Abstract
The unit dispatch problem is defined as the attribution of operational values to each generation unit inside a hydropower plant (HPP), given some criteria such as the total power to be generated, or the operational bounds of each unit. An optimal dispatch programming for hydroelectric units in HPP provides a larger production of electricity, with minimal water use. This paper presents an evolutionary approach to optimize the multicriteria electric dispatch problem in a general HPP, based on a Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm. The proposed approach integrates mathematical models and evolutionary swarm computation. The experimental analysis shows that the proposed MESH algorithm is able to reach competitive results when compared with classical evolutionary algorithms, the NGA-II and SPEA2 basing on ANOVA inference test. Results also show that the proposed MESH is able to save a large amount of water in the energy production process, supplying the requested load, and minimizing blackout risks and generating a profit around $275,000 monthly.
| Original language | English |
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| Title of host publication | 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings |
| Publisher | IEEE |
| Pages | 193-200 |
| ISBN (Electronic) | 9781728183923 |
| DOIs | |
| Publication status | Published - 9 Aug 2021 |
| Event | 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Virtual, Krakow, Poland Duration: 28 Jun 2021 → 1 Jul 2021 |
Publication series
| Name | 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings |
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Conference
| Conference | 2021 IEEE Congress on Evolutionary Computation, CEC 2021 |
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| Country/Territory | Poland |
| City | Virtual, Krakow |
| Period | 28/06/21 → 1/07/21 |
Funding
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. “The content of this publication does not reflect the official opinion of the European Union. Responsibility for the information and views expressed herein lies entirely with the author(s).” This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754382. This research has been partially supported by Ministerio de Econom?a y Competitividad of Spain (Grant Ref. TIN2017-85887-C2-2-P) and by Comunidad de Madrid, PROMINTCM 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. ?The content of this publication does not reflect the official opinion of the European Union. Responsibility for the information and views expressed herein lies entirely with the author(s).?