Distributionally Robust Generation Expansion Planning with Unimodality and Risk Constraints
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Distributionally Robust Generation Expansion Planning with Unimodality and Risk Constraints. / Pourahmadi, Farzaneh; Kazempour, Jalal.
I: IEEE Transactions on Power Systems, Bind 36, Nr. 5, 2021, s. 4281-4295.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Distributionally Robust Generation Expansion Planning with Unimodality and Risk Constraints
AU - Pourahmadi, Farzaneh
AU - Kazempour, Jalal
N1 - Publisher Copyright: © 1969-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - chance constraints
KW - CVaR constraints
KW - Distributionally robust optimization
KW - generation expansion planning
KW - unimodality information
U2 - 10.1109/TPWRS.2021.3057265
DO - 10.1109/TPWRS.2021.3057265
M3 - Journal article
AN - SCOPUS:85100838238
VL - 36
SP - 4281
EP - 4295
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 5
ER -
ID: 284197414