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Corrigendum to ‘Low-dimensional scenario generation method of solar and wind availability for representative days in energy modeling’[Applied Energy Volume 306 PB (2021) 118075]

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  • Densing, Martin
  • Wan, Yi

Abstract

We present a scenario generation method for representative days of wind and solar power availability for use in energy-system models. The method uses principal component analysis (PCA) such that the correlations between solar and wind can be captured. PCA is applied to daily time series of hourly profiles of regional solar and wind power availability to yield low-dimensional scenarios, which can be used in regional energy system or energy market models that represent the year with a limited set of representative days. Subsequently, the scenarios generated with PCA are used as building blocks for daily multi-regional scenarios under different assumption on dependence, which can include extreme joint events. As an application, the impact of variability of intermittent renewables with a – numerically tractable – low number of scenarios is applied in an electricity market model, where the increase in resulting price variation caused by solar and wind variability is investigated. Strengths and limits of the approach are also shown in terms of dimensional extensions and by comparison with hierarchical clustering. – The documented software code of the statistical analysis is freely available.
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Suggested Citation

  • Densing, Martin & Wan, Yi, 2025. "Corrigendum to ‘Low-dimensional scenario generation method of solar and wind availability for representative days in energy modeling’[Applied Energy Volume 306 PB (2021) 118075]," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s030626192402542x
    DOI: 10.1016/j.apenergy.2024.125158
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    2. Anderson Mitterhofer Iung & Fernando Luiz Cyrino Oliveira & André Luís Marques Marcato, 2023. "A Review on Modeling Variable Renewable Energy: Complementarity and Spatial–Temporal Dependence," Energies, MDPI, vol. 16(3), pages 1-24, January.
    3. Hwang Goh, Hui & Shi, Shuaiwei & Liang, Xue & Zhang, Dongdong & Dai, Wei & Liu, Hui & Yuong Wong, Shen & Agustiono Kurniawan, Tonni & Chen Goh, Kai & Leei Cham, Chin, 2022. "Optimal energy scheduling of grid-connected microgrids with demand side response considering uncertainty," Applied Energy, Elsevier, vol. 327(C).
    4. Simon, Emanuel & Schaeffer, Roberto & Szklo, Alexandre, 2025. "A solar and wind clustering framework with downscaling and bias correction of reanalysis data using singular value decomposition," Energy, Elsevier, vol. 319(C).
    5. Dong, Xiaochong & Sun, Yingyun & Yang, Yue & Mao, Zhihang, 2025. "Controllable renewable energy scenario generation based on pattern-guided diffusion models," Applied Energy, Elsevier, vol. 398(C).
    6. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    7. Liang Ma & Shigong Jiang & Yi Song & Chenyi Si & Xiaohan Li, 2025. "Multi-Time Scale Scenario Generation for Source–Load Modeling Through Temporal Generative Adversarial Networks," Energies, MDPI, vol. 18(6), pages 1-18, March.
    8. Xiaomei Ma & Yongqian Liu & Jie Yan & Han Wang, 2023. "A WGAN-GP-Based Scenarios Generation Method for Wind and Solar Power Complementary Study," Energies, MDPI, vol. 16(7), pages 1-20, March.
    9. Cheng, Xiong & Wan, Shixing & Zhengfeng, Bao & Wang, Lei & Li, Wenwu & Li, Xianshan & Zhong, Hao, 2025. "Credible capacity gain identification method of peak-shaving scheduling of cascade hydro-wind-solar complementary system," Renewable Energy, Elsevier, vol. 248(C).

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