期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 33, 期 5, 页码 5714-5726出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2018.2822784
关键词
Alternating direction method of multipliers; (ADMM); big data; distributed optimization; probabilistic forecasting; quantile regression; short-term forecasting
资金
- National Natural Science Foundation of China [51777155]
- Key Research and Development Program of Shaanxi [2017ZDCXL-GY-02-03]
Using off-site predictors to capture spatio-temporal correlations among geographically distributed wind farms is seen as one solution to improve the forecast accuracy of wind power generation. However, in practice, wind farm operators are usually unwilling to share their private data with each other because of competitive reasons and security considerations. To address this issue, this paper presents how wind power probabilistic forecasting using off-site information could be achieved in a privacy-preserving and distributed fashion. Wind power probabilistic forecasts are created by means of multiple quantile regression. The original large-scale forecasting problem is first decomposed into a large number of small-scale subproblems. The subproblem can be computed locally on each farm. Then, the closed-form solution to the subproblem is derived exactly for achieving high computational efficiency. The proposed approach offers a flexible framework for using off-site information, but without having to exchange commercially sensitive data among all participants. It relies on the alternating direction method of multipliers algorithm to achieve the cooperation among all participants and finally converges to the optimal solution. Case studies with real-world data validate improvements in the forecast accuracy when considering spatiotemporal correlations. Distributed approaches also show higher computational efficiency than traditional centralized approaches.
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