Improving Large Scale Day-Ahead Security Constrained Unit Commitment Performance

Chen, Yonghong; Casto, Aaron; Wang, Feng‐Yu; Wang, Qianfan; Wang, Xing; Wan, Jie

doi:10.1109/tpwrs.2016.2530811

Cited by 100 publications

(51 citation statements)

References 18 publications

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“…With (25) and (26), the minimum approximation error ε 0 * can be easily obtained. Theorem 2: essentially gives an analytical solution to problems (20) and (21). So, the solution time for solving this problem is negligible.…”

Section: Calculation Of Approximation Parametersmentioning

confidence: 99%

“…Remark 2: As analysed in Remark 1, ε l j 1 , j 2 must be repeatedly calculated during the whole procedure. With the analytical solution to problems (20) and (21) given in Theorem 1, the calculation time of ε l j 1 , j 2 can be negligible. Remark 3: Although not stated in the algorithm, it is better to eliminate the redundant transmission constraints previously before running Algorithm 1 (see Fig.…”

Section: Algorithmic Frameworkmentioning

confidence: 99%

“…(iii) (α 0 * , β 0 * ) obtained by (25) and (26) is the global optimal solution to problems (20) and (21).…”

Section: Proof Of Theoremmentioning

confidence: 99%

See 2 more Smart Citations

Robust transmission constrained unit commitment: a column merging method

Zhai

Guan

2020

IET Generation, Transmission & Distribution

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With rapid integration of power sources with uncertainty, robustness must be carefully considered in the transmission constrained unit commitment (TCUC) problem. The overall computational complexity of the robust TCUC methods is closely related to the vertex number of the uncertainty set. The vertex number is further associated with 1) the period number in the scheduling horizon as well as 2) the number of nodes with uncertain injections. In this paper, a column merging method (CMM) is proposed to reduce the computation burden by merging the uncertain nodes, while still guaranteeing the robustness of the solution. By the CMM, the transmission constraints are modified, with the parameters obtained based on an analytical solution of a uniform approximation problem, so that the computational time is negligible. The CMM is applied under a greedy-algorithm based framework, where the number of merged nodes and the approximation error can be well balanced. The CMM is designed as a preprocessing tool to improve the solution efficiency for robust TCUC problems and is compatible with many solution methods (like two-stage and multi-stage robust optimization methods). Numerical tests show the method is effective.

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Section: Calculation Of Approximation Parametersmentioning

confidence: 99%

Section: Algorithmic Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Robust transmission constrained unit commitment: a column merging method

Zhai

Guan

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…However, it is a challenge to model the full network model for large-scale power systems for both pre-and post-contingency states and have it solved within the required time window. As reported in [15], the Midcontinent Independent System Operator (MISO) manages a system with about 45,000 buses. In order to meet the market clearing time requirements, MISO employs a 1200 seconds time limit and a 0.1% MIP relative gap for the day-ahead (DA) SCUC.…”

Section: Industry Practicesmentioning

confidence: 99%

“…In order to meet the market clearing time requirements, MISO employs a 1200 seconds time limit and a 0.1% MIP relative gap for the day-ahead (DA) SCUC. As stated in [15], MISO constantly encounters performance challenges in solving its DA SCUC model with the aforementioned limits; thus, expanding the model in order to explicitly represent contingency scenarios within the model would be computationally burdensome. Therefore, in order to address the performance challenges that arise from large-scale systems, the ISOs in the United States currently rely on heuristics, approximations, and policies rather than solving stochastic programs.…”

Section: Industry Practicesmentioning

confidence: 99%