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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Scenario tree reduction in stochastic programming with recourse for hydropower operations

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Xu, Bin [1, 2, 3] ; Zhong, Ping-An [2, 3] ; Zambon, Renato C. [4] ; Zhao, Yunfa [5] ; Yeh, William W. -G. [1, 6]
Total Authors: 5
[1] Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA 90095 - USA
[2] Hohai Univ, Coll Hydrol & Water Resources, Nanjing, Jiangsu - Peoples R China
[3] Hohai Univ, Natl Engn Res Ctr Water Resources Efficient Utili, Nanjing, Jiangsu - Peoples R China
[4] Univ Sao Paulo, Dept Hydraul & Environm Engn, Polytech Sch, Sao Paulo - Brazil
[5] China Three Gorges Corp, Beijing - Peoples R China
[6] Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA - USA
Total Affiliations: 6
Document type: Journal article
Source: WATER RESOURCES RESEARCH; v. 51, n. 8, p. 6359-6380, AUG 2015.
Web of Science Citations: 15

A stochastic programming with recourse model requires the consequences of recourse actions be modeled for all possible realizations of the stochastic variables. Continuous stochastic variables are approximated by scenario trees. This paper evaluates the impact of scenario tree reduction on model performance for hydropower operations and suggests procedures to determine the optimal level of scenario tree reduction. We first establish a stochastic programming model for the optimal operation of a cascaded system of reservoirs for hydropower production. We then use the neural gas method to generate scenario trees and employ a Monte Carlo method to systematically reduce the scenario trees. We conduct in-sample and out-of-sample tests to evaluate the impact of scenario tree reduction on the objective function of the hydropower optimization model. We then apply a statistical hypothesis test to determine the significance of the impact due to scenario tree reduction. We develop a stochastic programming with recourse model and apply it to real-time operation for hydropower production to determine the loss in solution accuracy due to scenario tree reduction. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) the neural gas method preserves the mean value of the original streamflow series but introduces bias to variance, cross variance, and lag-one covariance due to information loss when the original tree is systematically reduced; (2) reducing the scenario number by as much as 40% results in insignificant change in the objective function and solution quality, but significantly reduces computational demand. (AU)

FAPESP's process: 13/03432-9 - Stochastic optimization with individualized hydropower plants for planning operation of Brazilian hydrothermal system
Grantee:Renato Carlos Zambon
Support Opportunities: Scholarships abroad - Research