LAPSE:2023.31905v1
Published Article
LAPSE:2023.31905v1
Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm
April 19, 2023
Abstract
Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem. This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.
Keywords
bio-inspired algorithms, coral reefs optimization algorithm, Energy Efficiency, generation scheduling, hydro-power plants, meta-heuristics
Suggested Citation
Marcelino CG, Camacho-Gómez C, Jiménez-Fernández S, Salcedo-Sanz S. Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm. (2023). LAPSE:2023.31905v1
Author Affiliations
Marcelino CG: Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain; Institute of Computing, Federal University of Rio de Janeiro, Rio de Janeiro 21941-972, Brazil [ORCID]
Camacho-Gómez C: Department of Information Systems, Universidad Politécnica de Madrid, Campus Sur, 28031 Madrid, Spain [ORCID]
Jiménez-Fernández S: Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain [ORCID]
Salcedo-Sanz S: Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain [ORCID]
Journal Name
Energies
Volume
14
Issue
9
First Page
2443
Year
2021
Publication Date
2021-04-25
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14092443, Publication Type: Journal Article
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LAPSE:2023.31905v1
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https://doi.org/10.3390/en14092443
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Apr 19, 2023
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