The optimal dispatch of integrated energy systems (IESs) is strongly affected by uncertainties on both the supply and demand sides. To model wind power uncertainty and embed it into dispatch decision-making, this paper develops a distributed stochastic scheduling method driven by Diffusion-TS-based scenario generation. First, a conditional Diffusion-TS model is developed to generate high-fidelity wind power scenarios from day-ahead forecasts, and a temperature parameter is introduced to balance scenario diversity and fidelity. Second, a distributed stochastic scheduling framework with chance constraints is established, in which the probabilistic constraints are reformulated into a mixed-integer linear programming problem to address source-load fluctuations while preserving subsystem privacy. Third, the block coordinate descent method is used to decompose the system into cooling, heating, and electricity subproblems for iterative solution. Case study results show that the average CRPS of the generated scenarios is 162.16 MW, which is 34% lower than that of the deterministic forecast benchmark. The total cost of distributed deterministic dispatch is 2.8% higher than that of centralized deterministic dispatch, while the total cost of distributed stochastic dispatch is 53.1% higher than that of distributed deterministic dispatch, reflecting the additional economic cost of uncertainty-aware scheduling. Compared with the traditional LHS-Kmeans method, the scenarios generated by Diffusion-TS are closer to the actual wind power output. Although the resulting dispatch cost is higher, the obtained scheduling results are more consistent with realistic wind power conditions. Overall, the proposed method provides a practical technical route for the secure and economical operation of IESs under uncertainty.
Xia et al. (Fri,) studied this question.