A Novel Scenario Generation Framework Based on the Knowledge of Existing Wind Power Plants

Abstract

As a kind of renewable energy with economic and environmental friendliness, wind energy is widely used, although the high uncertainty of wind speed has significant impact on the operation and planning of wind power systems. Thus, scenario generation of wind speed is the primary step to obtain optimal decisions. However, as to the wind power plants to be newly-built or expanded, adequate wind data may not be available, or even the wind data are missing or invalid, which may lead to the inaccuracy of data-driven scenario generation. In this paper, considering that multiple wind plants in neighboring areas may have similar wind patterns, a novel scenario generation framework is proposed to transfer the knowledge extracted from existing data-rich plants to help establish the uncertainty model of newly-built plant. Transfer component analysis (TCA) is used to minimize the distribution gap between multiple source plants and target plant, and the results of maximum mean discrepancy (MMD) can be obtained to construct the mixture distribution model of target wind speed. Experimental results show that the scenarios generated by transferring the knowledge of existing wind plants can better reflect the real characteristics of the target wind speed in the case of insufficient historical data.