Optimal Dispatch of Multi-Integrated Energy Systems with Spatio-Temporal Wind Forecasting and Bilateral Energy–Carbon Trading

With the increasing penetration of renewable energy, the efficient dispatch of integrated energy systems (IESs) is facing severe challenges. Addressing the uncertainty of renewable energy output and designing efficient market mechanisms are crucial for achieving economical and low-carbon operation of IES. To this end, this paper unveils a comprehensive modeling and optimization framework: Firstly, a Spatio-Temporal Diffusion Model (STDM) is proposed, which generates high-quality wind power forecasting data by accurately capturing its spatio-temporal correlations, thereby providing reliable input for IES dispatch. Subsequently, a stochastic optimal scheduling model for electricity–heat–carbon coupled IES is established, comprehensively considering carbon capture equipment and a carbon quota mechanism. Finally, a multi-IES Nash bargaining cooperative game model is developed, encompassing bilateral energy trading and bilateral carbon trading, to equitably distribute cooperative benefits. Simulation results demonstrate that the STDM model significantly outperforms baseline models in both forecasting accuracy and scenario quality, while the designed bilateral market mechanism enhances system economics by reducing the total operating cost by 19.63% and lowering the total carbon emissions by 4.09%.