Research on Optimal Scheduling of Integrated Energy System in Parks Based on Dynamic Carbon Emission Factors

Traditional research on optimizing integrated energy systems in industrial parks typically centers on electricity price incentives. From an “electricity” perspective, it is challenging for parks to respond to actual carbon emission changes. Therefore, this study develops an optimization strategy for integrated energy systems in industrial parks based on dynamic carbon emission factors. Unlike traditional methods that rely solely on economic signals like time-of-use electricity pricing, the proposed strategy introduces a dynamic carbon signal as a key scheduling driver. This signal directly reflects the temporal variations in grid-side carbon intensity, enabling the park to shift its optimization paradigm from a purely “economic-cost” perspective to a coordinated “economic-carbon” perspective. First, the structure and components of the integrated energy system are modeled. Then, an optimization dispatch model based on dynamic carbon emission factors is established according to the park’s objective function and constraints. Finally, this strategy is applied to the park’s optimization dispatch plan. Case studies validate that this strategy can effectively reduce carbon emissions in the distribution grid, lower electricity costs, and smooth out load fluctuations, demonstrating a synergistic optimization beyond the capability of price-only signals.