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

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  • 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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    Cited by:

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    3. Yekini Shehu & Lulu Liu & Xiaolong Qin & Qiao-Li Dong, 2022. "Reflected Iterative Method for Non-Monotone Equilibrium Problems with Applications to Nash-Cournot Equilibrium Models," Networks and Spatial Economics, Springer, vol. 22(1), pages 153-180, March.
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