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Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications

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  • Camal, S.
  • Teng, F.
  • Michiorri, A.
  • Kariniotakis, G.
  • Badesa, L.

Abstract

This paper proposes a methodology for an efficient generation of correlated scenarios of Wind, Photovoltaics (PV) and small Hydro production considering the power system application at hand. The merits of scenarios obtained from a direct probabilistic forecast of the aggregated production are compared with those of scenarios arising from separate production forecasts for each energy source, the correlations of which are modeled in a later stage with a multivariate copula. It is found that scenarios generated from separate forecasts reproduce globally better the variability of a multi-source aggregated production. Aggregating renewable power plants can potentially mitigate their uncertainty and improve their reliability when they offer regulation services. In this context, the first application of scenarios consists in devising an optimal day-ahead reserve bid made by a Wind-PV-Hydro Virtual Power Plant (VPP). Scenarios are fed into a two-stage stochastic optimization model, with chance-constraints to minimize the probability of failing to deploy reserve in real-time. Results of a case study show that scenarios generated by separately forecasting the production of each energy source leads to a higher Conditional Value at Risk than scenarios from direct aggregated forecasting. The alternative forecasting methods can also significantly affect the scheduling of future power systems with high penetration of weather-dependent renewable plants. The generated scenarios have a second application here as the inputs of a two-stage stochastic unit commitment model. The case study demonstrates that the direct forecast of aggregated production can effectively reduce the system operational cost, mainly through better covering the extreme cases. The comprehensive application-based assessment of scenario generation methodologies in this paper informs the decision-makers on the optimal way to generate short-term scenarios of aggregated RES production according to their risk aversion and to the contribution of each source in the aggregation.

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  • Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1396-1406
    DOI: 10.1016/j.apenergy.2019.03.112
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    7. Gaete-Morales, Carlos & Gallego-Schmid, Alejandro & Stamford, Laurence & Azapagic, Adisa, 2019. "A novel framework for development and optimisation of future electricity scenarios with high penetration of renewables and storage," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 250, pages 1657-1672.
    8. 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.
    9. Densing, Martin & Wan, Yi, 2022. "Low-dimensional scenario generation method of solar and wind availability for representative days in energy modeling," Applied Energy, Elsevier, vol. 306(PB).
    10. Chapaloglou, Spyridon & Varagnolo, Damiano & Marra, Francesco & Tedeschi, Elisabetta, 2022. "Data-driven energy management of isolated power systems under rapidly varying operating conditions," Applied Energy, Elsevier, vol. 314(C).
    11. Zhang, Juntao & Cheng, Chuntian & Yu, Shen & Su, Huaying, 2022. "Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems," Applied Energy, Elsevier, vol. 324(C).
    12. Dong, Wei & Chen, Xianqing & Yang, Qiang, 2022. "Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability," Applied Energy, Elsevier, vol. 308(C).
    13. Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
    14. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    15. Meng Zhang & Jinhai Feng & Ziwen Zhao & Wei Zhang & Junzhi Zhang & Beibei Xu, 2022. "A 1D-3D Coupling Model to Evaluate Hydropower Generation System Stability," Energies, MDPI, vol. 15(19), pages 1-13, September.
    16. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
    17. Faria, Victor A.D. & Rodrigo de Queiroz, Anderson & DeCarolis, Joseph F., 2023. "Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies," Energy, Elsevier, vol. 270(C).
    18. Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    19. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    20. Xiaoming Liu & Liang Wang & Yongji Cao & Ruicong Ma & Yao Wang & Changgang Li & Rui Liu & Shihao Zou, 2023. "Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length," Energies, MDPI, vol. 16(7), pages 1-16, March.
    21. González-Garrido, A. & Gaztañaga, H. & Saez-de-Ibarra, A. & Milo, A. & Eguia, P., 2020. "Electricity and reserve market bidding strategy including sizing evaluation and a novel renewable complementarity-based centralized control for storage lifetime enhancement," Applied Energy, Elsevier, vol. 262(C).
    22. 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.
    23. Gaete-Morales, Carlos & Gallego-Schmid, Alejandro & Stamford, Laurence & Azapagic, Adisa, 2019. "A novel framework for development and optimisation of future electricity scenarios with high penetration of renewables and storage," Applied Energy, Elsevier, vol. 250(C), pages 1657-1672.

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