Premium
Scheduling Model of Power System Based on Forecasting Error of Wind Power Plant Output
Author(s) -
Sun Kai,
Dou Zhenhai,
Zhu Yaling,
Liao Qingling,
Si Shuqian,
Dong Jun,
Wang Zichen,
Wang Chen
Publication year - 2021
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23326
Subject(s) - scheduling (production processes) , wind power , mathematical optimization , computer science , electric power system , forecast error , wind power forecasting , power (physics) , engineering , econometrics , mathematics , physics , electrical engineering , quantum mechanics
Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.