대한산업공학회지 (Journal of Korean Institute of Industrial Engineers)

  • 제39권6호
  • /
  • Pages.562-576
  • /
  • 2013
  • /
  • 1225-0988(pISSN)
  • /
  • 2234-6457(eISSN)
대한산업공학회 (Korean Institute of Industrial Engineers)
권호 표지
DOI QR코드

DOI QR Code

가상 전력 도매 시장의 최적 경매 가격 예측

Estimating Optimized Bidding Price in Virtual Electricity Wholesale Market

  • 신수진 (KAIST 산업 및 시스템공학과) ;
  • 이세훈 (KAIST 산업 및 시스템공학과) ;
  • 권윤중 (KAIST 산업 및 시스템공학과) ;
  • 차재강 (KAIST 산업 및 시스템공학과) ;
  • 문일철 (KAIST 산업 및 시스템공학과)
  • Shin, Su-Jin (Department of Industrial and Systems Engineering, KAIST) ;
  • Lee, SeHoon (Department of Industrial and Systems Engineering, KAIST) ;
  • Kwon, Yun-Jung (Department of Industrial and Systems Engineering, KAIST) ;
  • Cha, Jae-Gang (Department of Industrial and Systems Engineering, KAIST) ;
  • Moon, Il-Chul (Department of Industrial and Systems Engineering, KAIST)
  • 투고 : 2013.04.12
  • 심사 : 2013.08.09
  • 발행 : 2013.12.15
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Power TAC (Power Trading Agent Competition) is an agent-based simulation for competitions between electricity brokering agents on the smart grid. To win the competition, agents obtain electricity from the electricity wholesale market among the power plants. In this operation, a key to success is balancing the demand of the customer and the supply from the plants because any imbalance results in a significant penalty to the brokering agent. Given the bidding on the wholesale market requires the price and the quantity on the electricity, this paper proposes four different price estimation strategies: exponentially moving average, linear regression, fuzzy logic, and support vector regression. Our evaluations with the competition simulation show which strategy is better than which, and which strategy wins in the free-for-all situations. This result is a crucial component in designing an electricity brokering agent in both Power TAC and the real world.

