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A multiple objective mixed integer linear programming model for power generation expansion planning

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  • Antunes, C.Henggeler
  • Martins, A.Gomes
  • Brito, Isabel Sofia

Abstract

Power generation expansion planning inherently involves multiple, conflicting and incommensurate objectives. Therefore, mathematical models become more realistic if distinct evaluation aspects, such as cost and environmental concerns, are explicitly considered as objective functions rather than being encompassed by a single economic indicator. With the aid of multiple objective models, decision makers may grasp the conflicting nature and the trade-offs among the different objectives in order to select satisfactory compromise solutions. This paper presents a multiple objective mixed integer linear programming model for power generation expansion planning that allows the consideration of modular expansion capacity values of supply-side options. This characteristic of the model avoids the well-known problem associated with continuous capacity values that usually have to be discretized in a post-processing phase without feedback on the nature and importance of the changes in the attributes of the obtained solutions. Demand-side management (DSM) is also considered an option in the planning process, assuming there is a sufficiently large portion of the market under franchise conditions. As DSM full costs are accounted in the model, including lost revenues, it is possible to perform an evaluation of the rate impact in order to further inform the decision process.

Suggested Citation

  • Antunes, C.Henggeler & Martins, A.Gomes & Brito, Isabel Sofia, 2004. "A multiple objective mixed integer linear programming model for power generation expansion planning," Energy, Elsevier, vol. 29(4), pages 613-627.
    • Handle: RePEc:eee:energy:v:29:y:2004:i:4:p:613-627
    DOI: 10.1016/j.energy.2003.10.012
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    References listed on IDEAS

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    1. Narula, Subhash C. & Vassilev, Vassil, 1994. "An interactive algorithm for solving multiple objective integer linear programming problems," European Journal of Operational Research, Elsevier, vol. 79(3), pages 443-450, December.
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