Grey Wolf Optimizer for RES Capacity Factor Maximization at the Placement Planning Stage

被引:3
作者
Bramm, Andrey M. [1 ]
Eroshenko, Stanislav A. [1 ]
Khalyasmaa, Alexandra I. [1 ]
Matrenin, Pavel V. [1 ]
机构
[1] Ural Fed Univ First President Russia BN Yeltsin, Ural Power Engn Inst, Ekaterinburg 620002, Russia
关键词
capacity factor; forecasting; renewable energy sources; optimization; energy optimization; grey wolf optimizer; SOLAR IRRADIANCE; DESIGN;
D O I
10.3390/math11112545
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
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页数:22
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共 41 条
  • [1] Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms
    Ahmadi, Amirhossein
    Nabipour, Mojtaba
    Mohammadi-Ivatloo, Behnam
    Amani, Ali Moradi
    Rho, Seungmin
    Piran, Md. Jalil
    [J]. IEEE ACCESS, 2020, 8 : 151511 - 151522
  • [2] [Anonymous], 2013, W WIND SOLAR INTEGRA
  • [3] Atique S., 2020, P IEEE GREEN TECHN C
  • [4] The Potential Impact of Climate Change on the Efficiency and Reliability of Solar, Hydro, and Wind Energy Sources
    Bhatt, Uma S.
    Carreras, Benjamin A.
    Barredo, Jose Miguel Reynolds
    Newman, David E.
    Collet, Pere
    Gomila, Damia
    [J]. LAND, 2022, 11 (08)
  • [5] Bramm A., 2021, P 2021 18 INT SCI TE
  • [6] Cavalcante L, 2017, 2017 IEEE MANCHESTER POWERTECH
  • [7] docs, AM CERT ACTS GOV RUS
  • [8] Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression
    Dowell, Jethro
    Pinson, Pierre
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2016, 7 (02) : 763 - 770
  • [9] El-Sayed M.E., 2023, PLOS ONE, V18
  • [10] eur-lex, PERF SUPP EL REN SOU