Empowering sustainable energy communities: Optimizing multi-carrier energy systems with green energy, uncertainty management, and demand response

Energy management has emerged as a pressing concern in the planning and operation of smart cities, driven by the escalating demand for energy. Concurrently, demand response programs (DRPs) have gained significant attention in recent years, owing to their profound impact on the power system. This paper introduces an approach aimed at minimizing operational costs and environmental emissions within a smart city reliant on grid-connected power. A novel green energy scheduling for a multi-carrier energy community is presented to achieve a sustainable development. The proposed method places a premium on maximizing the utilization of renewable energy sources. Recognizing the inherent uncertainty associated with green energy resources, this approach seeks to enhance system reliability by addressing this challenge. This paper's primary objective is to tackle the intricate problem of power management in a smart city while factoring in the uncertainty stemming from green energy resources and the integration of DRPs. To address these complexities, the paper leverages the CPLEX solver embedded in the GAMS software application, while uncertainties in the system are modelled through the application of the Monte-Carlo algorithm. The results show that considering the uncertainty of green energy resources, the total cost of the system can be reduced by $504 by performing the DRPs.