Seguridad eléctrica en un sistema de potencia considerando fuentes intermitentes de energía eléctrica

dc.contributorCandelo Becerra, John Edwin
dc.creatorSerna Toro, Juan Sebastian
dc.date.accessioned2022-08-22T20:53:09Z
dc.date.accessioned2022-09-21T17:26:37Z
dc.date.available2022-08-22T20:53:09Z
dc.date.available2022-09-21T17:26:37Z
dc.date.created2022-08-22T20:53:09Z
dc.date.issued2022-05-15
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/81999
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3400668
dc.description.abstractEn este trabajo de grado se abordan los principales retos de la operación y planeación del sistema eléctrico bajo la regulación CREG 060 de 2019. Se aborda el tema de pronósticos de potencia de plantas solares haciendo uso de redes neuronales recurrentes. Se plantea una metodología para operar de manera segura el sistema teniendo en cuenta la variación de corto tiempo asociado a este tipo de plantas y finalmente se propone una modificación al calculo de la región segura de operación que considera la variación de las renovables. (Texto tomado de la fuente)
dc.description.abstractIn this degree work, the main challenges of the operation and planning of the electrical system under the CREG 060 regulation of 2019 are addressed. The issue of power forecasts of solar plants is addressed using recurrent neural networks. A methodology is proposed to safely operate the system, considering the short-time variation associated with this type of plant, and finally a modification to the calculation of the safe region of operation that considers the variation of renewables is proposed.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
dc.publisherMedellín - Minas - Maestría en Ingeniería - Ingeniería Eléctrica
dc.publisherDepartamento de Ingeniería Eléctrica y Automática
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
dc.relationO. Ellabban, H. Abu-Rub, and F. Blaabjerg, “Renewable energy resources: Current status, future prospects and their enabling technology,” Renewable and Sustainable Energy Reviews, vol. 39, pp. 748–764, 2014, doi: 10.1016/j.rser.2014.07.113.
dc.relation“RE-thinking 2050.” www.erec.org (accessed Sep. 02, 2013).
dc.relation“World Energy Outlook 2012.” www.iae.org (accessed Aug. 15, 2013).
dc.relationUPME, “‘Informe registro de proyectos’ - https://www1.upme.gov.co/Paginas/Registro.aspx" ,” Nov. 18, 2021.
dc.relationComisión de Regulación de Energía y Gas, “Resolución N° 060 de 2019.” p. 44, 2019.
dc.relationP. Li et al., “Operational flexibility of active distribution networks: Definition, quantified calculation and application,” International Journal of Electrical Power & Energy Systems, vol. 119, p. 105872, 2020, doi: https://doi.org/10.1016/j.ijepes.2020.105872.
dc.relationM. A. Bucher, S. Delikaraoglou, K. Heussen, P. Pinson, and G. Andersson, “On quantification of flexibility in power systems,” 2015 IEEE Eindhoven PowerTech, PowerTech 2015, 2015, doi: 10.1109/PTC.2015.7232514.
dc.relationJ. Zhang, G. Xiong, K. Meng, P. Yu, G. Yao, and Z. Dong, “An improved probabilistic load flow simulation method considering correlated stochastic variables,” International Journal of Electrical Power and Energy Systems, vol. 111, no. March, pp. 260–268, 2019, doi: 10.1016/j.ijepes.2019.04.007.
dc.relationF. F. Wu and S. Kumagai, “Steady-State Security Regions of Power Systems,” IEEE Transactions on Circuits and Systems, vol. 29, no. 11, pp. 703–711, 1982, doi: 10.1109/TCS.1982.1085091.
dc.relationF. F. Wu and S. Kumagai, “Steady-State Security Regions of Power Systems,” IEEE Transactions on Circuits and Systems, vol. 29, no. 11, pp. 703–711, 1982, doi: 10.1109/TCS.1982.1085091.
dc.relationC. Liu, “A New Method for the Construction of Maximal Steady-State Security Regions of Power Systems,” IEEE Power Engineering Review, vol. PER-6, no. 11, pp. 25–26, 1986, doi: 10.1109/MPER.1986.5527464.
dc.relationW. Dai et al., “Security region of renewable energy integration: Characterization and flexibility,” Energy, vol. 187, p. 115975, 2019, doi: 10.1016/j.energy.2019.115975.
dc.relationY. Hou, J. Yan, C. Peng, Z. Qin, S. Lei, and H. Ruan, “Risk assessment of critical time to renewable operation with steady-state security region,” Proceedings - 2014 Power Systems Computation Conference, PSCC 2014, no. 51277155, pp. 1–6, 2014, doi: 10.1109/PSCC.2014.7038341.
dc.relationF. F. Wu and S. Kumagai, “Steady-State Security Regions of Power Systems,” IEEE Transactions on Circuits and Systems, vol. 29, no. 11, pp. 703–711, 1982, doi: 10.1109/TCS.1982.1085091.
