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Optimal power system generation scheduling by multi-objective genetic algorithms with preferences

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  • Zio, E.
  • Baraldi, P.
  • Pedroni, N.

Abstract

Power system generation scheduling is an important issue both from the economical and environmental safety viewpoints. The scheduling involves decisions with regards to the units start-up and shut-down times and to the assignment of the load demands to the committed generating units for minimizing the system operation costs and the emission of atmospheric pollutants.

Suggested Citation

  • Zio, E. & Baraldi, P. & Pedroni, N., 2009. "Optimal power system generation scheduling by multi-objective genetic algorithms with preferences," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 432-444.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:2:p:432-444
    DOI: 10.1016/j.ress.2008.04.004
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    References listed on IDEAS

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    1. Bertrand Mareschal & Jean Pierre Brans, 1994. "PROMCALC & GAIA: a new decision support system for multicriteria decision aid," ULB Institutional Repository 2013/9349, ULB -- Universite Libre de Bruxelles.
    2. Marseguerra, M. & Zio, E. & Martorell, S., 2006. "Basics of genetic algorithms optimization for RAMS applications," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 977-991.
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    Citations

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    Cited by:

    1. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    2. Zio, E. & Bazzo, R., 2011. "Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 569-580.
    3. Ye, Zhisheng & Li, Zhizhong & Xie, Min, 2010. "Some improvements on adaptive genetic algorithms for reliability-related applications," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 120-126.
    4. F Cadini & E Zio & L R Golea & C A Petrescu, 2011. "Application of multi-objective genetic algorithms to two case studies of reliability efficiency analysis and optimal expansion of electrical transmission networks," Journal of Risk and Reliability, , vol. 225(3), pages 365-374, September.
    5. Luís A. C. Roque & Dalila B. M. M. Fontes & Fernando A. C. C. Fontes, 2017. "A Metaheuristic Approach to the Multi-Objective Unit Commitment Problem Combining Economic and Environmental Criteria," Energies, MDPI, vol. 10(12), pages 1-25, December.
    6. Zio, E. & Bazzo, R., 2011. "A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 624-634, May.

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