Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization Programming

1. College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
3. Department of Electrical Engineering, Tishreen University, Latakia 2230, Syria
4. National Technical University of Athens, Athens 15773, Greece
5. Department of Management & Innovation Systems, University of Salerno, Salerno 84084, Italy

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 62073121), the National Key R&D Program of China “Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption” (No. 2018YFB0904200), and eponymous Complement S&T Program of State Grid Corporation of China (No. SGLNDKOOKJJS1800266).

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    Regional photovoltaic (PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals (PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granule-based clustering (GC) and direct optimization programming (DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction (NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples’ utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

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  • Received:September 13,2022
  • Revised:November 28,2022
  • Adopted:
  • Online: September 20,2023
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