To address the problem of renewable energy curtailment and the need for operational economic optimization in integrated energy systems with high penetration of wind and solar power, a coordinated optimization method integrating thermoelectric decoupling, ammonia-blended combustion technology, and energy storage capacity leasing is proposed. First, a chaotic-improved Latin Hypercube Sampling (C-LHS) method, combined with an improved K-means clustering algorithm, is employed to generate representative wind–solar–load scenarios. This approach improves the efficiency of uncertainty scenario generation while reducing computational burden and maintaining solution accuracy. Secondly, by coordinating the operation of thermal energy storage and electric boilers, the “heat-led power generation” constraint is relaxed, and, in combination with ammonia-blended combustion in combined heat and power (CHP) units, the system’s flexibility and renewable energy accommodation capability are enhanced. Finally, with the objective of minimizing total operating cost, a day-ahead scheduling model incorporating electrical energy storage (EES) leasing optimization is established. For EES, under a shared energy storage market mechanism, the golden section search (GSS) algorithm is employed to optimize the day-ahead leasing capacity. The simulation results demonstrate that the proposed method improves renewable energy accommodation while maintaining economic performance, and effectively reduces the overall operating cost of the system. These findings confirm the effectiveness of the proposed strategy in enhancing both system flexibility and economic performance.
Fu et al. (Fri,) studied this question.