KIEE - The Transactions of the Korean Institute of Electrical Engineers

1

N. C. Chung, “Improvements of the Renewable Energy Legislation for the Realization of 2050 Carbon Neutral,” Justice, vol. 190, pp. 5-27, 2022. DOI : 10.29305/tj.2022.6.190.05

2

T. Xu, W. Gao, F. Qian, Y. Li, “The implementation limitation of variable renewable energies and its impacts on the public power grid,” Energy, vol. 239, pp. 1228-1233, 2022. DOI : 10.1016/j.energy.2021.121992

3

J. Huang, P. Yalla, T. Yong, “New real time market applications at the California independent system operator (CAISO),” IEEE PES Power Systems Conference and Exposition, vol. 3, pp. 1228-1233, 2004. DOI : 10.1109/PSCE.2004.1397599

4

Q. Wang, C. Zhang, Y. Ding, G. Xydis, J. Wang, J. Østergaard, “Review of real-time electricity markets for integrating Distributed Energy Resources and Demand Response,” Applied Energy, vol. 138, pp. 695-706, 2015. DOI : 10.1016/j.apenergy.2014.10.048

5

P. Denholm, M. Hand, “Grid flexibility and storage required to achieve very high penetration of variable renewable electricity,” Energy Policy, vol. 39, pp. 1817-1830, 2011. DOI : 10.1016/j.enpol.2011.01.019

6

S. W. Lee, “Pilot Project of Electricity Market Reform in JEJU Island,” The Korean Institute of Electrical Engineers, vol. 73, no. 9, pp. 7-14, 2024.

7

J. Li, C. Wang, H. Wang, “Optimal Energy Storage Scheduling for Wind Curtailment Reduction and Energy Arbitrage: A Deep Reinforcement Learning Approach,” ArXiv, vol. arXiv:2304.02239, 2023. DOI:10.48550/arXiv.2304.02239

8

M. Miri & M. McPherson, “Demand response programs: Comparing price signals and direct load control,” Energy, vol. 288, pp. 1-17, 2024. DOI : 10.1016/j.energy.2023.129673

9

M. H. Shams, H. Niaz, B. Hashemi, J. J. Liu, P. Siano, A. A. Moghaddam, “Artificial intelligence-based prediction and analysis of the oversupply of wind and solar energy in power systems,” Energy Conversion and Management, vol. 250, 2021. DOI : 10.1016/j.enconman.2021.114892

10

M. H. Shams, H. Niaz, J. Na, A. A. Moghaddam, J. J. Liu, “Machine learning-based utilization of renewable power curtailments under uncertainty by planning of hydrogen systems and battery storages,” Journal of Energy Storage, vol. 41, 2021. DOI : 10.1016/j.est.2021.103010.

11

E. Memmel, T. Steens, S. Schluters, R. Volker, F. Schuldt, K. V. Maydell, “Predicting Renewable Curtailment in Distribution Grids Using Neural Networks,” IEEE Access, vol. 11, pp. 20319-20336, 2023. DOI : 10.1109/ACCESS.2023.3249459

12

H. Hadian, F. Naderkhani, “Deep Learning-Based Models for Wind and Solar Curtailment Forecasting,” International Conference on Energy Harvesting, Storage, and Transfer, vol. 120, 2023. DOI : 10.11159/ehst23.120

13

A. Bunodiere, H. S. Lee, “Renewable Energy Curtailment: Prediction Using a Logic-Based Forecasting Method and Mitigation Measures in Kyushu, Japan,” Energy, vol. 13, no. 18, pp. 2-24, 2020. DOI : 10.3390/en13184703

14

R. Golden, B. Paulos, “Curtailment of Renewable Energy in California and Beyond,” The Electricity Journal, vol. 28, no. 6, pp. 36-50, 2015. DOI : 10.1016/j.tej.2015.06.008

15

J. R. Cancelo, A. Espasa, “Modelling and forecasting daily series of electricity demand,” Investigaciones Economicas, vol. 20, pp. 359-376, 1996.

16

Y. Y. Yan, “Climate and residential electricity consumption in Hong Kong,” Energy, vol. 23, no. 1, pp. 17-20, 1998. DOI : 10.1016/S0360-5442(97)00053-4

17

D. Markovics, M. J. Mayer, “Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction,” Renewable and Sustainable Energy Reviews, vol. 161, 2022. DOI : 10.1016/j.rser.2022.112364

18

C. Liu, X. Zhang, S. Mei, Z. Zhen, M. Jia, Z. Li, H. Tang, “Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness,” Applied Energy, vol. 313, 2022. DOI : j.apenergy.2022.118769

19

J. S. Stein, W. F. Holmgren, J. Forbess, C. W. Hansen, “PVLIB: Open source photovoltaic performance modeling functions for Matlab and Python,” 2016 IEEE 43rd PVSC, vol. 43, pp. 3425-3430, 2016. DOI : 10.1109/PVSC.2016.7750303

20

T. Chen, C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” Association for Computing Machinery, pp. 785–794, 2016. DOI : 10.1145/2939672.2939785

21

B. O. Kvalseth, “Cautionary Note about R2,” The American Statistician, vol. 39, no. 4, pp. 279-285, 1985. DOI : 10.2307/2683704

22

T. Chai, R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)?,” Geoscientific Model Development Discussions, vol. 7, pp. 1525-1534, 2014. DOI : 10.5194/gmdd-7-1525-2014

23

B. Acun, B. Morgan, H. Richardson, N. Steinsultz, C. J. Wu, “Unlocking the Potential of Renewable Energy Through Curtailment Prediction,” ArXiv, vol. abs/2405.18526, 2024. DOI : 10.48550/arXiv.2405.18526

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.

Journal Search

Journal XML

References

KIEEThe Transactions ofthe Korean Institute of Electrical Engineers