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Evaluating the quality of scenarios of short-term wind power generation

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  1. Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
  2. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
  3. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
  4. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
  5. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
  6. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
  7. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
  8. Xu, Jian & Wang, Jing & Liao, Siyang & Sun, Yuanzhang & Ke, Deping & Li, Xiong & Liu, Ji & Jiang, Yibo & Wei, Congying & Tang, Bowen, 2018. "Stochastic multi-objective optimization of photovoltaics integrated three-phase distribution network based on dynamic scenarios," Applied Energy, Elsevier, vol. 231(C), pages 985-996.
  9. Iversen, Emil B. & Morales, Juan M. & Møller, Jan K. & Madsen, Henrik, 2016. "Short-term probabilistic forecasting of wind speed using stochastic differential equations," International Journal of Forecasting, Elsevier, vol. 32(3), pages 981-990.
  10. Müller, Alfred & Reuber, Matthias, 2023. "A copula-based time series model for global horizontal irradiation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 869-883.
  11. Chen, Jun & Rabiti, Cristian, 2017. "Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems," Energy, Elsevier, vol. 120(C), pages 507-517.
  12. Roy, Sanjoy, 2014. "Performance prediction of active pitch-regulated wind turbine with short duration variations in source wind," Applied Energy, Elsevier, vol. 114(C), pages 700-708.
  13. Elberg, Christina & Hagspiel, Simeon, 2015. "Spatial dependencies of wind power and interrelations with spot price dynamics," European Journal of Operational Research, Elsevier, vol. 241(1), pages 260-272.
  14. Poncela, Marta & Poncela, Pilar & Perán, José Ramón, 2013. "Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting," Applied Energy, Elsevier, vol. 108(C), pages 349-362.
  15. Arrieta-Prieto, Mario & Schell, Kristen R., 2022. "Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model," International Journal of Forecasting, Elsevier, vol. 38(1), pages 300-320.
  16. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
  17. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Pan, Bo & Qi, Shiqiang, 2020. "Two-stage stochastic optimal operation of integrated electricity and heat system considering reserve of flexible devices and spatial-temporal correlation of wind power," Applied Energy, Elsevier, vol. 275(C).
  18. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
  19. Gilbert, Ciaran & Browell, Jethro & McMillan, David, 2021. "Probabilistic access forecasting for improved offshore operations," International Journal of Forecasting, Elsevier, vol. 37(1), pages 134-150.
  20. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
  21. Carol Alexander & Michael Coulon & Yang Han & Xiaochun Meng, 2021. "Evaluating the Discrimination Ability of Proper Multivariate Scoring Rules," Papers 2101.12693, arXiv.org.
  22. Chen, Xianqing & Dong, Wei & Yang, Qiang, 2022. "Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties," Applied Energy, Elsevier, vol. 323(C).
  23. 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.
  24. 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).
  25. Jon Olauson & Johan Bladh & Joakim Lönnberg & Mikael Bergkvist, 2016. "A New Approach to Obtain Synthetic Wind Power Forecasts for Integration Studies," Energies, MDPI, vol. 9(10), pages 1-16, October.
  26. Busiswe Skosana & Mukwanga W. Siti & Nsilulu T. Mbungu & Sonu Kumar & Willy Mulumba, 2023. "An Evaluation of Potential Strategies in Renewable Energy Systems and Their Importance for South Africa—A Review," Energies, MDPI, vol. 16(22), pages 1-27, November.
  27. Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
  28. Rachunok, Benjamin & Staid, Andrea & Watson, Jean-Paul & Woodruff, David L., 2020. "Assessment of wind power scenario creation methods for stochastic power systems operations," Applied Energy, Elsevier, vol. 268(C).
  29. Rahimiyan, Morteza, 2014. "A statistical cognitive model to assess impact of spatially correlated wind production on market behaviors," Applied Energy, Elsevier, vol. 122(C), pages 62-72.
  30. Muniain, Peru & Ziel, Florian, 2020. "Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1193-1210.
  31. Zheng, Kedi & Chen, Huiyao & Wang, Yi & Chen, Qixin, 2022. "Data-driven financial transmission right scenario generation and speculation," Energy, Elsevier, vol. 238(PC).
  32. Tang, Chenghui & Wang, Yishen & Xu, Jian & Sun, Yuanzhang & Zhang, Baosen, 2018. "Efficient scenario generation of multiple renewable power plants considering spatial and temporal correlations," Applied Energy, Elsevier, vol. 221(C), pages 348-357.
  33. Xu, Bin & Zhu, Feilin & Zhong, Ping-an & Chen, Juan & Liu, Weifeng & Ma, Yufei & Guo, Le & Deng, Xiaoliang, 2019. "Identifying long-term effects of using hydropower to complement wind power uncertainty through stochastic programming," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  34. 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.
  35. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
  36. Mike Ludkovski & Glen Swindle & Eric Grannan, 2022. "Large Scale Probabilistic Simulation of Renewables Production," Papers 2205.04736, arXiv.org.
  37. Peru Muniain & Florian Ziel, 2018. "Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating Peak and Off-Peak Prices," Papers 1810.08418, arXiv.org, revised Dec 2019.
  38. Yongjie Luo & Qinghao Meng & Yuan Chi & Qianggang Wang & Yongshou Zeng & Zaoming Deng & Yao Zou, 2024. "A Low-Carbon Optimal Operation Method for an Industrial Park Multi-Energy Coupling System Utilizing By-Product Hydrogen," Sustainability, MDPI, vol. 16(6), pages 1-24, March.
  39. Xiao, Yunpeng & Wang, Xifan & Wang, Xiuli & Dang, Can & Lu, Ming, 2016. "Behavior analysis of wind power producer in electricity market," Applied Energy, Elsevier, vol. 171(C), pages 325-335.
  40. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
  41. Golestaneh, Faranak & Gooi, Hoay Beng & Pinson, Pierre, 2016. "Generation and evaluation of space–time trajectories of photovoltaic power," Applied Energy, Elsevier, vol. 176(C), pages 80-91.
  42. Cramer, Eike & Witthaut, Dirk & Mitsos, Alexander & Dahmen, Manuel, 2023. "Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 346(C).
  43. Pinto, Mauro S.S. & Miranda, Vladimiro & Saavedra, Osvaldo R., 2016. "Risk and unit commitment decisions in scenarios of wind power uncertainty," Renewable Energy, Elsevier, vol. 97(C), pages 550-558.
  44. Florian Ziel & Kevin Berk, 2019. "Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules," Papers 1910.07325, arXiv.org.
  45. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
  46. Didem Sarı Ay & Sarah M. Ryan, 2019. "Observational data-based quality assessment of scenario generation for stochastic programs," Computational Management Science, Springer, vol. 16(3), pages 521-540, July.
  47. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
  48. Gao, Cuixia & Sun, Mei & Geng, Yong & Wu, Rui & Chen, Wei, 2016. "A bibliometric analysis based review on wind power price," Applied Energy, Elsevier, vol. 182(C), pages 602-612.
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