Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
권호 표지
DOI QR코드

DOI QR Code

Optimizing the Electricity Price Revenue of Wind Power Generation Captures in the South Korean Electricity Market

남한 전력시장에서 풍력발전점유의 전력가격수익 최적화

  • Eamon, Byrne (Department of Electrical and Electronic Engineering, Queens University Belfast) ;
  • Kim, Hyun-Goo (New-Renewable Energy Resource Center, Korea Institute of Energy Research) ;
  • Kang, Yong-Heack (New-Renewable Energy Resource Center, Korea Institute of Energy Research) ;
  • Yun, Chang-Yeol (New-Renewable Energy Resource Center, Korea Institute of Energy Research)
  • 에먼 번 (영국 벨파스트 퀸즈대학교 전기전자공학과, 한국에너지기술연구원 신재생에너지자원센터) ;
  • 김현구 (한국에너지기술연구원 신재생에너지자원센터) ;
  • 강용혁 (한국에너지기술연구원 신재생에너지자원센터) ;
  • 윤창열 (한국에너지기술연구원 신재생에너지자원센터)
  • Received : 2016.01.13
  • Accepted : 2016.02.22
  • Published : 2016.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

How effectively a wind farm captures high market prices can greatly influence a wind farm's viability. This research identifies and creates an understanding of the effects that result in various capture prices (average revenue earned per unit of generation) that can be seen among different wind farms, in the current and future competitive SMP (System Marginal Price) market in South Korea. Through the use of a neural network to simulate changes in SMP caused by increased renewables, based on the Korea Institute of Energy Research's extensive wind resource database for South Korea, the variances in current and future capture prices are modelled and analyzed for both onshore and offshore wind power generation. Simulation results shows a spread in capture price of 5.5% for the year 2035 that depends on both a locations wind characteristics and the generations' correlation with other wind power generation. Wind characteristics include the generations' correlation with SMP price, diurnal profile shape, and capacity factor. The wind revenue cannibalization effect reduces the capture price obtained by wind power generation that is located close to a substantial amount of other wind power generation. In onshore locations wind characteristics can differ significantly/ Hence it is recommended that possible wind development sites have suitable diurnal profiles that effectively capture high SMP prices. Also, as increasing wind power capacity becomes installed in South Korea, it is recommended that wind power generation be located in regions far from the expected wind power generation 'hotspots' in the future. Hence, a suitable site along the east mountain ridges of South Korea is predicted to be extremely effective in attaining high SMP capture prices. Attention to these factors will increase the revenues obtained by wind power generation in a competitive electricity market.

Keywords

References

  1. Shcherbakova, A., Kleit, A., Blumsack, S., Cho, J.H., Lee, W.N., Effect of Increased Wind Penetration on System Prices in Korea's Electricity Markets, Wind Energy, Vol. 17, pp. 1469-1482, 2014. https://doi.org/10.1002/we.1645
  2. Wurzburg, K., Labandeira, S., Linares, P., Renewable Generation and Electricity Prices: Taking Stock and New Evidence for Germany and Austria, Energy Economics, Vol. 40, pp. 159-171, 2013. https://doi.org/10.1016/j.eneco.2013.09.011
  3. Poyry Energy Consulting, Impact of Intermittency: How Wind Variability Could Change The Shape of The British and Irish Electricity Markets, Summary Report, 2009.
  4. KIER (Korea Institute of Energy Research), New & Renewable Energy Resource Atlas of Korea, http://www.kier-atlas.org/
  5. KPX (Korean Power Exchange), EPSIS (Electric Power Statistics Information System), http://epsis.kpx.or.kr/
  6. Azevedo, F., Vale, Z.A., Forecasting Electricity Prices with Historical Statistical Information using Neural Networks and Clustering Techniques, IEEE 2006 Power Systems Conference and Exposition, Atlanta, USA, pp. 44-50, 2006.
  7. Lee, J.K., Yang, K.M., Park, J.B., Shin, J.R., Lee, T.H., A Hybrid Neural Network Framework for Hour-Ahead System Marginal Price Forecasting, International Conference on Electrical Engineering, Sapporo, Japan, 2004.
  8. Lee, T.H., Lee, K.J., Jo, B.W., Kim, L.H., Yeo, Y.K., Assessment of Three Forecasting Methods for System Marginal Prices, Korean Journal of Chemical Engineering, Process Systems Engineering, Process Safety, Vol. 28, No. 6, pp 1331-1339, 2011.
  9. Poyry Energy Consulting, Wind Energy and Electricity Prices: Exploring the 'Merit Order Effect', European Wind Energy Association, 2010.
  10. Poyry Energy Consulting, Implications of Intermittency: A Multi-Client Study, 2009.
  11. Fox, B., Flynn, D., Bryans, L., Jenkins, N., Milborrow, D., O'Malley, M., Watson, R., Anaya-Lara, O., Wind Power Integration, Connection and System Operational Aspects, IET Power and Energy Series 50, The Institute of Engineering and Technology, UK, 2007

Cited by

  1. ELCC Approach for Calculating Capacity Credit of Wind Power in Korea vol.54, pp.5, 2017, https://doi.org/10.12972/ksmer.2017.54.5.491