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The combined value of wind and solar power forecasting improvements and electricity storage

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Cited by:

  1. Henrik Zsiborács & Gábor Pintér & András Vincze & Nóra Hegedűsné Baranyai, 2022. "Wind Power Generation Scheduling Accuracy in Europe: An Overview of ENTSO-E Countries," Sustainability, MDPI, vol. 14(24), pages 1-58, December.
  2. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
  3. Doubleday, Kate & Lara, José Daniel & Hodge, Bri-Mathias, 2022. "Investigation of stochastic unit commitment to enable advanced flexibility measures for high shares of solar PV," Applied Energy, Elsevier, vol. 321(C).
  4. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
  5. Kazmi, Hussain & Tao, Zhenmin, 2022. "How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead," Applied Energy, Elsevier, vol. 323(C).
  6. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
  7. Khan, Taimoor & Choi, Chang, 2025. "Attention enhanced dual stream network with advanced feature selection for power forecasting," Applied Energy, Elsevier, vol. 377(PC).
  8. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
  9. Wenhui Zhao & Rong Li & Shuan Zhu, 2024. "Subsidy Policies and Economic Analysis of Photovoltaic Energy Storage Integration in China," Energies, MDPI, vol. 17(10), pages 1-24, May.
  10. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
  11. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  12. Eppinger, Bernd & Zigan, Lars & Karl, Jürgen & Will, Stefan, 2020. "Pumped thermal energy storage with heat pump-ORC-systems: Comparison of latent and sensible thermal storages for various fluids," Applied Energy, Elsevier, vol. 280(C).
  13. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
  14. Hameedullah Zaheb & Mikaeel Ahmadi & Nisar Ahmad Rahmany & Mir Sayed Shah Danish & Habibullah Fedayi & Atsushi Yona, 2023. "Optimal Grid Flexibility Assessment for Integration of Variable Renewable-Based Electricity Generation," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
  15. Yunzhu Gao & Jun Wang & Lin Guo & Hong Peng, 2024. "Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems," Sustainability, MDPI, vol. 16(4), pages 1-18, February.
  16. Hou, Hui & Xu, Tao & Wu, Xixiu & Wang, Huan & Tang, Aihong & Chen, Yangyang, 2020. "Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system," Applied Energy, Elsevier, vol. 271(C).
  17. Yang, Chengwu & Xia, Xiaoxia & Peng, Bo & Wang, Zhiqi & Zhang, Hualong & Liang, Enxue, 2024. "Multi-objective optimization and influence degree analysis of the thermally integrated HP-ORC carnot battery based on the orthogonal design method and grey relational analysis," Energy, Elsevier, vol. 311(C).
  18. Craig, Michael & Guerra, Omar J. & Brancucci, Carlo & Pambour, Kwabena Addo & Hodge, Bri-Mathias, 2020. "Valuing intra-day coordination of electric power and natural gas system operations," Energy Policy, Elsevier, vol. 141(C).
  19. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
  20. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
  21. Gandhi, Oktoviano & Zhang, Wenjie & Kumar, Dhivya Sampath & Rodríguez-Gallegos, Carlos D. & Yagli, Gokhan Mert & Yang, Dazhi & Reindl, Thomas & Srinivasan, Dipti, 2024. "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  22. CH Hussaian Basha & C Rani, 2020. "Different Conventional and Soft Computing MPPT Techniques for Solar PV Systems with High Step-Up Boost Converters: A Comprehensive Analysis," Energies, MDPI, vol. 13(2), pages 1-27, January.
  23. Li, Xiao & Liu, Pan & Feng, Maoyuan & Jordaan, Sarah M. & Cheng, Lei & Ming, Bo & Chen, Jie & Xie, Kang & Liu, Weibo, 2024. "Energy transition paradox: Solar and wind growth can hinder decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  24. An, Xugang & He, Qing & Zhang, Qianxu & Liu, Ruonan & Lu, Chang & Du, Dongmei, 2024. "Physical modeling and dynamic characteristics of pumped thermal energy storage system," Energy, Elsevier, vol. 290(C).
  25. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
  26. Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
  27. Eppinger, Bernd & Steger, Daniel & Regensburger, Christoph & Karl, Jürgen & Schlücker, Eberhard & Will, Stefan, 2021. "Carnot battery: Simulation and design of a reversible heat pump-organic Rankine cycle pilot plant," Applied Energy, Elsevier, vol. 288(C).
  28. Bernd Eppinger & Mustafa Muradi & Daniel Scharrer & Lars Zigan & Peter Bazan & Reinhard German & Stefan Will, 2021. "Simulation of the Part Load Behavior of Combined Heat Pump-Organic Rankine Cycle Systems," Energies, MDPI, vol. 14(13), pages 1-18, June.
  29. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
  30. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Mohammed Elsayed Lotfy & Theophilus Amara & Keifa Vamba Konneh & Tomonobu Senjyu, 2019. "Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning," Future Internet, MDPI, vol. 11(10), pages 1-16, October.
  31. Wyman-Pain, Heather & Bian, Yuankai & Thomas, Cain & Li, Furong, 2018. "The economics of different generation technologies for frequency response provision," Applied Energy, Elsevier, vol. 222(C), pages 554-563.
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