• DocumentCode
    692471
  • Title

    Comparing MOPSO Approaches for Hydrothermal Systems Operation Planning

  • Author

    Cardoso Silva, Jonathan ; Cruz, G. ; Vinhal, C. ; Silva, Diego R. C. ; Bastos-Filho, Carmelo J. A.

  • Author_Institution
    Sch. of Electr., Fed. Univ. of Goias Goiania, Goiania, Brazil
  • fYear
    2013
  • fDate
    8-11 Sept. 2013
  • Firstpage
    551
  • Lastpage
    556
  • Abstract
    Hydrothermal operational planning is categorized as an optimization problem that demands operational strategies of hydroelectric power plants in order to minimize the use of thermoelectric power plants, while maintaining the highest possible level of system´s reservoirs during planning period. Moreover, the problem must meet a set of complex constraints. We showed in this paper that it is possible to tackle the medium-term planning of hydrothermal systems as a multi-objective problem. The particles were represented as vectors indicating the monthly generation of hydropower. We applied some three recent swarm based multi-objective optimizers, MOPSO-CDR, MOPSO-DFR and SMPSO. This trade-off is presented in Pareto Fronts, which can be used for decision making. Among the assessed approaches involving a system composed of eight Brazilian hydroelectric plants, we observed that the MOPSO-CDR returned the best results and it is worth to include seeds from mono-objective approaches to improve the convergence capacity. We included the result achieved by the PSO-CLANM algorithm and it generated effective results.
  • Keywords
    Pareto optimisation; hydroelectric power stations; hydrothermal power systems; particle swarm optimisation; power engineering computing; power system planning; Brazilian hydroelectric plants; MOPSO-CDR; MOPSO-DFR; PSO-CLANM algorithm; Pareto fronts; SMPSO; complex constraints; convergence capacity; decision making; hydroelectric power plants; hydropower; hydrothermal systems operational planning; medium-term planning; mono-objective approaches; multiobjective problem; operational strategies; optimization problem; planning period; swarm based multiobjective optimizers; system reservoirs; thermoelectric power plants; Optimization; Particle swarm optimization; Planning; Power generation; Reservoirs; Vectors; Hydrothermal Systems; Otimization; Power Operation Planning; Swarm Intelligence;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on
  • Conference_Location
    Ipojuca
  • Type

    conf

  • DOI
    10.1109/BRICS-CCI-CBIC.2013.97
  • Filename
    6855906