DOI QR코드

DOI QR Code

Study of Peak Load Demand Estimation Methodology by Pearson Correlation Analysis with Macro-economic Indices and Power Generation Considering Power Supply Interruption

  • Song, Jiyoung (Korea Electric Power Corporation (KEPCO) Research Institute) ;
  • Lee, Jaegul (Korea Electric Power Corporation (KEPCO) Research Institute) ;
  • Kim, Taekyun (Korea Electric Power Corporation (KEPCO) Research Institute) ;
  • Yoon, Yongbeum (Korea Electric Power Corporation (KEPCO) Research Institute)
  • Received : 2016.10.27
  • Accepted : 2017.04.12
  • Published : 2017.07.01

Abstract

Since the late 2000s, there has been growing preparation in South Korea for a sudden reunification of South and North Korea. Particularly in the power industry field, thorough preparations for the construction of a power infrastructure after reunification are necessary. The first step is to estimate the peak load demand. In this paper, we suggest a new peak demand estimation methodology by integrating existing correlation analysis methods between economic indicators and power generation quantities with a power supply interruption model in consideration of power consumption patterns. Through this, the potential peak demand and actual peak demand of the Nation, which experiences power supply interruption can be estimated. For case studies on North Korea after reunification, the potential peak demand in 2015 was estimated at 5,189 MW, while the actual peak demand within the same year was recorded as 2,461 MW. The estimated potential peak demand can be utilized as an important factor when planning the construction of power system facilities in preparation for reunification.

Keywords

References

  1. S. S. Lee, Y. S. Jang, G. P. Park, S. K. Kim, Y. C. Kim, J. K. Park, S. I. Moon, Y. T. Yoon, "Northeast Asia Power System Interconnection Part 3: North- South Korea Transaction Analysis," in Proc. KIEE, 2010, pp. 113-115.
  2. J.Y. Yun, "South and North Korea Integrated Power Grid," KEER, vol. 6, pp. 155-173, Jun. 2007.
  3. E. H. Barakat, M. A. M. Eissa, "Forecasting monthly peak demand in fast growing electric utility using a composite multiregression-decomposition model," in Proc. IEE, 1989, pp. 35-41.
  4. R. J. Hyndman, S. Fan, "Density Forecasting for Long-Term Peak Electricity Demand," IEEE Transaction on Power Systems, vol. 25, pp. 1142-1153, May 2010. https://doi.org/10.1109/TPWRS.2009.2036017
  5. J. Deng, "Energy Demand Estimation of China Using Artificial Neural Network," in Proc. BIFE, 2010, pp. 32-34.
  6. K. B. Song, "Various Models of Fuzzy Least-Squares Linear Regression for Load Forecasting," KIEE, vol. 21, pp. 61-67, Aug. 2007.
  7. K. J. Hwang, H. R. Ju, M. H Yi, D. H. Ahn, "Yearly Load Forecasting Algorithm for Annual Electric Energy Supply Plan," in Proc. KIEE, 2006, pp. 76-77.
  8. N. Hasan, M. N. S. K. Shabbir, S. A. M. B. Uddin, T. Islam, A. Islam, "A Strategic Numerical Approach to Estimate Actual Demand in case of Systematic Data Scarcity," in Proc. IFOST, 2014, pp. 369-372.
  9. Hyundai Research Institute, 2050 Reunified Korea's Future Economy, 2014.
  10. S. J. Lee, C. K. Kim, S. H. Park, W. H. Shin "Development issues of the growth centers in North Korea for preparing Korean unification (I)," Korea Research Institute for Human Settlements, vol. 50, 2011
  11. Y. H. An, S. M. Moon, "Research on inter-Korean Economic Integration Scheme after Reunification," Korea Institute of Finance, vol. 291, Jan. 2007.
  12. S. M. Moon, B. H. Yu, "The Effects of Inter-Korean Integration Type on Economic Performance: the Role of Wage Policy," Korea Institute of Finance, vol. 477, Jul. 2012.
  13. J. Y. Song, J. G. Lee, T. K. Kim, J. H. Shin, S. W. Han, B. K. Ko, S. T. Cha, J. H. Choi, "Study on Peak Demand Estimation Methodology by using Correlation with Economic Index and Total generation," in Proc. ISGC, 2015, pp. 504-507.
  14. KOSIS web site, Korea Statistical Information Service, http://kosis.kr, Nov 30th 2014.
  15. Korea Finance Corporation, The North Korea Industry, 2010.
  16. A. M. Neto, A. C. Victorino, "Real-Time Dynamic Power Management based on Pearson's Correlation Coefficient," in Proc. ICAR, 2011, pp. 304-309.
  17. World Bank web site, http://data.worldbank.org/indicator, Nov 30th 2014.
  18. Z. W. Geem, W. E. Roper, "Energy demand estimation of South Korea using artificial neural network," Energy Policy, vol. 37, pp. 4049-4054, 2009. https://doi.org/10.1016/j.enpol.2009.04.049
  19. K. H. Yoon, B. H. Ku, J. M. Cha, J. S. Choi, U. K. Baek, "A Study for Annual Load Forecasting by Using Trend Method with Considering Peak Load," in Proc. KIEE, 2010, pp.188-190.
  20. J. M. Cha, K. H. Yooun, B. H. Ku, "Annual Yearly Load Forecasting by Using Seasonal Load Characteristics with Considering Weekly Normalization," in Proc. KIEE, 2011, pp. 199-200,
  21. Korea Power Exchange(KPX) historical data, www.kpx.or.kr. Oct 31st 2014.
  22. R. Morris, R. Brown, "Extension of validity of GRG method in optimal control calculation," IEEE Journals & Magazines, vol. 21, pp. 420-422, 1976.
  23. L. S. Lasdon, R. L. Fox, M. W. Ratner, "Nonlinear optimization using the generalized reduced gradient method," R.A.I.R.O., vol. 3, pp. 73-103, 1974.