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A Stochastic Multi-agent Optimization Model for Energy Infrastructure Planning under Uncertainty in An Oligopolistic Market


  • Zhaomiao Guo

    (University of California)

  • Yueyue Fan

    () (University of California)


Abstract This paper presents a mathematical model for analyzing long-term infrastructure investment decisions in a deregulated electricity market, such as the case in the United States. The interdependence between different decision entities in the system is captured in a network-based stochastic multi-agent optimization model, where new entrants of investors compete among themselves and with existing generators for natural resources, transmission capacities, and demand markets. To overcome computational challenges involved in stochastic multi-agent optimization problems, we have developed a solution method by combining stochastic decomposition and variational inequalities, which converts the original problem to many smaller problems that can be solved more easily.

Suggested Citation

  • Zhaomiao Guo & Yueyue Fan, 2017. "A Stochastic Multi-agent Optimization Model for Energy Infrastructure Planning under Uncertainty in An Oligopolistic Market," Networks and Spatial Economics, Springer, vol. 17(2), pages 581-609, June.
  • Handle: RePEc:kap:netspa:v:17:y:2017:i:2:d:10.1007_s11067-016-9336-8
    DOI: 10.1007/s11067-016-9336-8

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    References listed on IDEAS

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    Cited by:

    1. Dávid Csercsik & László Á. Kóczy, 2017. "Efficiency and Stability in Electrical Power Transmission Networks: a Partition Function Form Approach," Networks and Spatial Economics, Springer, vol. 17(4), pages 1161-1184, December.
    2. Fan, Yueyue & Zhang, Yunteng, 2019. "Next-Generation Transit System Design During a Revolution of Shared Mobility," Institute of Transportation Studies, Working Paper Series qt77t6g3w4, Institute of Transportation Studies, UC Davis.
    3. Xie, Fei & Huang, Yongxi, 2018. "A multistage stochastic programming model for a multi-period strategic expansion of biofuel supply chain under evolving uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 130-148.


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