Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity

This study proposes a multi-time scale dynamic optimization (MSDO) method for ultra-short-term scheduling in industrial electricity-heat-gas integrated energy systems. The aim is to enhance the feasibility of scheduling schemes and to utilize flexibility potential. The MSDO method considers equipment response differences and demand fulfillment time disparities. The dynamic aspect is reflected in the supply-demand matching within the scheduling time window. A single-layer model with unified temporal granularity is constructed based on lag parameter identification. The study proposes various forms of energy demand elasticity, including steam networks and cumulative demand, and investigates the optimal minimum temporal granularity for scheduling. Case results demonstrate that the MSDO method effectively characterizes the multi-energy time lag of equipment. Compared with methods disregarding lag parameters, the MSDO method enhances total profit by yen 120711.42 and improves operation stability by 2.96%. The MSDO method outperforms baseline methods in cost and stability, accommodating a 160% increase in renewable energy growth. Under different elasticity spaces, the profit of the cumulative demand method exceeds that of the average method. The time lag of different equipment impacts scheduling profits. An optimal choice exists for the minimum temporal granularity to strike a balance between computational efficiency, economy, and feasibility.