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Design allocation of multistate series-parallel systems for power systems planning: A multiple objective evolutionary approach

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  • H A Taboada
  • J F Espiritu
  • D W Coit

Abstract

This paper presents an extension and application of a recent developed multiple objective evolutionary algorithm to solve design allocation problems commonly found in the power systems area. The evolutionary algorithm introduced is called MOMS-GA, a multiobjective genetic algorithm developed to solve multistate design allocation problems. MOMS-GA works under the assumption that both the system and its components can experience more than two possible states of performance. MOMS-GA uses the universal moment generating function (UMGF) approach to evaluate the different reliability indices of the system. Therefore, system availability is represented by a multistate availability function which extends the traditional binary state availability. Three different design allocation problems commonly found in power systems planning are solved to show the performance of the algorithm. The multiobjective formulation considered in the first two examples corresponds to the maximization of system availability, minimization of system investment cost, and maximization of expected system capacity. In the third example the multiobjective formulation seeks to maximize system availability, minimize system investment cost, and minimize expected unsupplied demand.

Suggested Citation

  • H A Taboada & J F Espiritu & D W Coit, 2008. "Design allocation of multistate series-parallel systems for power systems planning: A multiple objective evolutionary approach," Journal of Risk and Reliability, , vol. 222(3), pages 381-391, September.
    • Handle: RePEc:sae:risrel:v:222:y:2008:i:3:p:381-391
    DOI: 10.1243/1748006XJRR151
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    References listed on IDEAS

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    1. Chassin, David P. & Posse, Christian, 2005. "Evaluating North American electric grid reliability using the Barabási–Albert network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 667-677.
    2. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, September.
    3. Taboada, Heidi A. & Baheranwala, Fatema & Coit, David W. & Wattanapongsakorn, Naruemon, 2007. "Practical solutions for multi-objective optimization: An application to system reliability design problems," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 314-322.
    4. Rashika Gupta & Manju Agarwal, 2006. "Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system," Journal of Combinatorial Optimization, Springer, vol. 12(3), pages 257-277, November.
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    Cited by:

    1. Lai, Chyh-Ming & Yeh, Wei-Chang, 2016. "Two-stage simplified swarm optimization for the redundancy allocation problem in a multi-state bridge system," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 148-158.

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