Integrated energy systems (IES) play a crucial role in promoting efficient utilization of clean energy and multienergy complementarity in industrial parks. However, the safe and efficient operation of IES is exposed to the dynamic fluctuations in renewable energy and load demand, so that traditional optimization methods face problems such as overly conservative results or high computational complexity. To address these issues, this paper proposes a two‐stage distributionally robust optimization model based on the Wasserstein distance, which minimizes the total cost of IES considering the worst case of minimum photovoltaic (PV) output and maximum load deviation. The first stage minimizes the cost of procurement and equipment maintenance, and the second stage optimizes the PV curtailment penalty and load variation penalty due to the former stage. Case studies demonstrate that the proposed strategy achieves a good balance between economy and robustness when selecting a proper Wasserstein radius and substantially enhances the PV consumption rate.
Ma et al. (Fri,) studied this question.