The widespread adoption of renewable energy within integrated energy systems introduces significant uncertainty, posing challenges to their stable and economical operation. Mitigating the costly degradation of electrochemical energy storage equipment while effectively managing fluctuations on both the generation and load sides has become a critical issue. Against this backdrop, this paper proposes a scheduling framework integrating interval-type robust optimisation with a two-layer model predictive control approach. During the day-ahead planning phase, an interval-type robust scheduling model is constructed. This model employs k-means clustering to partition uncertainty sets for renewable generation and load demand, thereby circumventing the excessive conservatism inherent in traditional robust optimisation. For the intraday phase, a two-layer model predictive control framework is established to smooth the rolling optimisation time scale differences across electricity, heat, gas, and hydrogen loads. Concurrently, a storage lifetime cost term is incorporated into the optimisation objective to extend the energy storage system's lifespan. This is combined with an electricity price-based demand response model to enhance scheduling flexibility. Simulation results demonstrate the proposed method's multifaceted advantages. Compared to conventional robust optimisation, the interval-based robust optimisation model maintains high user satisfaction (96.43%) while reducing average daily system operating costs by approximately 8.24% and lowering the monthly capacity degradation rate of the energy storage system from 1.831% to 1.617%. The intraday robust model predictive control (MPC) demonstrated robust performance under extreme scenarios, with total cost increases of merely 2.91%, significantly lower than the 16.23% observed in conventional MPC. The proposed scheduling strategy, integrating interval robust optimisation with model predictive control, offers an effective solution for addressing robustness, economic efficiency, and equipment lifespan management within integrated energy systems under renewable energy and load uncertainty.
Haoyu Mao (Thu,) studied this question.