IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v87y2010i3p843-855.html
   My bibliography  Save this item

A methodology to generate statistically dependent wind speed scenarios

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Deng, Jingchuan & Li, Hongru & Hu, Jinxing & Liu, Zhenyu, 2021. "A new wind speed scenario generation method based on spatiotemporal dependency structure," Renewable Energy, Elsevier, vol. 163(C), pages 1951-1962.
  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. Gómez-Pérez, Jesús D. & Latorre-Canteli, Jesus M. & Ramos, Andres & Perea, Alejandro & Sanz, Pablo & Hernández, Francisco, 2024. "Improving operating policies in stochastic optimization: An application to the medium-term hydrothermal scheduling problem," Applied Energy, Elsevier, vol. 359(C).
  4. 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.
  5. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
  6. Zárate-Miñano, Rafael & Anghel, Marian & Milano, Federico, 2013. "Continuous wind speed models based on stochastic differential equations," Applied Energy, Elsevier, vol. 104(C), pages 42-49.
  7. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
  8. 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).
  9. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
  10. Domínguez, R. & Conejo, A.J. & Carrión, M., 2014. "Operation of a fully renewable electric energy system with CSP plants," Applied Energy, Elsevier, vol. 119(C), pages 417-430.
  11. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Impact of spatio-temporal correlation of wind production on clearing outcomes of a competitive pool market," Renewable Energy, Elsevier, vol. 86(C), pages 216-227.
  12. Zhang, Wenyu & Wu, Jie & Wang, Jianzhou & Zhao, Weigang & Shen, Lin, 2012. "Performance analysis of four modified approaches for wind speed forecasting," Applied Energy, Elsevier, vol. 99(C), pages 324-333.
  13. Krishna, Attoti Bharath & Abhyankar, Abhijit R., 2023. "Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method," Energy, Elsevier, vol. 265(C).
  14. 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.
  15. Wang, Zhiwen & Shen, Chen & Liu, Feng, 2018. "A conditional model of wind power forecast errors and its application in scenario generation," Applied Energy, Elsevier, vol. 212(C), pages 771-785.
  16. Simoglou, Christos K. & Kardakos, Evaggelos G. & Bakirtzis, Emmanouil A. & Chatzigiannis, Dimitris I. & Vagropoulos, Stylianos I. & Ntomaris, Andreas V. & Biskas, Pandelis N. & Gigantidou, Antiopi & T, 2014. "An advanced model for the efficient and reliable short-term operation of insular electricity networks with high renewable energy sources penetration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 415-427.
  17. Mo, Hua-Dong & Li, Yan-Fu & Zio, Enrico, 2016. "A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks," Applied Energy, Elsevier, vol. 183(C), pages 805-822.
  18. Alipour, M. & Hafezi, R. & Amer, M. & Akhavan, A.N., 2017. "A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post–sanction period," Energy, Elsevier, vol. 135(C), pages 851-864.
  19. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
  20. Arenas-López, J. Pablo & Badaoui, Mohamed, 2020. "Stochastic modelling of wind speeds based on turbulence intensity," Renewable Energy, Elsevier, vol. 155(C), pages 10-22.
  21. Gupta, Neeraj, 2016. "Probabilistic load flow with detailed wind generator models considering correlated wind generation and correlated loads," Renewable Energy, Elsevier, vol. 94(C), pages 96-105.
  22. Zamani-Dehkordi, Payam & Rakai, Logan & Zareipour, Hamidreza, 2016. "Deciding on the support schemes for upcoming wind farms in competitive electricity markets," Energy, Elsevier, vol. 116(P1), pages 8-19.
  23. 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.
  24. Hui Hwang Goh & Gumeng Peng & Dongdong Zhang & Wei Dai & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories," Energies, MDPI, vol. 15(7), pages 1-21, April.
  25. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
  26. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
  27. 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).
  28. Pineda, Salvador & Morales, Juan M. & Boomsma, Trine K., 2016. "Impact of forecast errors on expansion planning of power systems with a renewables target," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1113-1122.
  29. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José, 2016. "Wind power scenario generation through state-space specifications for uncertainty analysis of wind power plants," Applied Energy, Elsevier, vol. 162(C), pages 21-30.
  30. 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).
  31. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
  32. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
  33. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
  34. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
  35. Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
  36. Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
  37. 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.
  38. Hua, Weiqi & Chen, Ying & Qadrdan, Meysam & Jiang, Jing & Sun, Hongjian & Wu, Jianzhong, 2022. "Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  39. Site Wang & Harsha Gangammanavar & Sandra Ekşioğlu & Scott J. Mason, 2020. "Statistical estimation of operating reserve requirements using rolling horizon stochastic optimization," Annals of Operations Research, Springer, vol. 292(1), pages 371-397, September.
