microgrid, which incorporates renewable resources and meets both electrical and thermal demands, presents a substantial challenge. In this study, the stochastic optimization of a multi-carrier energy microgrid system with the presence of renewable energy sources is addressed. To control and optimally allocate costs and create a better profile for electrical and thermal loads, a demand response program is utilized. Given the uncertainty of renewable resources, scenario-based planning and reduction are proposed to address this issue. For realistic planning, this study models a 24-hour ahead scheduling, with objective functions based on energy purchase costs, fuel costs, profits from energy sales, and the reduction of greenhouse gas emissions. The proposed model and method are then applied to a sample system in a Python software environment using solvers. The results are analyzed under various scenarios, considering different parameters, including uncertainty and demand response programs. According to the obtained results, the total cost of meeting the electrical and thermal demand of consumers through the electricity and gas network is 279,910 cents, whereas the cost of meeting the same demand by the optimized system is 164,682 cents, achieving approximately 41% savings.