Multi-Timescale Scheduling Optimization of Hospital Integrated Energy Systems for Intelligent Energy Management

To address the limitations of traditional hospital energy management strategies in responding to real-time medical demands, this paper proposes a coordinated optimization approach for multi-timescale scheduling in diversified hospital energy systems. The long-term scheduling problem is first formulated as a Markov Decision Process, with fine-grained short-term energy supply plans embedded in each decision step through an optimal model. Deep reinforcement learning is then employed to reduce the dimensionality of long-term decision variables, while hybrid integer linear programming is integrated to strictly enforce critical load operation constraints. A hybrid data- and model-driven framework is constructed to simultaneously enhance computational efficiency and power supply reliability. Empirical studies demonstrate that, compared with traditional scenario-based and robust optimization methods, the proposed approach significantly improves energy resource utilization—raising the distributed renewable energy utilization rate from 82.45% to 96.72%—and reduces the power interruption rate for critical loads from 2.8% to 0.15%. This ensures the continuity of medical services while minimizing energy waste. The proposed method provides both theoretical and practical guidance for intelligent scheduling and energy management in complex hospital integrated energy systems.