TY - JOUR
T1 - A linear AC unit commitment formulation
T2 - An application of data-driven linear power flow model
AU - Shao, Zhentong
AU - Zhai, Qiaozhu
AU - Han, Zhihan
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/2
Y1 - 2023/2
N2 - Unit commitment is an important schedule procedure in power system daily operations, and an ideal version is to combine the unit commitment with the optimal power flow, thereby, it achieves an AC-power-flow-constrained unit commitment problem, which is referred to as the AC unit commitment. The AC unit commitment is quite benefit for system operation, however, the AC unit commitment problem is intractable since it is generally a large-scale non-convex and nonlinear program. To overcome this difficulty, this paper proposes a linear AC unit commitment model, which achieves a high-accuracy solution that is close to the solution of the exact AC unit commitment, while it keeps the problem from getting into a huge computational burden. To achieve a high accuracy, the recent data-driven linear power flow model is adopted, and for making the data-driven linear power flow model applicable for the unit commitment problem, a chance-constrained support vector regression approach is proposed to the replace the common least-squares regression in former data-driven linear power flow models, and physical models are used to deduce aid formulations to make the data-driven linear power flow model more robust. The resultant linear unit commitment model is tested on several standard systems, including a 2000-bus system, and the results verify the effectiveness of the proposed method, and show that the accuracy of the proposed data-driven linear power flow model improved about two-folds compared with the classical one.
AB - Unit commitment is an important schedule procedure in power system daily operations, and an ideal version is to combine the unit commitment with the optimal power flow, thereby, it achieves an AC-power-flow-constrained unit commitment problem, which is referred to as the AC unit commitment. The AC unit commitment is quite benefit for system operation, however, the AC unit commitment problem is intractable since it is generally a large-scale non-convex and nonlinear program. To overcome this difficulty, this paper proposes a linear AC unit commitment model, which achieves a high-accuracy solution that is close to the solution of the exact AC unit commitment, while it keeps the problem from getting into a huge computational burden. To achieve a high accuracy, the recent data-driven linear power flow model is adopted, and for making the data-driven linear power flow model applicable for the unit commitment problem, a chance-constrained support vector regression approach is proposed to the replace the common least-squares regression in former data-driven linear power flow models, and physical models are used to deduce aid formulations to make the data-driven linear power flow model more robust. The resultant linear unit commitment model is tested on several standard systems, including a 2000-bus system, and the results verify the effectiveness of the proposed method, and show that the accuracy of the proposed data-driven linear power flow model improved about two-folds compared with the classical one.
KW - AC power flow
KW - Data driven
KW - Linear power flow
KW - Mixed integer linear programming
KW - Unit commitment
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U2 - 10.1016/j.ijepes.2022.108673
DO - 10.1016/j.ijepes.2022.108673
M3 - Article
AN - SCOPUS:85139590694
SN - 0142-0615
VL - 145
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 108673
ER -