Browse > Article

Unit Commitment of a GENCO Under the Competitive Environment Considering the Uncertainty of Market Prices  

정정원 (경성대학교 전기전자컴퓨터공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers A / v.52, no.4, 2003 , pp. 234-239 More about this Journal
Abstract
In recent decades, many countries have introduced competition in the electricity industry. Now, unit commitment becomes not a problem to be solved by a monopoly company but the one to be tackled by each generation company(GENCO). Its aim has been altered from the global cost minimization to the each GENCO's profit maximization. In this paper, the author proposes the scheme of unit commitment of a GENCO to maximize profit considering the uncertainty of market clearing price. The type of the assumed market is a uniform price market. A genetic algorithm is used for the maximization of the profit.
Keywords
unit commitment; electricity market; price uncertainty; genetic algorithm;
Citations & Related Records
연도 인용수 순위
  • Reference
1 C.W. Richter and G.B. Sheble, 'A profit-based unit commitment GA for the competitive environment,' IEEE Trans. on Power Systems, Vol. 15, No. 2, May 2000   DOI   ScienceOn
2 K.S. Swarup, S. Yamashiro, 'Unit commitment solution methodology using genetic algorithm,' IEEE Trans. on Power System, Vol. 17, No. 1, 2002   DOI   ScienceOn
3 A. Rudolf, R. Bayrleithner, 'A genetic algorithm for solving the unit commitment problem of a hydro thermal power system,' IEEE Trans. on Power System, Vol. 14, No. 4, 1999   DOI   ScienceOn
4 한국전력거래소, 2002년 운용실적, www.kpx.or.kr
5 W. Xing, F. Wu, 'Genetic algorithm based unit commitment with energy contracts,' Electrical Power and Energy Systems 24, 2002   DOI   ScienceOn
6 P.L. Joskow, Deregulation and regulatory reform in the U.S. electric power sector, AEI Conference on Deregulation in Network Industries, Dec. 1999
7 H. T. Yang, P. C. Yang, C. L. Huang, 'A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks,' IEEE Trans. on Power Systems, Vol. 12, No.2, 1997   DOI   ScienceOn
8 H.T. Yang, P.C. Yang, C.L. Huang, 'Applications of the genetic algorithm to the unit commitment problem in power generation industry,' International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE International Conference on , Volume: 1 , 20-24 Mar 1995   DOI
9 A.C. Homaifar, C. Qi, and S. Lai, 'Constrained optimization via genetic algorithms,' Simulation, Vol. 62, No.4, 1994   DOI   ScienceOn
10 D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Publishing Company, Inc., 1989
11 J. Joines, C. Houck, 'On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs,' Proceedings of the first IEEE conference on evolutionary computations, 1994   DOI
12 M. Gen, R. Cheng, Genetic algorithms and engineering design, John Wiley & Sons, Inc., 1997
13 Z. Michalewicz, 'A survey of constraint handling techniques in evolutionary computation method,' Evolutionary programming IV, 1995
14 T. Yokota, M. Gen, K. Ida, T. Taguchi, 'Optimal design of system reliability by an approved genetic algorithm,' Trans. of institute of electronics, Information and Communication Engineers, Vol. J78A, No.6, 1995