A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm

In the stochastic optimal scheduling of microgrid with multiple wind farms, the accurate description of uncertainties is a critical issue. Scenario generation provides an effective way to represent the strong randomness and interdependence between wind speeds. However, there may be very limited data or no historical information in the beginning stage of a newly-built wind farm operation, which will lead to the inaccuracy of scenario generation and thus affect the reliability of decision results. In this paper, considering that multiple wind farms in the adjacent areas may have similar weather conditions, a novel transfer learning-based scenario generation method is proposed to utilize the historical information from other existing data-rich farms for generating wind speed scenarios of the new farm. The scenario generation tasks are constructed as a cross-domain adaption problem. To model the target wind speed, joint distribution adaption (JDA) is adopted to explore the underlying relationship between multiple source farms and the target farm. Experimental results show that the scenarios generated by our proposed method can better describe the properties of target wind speed, and the microgrid scheduling results can be more reliable in the case of very limited data.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In the stochastic optimal scheduling of microgrid with multiple wind farms, accurately describing uncertainties is crucial. This paper proposes a novel transfer learning-based scenario generation method to generate wind speed scenarios for a newly-built wind farm by utilizing historical information from other existing data-rich farms. Experimental results demonstrate that the generated scenarios better describe the properties of target wind speed and improve the reliability of microgrid scheduling results when data is very limited.