| 引用本文: | 李夫刚,李夫海,琚承乾,等.新能源不确定性表征方法及关键技术问题研究现状、挑战及展望[J].电力系统保护与控制,2025,53(15):172-187.[点击复制] |
| LI Fugang,LI Fuhai,JU Chengqian,et al.Methods for characterizing renewable energy uncertainty and key technical issues: research status, challenges, and prospects[J].Power System Protection and Control,2025,53(15):172-187[点击复制] |
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| 摘要: |
| 随着清洁能源的大规模发展，风、光等清洁能源的接入日益增多，水、风、光多能互补系统的应用越来越广泛。如何对水、风、光等能源的出力变化以及负荷增长的不确定性进行建模，给电网的安全、经济运行调度和规划带来了许多挑战。采用基于传统概率建模和人工智能技术对不确定性进行量化是推动新型电力系统不确定优化技术发展的关键。针对现有新能源不确定性表征问题，全面综述了相关研究。首先阐述了不确定性量化的概念，新能源与气象耦合的关系及量化方法。其次从研究对象和数学问题两个方面阐述了电力系统新能源不确定性的基本概念，回顾了现有的研究方法、评估指标和典型场景的应用现状。最后总结了当前研究中所存在的问题，并展望了未来的发展趋势和挑战，旨在为相关研究提供参考与借鉴。 |
| 关键词: 碳达峰碳中和 不确定性表征 传统概率建模 深度学习 |
| DOI：10.19783/j.cnki.pspc.241293 |
| 投稿时间：2024-09-24修订日期：2025-01-20 |
| 基金项目:国家重点研发计划项目资助(2018YFB0905204)；四川省科技计划项目资助(22ZDYF2707) |
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| Methods for characterizing renewable energy uncertainty and key technical issues: research status, challenges, and prospects |
| LI Fugang1,LI Fuhai2,JU Chengqian3,CHEN Shijun1,YANG Yadong4,DING Fan5 |
| (1. Sichuan University, Chengdu 610065, China; 2. SGICT Group Beijing China-Power Information Technology Co., Ltd.,
Beijing 102211, China; 3. Huaneng Lancang River Hydropower Co., Ltd., Kunming 650214, China; 4. State Grid Shizuishan
Power Supply Company, Shizuishan 750001, China; 5. State Grid Ningxia Electric Power Co., Ltd., Lingwu 750001, China) |
| Abstract: |
| With the large-scale development of clean energy, the integration of wind and solar power is increasing, and multi-energy complementary systems involving hydro, wind, and solar power are becoming more widely applied. However, accurately modeling the uncertainty in the output of these energy sources, as well as load growth, poses significant challenges for the safe and economical operation, dispatch, and planning of power grids. Characterizing this uncertainty using traditional probabilistic modeling and artificial intelligence technologies is crucial for advancing uncertainty-based optimization techniques in modern power systems. This paper presents a comprehensive review of the current research on renewable energy uncertainty characterization. First, it introduces the concept of uncertainty quantification and explores the coupling relationship between renewable energy sources and meteorological factors, along with relevant quantification methods. It then discusses the basic concepts of renewable energy uncertainty in power systems from the perspectives of research objects and mathematical problems, reviewing existing research methods, assessment indicators, and the application of typical scenarios. Finally, it summarizes the current research challenges and discusses future development trends and directions, aiming to provide references and insights for related studies. |
| Key words: carbon peaking and carbon neutrality uncertainty characterization traditional probabilistic modeling deep learning |
