Operation Management on Autonomous Power System

  • E. S. Karapidakis
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 28)


The running developments in the field of power systems have, as a result, the maximization of complexity and their marginal operation with regard to their dynamic security. This becomes more perceptible in autonomous power systems. Consequently, in the modern energy environment, the use of enhanced operation management and monitoring programs is judged necessary. The results that are arrived at in the present chapter were developed via a concretization of algorithms, which were incorporated in an implemented operation and planning management system. The presented algorithms have as object to improve the level of combination between unit commitment, economic dispatch and dynamic security assessment (DSA).


Unit Commitment Load Demand Power Generation Unit Wind Power Generation Energy Management System 


  1. 1.
    Androutsos A, Papadopoulos M (1999) Logistics modeling and its real time implementation on large isolated powe system with high wind penetration. EWEC 99, Nice, FranceGoogle Scholar
  2. 2.
    Box GE, Jenkins GM (1976) Time series analysis forecasting and control. Holden-Day, San Francisco.MATHGoogle Scholar
  3. 3.
    Contaxis G, Vlachos A (1999) Constrained optimal power flow in electrical energy grids with large integration of dispatchable wind energy and independent wind power producers. EWEC 99, Nice, FranceGoogle Scholar
  4. 4.
    Dy-Liacco ΤΕ (1988) System security: the computer’s role. IEEE Spectrum, pp 45–50Google Scholar
  5. 5.
    Dy-Liacco ΤΕ (2002) Control centers are here to stay. IEEE Comput Appl Power 15:18–23CrossRefGoogle Scholar
  6. 6.
    Ejebe GC, Jing C, Waight JG, Vittal V, Pieper G, Jamshidian F, Hirsch P, Sobajic D (1998) Online dynamic security assessment in an EMS. IEEE Comput Appl Power 11:43–47CrossRefGoogle Scholar
  7. 7.
    Fred I Denny (2002) Prospective on computer applications in power. IEEE Comput Appl Power 15:24–29Google Scholar
  8. 8.
    Gooi HB, Mendes DP, W.Bell KR, Kirschen DS (1999) Optimal scheduling of spinning reserve. IEEE Trans Power Syst 14:1485–1490CrossRefGoogle Scholar
  9. 9.
    Gross G, Galiana FD (1987) Short term load forecasting. In Proc IEEE, 75:1558–1573CrossRefGoogle Scholar
  10. 10.
    Haili Ma, Shahidehpour SM (1999) Unit commitment with transmission security and voltage constraints. IEEE Trans Power Syst 14:757–764CrossRefGoogle Scholar
  11. 11.
    Hatziargyriou ND (1998) Dynamic security assessment of isolated power systems with increased wind power integration. Group 38, Pref. Subject 3, 37th Session, CIGRE, ParisGoogle Scholar
  12. 12.
    Highly DD, Hilmes TJ (1993) Load forecasting by ANN. IEEE Comput Appl Power 6:10–15CrossRefGoogle Scholar
  13. 13.
    Jamnicsky L (1996) EMS network security applications of the future. IEEE Comput Appl Power 9(2):42–46Google Scholar
  14. 14.
    Karapidakis ES, Hatziargyriou ND (2002) On-line preventive dynamic security of isolated power systems using decision trees. IEEE Trans Power Syst 17:297–304CrossRefGoogle Scholar
  15. 15.
    Kariniotakis G, Matos M, Miranda V (1999) Assessment of benefits from advanced load and wind forecasting in autonomous power systems. EWEC 99, Nice, FranceGoogle Scholar
  16. 16.
    Kazarlis K, Bakirtzis A, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11:83–90CrossRefGoogle Scholar
  17. 17.
    Kumar ΑΒR, Brandwajn V, Ipakchi A, Rambabu Adapa (1998) Integrated framework for dynamic security analysis. IEEE Trans Power Syst 13(3):816–821CrossRefGoogle Scholar
  18. 18.
    Lee KY, Cha YT, Park JH (1992) Short term load forecasting using an artificial neural network. IEEE Trans PAS 7:124–131Google Scholar
  19. 19.
    Park DC, El Sharkawi MA, Marks RJ, Atlasm LE, Damborg J (1991) Electric load forecasting using an artificial neural network. IEEE Trans PAS 6:442–448Google Scholar
  20. 20.
    Pecas Lopes JA (1998) Application of neural network based stability assessment tools to an operational environment of large autonomous power systems. Group 38, Pref. Subject 1, 37th Session, CIGRE, ParisGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • E. S. Karapidakis
    • 1
  1. 1.Technological Educational Institute of Crete73133, ChaniaGreece

Personalised recommendations