DocumentCode :
743819
Title :
Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals
Author :
Hao Quan ; Srinivasan, Dipti ; Khosravi, Abbas
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
Volume :
26
Issue :
9
fYear :
2015
Firstpage :
2123
Lastpage :
2135
Abstract :
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.
Keywords :
Monte Carlo methods; forecasting theory; genetic algorithms; neural nets; power engineering computing; power generation dispatch; power generation scheduling; stochastic processes; wind power; ECDF; Monte Carlo simulation method; economic dispatch reserves; empirical cumulative distribution function; forecast uncertainty quantification; generation costs; heuristic genetic algorithm; neural network-based prediction intervals; nonparametric neural network; scenario generation method; stochastic SCUC problem; stochastic security-constrained unit commitment; wind power forecast uncertainties; Decision making; Forecast uncertainty; Stochastic processes; Wind forecasting; Wind power generation; Decision making; genetic algorithm (GA); prediction interval (PI); scenario generation; smart grid; stochastic model; uncertainties; unit commitment (UC); wind power; wind power.;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2014.2376696
Filename :
6987360
Link To Document :
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