Transmission Network Expansion Planning Considering Uncertainty in Demand with Global and Nodal Approach

Authors

Keywords:

Stochastic, Transmission Network Expansion Planning, Uncertainty, Liner Optimization, Cluster

Abstract

Transmission expansion planning aims to establish when and where to install new infrastructure such as transmission lines, cables, generators and transformers in the electrical power system. The planning must be motivated mainly to satisfy the increase in demand, consequently increase the reliability of the system and provide non-discriminatory access for generators and consumers to the electrical grid.  In this sense, this work aims to propose a methodology to handle demand uncertainty by reducing scenarios through the K-means clustering algorithm, which is used to construct representative demand curves that allow using a static model of stochastic linear optimization with less computational effort, which seeks to minimize the investment and operating costs of the electrical system, meeting the total demand of the system. The global demand and nodal demand approach of the system is compared, observing the behaviour of investment and operating costs, as well as their advantages. The results demonstrate that the formulation can be estimate the number of scenarios through mathematical metrics and the global demand approach has the advantage of only needing data on the behavior of the total demand of the system.

Downloads

Download data is not yet available.

Author Biographies

Nestor Gonzalez-Cabrera, UNAM

N. González-Cabrera (IEEE Member) works at the Department of Electrical Energy at the National Autonomous University of Mexico (UNAM). His areas of interest are power system planning.

Daniel Ernesto Hernandez Reyes, Comisión Federal de Electricidad

D.E. Hernández Reyes works in the Energy Management and Associated Products Unit of the  Federal Electricity Commission (CFE). He collaborates in the technical and economic feasibility analysis of generation projects.

Vicente Torres García, National Technology of Mexico (TecNM) / IT Morelia

V. Torres-García (Senior Member IEEE) received his PhD degree from TecNM/Instituto Tecnológico de Morelia in 2014. He is currently Professor of the PGIIE of the same institute and his areas of interest are operation of electrical power systems, electrical protections and electromagnetic transients.

References

. L. L. Garver, “Transmission Network Estimation Using Linear Programming,” IEEE Transactions on Power Apparatus and Systems, vol. 89, nº 7, pp. 1688-1696, 1970. DOI: 10.1109/TPAS.1970.292825

. A. J. Conejo, L. Baringo, S. J. Kazempour, A. S. Siddiqui, “Investment in Electricity Generation and Transmission: Decision Making under uncertainty,” Switzerland: Springer, 2016. DOI: https://doi.org/10.1007/978-3-319-29501-5

. S. Lumbreras, A. Ramos, “The new challenges to transmission expansion planning. Survey of recent practice and literature review,” Electric Power Systems Research, vol. 134, pp. 19-29, 2016. DOI: https://doi.org/10.1016/j.epsr.2015.10.013

. Z. M. Al-Hamouz, A. S. Al-Faraj, “Transmission-expansion planning based on a non-linear programming algorithm,” Applied Energy, vol. 76, nº 1-3, pp. 169-177, 2003. DOI: https://doi.org/10.1016/S0306-2619(03)00060-6

. L. Bahiense, G. C. Oliveira, M. Pereira, S. Granville, “A mixed integer disjunctive model for transmission network expansion,” IEEE Transactions on Power Systems, vol. 16, nº 3, pp. 560-565, 2001. DOI: 10.1109/PICA.2001.932383

. M. Qorbani, T. Amraee, “Long term transmission expansion planning to improve power system resilience against cascading outages,” Electric Power Systems Research, nº 106972, 2021. DOI: https://doi.org/10.1016/j.epsr.2020.106972

. P. Villaca Gomes, J. Tomé Saraiva, “State-of-the-art of transmission expansion planning: A survey from restructuring to renewable and distributed electricity markets,” International Electrical Power and Energy Systems, nº 111, pp. 411-424, 2019. DOI: https://doi.org/10.1016/j.ijepes.2019.04.035

. E. Mortaz, J. Valenzuela, “Evaluating the impact of renewable generation on transmission expansion planning,» Electric Power Systems Research, vol. 169, pp. 35-44, 2019. DOI: https://doi.org/10.1016/j.epsr.2018.12.007

