Data-driven Model-free Hosting Capacity Estimation in a Low Voltage Prosumer
DOI:
https://doi.org/10.37431/conectividad.v5i3.134Keywords:
Hosting Capacity, Data-driven analyses, Linear regression model, Distributed energy resourceAbstract
Currently, distributed generation systems have been proposed as a way to increase the energy potential of the countries introducing them, however, the entry of this new form of energy generation can cause some negative effects on the correct operation of the distribution network, one of these negative effects are the overvoltages that can exceed the levels allowed by the local operator. For this reason, it is necessary to limit in some way the injection of distributed generation into the grid. This parameter has been called Hosting Capacity, which is a new parameter developed to limit the power coming from distributed sources while maintaining the proper functioning of the grid. Many studies have been developed to define and calculate the Hosting Capacity, mainly based on scenario simulations, however, these need detailed and accurate network models which at low voltage levels is very difficult to have. The present work focuses on determining a Hosting Capacity value in a node at low voltage using values taken from a smart meter, the methodology uses a regression model considering voltage and power consumption values at the point. The results show a good approximation of Hosting Capacity validated through a network model of the connection node obtaining voltage values close to the limit, but lower than it, which allows the conclusion that the planted methodology is useful for the calculation of Hosting Capacity without the need for having a network model.
References
Bassi, V., Jaglal, D., Ochoa, L., Alpcan, T., & Leckie, C. (2022a). Deliverable 0 «Concept, Smart Meter Data, and Initial Findings». https://doi.org/10.13140/RG.2.2.29492.94089/1
Bassi, V., Jaglal, D., Ochoa, L., Alpcan, T., & Leckie, C. (2022b). Deliverable 0 «Concept, Smart Meter Data, and Initial Findings».
Bassi, V., Ochoa, L., & Alpcan, T. (2021a). Model-Free Voltage Calculations for PV-Rich LV Networks: Smart Meter Data and Deep Neural Networks. https://doi.org/10.1109/PowerTech46648.2021.9494847
Bassi, V., Ochoa, L., & Alpcan, T. (2021b). Model-Free Voltage Calculations for PV-Rich LV Networks: Smart Meter Data and Deep Neural Networks. 2021 IEEE Madrid PowerTech, 1-6. https://doi.org/10.1109/PowerTech46648.2021.9494847
Castelo de Oliveira, T. E., Ribeiro, P. F., Zobaa, A. F., Abdel Aleem, S. H. E., & Ismael, S. M. (2020). An Overview of Hosting Capacity for Modern Power Grids. En A. F. Zobaa, S. H. E. Abdel Aleem, S. M. Ismael, & P. F. Ribeiro (Eds.), Hosting Capacity for Smart Power Grids (pp. 1-9). Springer International Publishing. https://doi.org/10.1007/978-3-030-40029-3_1
Cunha, V. C., Freitas, W., Trindade, F. C. L., & Santoso, S. (2020). Automated Determination of Topology and Line Parameters in Low Voltage Systems Using Smart Meters Measurements. IEEE Transactions on Smart Grid, 11(6), 5028-5038. https://doi.org/10.1109/TSG.2020.3004096
e Silva, L. E. S., & Vieira, J. P. A. (2022). Combined PV-PEV Hosting Capacity Analysis in Low-Voltage Distribution Networks. Electric Power Systems Research, 206, 107829. https://doi.org/10.1016/j.epsr.2022.107829
Esau, Z., Ryoichi, H., & Hiroyuki, K. (2023). A flexible stochastic PV hosting capacity framework considering network over-voltage tolerance. Energy Reports, 9, 529-538. https://doi.org/10.1016/j.egyr.2022.11.101
Fatima, S., Püvi, V., & Lehtonen, M. (2020). Review on the PV Hosting Capacity in Distribution Networks. Energies, 13(18), Article 18. https://doi.org/10.3390/en13184756
Guo, Y., Yuan, Y., & Wang, Z. (2022). Distribution Grid Modeling Using Smart Meter Data. IEEE Transactions on Power Systems, 37(3), 1995-2004. https://doi.org/10.1109/TPWRS.2021.3118004
Huo, Y., Li, P., Ji, H., Yu, H., Yan, J., Wu, J., & Wang, C. (2023). Data-Driven Coordinated Voltage Control Method of Distribution Networks With High DG Penetration. IEEE Transactions on Power Systems, 38(2), 1543-1557. https://doi.org/10.1109/TPWRS.2022.3172667
Ismael, S. M., Abdel Aleem, S. H. E., Abdelaziz, A. Y., & Zobaa, A. F. (2019). State-of-the-art of hosting capacity in modern power systems with distributed generation. Renewable Energy, 130, 1002-1020. https://doi.org/10.1016/j.renene.2018.07.008
Munikoti, S., Abujubbeh, M., Jhala, K., & Natarajan, B. (2022). A novel framework for hosting capacity analysis with spatio-temporal probabilistic voltage sensitivity analysis. International Journal of Electrical Power & Energy Systems, 134, 107426. https://doi.org/10.1016/j.ijepes.2021.107426
Procopiou, A., Liu, M., Ochoa, L., Langstaff, T., & Harding, J. (2020). Smart meter-driven estimation of PV hosting capacity. https://doi.org/10.1049/oap-cired.2021.0287
Procopiou, A. T., Liu, M. Z., Ochoa, L. F., Langstaff, T., & Harding, J. (2020). Smart meter-driven estimation of PV hosting capacity. CIRED 2020 Berlin Workshop (CIRED 2020), 2020, 128-131. https://doi.org/10.1049/oap-cired.2021.0287
Qamar, N., Arshad, A., Mahmoud, K., & Lehtonen, M. (2023a). Hosting Capacity in Distribution Grids: A Review of Definitions, Performance Indices, Determination Methodologies, and Enhancement Techniques. Energy Science & Engineering, 11. https://doi.org/10.1002/ese3.1389
Qamar, N., Arshad, A., Mahmoud, K., & Lehtonen, M. (2023b). Hosting capacity in distribution grids: A review of definitions, performance indices, determination methodologies, and enhancement techniques. Energy Science & Engineering, 11(4), 1536-1559. https://doi.org/10.1002/ese3.1389
Rajabi, A., Elphick, S., David, J., Pors, A., & Robinson, D. (2022). Innovative approaches for assessing and enhancing the hosting capacity of PV-rich distribution networks: An Australian perspective. Renewable and Sustainable Energy Reviews, 161, 112365. https://doi.org/10.1016/j.rser.2022.112365
Taheri, S., Jalali, M., Kekatos, V., & Tong, L. (2021). Fast Probabilistic Hosting Capacity Analysis for Active Distribution Systems. IEEE Transactions on Smart Grid, 12(3), 2000-2012. https://doi.org/10.1109/TSG.2020.3038651
Wu, J., Yuan, J., Weng, Y., & Ayyanar, R. (2023). Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids. IEEE Transactions on Smart Grid, 14(1), 354-364. https://doi.org/10.1109/TSG.2022.3196943
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Instituto Superior Tecnológico Universitario Rumiñahui
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The originals published in the electronic edition under the first publication rights of the journal belong to the Instituto Superior Tecnológico Universitario Rumiñahui; therefore, it is necessary to cite the source in any partial or total reproduction. All the contents of the electronic journal are distributed under a Creative Commons Attribution-Noncommercial 4.0 International (CC-BY-NC 4.0) license.
2.png){kind=logo}
