Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks
Appropriate operation of the dam reservoir in a hydroelectric power plant (HEPP) is necessary for energy planning, reservoir management, and efficient operation. For good energy planning, the operator needs to make an accurate estimate of the energy production capacity for the next day and plan for...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| author | Sibel Akkaya Oy Serkan İnal Ali Ekber Özdemir |
| author_facet | Sibel Akkaya Oy Serkan İnal Ali Ekber Özdemir |
| author_sort | Sibel Akkaya Oy |
| collection | DOAJ |
| description | Appropriate operation of the dam reservoir in a hydroelectric power plant (HEPP) is necessary for energy planning, reservoir management, and efficient operation. For good energy planning, the operator needs to make an accurate estimate of the energy production capacity for the next day and plan for production when the energy need is highest. The energy produced in HEPPs depends on the level of water stored in the reservoir, which is directly connected to the reservoir flow. As the water level in the reservoir varies throughout the year depending on climatic conditions, it is important to estimate energy production in order to operate the HEPP most effectively. In this study, the next-day energy production of the HEPP was estimated using a neural network with two hidden layers, each with 10 neurons. A neural network with a hidden layer of 20 neurons was used to estimate future electricity prices and the best hours for market clearing price (MCP). This study found that using short-term training provided the best hourly estimation of MCP, with an average accuracy of 90%; the daily estimation of MCP was ≥95%. |
| format | Article |
| id | doaj-art-3c5b1573570744bdb3d8691ee3c3a1da |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3c5b1573570744bdb3d8691ee3c3a1da2025-01-10T13:14:43ZengMDPI AGApplied Sciences2076-34172024-12-0115118310.3390/app15010183Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural NetworksSibel Akkaya Oy0Serkan İnal1Ali Ekber Özdemir2Fatsa Faculty of Marine Sciences, Ordu University, Ordu 52400, TurkeyDarıca—2 Hydroelectric Power Plant, Kabadüz, Ordu 52020, TurkeyFatsa Faculty of Marine Sciences, Ordu University, Ordu 52400, TurkeyAppropriate operation of the dam reservoir in a hydroelectric power plant (HEPP) is necessary for energy planning, reservoir management, and efficient operation. For good energy planning, the operator needs to make an accurate estimate of the energy production capacity for the next day and plan for production when the energy need is highest. The energy produced in HEPPs depends on the level of water stored in the reservoir, which is directly connected to the reservoir flow. As the water level in the reservoir varies throughout the year depending on climatic conditions, it is important to estimate energy production in order to operate the HEPP most effectively. In this study, the next-day energy production of the HEPP was estimated using a neural network with two hidden layers, each with 10 neurons. A neural network with a hidden layer of 20 neurons was used to estimate future electricity prices and the best hours for market clearing price (MCP). This study found that using short-term training provided the best hourly estimation of MCP, with an average accuracy of 90%; the daily estimation of MCP was ≥95%.https://www.mdpi.com/2076-3417/15/1/183hydropower generationenergy productionelectricity priceartificial intelligencealgorithmartificial neural network |
| spellingShingle | Sibel Akkaya Oy Serkan İnal Ali Ekber Özdemir Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks Applied Sciences hydropower generation energy production electricity price artificial intelligence algorithm artificial neural network |
| title | Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks |
| title_full | Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks |
| title_fullStr | Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks |
| title_full_unstemmed | Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks |
| title_short | Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks |
| title_sort | operation of hydroelectric power plants dam reservoirs and energy trade using artificial neural networks |
| topic | hydropower generation energy production electricity price artificial intelligence algorithm artificial neural network |
| url | https://www.mdpi.com/2076-3417/15/1/183 |
| work_keys_str_mv | AT sibelakkayaoy operationofhydroelectricpowerplantsdamreservoirsandenergytradeusingartificialneuralnetworks AT serkaninal operationofhydroelectricpowerplantsdamreservoirsandenergytradeusingartificialneuralnetworks AT aliekberozdemir operationofhydroelectricpowerplantsdamreservoirsandenergytradeusingartificialneuralnetworks |