Uncertainty-Aware Economic Dispatch of Integrated Energy Systems with Demand-Response and Carbon-Emission Costs

This study investigates the economic operation of integrated energy systems under uncertainty, aiming to boost operational efficiency and cost-effectiveness while reducing carbon emissions. Unlike existing methods that either ignore the demand response or treat uncertainties separately, we introduce a two-stage robust optimization scheduling framework that simultaneously integrates demand-response mechanisms and carbon-emission costs. In Stage I, a preliminary dispatch is obtained for deterministic scenarios based on forecasted values of renewable outputs and load demands; in Stage II, the solution is refined against worst-case fluctuations in renewable output and load demand. A column-and-constraint generation algorithm facilitates efficient, iterative coordination between the two stages, resulting in an optimal and robust dispatch strategy. To validate our approach, we performed detailed numerical simulations on a standard benchmark for integrated energy systems commonly used in the literature. The results show that by accounting for multiple sources of uncertainty, the system’s energy cost fell from 7091.03 RMB to 6489.18 RMB—a saving of 8.49%—while the carbon emissions dropped from 6165.57 kg to 5732.54 kg, a reduction of 7.02%. Compared with conventional scenario-based dispatch methods, the proposed two-stage framework demonstrates superior adaptability and robustness in handling renewable generation and load uncertainties, providing strong technical backing and theoretical insights for the sustainable operation of integrated energy systems in uncertain environments.