Distributionally Robust Generation Expansion Planning with Unimodality and Risk Constraints

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  • Farzaneh Pourahmadi
  • Jalal Kazempour

As more renewables are integrated into the power system, capacity expansion planners need more advanced long-term decision-making tools to properly model short-term stochastic production uncertainty and to explore its effects on expansion decisions. We develop a distributionally robust generation expansion planning model, accounting for a family of potential probability distributions of wind forecast error uncertainty. Aiming to include more realistic distributions, we construct more informed moment-based ambiguity sets by adding structural information of unimodality. We include operational-stage unit commitment constraints and model the risk of operational limit violations in two distinct forms: chance and conditional value-at-risk (CVaR) constraints. In both forms, the resulting expansion planning model is a mixed-integer second-order cone program. Using a thorough out-of-sample numerical analysis, we conclude: (i) the distributionally robust chance-constrained generation expansion planning model exhibits a better out-of-sample performance only if sufficiently accurate information about the first- and the second-order moments as well as the mode location of potential distributions is available; (ii) conversely, if such accurate information is unavailable, the distributionally robust CVaR-constrained generation expansion planning model outperforms; (iii) these two models have a similar performance when unimodality information is excluded.

Original languageEnglish
JournalIEEE Transactions on Power Systems
Volume36
Issue number5
Pages (from-to)4281-4295
ISSN0885-8950
DOIs
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 1969-2012 IEEE.

    Research areas

  • chance constraints, CVaR constraints, Distributionally robust optimization, generation expansion planning, unimodality information

ID: 284197414