• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2023-04 (Vol.72 No.04)
  4. 10.5370/KIEE.2023.72.4.503
XML PDF INFO REF

References

1 
Ministry of Commerce Industry and Energy, 2020, The 9th Basic Plan of Long-Term Electricity Supply and Demand, DOI
2 
Hyun-Goo Kim, Jin-Young Kim, Chang Ki Kim, 2018, Analysis of Ramp Characteristics of Shinan Wind Power Plant, Journal of Wind Energy, Vol. 9, No. 3, pp. 13-18DOI
3 
National Assembly Research Service, 2012, Power System Management System (EMS) Operation Status and Improvement Plan, DOI
4 
Korea Power Exchange, 2015, Overseas Power Market Trends in 2015 NYISO, , Vol. , No. , pp. -DOI
5 
Tong Wu, M. Rothleder, Z. Alaywan, A. D. Papalexopoulos, feb. 2004, Pricing energy and ancillary services in integrated market systems by an optimal power flow, in IEEE Transactions on Power Systems, Vol. 19, No. 1, pp. 339-347DOI
6 
D. Shchetinin, T. T. De Rubira, G. Hug, march 2019, On the Construction of Linear Approximations of Line Flow Constraints for AC Optimal Power Flow, in IEEE Transactions on Power Systems, Vol. 34, No. 2, pp. 1182-1192DOI
7 
Rahman, Jubeyer, Cong Feng, , A learning- augmented approach for AC optimal power flow, International Journal of Electrical Power &Energy Systems 130 (2021): 106908.DOI
8 
Fioretto, Ferdinando, Terrence WK Mak, 2020, Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods, Proceedings of the AAAI Conference on Artificial Intelligence., Vol. 34, No. 1DOI
9 
Zhao, Tianyu, 2020, DeepOPF+: A deep neural network approach for DC optimal power flow for ensuring feasibility, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEEDOI
10 
K. Baker, , Learning Warm-Start Points For Ac Optimal Power Flow, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Vol. 2019, No. , pp. 1-6DOI
11 
A. S. Zamzam, K. Baker, 2020, Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), pp. 1-6DOI
12 
D. Deka, S. Misra, , Learning for DC-OPF: Classifying active sets using neural nets, 2019 IEEE Milan PowerTech, Vol. 2019, No. , pp. 1-6DOI
13 
J. Carpentier, 1962, Contribution e letude do Dispatching Economique, Bulleting de la Societe Francaise des Electiciens Ser 8, Vol. 3, pp. 431-447DOI
14 
Wood, Allen J., Bruce F. Wollenberg, Gerald B. Sheblé., 2013, Power generation, operation, and control., 2DOI
15 
Glover, J. Duncan, Mulukutla S. Sarma, 2012, Power system analysis &design, SI version. Cengage Learning, DOI
16 
Staffell, Iain, Richard Green, 2015, Is there still merit in the merit order stack? The impact of dynamic constraints on optimal plant mix, IEEE Transactions on Power Systems, Vol. 31, No. 1, pp. 43-53DOI
17 
Cludius, Johanna, 2014, The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications,“ Energy economics 44, pp. 302-313DOI
18 
Sungwoo Lee, Hyoungtae Kim, Wook Kim, 2017, Fast mixed- integer AC optimal power flow based on the outer approximation method, Journal of Electrical Engineering and Technology, Vol. 12, No. 6, pp. 2187-2195DOI