Algoritmo para la Localización y Despacho de Bancos de Baterías en Redes de Distribución
Contenido principal del artículo
Resumen
La utilización de medios de almacenamiento de energía en redes eléctricas se encuentra en pleno crecimiento en razón de los múltiples beneficios que aportan al sistema. En este trabajo, se presenta un algoritmo de optimización orientado a la gestión de bancos de baterías en redes radiales de distribución. El objetivo consiste en seleccionar la combinación de capacidades, posiciones en la red y la estrategia de operación más adecuada de los bancos de baterías. Se pretende optimizar un indicador global que incluye costos asociados a la adquisición de la energía por parte de la empresa distribuidora, pérdidas, operación y mantenimiento de las baterías, y penalizaciones por energía no suministrada ante escenarios de falla. La resolución se plantea en dos etapas acopladas: un algoritmo de Optimización por Enjambre de Partículas establece la ubicación y capacidades de los bancos a incorporar, y otros métodos tradicionales basados en gradientes obtienen el despacho de potencias, las pérdidas en la red y minimizan el impacto de los cortes de suministro. El algoritmo se evalúa sobre una red de distribución real, demostrando su validez para abordar un problema complejo con un número elevado de variables, siendo efectivo para optimizar el costo global.
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Citas
Ahmadi, M., Adewuyi, O. B., Danish, M. S. S., Mandal, P., Yona, A., & Senjyu, T. (2021). Optimum coordination of centralized and distributed renewable power generation incorporating battery storage system into the electric distribution network. International Journal of Electrical Power and Energy Systems, 125 (September 2020), 106458. https://doi.org/10.1016/j.ijepes.2020.106458
Ahmed, H. M. A., Awad, A. S. A., Ahmed, M. H., & Salama, M. M. A. (2020). Mitigating voltage-sag and voltage-deviation problems in distribution networks using battery energy storage systems. Electric Power Systems Research, 184 (February), 106294. https://doi.org/10.1016/j.epsr.2020.106294
AlRashidi, M. R., El-Hawary, M. E. (2009). A Survey of Particle Swarm Optimization Applications in Electric Power Systems. IEEE Transactions on Evolutionary Computation, 13(4), 913-918. https://doi.org/10.1109/TEVC.2006.880326
Babacan, O., Torre, W., & Kleissl, J. (2017). Siting and sizing of distributed energy storage to mitigate voltage impact by solar PV in distribution systems. Solar Energy, 146, 199–208. https://doi.org/10.1016/j.solener.2017.02.047
Baldick, R. (1995). The generalized unit commitment problem. IEEE Transactions on Power Systems, 10, 465-475. https://doi.org/10.1109/59.373972
Hajeforosh, S., Nazir, Z., & Bollen, M. (2020). Reliability aspects of battery energy storage in the power grid. IEEE PES Innovative Smart Grid Technologies Conference Europe, 2020-Octob, 121–125. https://doi.org/10.1109/ISGT-Europe47291.2020.9248757
Kebede, A. A., Kalogiannis, T., Van Mierlo, J., & Berecibar, M. (2022). A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration. Renewable and Sustainable Energy Reviews, 159, 112213. https://doi.org/10.1016/j.rser.2022.112213
Kim, I. (2017). A case study on the effect of storage systems on a distribution network enhanced by high-capacity photovoltaic systems. Journal of Energy Storage, 12, 121–131. https://doi.org/10.1016/j.est.2017.04.010
Loyarte, A., Sangoi, E., Clementi, L., & Vega, J. (2017). Optimal distribution of battery banks in microgrids with high photovoltaic penetration. XVII Workshop on Information Processing and Control (RPIC), Mar del Plata, Argentina, 20-22 Sept. 2017. https://doi.org/10.23919/RPIC.2017.8214369
Lu, Z., Xu, X., & Yan, Z. (2020). Data-driven stochastic programming for energy storage system planning in high PV-penetrated distribution network. International Journal of Electrical Power and Energy Systems, 123 (January), 106326. https://doi.org/10.1016/j.ijepes.2020.106326
Luo, X., Wang, J., Dooner, M., & Clarke, J. (2015). Overview of current development in electrical energy storage technologies and the application potential in power system operation. Applied Energy, 137, 511–536. https://doi.org/10.1016/j.apenergy.2014.09.081
Mohamed, F. (2008). MicroGrid Modelling and Online Management [PhD. Thesis, Helsinki University of Technology, Control Engineering Laboratory], 49-50. ISBN 978-951-22-9234-9.
Mongird, K., Viswanathan, V., Balducci, P., Alam, J., Fotedar, V., Koritarov, V., & Hadjerioua, B. (2020). An evaluation of energy storage cost and performance characteristics. Energies, 13(13), 3307. https://doi.org/10.3390/en13133307
Monticelli, A. (1999). Power Flow Equations. In A. Monticelli, State Estimation in Electric Power Systems (pp. 63-102). Springer.
Moura, A. P., Moura, A. A. (2013). Newton–Raphson power flow with constant matrices: A comparison with decoupled power flow methods. International Journal of Electrical Power & Energy Systems, 46, 108-114. https://doi.org/10.1016/j.ijepes.2012.10.038
Nayak, M. R., Behura, D., & Kasturi, K. (2021). Optimal allocation of energy storage system and its benefit analysis for unbalanced distribution network with wind generation. Journal of Computational Science, 51 (February), 101319. https://doi.org/10.1016/j.jocs.2021.101319
Nocedal, J., Wright, S. (2006). Sequential quadratic programming. In J. Nocedal and S. Wrigth, Numerical Optimization (pp. 529-562). Springer.
Ribeiro, P. F., Johnson, B. K., Crow, M. L., Arsoy, A., & Liu, Y. (2001). Energy Storage systems for Advances Power Applications. Proceedings of the IEEE, 89 (12), 1744–1756. https://doi.org/10.1109/5.975900
Siddique, R., Raza, S., Mannan, A., Khalil, L., Alwaz, N., & Riaz, M. (2021). A modified NSGA approach for optimal sizing and allocation of distributed resources and battery energy storage system in distribution network. Materials Today: Proceedings, 47, S102–S109. https://doi.org/10.1016/j.matpr.2020.05.669
Wali, S. B., Hannan, M. A., Ker, P. J., Rahman, M. A., Mansor, M., Muttaqi, K. M., Mahlia, T. M. I., & Begum, R. A. (2022). Grid-connected lithium-ion battery energy storage system: A bibliometric analysis for emerging future directions. Journal of Cleaner Production, 334, 130272. https://doi.org/10.1016/j.jclepro.2021.130272
Virtanen, P. et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261-272. https://doi.org/10.1038/s41592-019-0686-2
Wong, L. A., Ramachandaramurthy, V. K., Taylor, P., Ekanayake, J. B., Walker, S. L., & Padmanaban, S. (2019). Review on the optimal placement, sizing and control of an energy storage system in the distribution network. Journal of Energy Storage, 21 (December 2018), 489–504. https://doi.org/10.1016/j.est.2018.12.015
Yuan, Z., Wang, W., Wang, H., & Yildizbasi, A. (2020). A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction. Journal of Energy Storage, 29 (January), 101368. https://doi.org/10.1016/j.est.2020.101368
Zimmerman, R. D., Murillo-Sánchez C. E., Thomas, R. J. (2011). MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 26, 12-19. https://doi.org/0.1109/TPWRS.2010.2051168