Using Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Control
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Date
2014-12-17
Authors
Menšík, Pavel
Starý, Miloš
Marton, Daniel
0000-0003-4697-5033Advisor
Referee
Mark
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Publisher
Elsevier Science Publishers
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Abstract
Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.
Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.
Conventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.
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Citation
Procedia Engineering. 2014, vol. 89, issue 12, p. 1486-1492.
http://www.sciencedirect.com/science/article/pii/S1877705814025508
http://www.sciencedirect.com/science/article/pii/S1877705814025508
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Peer-reviewed
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en
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Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported
http://creativecommons.org/licenses/by-nc-nd/3.0/
http://creativecommons.org/licenses/by-nc-nd/3.0/