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山东大学学报 (工学版) ›› 2025, Vol. 55 ›› Issue (3): 149-157.doi: 10.6040/j.issn.1672-3961.0.2024.139

• 电气工程 • 上一篇    

基于岭回归的配电网分布式光伏消纳能力预测方法

孙东磊1,孙毅1,刘蕊1,孙鹏凯2*,张玉敏2   

  1. 1.国网山东省电力公司经济技术研究院, 山东 济南 250021;2.电网智能化调度与控制教育部重点实验室(山东大学), 山东 济南 250061
  • 发布日期:2025-06-05
  • 作者简介:孙东磊(1988— ),男,高级工程师,博士,主要研究方向为电力系统规划技术. E-mail:sdusdlei@sina.com. *通信作者简介:孙鹏凯(1997— ),男,硕士研究生,主要研究方向为电力系统运行与控制. E-mail:pksun0929@163.com
  • 基金资助:
    国家电网有限公司总部管理科技资助项目(5400-202216412A-2-0-ZN)

Ridge regression-based method for predicting distributed photovoltaic consumption capacity in distribution networks

SUN Donglei1, SUN Yi1, LIU Rui1, SUN Pengkai2*, ZHANG Yumin2   

  1. SUN Donglei1, SUN Yi1, LIU Rui1, SUN Pengkai2*, ZHANG Yumin2(1. State Grid Shandong Electric Power Company Economic and Technological Research Institute, Jinan 250021, Shandong, China;
    2. Key Laboratory of Power System Intelligent Dispatch and Control, Ministry of Education(Shandong University), Jinan 250061, Shandong, China
  • Published:2025-06-05

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摘要： 针对分布式光伏大量接入电网导致的弃光问题,提出基于岭回归的配电网分布式光伏消纳能力预测方法。剖析分布式光伏消纳的影响因素,引入分布式光伏消纳影响因素贡献度,提出一种灰色关联度分析方法评估不同影响因素与分布式光伏消纳的关联程度。构建基于岭回归方法的分布式光伏消纳能力预测模型,将分布式光伏消纳影响因素关联度高的评价项纳入预测模型中,推导出分布式光伏消纳驱动因素与预测模型的关联映射关系,结合情景模拟方法,制定未来光伏消纳能力的发展策略。在SPSSPRO系统中进行仿真计算,结果表明,所提方法能够较好地预测分布式光伏消纳能力,为未来光伏消纳能力的提升提供策略建议。

关键词: 分布式光伏, 贡献度, 灰色关联度分析, 岭回归, 消纳能力

Abstract: To address curtailment issues caused by large-scale grid integration of distributed photovoltaic systems, a ridge regression-based method for predicting distributed photovoltaic consumption capacity in distribution networks was proposed. Key factors influencing distributed photovoltaic consumption capacity were analyzed, with the contribution degree of these factors quantified through grey relational analysis. A ridge regression-based prediction model was developed by incorporating high-correlation evaluation indicators. The mapping relationships between driving factors and consumption capacity were derived, followed by scenario simulations to formulate strategic recommendations for future photovoltaic absorption improvement. Simulations implemented in the SPSSPRO platform demonstrated that the proposed method accurately predicted photovoltaic consumption capacity, providing actionable insights for enhancing system-level photovoltaic consumption capacity.

Key words: distributed photovoltaic, contribution degree, grey relational analysis, ridge regression, consumption capacity

中图分类号: 

  • TM73
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