广东电力2026，Vol.39Issue(3)：1-10,10.DOI:10.3969/j.issn.1007-290X.2026.03.001
计及发电量不确定性的风电场配储容量优化
Optimization of Wind Farm Storage Capacity Considering Uncertainty of Power Generation
摘要
Abstract
The wind power industry generally uses the comprehensive reduction coefficient to estimate power generation,but this approach is overly simplistic for the distribution and storage decision.The determination of energy storage capacity for a wind farm should not only prioritize high economic benefits but also control the risk to deal with different working conditions.In order to quantitatively evaluate the impact of exceedance probability on the energy storage capacity configuration and economy of wind farms,this paper takes a wind farm in Shanxi as a case study,employs Monte Carlo simulation to develop a normal probability distribution model for reduction factors and calculates the exceedance probability of the wind farm's power generation.Subsequently,the research optimizes the energy storage capacity by taking the power generation as a variable and aiming to maximize the net profit of the wind storage system.The findings indicate that the power generation forecasts for the same wind power project vary significantly across different exceedance probabilities,leading to divergent optimal energy storage capacities based on these varying power generation outcomes.Consequently,decision-makers must account for uncertainty risks in the allocation and storage strategies to enhance the overall economic viability of the wind storage project.The incorporation of exceedance probability enhances the flexibility and precision of decision-making in wind power projects,thereby facilitating the effective allocation of reserves for such initiatives.关键词
发电量计算/不确定性/风储系统/全寿命周期成本/容量配置Key words
power generation calculation/uncertainty/wind storage system/full life cycle cost/capacity configuration分类
信息技术与安全科学引用本文复制引用
孙锐,陈安新,冯秦阳,许昌,韩星星..计及发电量不确定性的风电场配储容量优化[J].广东电力,2026,39(3):1-10,10.基金项目
山东省工业和信息化厅课题(202350100877) （202350100877）
国家自然科学基金项目(52106238) （52106238）
国家重点研发计划-战略性科技创新合作重点专项(2024YFE0212200) （2024YFE0212200）
