DocumentCode :
1365750
Title :
Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy
Author :
Amjady, Nima ; Keynia, Farshid ; Zareipour, Hamidreza
Author_Institution :
Dept. of Electr. Eng., Semnan Univ., Semnan, Iran
Volume :
1
Issue :
3
fYear :
2010
Firstpage :
286
Lastpage :
294
Abstract :
Microgrids are a rapidly growing sector of smart grids, which will be an essential component in the trend toward distributed electricity generation. In the operation of a microgrid, forecasting the short-term load is an important task. With a more accurate short-term loaf forecast (STLF), the microgrid can enhance the management of its renewable and conventional resources and improve the economics of energy trade with electricity markets. However, STLF for microgrids is a complex forecast process, mainly because of the highly nonsmooth and nonlinear behavior of the load time series. In this paper, characteristics of the load time series of a typical microgrid are discussed and the differences with the load time series of traditional power systems are described. In addition, a new bilevel prediction strategy is proposed for STLF of microgrids. The proposed strategy is composed of a feature selection technique and a forecast engine (including neural network and evolutionary algorithm) in the lower level as the forecaster and an enhanced differential evolution algorithm in the upper level for optimizing the performance of the forecaster. The effectiveness of the proposed prediction strategy is evaluated by the real-life data of a university campus in Canada.
Keywords :
distributed power generation; load forecasting; optimisation; power generation economics; power markets; smart power grids; time series; STLF; bilevel prediction strategy; distributed electricity generation; electricity markets; energy trade economics; evolution algorithm; feature selection technique; forecast engine; load time series; microgrids; power systems; short-term load forecast; smart grids; Load forecasting; Neural networks; Optimization; Prediction algorithms; Time series analysis; Differential evolution algorithm; load forecast; microgrid; neural networks;
fLanguage :
English
Journal_Title :
Smart Grid, IEEE Transactions on
Publisher :
ieee
ISSN :
1949-3053
Type :
jour
DOI :
10.1109/TSG.2010.2078842
Filename :
5613970
Link To Document :
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