Start-Up Optimisation of a Combined Cycle Power Plant with Multiobjective Evolutionary Algorithms

  • Ilaria Bertini
  • Matteo De Felice
  • Fabio Moretti
  • Stefano Pizzuti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6025)


In this paper we present a study of the application of Evolutionary Computation methods to the optimisation of the start-up of a combined cycle power plant. We propose a multiobjective approach considering different objectives for the optimisation in order to reduce the pollution emissions and to maximise the efficiency of the plant. We compare a multiobjective evolutionary algorithm (NSGA-II) with 2 and 5 objectives on a software simulator and then we use different metrics to measure the performances. We show that NSGA-II algorithm is able to provide a set of solutions, defined as Pareto Front, that represent the best trade-off on the different objectives among those the decision maker can choose.


Pareto Front Multiobjective Optimisation Steam Turbine Multiobjective Evolutionary Algorithm Multiobjective Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cutello, V.: A multi-objective evolutionary approach to the protein structure prediction problem. Journal of The Royal Society Interface 3, 139–151 (2006)CrossRefGoogle Scholar
  2. 2.
    Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multi-objective optimization: Formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423 (1993)Google Scholar
  3. 3.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar
  4. 4.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) PPSN VI 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)zbMATHGoogle Scholar
  6. 6.
    Coello, C.A.: An updated survey of ga-based multiobjective optimization techniques. ACM Comput. Surv. 32(2), 109–143 (2000)CrossRefGoogle Scholar
  7. 7.
    Alobaida, F., Postlera, R., Ströhlea, J., Epplea, B., Kimb, H.-G.: Modeling and investigation start-up procedures of a combined cycle power plant. Applied Energy 85(12), 1173–1189 (2008)CrossRefGoogle Scholar
  8. 8.
    Tetsuya, F.: An optimum start up algorithm for combined cycle. Transactions of the Japan Society of Mechanical Engineers 67(660), 2129–2134 (2001)Google Scholar
  9. 9.
    Casella, F., Pretolani, F.: Fast Start-up of a Combined-Cycle Power Plant: a Simulation Study with Modelica. In: Proceedings 5th International Modelica Conference, Vienna, Austria, September 6-8, pp. 3–10 (2006)Google Scholar
  10. 10.
    Knowles, J., Corne, J.: On metrics for comparing non-dominated sets. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 711–716 (2002)Google Scholar
  11. 11.
    Schott, J.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Masters thesis Department of Aeronautics and Astronautics. Massachusetts Institute of Technology (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ilaria Bertini
    • 1
  • Matteo De Felice
    • 1
    • 2
  • Fabio Moretti
    • 2
  • Stefano Pizzuti
    • 1
  1. 1.ENEA (Italian Energy New Technology and Environment Agency) 
  2. 2.Dipartimento di Informatica e AutomazioneUniversità degli Studi “Roma Tre” 

Personalised recommendations