Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage
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| Автор |
Bramm, A. M.
Eroshenko, S. A. Khalyasmaa, A. I. Matrenin, P. V. |
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| Тематика |
CAPACITY FACTOR
ENERGY OPTIMIZATION FORECASTING GREY WOLF OPTIMIZER OPTIMIZATION RENEWABLE ENERGY SOURCES |
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| Описание |
At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the installed capacity utilization factor, which is extremely important for optimally placing power facilities based on renewable energy sources and for the successful development of renewable energy. Efficient placement maximizes the installed capacity utilization factor of a power facility, increasing energy efficiency and the payback period. The installed capacity utilization factor depends on the assumed meteorological factors relating to geographical location and the technical characteristics of power generation. However, the installed capacity utilization factor cannot be accurately predicted, since it is necessary to know the volume of electricity produced by the power facility. A novel approach to the optimization of placement of renewable energy source power plants and their capacity factor forecasting was proposed in this article. This approach combines a machine learning forecasting algorithm (random forest regressor) with a metaheuristic optimization algorithm (grey wolf optimizer). Although the proposed approach assumes the use of only open-source data, the simulations show better results than commonly used algorithms, such as random search, particle swarm optimizer, and firefly algorithm. © 2023 by the authors.
Ministry of Education and Science of the Russian Federation, Minobrnauka: FEUZ-2022-0030 The research was carried out within the state assignment with the financial support of the Ministry of Science and Higher Education of the Russian Federation (subject No. FEUZ-2022-0030 Development of an intelligent multi-agent system for modeling deeply integrated technological systems in the power industry). |
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| Дата |
2024-04-05T16:25:36Z
2024-04-05T16:25:36Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Bramm, AM, Eroshenko, SA, Khalyasmaa, AI & Matrenin, PV 2023, 'Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage', Mathematics, Том. 11, № 11, 2545. https://doi.org/10.3390/math11112545
Bramm, A. M., Eroshenko, S. A., Khalyasmaa, A. I., & Matrenin, P. V. (2023). Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage. Mathematics, 11(11), [2545]. https://doi.org/10.3390/math11112545 2227-7390 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161291926&doi=10.3390%2fmath11112545&partnerID=40&md5=16ec9e7e6100ec4a4bb352a07b0b5566 https://www.mdpi.com/2227-7390/11/11/2545/pdf?version=1685610137 http://elar.urfu.ru/handle/10995/130546 10.3390/math11112545 85161291926 001003680800001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
MDPI
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| Источник |
Mathematics
Mathematics |
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