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Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning

Author

Listed:
  • Jikai Zou

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Shabbir Ahmed

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Xu Andy Sun

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

Electric power generation expansion planning (GEP) is the problem of determining an optimal construction and generation plan of both new and existing electric power plants to meet future electricity demand. We consider a stochastic optimization approach for this capacity expansion problem under demand and fuel price uncertainty. In a two-stage stochastic optimization model for GEP, the capacity expansion plan for the entire planning horizon is decided prior to the uncertainty realized and hence allows no adaptivity to uncertainty evolution over time. In comparison, a multistage stochastic optimization model allows full adaptivity to the uncertainty evolution but is extremely difficult to solve. To reconcile the trade-off between adaptivity and tractability, we propose a partially adaptive stochastic mixed integer optimization model in which the capacity expansion plan is fully adaptive to the uncertainty evolution up to a certain period and follows the two-stage approach thereafter. Any solution to the partially adaptive model is feasible to the multistage model, and we provide analytical bounds on the quality of such a solution. We propose an efficient algorithm that solves a sequence of partially adaptive models, to recursively construct an approximate solution to the multistage problem. We identify sufficient conditions under which this algorithm recovers an optimal solution to the multistage problem. Finally, we conduct extensive test of our algorithm on a realistic GEP problem. Experiments show that, within a reasonable computation time limit, the proposed algorithm produces a significantly better solution than solving the multistage model directly.

Suggested Citation

  • Jikai Zou & Shabbir Ahmed & Xu Andy Sun, 2018. "Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 388-401, May.
  • Handle: RePEc:inm:orijoc:v:30:y:2018:i:2:p:388-401
    DOI: 10.1287/ijoc.2017.0782
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    References listed on IDEAS

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    Cited by:

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    2. Sajad Aliakbari Sani & Olivier Bahn & Erick Delage & Rinel Foguen Tchuendom, 2022. "Robust Integration of Electric Vehicles Charging Load in Smart Grid’s Capacity Expansion Planning," Dynamic Games and Applications, Springer, vol. 12(3), pages 1010-1041, September.
    3. Zhouchun Huang & Qipeng P. Zheng & Andrew L. Liu, 2022. "A Nested Cross Decomposition Algorithm for Power System Capacity Expansion with Multiscale Uncertainties," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 1919-1939, July.
    4. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    5. J. Cole Smith, 2019. "In Memoriam: Shabbir Ahmed (1969–2019)," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 633-635, October.
    6. Xin Shi & Alberto J. Lamadrid L. & Luis F. Zuluaga, 2021. "Revenue Adequate Prices for Chance-Constrained Electricity Markets with Variable Renewable Energy Sources," Papers 2105.01233, arXiv.org.

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