An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

C.G. Marcelino; G.M.C. Leite; C.A.D.M. Delgado; L.B. de Oliveira; E.F. Wanner; S. Jiménez-Fernández; S. Salcedo-Sanz

doi:10.1016/j.eswa.2021.115638

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

C.G. Marcelino, G.M.C. Leite, C.A.D.M. Delgado, L.B. de Oliveira, E.F. Wanner, S. Jiménez-Fernández, S. Salcedo-Sanz

Research output: Contribution to journal › Article › peer-review

Abstract

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.

Original language	English
Article number	115638
Number of pages	18
Journal	Expert Systems with Applications
Volume	185
Early online date	28 Jul 2021
DOIs	https://doi.org/10.1016/j.eswa.2021.115638
Publication status	Published - 15 Dec 2021

Bibliographical note

Copyright © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

Funding & Acknowledgements: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754382. This research has been partially supported by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P) and by Comunidad de Madrid, PROMINT-CM project (grant No. P2018/EMT-4366). The authors thank UAH, UFRJ and CEFET-MG for the infrastructure used to conduct this work, and Brazilian research agencies: CAPES (Finance Code 001) and CNPq for support.

Keywords

Cascading hydro-power plant modeling
Multi-objective optimization
Swarm intelligence
MESH
Energy production

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10.1016/j.eswa.2021.115638

CG Marcellino et al_multi-objective evolutionary approach_multi-reservoir system scheduling in hydro-power plants
Copyright © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
Final published version, 4.63 MBLicence: CC BY 4.0

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Marcelino, C. G., Leite, G. M. C., Delgado, C. A. D. M., Oliveira, L. B. D., Wanner, E. F., Jiménez-Fernández, S., & Salcedo-Sanz, S. (2021). An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants. Expert Systems with Applications, 185, Article 115638. https://doi.org/10.1016/j.eswa.2021.115638

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abstract = "This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.",

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note = "Copyright {\textcopyright} 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). Funding & Acknowledgements: This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie grant agreement No 754382. This research has been partially supported by Ministerio de Econom{\'i}a y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P) and by Comunidad de Madrid, PROMINT-CM project (grant No. P2018/EMT-4366). The authors thank UAH, UFRJ and CEFET-MG for the infrastructure used to conduct this work, and Brazilian research agencies: CAPES (Finance Code 001) and CNPq for support.",

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AU - Oliveira, L.B. de

AU - Wanner, E.F.

AU - Jiménez-Fernández, S.

AU - Salcedo-Sanz, S.

N1 - Copyright © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). Funding & Acknowledgements: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754382. This research has been partially supported by Ministerio de Economía y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P) and by Comunidad de Madrid, PROMINT-CM project (grant No. P2018/EMT-4366). The authors thank UAH, UFRJ and CEFET-MG for the infrastructure used to conduct this work, and Brazilian research agencies: CAPES (Finance Code 001) and CNPq for support.

PY - 2021/12/15

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N2 - This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.

AB - This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out.

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JO - Expert Systems with Applications

JF - Expert Systems with Applications

M1 - 115638

ER -

An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

Abstract

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Keywords

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