Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm
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-gen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/9/2443 |
| _version_ | 1797536387855024128 |
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| author | Carolina Gil Marcelino Carlos Camacho-Gómez Silvia Jiménez-Fernández Sancho Salcedo-Sanz |
| author_facet | Carolina Gil Marcelino Carlos Camacho-Gómez Silvia Jiménez-Fernández Sancho Salcedo-Sanz |
| author_sort | Carolina Gil Marcelino |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:59:00Z |
| format | Article |
| id | doaj.art-7de03023f9a5458fa36a2b2ca484ee84 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T11:59:00Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7de03023f9a5458fa36a2b2ca484ee842023-11-21T17:04:23ZengMDPI AGEnergies1996-10732021-04-01149244310.3390/en14092443Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization AlgorithmCarolina Gil Marcelino0Carlos Camacho-Gómez1Silvia Jiménez-Fernández2Sancho Salcedo-Sanz3Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, SpainDepartment of Information Systems, Universidad Politécnica de Madrid, Campus Sur, 28031 Madrid, SpainDepartment of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, SpainDepartment of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, SpainHydro-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.https://www.mdpi.com/1996-1073/14/9/2443generation schedulinghydro-power plantscoral reefs optimization algorithmmeta-heuristicsbio-inspired algorithmsenergy efficiency |
| spellingShingle | Carolina Gil Marcelino Carlos Camacho-Gómez Silvia Jiménez-Fernández Sancho Salcedo-Sanz Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm Energies generation scheduling hydro-power plants coral reefs optimization algorithm meta-heuristics bio-inspired algorithms energy efficiency |
| title | Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm |
| title_full | Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm |
| title_fullStr | Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm |
| title_full_unstemmed | Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm |
| title_short | Optimal Generation Scheduling in Hydro-Power Plants with the Coral Reefs Optimization Algorithm |
| title_sort | optimal generation scheduling in hydro power plants with the coral reefs optimization algorithm |
| topic | generation scheduling hydro-power plants coral reefs optimization algorithm meta-heuristics bio-inspired algorithms energy efficiency |
| url | https://www.mdpi.com/1996-1073/14/9/2443 |
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