. S. Lumbreras, A. Ramos, F. Banez-Chicharro, “Optimal transmission network expansion planning in real-sized power systems with high renewable penetration,” Electric Power Systems Research, vol. 149, pp. 76-88, 2017. DOI: https://doi.org/10.1016/j.epsr.2017.04.020

. G. Becerik, E. Karatepe, “Stochastic chance constrained transmission expansion decisions for different investment budgets,” Proceedings - 2018 6th International Istanbul Smart Grids and Cities Congress and Fair, ICSG 2018, pp. 76-80, 2018. DOI: 10.1109/SGCF.2018.8408946

. C. Ruiz, A. J. Conejo, “Robust transmission expansion planning,” European Journal of Operational Research, vol. 242, nº 2, pp. 390-401, 2015. DOI: https://doi.org/10.1016/j.ejor.2014.10.030

. P. F. Freitas, L. H. Macedo, R. Romero, “A strategy for transmission network expansion planning considering multiple generation scenarios,” Electric Power Systems Research, vol. 172, pp. 22-31, 2019. DOI: https://doi.org/10.1016/j.epsr.2019.02.018

. Z. Zhuo, E. Du, N. Zhang, C. Kang, Q. Xia, Z. Wang, “Incorporating Massive Scenarios in Transmission Expansion Planning With High Renewable Energy Penetration,” IEEE Transactions on Power Systems, vol. 35, nº 2, pp. 1061-1074, 2020. DOI: 10.1109/TPWRS.2019.2938618

. M. Kantardzic, “Data mining: Concepts, Models, Methods and Algorithms,” New Jersey, USA: John Wiley & Sons, Inc., 2020.

. I. De J. Silva, M. Rider, R. Romero, C. Murari, “Transmission Network Expansion Planning Considering Uncertainty in Demand,” IEEE Transactions on Power Systems, vol. 21, nº 4, pp. 1565-1573, 2006. DOI: 10.1109/TDC-LA.2008.4641803

. H. Zhang, “Transmission Expansion Planning for Large Power Systems,” Arizona State University, Arizona, USA, 2013

. D. E. Hernández Reyes, “Planificación de la expansión de la transmisión considerando incertidumbre en la demanda,” Posgrado de energías, UNAM, CDMX, 2022.

. ERCOT, «Load,» 2021. [En línea]. Available: http://www.ercot.com/gridinfo/load. [Último acceso: diciembre 2020].

. The MathWorks, Inc., MATLAB, R2017b ed., Matworks.

. P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53-65, 1987. DOI: https://doi.org/10.1016/0377-0427(87)90125-7

. R. Green, I. Staffell, N. Vasilakos, “Divide and Conquer? k-means clustering of demand data allows rapid and accurate simulations of the British Electricity System,” IEEE Transactions on engineering management, vol. 61, nº 2, pp. 251-260, 2014 DOI: 10.1109/TEM.2013.2284386.

. Aprende IA, “Seleccionar el número adecuado de clústeres,” [En línea]. Available: https://aprendeia.com/seleccionar-el-numero-adecuado-de-clusteres/. [Último acceso: diciembre 2020].

. Subcommittee, Probability, “IEEE Reliability Test System,” IEEE Transactions on Power Apparatus and Systems, Vol. 12, nº 6, pp. 2047-2054, 1979. DOI: 10.1109/TPAS.1979.319398

. C. Roldan, R. Minguez, R. Garcia-Bertrand, J. M. Arroyo, “Robust Transmission Network Expansion Planning Under Correlated Uncertainty,” IEEE Transactions on Power Systems, vol. 34, nº 3, pp. 2071-2082, 2019. DOI: 10.1109/TPWRS.2018.2889032

Downloads

Published

2024-09-29

How to Cite

Gonzalez-Cabrera, N., Hernandez Reyes, D. E., & Torres García, V. (2024). Transmission Network Expansion Planning Considering Uncertainty in Demand with Global and Nodal Approach. IEEE Latin America Transactions, 22(10), 864–870. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8994

Issue

Vol. 22 No. 10 (2024): Ordinary Issue

Section

Electric Energy

Most read articles by the same author(s)