针对新型电力系统中,风光出力波动性和间接性给电网安全运行带来巨大挑战等问题,提出了一种考虑抽蓄的风光水火多能互补的联合发电系统双层优化调度模型:以系统净负荷最小、风光出力最大以及弃电量最小为目标函数的水风光蓄联合调度上层优化模型;以火电机组运行成本、抽水成本以及弃电惩罚成本为目标函数,并考虑联合发电系统经济性的下层优化模型。引入CO₂排放强度和火电机组出力系数作为评价指标,并设置3种不同方案进行优化仿真,采用CPLEX求解器求解。实际算例分析结果表明,考虑抽水蓄能电站后,夏季典型日综合运行成本降低4.6万元,CO₂排放强度降低0.012 kg/(kW·h),火电出力波动系数从33.34%降至7.88%;冬季典型日也有类似改善效果。因此,加入抽水蓄能电站,可以明显降低火电机组出力波动性,降低系统运行成本,以及提高新能源的消纳能力。

[Objective] In response to the operational challenge caused by high penetration of wind and solar power in modern power systems, this study aims to propose a bi-level optimized scheduling model for a multi-energy complementary power generation system incorporating pumped storage. The model seeks to enhance renewable energy utilization, optimize system economic performance, and improve system stability. The novelty lies in the integration of a bi-level optimization framework with a deep peak shaving strategy, while introducing CO₂ emission intensity and thermal power output coefficient as evaluation indicators for multi-objective coordination of economy, environmental performance, and stability. [Methods] The upper-level model optimized the joint dispatch of wind, solar, hydro, and pumped storage with objectives of maximizing wind and solar output, minimizing net load fluctuation, and minimizing curtailed electricity. The lower-level model optimized the economic performance of the system, aiming at minimizing thermal power operational costs, pumped storage costs, and curtailed electricity penalties. Constraints included wind and solar output limits, hydro and pumped storage capacity limits, thermal unit ramping capabilities, and power balance requirements. The CPLEX solver combined with the YALMIP toolbox was employed to solve the high-dimensional nonlinear mixed-integer programming problem. CO₂ emission intensity and thermal power output fluctuation coefficient were adopted as additional evaluation metrics to quantify environmental performance and system stability. [Results] Simulation results indicated that integrating pumped storage reduced total cost by 46 000 CNY (1.02%) and CO₂ emission intensity by 6.4% in the summer scenario, while the thermal power output fluctuation coefficient decreased from 33.34% to 7.88%. In winter, thermal output stability improved to 7.67%. Increasing wind-solar penetration from 31.25% to 47.62% lowered system costs by 39.5% and reduced CO₂ emission intensity by 58.3%. Enhancing deep peak shaving from 50% to 70% reduced total cost by 19.2% and decreased thermal power output fluctuation coefficient by 44.2%. [Conclusions] The introduction of pumped storage power station significantly enhances system flexibility, increasing renewable energy utilization by over 12% and reducing thermal unit peak regulation pressure by 50%. The bi-level optimization model ensures low-cost operation while reducing CO₂ emission intensity by more than 0.1 kg/kWh and maintaining thermal power output fluctuation coefficient below 8%. A combination of high wind-solar penetration (>40%) and deep peak shaving (70%) achieves optimal comprehensive benefits, providing theoretical support for scheduling of high renewable energy penetration power systems. This study provides an innovative methodology for the design and optimization of wind-solar-hydro-thermal-pumped storage multi-energy systems, and the findings can be generalized to other clean energy bases.