Abstract
The transmission expansion planning (TEP) problem is one of the perilous issues, which allows electricity transmission planners to design a cost-effective and reliable strategic model for the implementation of optimal transmission reinforcements in existing power grid networks. In this paper, a novel TEP model is proposed considering long- and short- term uncertain factors. The three-stage adaptive robust optimization (ARO) method deals with long-term uncertainties while prudently representing short-term uncertain parameters via scenarios. The formulated strategic scheme is elucidated through a modified decomposition algorithm that applies primal cutting planes and focuses on the subproblem feasible solution. The efficacy of the presented model is demonstrated through realistic case studies based on a 6-bus test system.
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References
Lumbreras, S., Ramos, A.: The new challenges to transmission expansion planning: survey of recent practice and literature review. Electric Power Syst. Res. 134, 19–29 (2016)
Rahim, S., Ahmad, H.: Data-driven multi-layered intelligent energy management system for domestic decentralized power distribution systems. J. Build. Eng. 68, 106113 (2023)
Sarker, E., Seyedmahmoudian, M., Jamei, E., Horan, B., Stojcevski, A.: Optimal management of home loads with renewable energy integration and demand response strategy. Energy 210, 118602 (2020)
Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)
Ude, N.G., Yskandar, H., Graham, R.C.: A comprehensive state of- the-art survey on the transmission network expansion planning optimization algorithms. IEEE Access 7, 123158–123181 (2019)
Jabr, R.A.: Robust transmission network expansion planning with uncertain renewable generation and loads. IEEE Trans. Power Syst. 28(4), 4558–4567 (2017)
García-Cerezo, A., Baringo, L., García-Bertrand, R.: Representative days for expansion decisions in power systems. Energies 13(2), 335 (2020)
Romero, R., Monticelli, A., Garcia, A., Haffner, S.: Test systems and mathematical models for transmission network expansion planning. IET Proc.—Gener. Trans. Distribut. 149(1), 27–36 (2002)
Alguacil, N., Motto, A.L., Conejo, A.J.: Trasmission expansion planning: a minxed-integer LP approach. IEEE Trans. Power Syst. 18(3), 1070–1077 (2003)
Binato, S., Pereira, M.V.F., Granville, S.: A new Benders decomposition approach to solve power transmission network design problems. IEEE Trans. Power Syst. 16(2), 235–240 (2001)
Rahim, S., Siano, P.: A survey and comparison of leading-edge uncertainty handling methods for power grid modernization. Expert Syst. Appl. 204, 117590 (2022)
Rahim, S., Wang, Z., Ju, P.: Overview and applications of Robust optimization in the avant-garde energy grid infrastructure: a systematic review. Appl. Energy 319, 119140 (2022)
Conejo, A. J., Baringo, L., Kazempour, S. J., & Siddiqui, A. S. (2016). Investment in electricity generation and transmission. Decision making under uncertainty. Springer
López, J.A., Ponnambalam, K., Quintana, V.H.: Generation and transmission expansion under risk using stochastic programming. IEEE Trans. Power Syst. 22(3), 1369–1378 (2007)
de la Torre, S., Conejo, A.J., Contreras, J.: Transmission expansion planning in electricity markets. IEEE Trans. Power Syst. 23(1), 238–248 (2008)
Sima, C.A., Lazaroiu, G.C., Dumbrava, V., Roscia, M., Zaninelli, D., Duquenne, P.: Stochastic programming approach for TEP optimization considering RES integration in electricity market. In International Conference On Energy and Environment (CIEM), pp. 485–489. IEEE (2017)
Jabr, R.A.: Robust transmission network expansion planning with uncertain renewable generation and loads. IEEE Trans. Power Syst. 28(4), 4558–4567 (2013)
Ruiz, C., Conejo, A.J.: Robust tranmission expansion planning. Euro. J. Oper. Res. 242, 390–401 (2015)
M´ınguez, R., Garc´ıa-Bertrand, R.: Robust transmission network expansion planning in energy systems: Improving computational performance. Euro. J. Oper. Res., 248, 21–32 (2016)
Zhang, X., Conejo, A.J.: Robust transmission expansion planning representing long-and short-term uncertainty. IEEE Trans. Power Syst. 33(2), 1329–1338 (2017)
Roldán, C., García-Bertrand, R., Mínguez, R.: Robust transmission expansion planning with uncertain generations and loads using full probabilistic information. Electric Power Syst. Res. 189, 106793 (2020)
Liang, Z., Chen, H., Chen, S., Wang, Y., Zhang, C., Kang, C.: Robust transmission expansion planning based on adaptive uncertainty set optimization under high-penetration wind power generation. IEEE Trans. Power Syst. 36(4), 2798–2814 (2021)
Hu, H., Liu, J., Zhang, X., Fang, M.: An effective and adaptable K-means algorithm for big data cluster analysis. Patt. Recogn. 139, 109404 (2023)
“Miso: Actual load - lcg consulting,” May 2023. [Online]. Available: http://www.energyonline.com/Data/GenericData.aspx?DataId=10&Amp;MISOActual Load.
“Grid data and tools. (Jun 2022). [Online]. Available: https://www.nrel.gov/grid/grid-data-tools.html.
“Markets & operations. (Jun 2022). [Online]. Available: https://www.pjm.com/markets-and-operations.
Acknowledgement
This work is supported by the Science and Technology Project of State Grid, Zhejiang Electric Power Company (No. 5100-202119559A-0–5-SF).
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Rahim, S., Li, F., Wang, Z., Dai, P., Yang, H. (2024). Dynamic Transmission Expansion Planning Using Adaptive Robust Optimization Under Uncertainties. In: Xue, Y., Zheng, Y., Gómez-Expósito, A. (eds) Proceedings of the 8th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control (PMF2023). PMF 2023. Lecture Notes in Electrical Engineering, vol 1129. Springer, Singapore. https://doi.org/10.1007/978-981-99-9251-5_28
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DOI: https://doi.org/10.1007/978-981-99-9251-5_28
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