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Optimizing Multi-Timestep Security-Constrained Optimal Power Flow for Large Power Grids (2311.15175v1)
Published 26 Nov 2023 in math.OC, cs.SY, and eess.SY
Abstract: This work proposes a novel method for scaling multi-timestep security-constrained optimal power flow in large power grids. The challenge arises from dealing with millions of variables and constraints, including binary variables and nonconvex, nonlinear characteristics. To navigate these complexities, techniques such as constraint relaxation, linearization, sequential optimization, and problem reformulation are employed. By leveraging these methods, complex power grid problems are solved while achieving high-quality solutions and meeting time constraints. The innovative solution approach showcases great robustness and consistently outperforms benchmark standards.
- G. B. Giannakis, V. Kekatos, N. Gatsis, S. J. Kim, H. Zhu, and B. F. Wollenberg, “Monitoring and optimization for power grids: a signal processing perspective,” IEEE Signal Process. Mag., vol. 30, no. 5, pp. 107–128, 2013, doi: 10.1109/MSP.2013.2245726.
- M. Gao, J. Yu, Z. Yang, and J. Zhao, “A physics-guided graph convolution neural network for optimal power flow,” IEEE Trans. Power Syst., vol. PP, pp. 1–11, 2023, doi: 10.1109/tpwrs.2023.3238377.
- J. K. Skolfield and A. R. Escobedo, “Operations research in optimal power flow: a guide to recent and emerging methodologies and applications,” Eur. J. Oper. Res., vol. 300, no. 2, pp. 387–404, 2022, doi: 10.1016/j.ejor.2021.10.003.
- ARPA-E, “Grid optimization challenge 3.” https://gocompetition.energy.gov/(accessed Oct. 28, 2023)
- S. H. Low, “Convex relaxation of optimal power flow - part i: formulations and equivalence,” IEEE Trans. Control Netw. Syst., vol. 1, no. 1, pp. 15–27, 2014, doi: 10.1109/TCNS.2014.2309732.
- A. R. Aldik and B. Venkatesh, “Fast QC relaxation of the optimal power flow using the line-wise model for representing meshed transmission networks,” IEEE Access, vol. 11, no. October 2022, pp. 2775–2786, 2023, doi: 10.1109/ACCESS.2022.3233633.
- R. Madani, S. Sojoudi, and J. Lavaei, “Convex relaxation for optimal power flow problem: mesh networks,” IEEE Trans. Power Syst., vol. 30, no. 1, pp. 199–211, 2015, doi: 10.1109/TPWRS.2014.2322051.
- D. K. Molzahn, J. T. Holzer, B. C. Lesieutre, and C. L. DeMarco, “Implementation of a large-scale optimal power flow solver based on semidefinite programming,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 3987–3998, 2013, doi: 10.1109/TPWRS.2013.2258044.
- B. C. Lesieutre, D. K. Molzahn, A. R. Borden, and C. L. DeMarco, “Examining the limits of the application of semidefinite programming to power flow problems,” in Proc. 49th Annu. Allerton Conf. Commun., Control, Comput. (Allerton), Sep. 2011, pp. 1492–1499
- C. Coffrin, H. L. Hijazi, and P. Van Hentenryck, “The QC relaxation: A theoretical and computational study on optimal power flow,” IEEE Trans. Power Syst., vol. 31, no. 4, pp. 3008–3018, Jul. 2016
- H. T. Kahraman, M. Akbel, and S. Duman, “Optimization of optimal power flow problem using multi-objective manta ray foraging optimizer,” Appl. Soft Comput., vol. 116, p. 108334, 2022, doi: 10.1016/j.asoc.2021.108334.
- D. Liu, C. Zhang, G. Chen, Y. Xu, and Z. Y. Dong, “Stochastic security-constrained optimal power flow for a microgrid considering tie-line switching,” Int. J. Electr. Power Energy Syst., vol. 134, no. May 2021, p. 107357, 2021, doi: 10.1016/j.ijepes.2021.107357.