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Distributionally robust chance-constrained generation expansion planning

Pourahmadi, F ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1109/TPWRS.2019.2958850
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2020
  4. Abstract:
  5. This article addresses a centralized generation expansion planning problem, accounting for both long- and short-term uncertainties. The long-term uncertainty (demand growth) is modeled via a set of scenarios, while the short-term uncertainty (wind power generation) is described by a family of probability distributions with the same first- and second-order moments obtained from historical data. The resulting model is a distributionally robust chance-constrained optimization problem, which selects the conventional generating units to be built among predefined discrete options. This model includes a detailed representation of unit commitment constraints. To achieve computational tractability, we use a tight relaxation approach to convexify unit commitment constraints and solve the model with linear decision rules, resulting in a mixed-integer second-order cone program. It is observed that the proposed model exhibits better out-of-sample performance in terms of total expected system cost and its standard deviation compared to a chance-constrained model that assumes a Gaussian distribution of short-term uncertainty. A similar observation is made when comparing the proposed model against a chance-constrained program that uses empirical renewable power generation data with unknown type of distribution, recasting as either a robust optimization or a stochastic program. © 1969-2012 IEEE
  6. Keywords:
  7. Conic programming ; Distributionally robust chance-constrained optimization ; Out-of-sample analysis ; Unit commitment ; Constrained optimization ; Electric power generation ; Expansion ; Integer programming ; Stochastic models ; Stochastic systems ; Wind power ; Centralized generation ; Chance constrained optimization problems ; Chance-constrained model ; Computational tractability ; Generation expansion planning ; Linear decision rules ; Renewable power generation ; Second order cone programs ; Probability distributions
  8. Source: IEEE Transactions on Power Systems ; Volume 35, Issue 4 , 2020 , Pages 2888-2903
  9. URL: https://ieeexplore.ieee.org/document/8933104