Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (3): 149-157.doi: 10.6040/j.issn.1672-3961.0.2024.139

• Electrical Engineering • Previous Articles    

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

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

CLC Number: 

  • TM73
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