  40. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & Wood, David A., 2019. "A Layered Uncertainties Scenario Synthesizing (LUSS) model applied to evaluate multiple potential long-run outcomes for Iran's natural gas exports," Energy, Elsevier, vol. 169(C), pages 646-659.
  41. Wenlei Bai & Duehee Lee & Kwang Y. Lee, 2017. "Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model," Energies, MDPI, vol. 10(12), pages 1-19, December.
  42. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
  43. Kirchner-Bossi, N. & Prieto, L. & García-Herrera, R. & Carro-Calvo, L. & Salcedo-Sanz, S., 2013. "Multi-decadal variability in a centennial reconstruction of daily wind," Applied Energy, Elsevier, vol. 105(C), pages 30-46.
  44. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
  45. Guglielmo D’Amico & Giovanni Masala & Filippo Petroni & Robert Adam Sobolewski, 2020. "Managing Wind Power Generation via Indexed Semi-Markov Model and Copula," Energies, MDPI, vol. 13(16), pages 1-21, August.
  46. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
  47. Liu, Yin & Davanloo Tajbakhsh, Sam & Conejo, Antonio J., 2021. "Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 812-824.
  48. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
  49. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
  50. 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.
  51. Gupta, Neeraj, 2016. "A review on the inclusion of wind generation in power system studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 530-543.
  52. Sui, Quan & Zhang, Rui & Wu, Chuantao & Wei, Fanrong & Lin, Xiangning & Li, Zhengtian, 2020. "Stochastic scheduling of an electric vessel-based energy management system in pelagic clustering islands," Applied Energy, Elsevier, vol. 259(C).
  53. Yilan Luo & Deniz Sezer & David Wood & Mingkuan Wu & Hamid Zareipour, 2019. "Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada," Energies, MDPI, vol. 12(10), pages 1-29, May.
  54. Luo, Zheng & Lin, Xiaojie & Qiu, Tianyue & Li, Manjie & Zhong, Wei & Zhu, Lingkai & Liu, Shuangcui, 2024. "Investigation of hybrid adversarial-diffusion sample generation method of substations in district heating system," Energy, Elsevier, vol. 288(C).
  55. Jin, Ming & Feng, Wei & Liu, Ping & Marnay, Chris & Spanos, Costas, 2017. "MOD-DR: Microgrid optimal dispatch with demand response," Applied Energy, Elsevier, vol. 187(C), pages 758-776.
  56. Juan. J. Flores & José R. Cedeño González & Héctor Rodríguez & Mario Graff & Rodrigo Lopez-Farias & Felix Calderon, 2019. "Soft Computing Methods with Phase Space Reconstruction for Wind Speed Forecasting—A Performance Comparison," Energies, MDPI, vol. 12(18), pages 1-19, September.
  57. Zhu, Xiaojie & Guo, Ruipeng & Chen, Bin & Zhang, Jing & Hayat, Tasawar & Alsaedi, Ahmed, 2015. "Embodiment of virtual water of power generation in the electric power system in China," Applied Energy, Elsevier, vol. 151(C), pages 345-354.
  58. Dirin, Sepehr & Rahimiyan, Morteza & Baringo, Luis, 2023. "Optimal offering strategy for wind-storage systems under correlated wind production," Applied Energy, Elsevier, vol. 333(C).
  59. Qin, Li & Liu, Shi & Long, Teng & Shahzad, Muhammad Ali & Schlaberg, H. Inaki & Yan, Song An, 2018. "Wind field reconstruction using dimension-reduction of CFD data with experimental validation," Energy, Elsevier, vol. 151(C), pages 272-288.
  60. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
  61. Ye, Lin & Peng, Yishu & Li, Yilin & Li, Zhuo, 2024. "A novel informer-time-series generative adversarial networks for day-ahead scenario generation of wind power," Applied Energy, Elsevier, vol. 364(C).
  62. Pei Du & Yu Jin & Kequan Zhang, 2016. "A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed," Sustainability, MDPI, vol. 8(8), pages 1-25, August.
  63. Chang, G.W. & Lu, H.J. & Chang, Y.R. & Lee, Y.D., 2017. "An improved neural network-based approach for short-term wind speed and power forecast," Renewable Energy, Elsevier, vol. 105(C), pages 301-311.
  64. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
  65. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
  66. Melício, R. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "Transient analysis of variable-speed wind turbines at wind speed disturbances and a pitch control malfunction," Applied Energy, Elsevier, vol. 88(4), pages 1322-1330, April.
  67. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
  68. Ceyhun Yıldız & Mustafa Tekin & Ahmet Gani & Ö. Fatih Keçecioğlu & Hakan Açıkgöz & Mustafa Şekkeli, 2017. "A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
  69. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.