Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Quan, Hao, Srinivasan, Dipti and Khosravi, Abbas 2015, Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals, IEEE Transactions on neural networks and learning systems, vol. 26, no. 9, pp. 2123-2135, doi: 10.1109/TNNLS.2014.2376696.

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Title Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals
Author(s) Quan, Hao
Srinivasan, Dipti
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Journal name IEEE Transactions on neural networks and learning systems
Volume number 26
Issue number 9
Start page 2123
End page 2135
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-09
ISSN 2162-2388
Keyword(s) Decision making
genetic algorithm (GA)
prediction interval (PI)
scenario generation
smart grid
stochastic model
uncertainties
unit commitment (UC)
wind power
Summary 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.
Language eng
DOI 10.1109/TNNLS.2014.2376696
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage, Distribution and Supply not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082512

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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