dc.relationH. Jahangir, M. A. Golkar, F. Alhameli, A. Mazouz, A. Ahmadian, and A. Elkamel, “Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN,” Sustainable Energy Technologies and Assessments, vol. 38, no. December 2019, 2020, doi: 10.1016/j.seta.2019.100601.
dc.relationH. Zang, L. Cheng, T. Ding, K. W. Cheung, Z. Wei, and G. Sun, “Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning,” International Journal of Electrical Power and Energy Systems, vol. 118, no. December 2019, p. 105790, 2020, doi: 10.1016/j.ijepes.2019.105790.
dc.relationS. Xuewei et al., “Research on Energy Storage Configuration Method Based on Wind and Solar Volatility; Research on Energy Storage Configuration Method Based on Wind and Solar Volatility,” 2020 10th International Conference on Power and Energy Systems (ICPES), 2020, doi: 10.1109/ICPES51309.2020.9349645/20/$31.00.
dc.relationC. O. Inacio and C. L. T. Borges, “Stochastic Model for Generation of High-Resolution Irradiance Data and Estimation of Power Output of Photovoltaic Plants,” IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 952–960, Apr. 2018, doi: 10.1109/TSTE.2017.2767780.
dc.relationK. Touafek et al., “Improvement of Energy Efficiency of Solar Hybrid Water Collectors; Improvement of Energy Efficiency of Solar Hybrid Water Collectors,” 2017.
dc.relationJ. Windarta, D. Denis, S. Saptadi, J. S. Silaen, and D. A. Satrio, “Implementation and Testing of Rooftop Solar Power Plant with On-Grid System 1215 Wp Household Scale,” in 7th International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2020 - Proceedings, Sep. 2020, pp. 294–299. doi: 10.1109/ICITACEE50144.2020.9239239.
dc.relationM. S. Hossain and H. Mahmood, “Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast,” IEEE Access, vol. 8, pp. 172524–172533, 2020, doi: 10.1109/ACCESS.2020.3024901.
dc.relationL. Alvarado-Barrios, J. M. Mauricio, J. M. Maza-Ortega, and A. Gómez-Expósito, “Control strategy for a mid-size Wind Energy Convertion System,” in SPEEDAM 2010 - International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 2010, pp. 396–402. doi: 10.1109/SPEEDAM.2010.5542228.
dc.relationL. F. Donoso, “Energía Eólica en Chile,” Chile más Energía, 2011. http://chilemasenergia.blogspot.com/2011/11/energia-eolica-en-chile.html (accessed Feb. 24, 2020).
dc.relationB. F. W. Allen J. Wood, Power Generation operation and control, Second edition. 1996.
dc.relationD. A. Rivera, S. M. Matute, J. v. Mendoza, and G. Mejía, “Low active and reactive power reserve in Honduras Northwest’s transmission system,” Dec. 2020. doi: 10.1109/ARGENCON49523.2020.9505325.
dc.relationComisión de regulación de energía eléctrica y gas, “Creg 060.” Ministerio de minas y energía, Jun. 20, 2019.
dc.relationR. Jiao, T. Zhang, Y. Jiang, and H. He, “Short-term non-residential load forecasting based on multiple sequences LSTM recurrent neural network,” IEEE Access, vol. 6, pp. 59438–59448, 2018, doi: 10.1109/ACCESS.2018.2873712.
dc.relationJ. S. Sepp Hochreiter, “Long Short-Term Memory,” Neural Computation 9, 1997.
dc.relationM. S. Hossain and H. Mahmood, “Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast,” IEEE Access, vol. 8, pp. 172524–172533, 2020, doi: 10.1109/ACCESS.2020.3024901.
dc.relationSouth Dakota State University, IEEE Region 4, and Institute of Electrical and Electronics Engineers, Classification of Electricity Load Profile Data and The Prediction of Load Demand Variability.
dc.relationM. Lave, J. Kleissl, and E. Arias-Castro, “High-frequency irradiance fluctuations and geographic smoothing,” Solar Energy, vol. 86, no. 8. pp. 2190–2199, Aug. 2012. doi: 10.1016/j.solener.2011.06.031.
dc.relationGutierrez Eduardo and Vladimirovna Olga, “Estadistica inferencial para ingenieria y ciencias,” Azcapotzalco, Ciudad de México: Grupo editorial Patria, 2016, pp. 306–307.
dc.relationH. Adolfo. Quevedo Urías and B. Rosa. Pérez Salvador, Estadística para inegeniería y ciencias. Larousse - Grupo Editorial Patria, 2000.
dc.relationDIgSILENT GmbH, “39 Bus New England System,” vol. V15,2, pp. 1–18, 2018.
dc.rightsReconocimiento 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleSeguridad eléctrica en un sistema de potencia considerando fuentes intermitentes de energía eléctrica
dc.typeTesis


Este ítem pertenece a la siguiente institución