Optimizing the Electricity Price Revenue of Wind Power Generation Captures in the South Korean Electricity Market = 남한 전력시장에서 풍력발전점유의 전력가격수익 최적화

다국어 초록 (Multilingual 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.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Data and Methodology
• 3. Results
• 4. Discussion
• 5. Conclusion
• Reference

참고문헌 (Reference)

1 Fox, B., "Wind Power Integration, Connection and System Operational Aspects" The Institute of Engineering and Technology 2007

2 Pöyry Energy Consulting, "Wind Energy and Electricity Prices: Exploring the ‘Merit Order Effect’" European Wind Energy Association 2010

3 Wurzburg, K., "Renewable Generation and Electricity Prices:Taking Stock and New Evidence for Germany and Austria" 40 : 159-171, 2013

4 Korea Institute of Energy Research, "New & Renewable Energy Resource Atlas of Korea"

5 Pöyry Energy Consulting, "Implications of Intermittency: A Multi-Client Study" 2009

6 Pöyry Energy Consulting, "Impact of Intermittency:How Wind Variability Could Change The Shape of The British and Irish Electricity Markets" 2009

7 Azevedo, F., "Forecasting Electricity Prices with Historical Statistical Information using Neural Networks and Clustering Techniques" 44-50, 2006

8 Korean Power Exchange, "Electric Power Statistics Information System"

9 Shcherbakova, A., "Effect of Increased Wind Penetration on System Prices in Korea’s Electricity Markets" 17 : 1469-1482, 2014

10 이태환, "Assessment of three forecasting methods for system marginal prices" 한국화학공학회 28 (28): 1331-1339, 2011

11 Lee, J. K., "A Hybrid Neural Network Framework for Hour-Ahead System Marginal Price Forecasting" 2004