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水力发电学报 ›› 2024, Vol. 43 ›› Issue (10): 1-16.doi: 10.11660/slfdxb.20241001

• •    下一篇

编辑部推荐论文:混合抽蓄-风-光多能互补系统容量配置研究

  

  • 出版日期:2024-10-25 发布日期:2024-10-25

Study on capacity design for hybrid pumped storage-wind-photovoltaic multi-energy complementary system

  • Online:2024-10-25 Published:2024-10-25

摘要: 混合抽蓄-风-光多能互补系统具有广阔应用前景,其容量配置需面临复杂水力水量、电力电量关系表征问题,同时其经济性评价需考虑电力市场规则。本研究提出了运行优化与容量配置决策两阶段优化思路,首先提出了适应中长期市场的月度统一电力送出模式,其次构建了混合抽蓄-风-光多能互补系统中长期运行多目标优化模型,然后基于海量容量情景模拟获取离散决策空间,最后从中优选得到最优新能源容量配置及运行方案。黄河上游清洁能源基地算例结果表明:失负荷风险接受度高、中、低分别对应新能源容量配置为混合抽蓄容量的3.2 ~ 3.9倍、2.4 ~ 3.0倍、1.6 ~ 2.1倍;系统月尺度打包并网电量峰谷比介于1.36 ~ 1.45,说明系统内各电源在中长期尺度的互补性较好。

关键词: 混合抽蓄, 容量配置, 两阶段优化, 多目标优化

Abstract: The hybrid pumped storage-wind-photovoltaic multi-energy complementary system has broad application prospects. However, its capacity design needs to characterize the complex relationship between the water volume and electric power, and its economic evaluation should consider the rules of electricity markets. This paper describes a new two-stage optimization framework for optimizing operation and capacity decision. First, a consistent assumption for the target gross output is presented; and a double-objective operation optimization model is developed. Then, a discrete decision space is obtained through optimization based on a large number of medium and long-term operation cases. Finally, the scheme with the maximized net present value (NPV) is selected. Application in a case study of the clean energy base in the upper Yellow River gives the conclusion as follows. New energy capacities corresponding to high, medium and low acceptance degrees of load loss risks are 3.2-3.9 times, 2.4-3.0 times, and 1.6-2.1 times that of the mixed pumping and storage capacity, respectively. The peak to valley ratios of the system's monthly electricity delivery range from 1.36 to 1.45, indicating the power sources in the system are well complementary on the medium and long time scales.

Key words: hybrid pumping and storage, capacity design, two-stage optimization, multi-objective optimization

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