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面向高维平衡域的时序-概率电力电量平衡量化评估方法OA

Quantitative Evaluation Method for Time-series-Probabilistic Power and Energy Balance Oriented to High-dimensional Balance Region

中文摘要英文摘要

各类新兴要素的规模化引入使得新型电力系统的电力电量平衡呈现多向互动、概率平衡和时序耦合新特点,如何实现新形势下电力电量平衡的量化分析成为重要问题.文中以时序-概率电力电量平衡理论为基础,提出了面向高维平衡域的量化评估方法,包括提出平衡概率、不平衡量、不平衡风险等新的评估指标,以及基于最大内近似平衡域的鲁棒评估模型及其算法.由于近似域是真实平衡域的最大内填充子集,评估结果的可靠性和精度要求得以保证;由于大量重复模拟和复杂积分运算被避免,计算效率得到了提高,满足工程实用化需求.最后,基于中国某区域2050年规划电网进行了算例分析,验证了所提方法的有效性.

The large-scale integration of various emerging elements has led to new characteristics of the power and energy balance of new power systems as multi-directional interaction,probabilistic balance,and temporal coupling.How to achieve quantitative analysis of power and energy balance under the new situation has become an important issue.Based on the theory of the time-series-probabilistic power and energy balance,the quantitative evaluation method oriented to high-dimensional balance region is proposed,including new evaluation indicators such as balance probability,unbalanced value,and unbalanced risk.And a robust evaluation model with its algorithm based on the maximum inner approximation of the balance region is also proposed.Since the approximate region is the largest inner filled subset of the real balance region,the reliability and the calculation accuracy of the evaluation results is guaranteed.Due to the avoidance of numerous repeated simulation and complex integral operation,the computational efficiency is improved,and the requirement of practical engineering can be met.Case study results show the validity of the proposed method based on the 2050 planned power system of a region of China.

林弋莎;鲁宗相;乔颖;李海波

国网泉州供电公司,福建省 泉州市 362000||清华大学电机工程与应用电子技术系,北京市 100084清华大学电机工程与应用电子技术系,北京市 100084||清华四川能源互联网研究院,四川省 成都市 610213清华大学电机工程与应用电子技术系,北京市 100084||清华四川能源互联网研究院,四川省 成都市 610213清华四川能源互联网研究院,四川省 成都市 610213

电力电量平衡平衡域时序耦合不确定性鲁棒优化

power and energy balancebalance regiontime-series couplinguncertaintyrobust optimization

《电力系统自动化》 2026 (8)

94-105,12

国家自然科学基金资助项目(U23B20111). This work is supported by National Natural Science Foundation of China(No.U23B20111).

10.7500/AEPS20250114001

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