키워드

참고문헌

  1. Amin, S. M. and Wollenberg, B. F. (2005), Toward a smart grid : power delivery for the 21st century, IEEE Power and Energy Magazine, 3(5), 34-41.
  2. Babic, J., Matetic, S., Matijas, M., Buljevic, I., Pranjic, I., Mijic, M., and Augustinovic, M. (2012), The CrocodileAgent 2012: Research for Efficient Agent-based Electricity Trading Mechanisms, In Proceedings of the Special Session on Trading Agent Competi-tion @ KES-AMSTA 2012, Dubrovnik, Croatia, 1-13.
  3. Bichler, M., Gupta, A., and Ketter, W. (2010), Designing Smart Markets, Information Systems Research, 21(4), 688-699. https://doi.org/10.1287/isre.1100.0316
  4. Block, C., Collins, J., Ketter, W., and Weinhardt, C. (2009), A Multi-Agent Energy Trading Competition, Technical Report ERS-2009-054-LIS, RSM Erasmus University, Rotterdam, The Netherlands.
  5. Cai, K., Gerding, E., Mcburney, P., Niu, J., Parsons, S., and Phelps, S. (2009), Overview of CAT : A Market Design Competition, Technical Report ULCS-09-005, Department of Computer Science, University of Liverpool, Liverpool, UK, 2009. Version 2.0.
  6. Collins, J., Ketter, W., and Sadeh, N. (2010), Pushing the Limits of Rational Agents : The Trading Agent Competition for Supply Chain Management, AI Magazine, 31(2), 63-80.
  7. Diamantopoulos, T. G., Symeonidis, A. L., and Chrysopoulos, A. C. (2012), Designing Robust Strategies for Continuous Trading in Contemporary Power Markets, Trading Agent Design and Analysis(TADA) and Agent-Mediated Electronic Commerce (AMEC), Valencia, Spain, June 4-8, 203-216.
  8. Eom, J. Y., Yun, M. Y., Lim, S. H., and Um, W.-S. (2008), A study on the design of adaptive trading agent for SCM, 2008 Korean Institute of Industrial Engineers Autumn Conference.
  9. Graham, S. (2002), Hot Topics in European Electricity : what is relevant and what isn't?, The Electricity Journal, 15(8), 25-39.
  10. Gunn, S. R. (1998), Support vector machines for classification and regression Tech. Rep., Image Speech and Intelligent Systems Research Group, University of Southampton, Southampton, U.K..
  11. Hunter, J. S. (1986), The Exponentially Weighted Moving Average, Journal of Quality Technology, 18(4), 203-210.
  12. Jordan, P. R. and Wellman, M. P. (2009), Designing an Ad Auctions Game for the Trading Agent Competition, IJCAI-09 Workshop on Trading Agent Design and Analysis, 147-162.
  13. Ketter, W., Collins, J., Reddy, P., and Weerdt, M. de. (2012), The 2012 Power Trading Agent Competition, Technical Report ERS-2012-010-LIS, RSM Erasmus University, Rotterdam, The Netherlands.
  14. Ketter, W., Kryzhnyaya, E., Damer, S., McMillen, C., Agovic, A., Collins, J., and Gini, M. (2004), Analysis and design of supply-driven strategies in TAC-SCM, In AAMAS-04 Workshop on Trading Agent Design and Analysis, 44-51.
  15. Kim, J. S. (2009), A Study on the Policy Changes of Restructuring of Electric Power Industry in Korea, Master's Thesis, The Graduate School of Seoul National University.
  16. Kim, H. S. and Shin, H. J. (2013), Electricity Price Prediction Based on Semi-Supervised Learning and Neural Network Algorithms, Journal of the Korean Institute of Industrial Engineers, 39(1), 30-45. https://doi.org/10.7232/JKIIE.2013.39.1.030
  17. Lee, G. J. (2011), Considerations for Electric Power Industry development regarding Blackout Case Analysis, KIEE Power Engineering Society Annual Meeting, 26-27.
  18. Ma, Y. B. (2007), Local Brokering-based Resource Management Model for Reliable Resource Management in Grid Environment, Master's Thesis, The Graduate School of Inha University.
  19. Mamdani, E. H. and Assilian, S. (1975), An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, 7(1), 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
  20. Montgomery, D. C. and Peck, E. A. (1992), Introduction to Linear Regression Analysis, 2nd edition, John Wiley and Sons, Inc..
  21. Moon, K. (2007), Trading Trend in Electricity Wholesale Market - Focusing on Trading Rules and Trading Trend, Journal of electrical world, (362), 8-10.
  22. Park, Y. J. (2010), Development of Automated Trading Agent in Real-Time Supply Chain Environment, Journal of the Korea Academia-Industrial cooperation Society, 11(11), 4282-4290. https://doi.org/10.5762/KAIS.2010.11.11.4282
  23. Pourbeik, P., Kundur, P. S., and Taylor, C. W. (2006), The Anatomy of a Power Grid Blackout, IEEE Power and Energy Magazine, 4(5), 22-29.
  24. Sugeno, M. and Kang, G. T. (1988), Structure Identification of Fuzzy Model, Fuzzy Sets and Systems, 28(1), 15-33. https://doi.org/10.1016/0165-0114(88)90113-3
  25. Vapnik, V. (1995), The Nature of Statistical Learning Theory, Springer-Verlag.
  26. Vapnik, V., Golowich, S., and Smola, A. (1997), Support vector method for function approximation, regression estimation, and signal processing, Advances in Neural Information Processing Systems, 9, 281-287.
  27. Wellman, M. P., Greenwald, A., Stone, P., and Wurman, P. R. (2003), The 2001 Trading Agent Competition, Electronic Markets, 13(1), 4-12. https://doi.org/10.1080/1019678032000062212
  28. Wellman, M. P., Wurman, P. R., Malley, K. O., Lin, S. De, Reeves, D., and Walsh, W. E. (2001), Designing the Market Game for a Trading, IEEE Internet Computing, 5(2), 43-51. https://doi.org/10.1109/4236.914647
  29. Zadeh, L. A. (1965), Fuzzy Sets, Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  30. Zhang, L., Luh, P. B., and Kasiviswanathan, K. (2003), Energy clearing price prediction and confidence interval estimation with cascaded neural networks, IEEE Transactions on Power Systems, 18(1), 99-105. https://doi.org/10.1109/TPWRS.2002.807062

피인용 문헌

  1. The Relationship between Mobile Termination Rates and Retail Prices : Some Arguments and Empirical analyses vol.40, pp.6, 2014, https://doi.org/10.7232/JKIIE.2014.40.6.609