
1. 国家电网有限公司华东分部,上海,200120
2. 上海电力大学 电气工程学院,上海,200090
网络出版:2025-07-20,
纸质出版:2025-07-20
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李建华,李成奥,陆建宇,许江蛟.基于出力特征的华东电网新能源统计学特性研究[J].智慧电力,2025,53(7):53-60.
LI Jianhua, LI Chen’gao, LU Jianyu, et al. Research on Statistical Characteristics of Renewable Energy in East China Power Grid Based on Output Features[J]. 2025, 53(7): 53-60.
李建华,李成奥,陆建宇,许江蛟.基于出力特征的华东电网新能源统计学特性研究[J].智慧电力,2025,53(7):53-60. DOI: 10.20204/j.sp.2025.07007.
LI Jianhua, LI Chen’gao, LU Jianyu, et al. Research on Statistical Characteristics of Renewable Energy in East China Power Grid Based on Output Features[J]. 2025, 53(7): 53-60. DOI: 10.20204/j.sp.2025.07007.
可再生能源的迅速发展加快了新型电力系统的建设,而新能源波动性强、随机性高等特点限制了智能电网的应用。鉴于华东电网呈现大受端、高密度、强耦合特性,因此以其为例采用统计学方法分析该区域新能源资源特性。首先,从供需平衡角度探讨新能源出力在不同参与度下的电网运行情况,定量分析其对供电可靠性的影响。其次,提出基于出力特征的精细化多尺度模型,将出力同时率融入小时级、分钟级时间尺度,揭示不同时空的新能源出力水平。再次,建立网省站三级资源架构模型,基于资源转移状态分析频率分布特征和时空相关性,进而辨识新能源发电的延续性趋势。研究成果可为电网运行和电网规划提供重要依据。
The rapid development of renewable energy has accelerated the construction of the new power system. However, characteristics such as the strong volatility and high randomness of renewable energy limit the application of smart grids. Given that the East China Power Grid exhibits large power-receiving, high-density, and strongly coupled characteristics, it serves as an example for analyzing the resource characteristics of the renewable energy in this region using statistical methods. First, from the perspective of supply-demand balance, the grid operation under the different levels of renewable energy participation is explored, quantitatively analyzing its impact on power supply reliability. Second, a refined multi-scale model based on output features is proposed. This model integrates the output simultaneity rate into hourly and minute-level time scales, revealing the renewable energy output levels across different times and spaces. Third, a three-level resource architecture model(grid-province-station) is established. Based on resource transition states, the frequency distribution characteristics and spatiotemporal correlations are analyzed, thereby identifying the continuity trends of renewable energy power generation. The research findings can provide an important basis for power grid operation and planning.
任大伟,肖晋宇,侯金鸣,等.双碳目标下我国新型电力系统的构建与演变研究[J].电网技术,2022,46(10):3831-3839.
卓振宇,张宁,谢小荣,等.高比例可再生能源电力系统关键技术及发展挑战[J].电力系统自动化,2021,45(9):171-191.
黄效喜,桑妲,徐玲君.华东地区风光储蓄协调运行研究[J].电力与能源,2020,41(1):87-90.
黄瀚燕.考虑大规模风电接入的电力系统备用确定方法研究[D].北京:华北电力大学,2022.
崔杨,张家瑞,仲悟之,等.考虑源-荷多时间尺度协调优化的大规模风电接入多源电力系统调度策略[J].电网技术,2021,45(5):1828-1837.
王建学,张耀,万筱钟,等.面向电网运行的新能源出力特性指标体系研究——风电出力特性指标体系[J].电网与清洁能源,2016,32(2):42-51,57.
葛朝晖,张倩茅,齐晓光,等.考虑灵活性需求的可再生能源出力特性指标体系[J].电力系统及其自动化学报,2018,30(7):30-37.
向异,金吉良,赵鑫,等.基于统计学特征的西北电网新能源资源特性分析[J].电网与清洁能源,2023,39(1):120-127.
吕思潼,李建国,郭永鑫,等.基于实测数据的风电场相邻日有功出力相关性研究[J].智慧电力,2020,48(5):47-52,79.
项丽,常康,李笑宇,等.宁夏风电出力时空特性研究[J].陕西电力,2011,39(12):43-49.
孙骁强,马晓伟,张小奇,等.基于相依关系的新能源功率预测场景生成及调度应用[J].电力系统自动化,2019,43(15):10-17,44.
贾文昭,康重庆,李丹,等.基于日前风功率预测的风电消纳能力评估方法[J].电网技术,2012,36(8):69-75.
兑潇玮,朱桂萍,刘艳章.考虑预测误差的风电场储能配置优化方法[J].电网技术,2017,41(2):434-439.
万灿,宋永华.新能源电力系统概率预测理论与方法及其应用[J].电力系统自动化,2021,45(1):2-16.
HOU Q C,DU E S,ZHANG N,et al. Impact of high renewable penetration on the power system operation mode:a data-driven approach[J]. IEEE Transactions on Power Systems,2020,35(1):731-741.
LI P,YANG M,WU QW. Confidence interval based distributionally robust real-time economic dispatch approach considering wind power accommodation risk[J].IEEE Transactions on Sustainable Energy,2021,12(1):58-69.
张振宇,王文倬,马晓伟,等.基于风险控制的新能源纳入电力系统备用方法[J].电网技术,2020,44(9):3375-3382.
吕循岩,肖晋宇,侯金鸣,等.连续多日低风速事件的时空分布特性及对电力供应影响初探[J].电力系统自动化,2025,49(4):79-89.
程林,何剑.电力系统可靠性原理和应用[M].北京:清华大学出版社,2015:2.
刘天琪.现代电力系统分析理论与方法[M].北京:中国电力出版社,2016:129-130.
国家市场监督管理总局,国家标准化管理委员会.电力系统技术导则:GB/T 38969—2020[S].北京:中国标准出版社,2020.
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