- -

Optimal Scheduling for Energy Storage Systems in Distribution Networks

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Optimal Scheduling for Energy Storage Systems in Distribution Networks

Mostrar el registro completo del ítem

Escoto Simó, M.; Montagud, M.; González-Cobos, N.; Belinchón, A.; Trujillo, AV.; Romero-Chavarro, JC.; Diaz-Cabrera, JC.... (2020). Optimal Scheduling for Energy Storage Systems in Distribution Networks. Energies. 13(15):1-13. https://doi.org/10.3390/en13153921

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/167459

Ficheros en el ítem

Metadatos del ítem

Título: Optimal Scheduling for Energy Storage Systems in Distribution Networks
Autor: Escoto Simó, Miquel Montagud, Mario González-Cobos, Noemí Belinchón, Alejandro Trujillo, Adriana Valentina Romero-Chavarro, Julián Camilo Diaz-Cabrera, Julio César GARCÍA PELLICER, MARTA Quijano-Lopez, Alfredo
Entidad UPV: Universitat Politècnica de València. Instituto de Tecnología Eléctrica - Institut de Tecnologia Elèctrica
Universitat Politècnica de València. Departamento de Ingeniería Eléctrica - Departament d'Enginyeria Elèctrica
Fecha difusión:
Resumen:
[EN] Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility ...[+]
Palabras clave: Energy storage system management , Demand and generation forecast , Optimal scheduling of distributed energy storage , Distribution network modelling and simulation , Optimization models
Derechos de uso: Reconocimiento (by)
Fuente:
Energies. (eissn: 1996-1073 )
DOI: 10.3390/en13153921
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/en13153921
Código del Proyecto:
info:eu-repo/grantAgreement/CDTI//CER-20191019/
info:eu-repo/grantAgreement/IVACE//IMDEEA%2F2019%2F38/ES/SOFI. Servicios para la Operación agregada de la Flexibilidad de forma Inteligente en el entorno de las smartgrid/
Agradecimientos:
This research was funded by European Regional Development Fund (Comunidad Valenciana FEDER 2014-2020 PO, CCI number: 2014ES16RFOP013) and the ITE-IVACE collaboration agreement corresponding to the annuity 2019 (file: ...[+]
Tipo: Artículo

References

The Impact of the Covid-19 Crisis on Clean Energy Progresshttps://www.iea.org/articles/the-impact-of-the-covid-19-crisis-on-clean-energy-progress

Sustainable Development Goalshttps://www.un.org/sustainabledevelopment/

Mesarić, P., & Krajcar, S. (2015). Home demand side management integrated with electric vehicles and renewable energy sources. Energy and Buildings, 108, 1-9. doi:10.1016/j.enbuild.2015.09.001 [+]
The Impact of the Covid-19 Crisis on Clean Energy Progresshttps://www.iea.org/articles/the-impact-of-the-covid-19-crisis-on-clean-energy-progress

Sustainable Development Goalshttps://www.un.org/sustainabledevelopment/

Mesarić, P., & Krajcar, S. (2015). Home demand side management integrated with electric vehicles and renewable energy sources. Energy and Buildings, 108, 1-9. doi:10.1016/j.enbuild.2015.09.001

Rodrigues, E. M. G., Godina, R., Santos, S. F., Bizuayehu, A. W., Contreras, J., & Catalão, J. P. S. (2014). Energy storage systems supporting increased penetration of renewables in islanded systems. Energy, 75, 265-280. doi:10.1016/j.energy.2014.07.072

Hirsch, A., Parag, Y., & Guerrero, J. (2018). Microgrids: A review of technologies, key drivers, and outstanding issues. Renewable and Sustainable Energy Reviews, 90, 402-411. doi:10.1016/j.rser.2018.03.040

Clean Energy for All Europeans Packagehttps://ec.europa.eu/energy/topics/energy-strategy/clean-energy-all-europeans_en

VISION 2050 Integrating Smart Networks for the Energy Transition: Serving Society and Protecting the Environmenthttps://www.etip-snet.eu/etip_publ/etip-snet-vision-2050/

Staying on Course: Renewable Energy in the Time of COVID-19https://www.irena.org/newsroom/pressreleases/2020/Apr/Staying-on-Course-Renewable-Energy-in-the-time-of-COVID19

ElNozahy, M. S., Abdel-Galil, T. K., & Salama, M. M. A. (2015). Probabilistic ESS sizing and scheduling for improved integration of PHEVs and PV systems in residential distribution systems. Electric Power Systems Research, 125, 55-66. doi:10.1016/j.epsr.2015.03.029

Li, Y., Yang, Z., Li, G., Zhao, D., & Tian, W. (2019). Optimal Scheduling of an Isolated Microgrid With Battery Storage Considering Load and Renewable Generation Uncertainties. IEEE Transactions on Industrial Electronics, 66(2), 1565-1575. doi:10.1109/tie.2018.2840498

Ciupăgeanu, D.-A., Lăzăroiu, G., & Barelli, L. (2019). Wind energy integration: Variability analysis and power system impact assessment. Energy, 185, 1183-1196. doi:10.1016/j.energy.2019.07.136

Hemmati, R., Saboori, H., & Jirdehi, M. A. (2017). Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources. Energy, 133, 380-387. doi:10.1016/j.energy.2017.05.167

Xie, S., Hu, Z., & Wang, J. (2020). Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks. Applied Energy, 261, 114412. doi:10.1016/j.apenergy.2019.114412

Saboori, H., & Jadid, S. (2020). Optimal scheduling of mobile utility-scale battery energy storage systems in electric power distribution networks. Journal of Energy Storage, 31, 101615. doi:10.1016/j.est.2020.101615

Kassai, M. (2017). Prediction of the HVAC Energy Demand and Consumption of a Single Family House with Different Calculation Methods. Energy Procedia, 112, 585-594. doi:10.1016/j.egypro.2017.03.1121

Zheng, Y., Zhao, J., Song, Y., Luo, F., Meng, K., Qiu, J., & Hill, D. J. (2018). Optimal Operation of Battery Energy Storage System Considering Distribution System Uncertainty. IEEE Transactions on Sustainable Energy, 9(3), 1051-1060. doi:10.1109/tste.2017.2762364

Jayasekara, N., Masoum, M. A. S., & Wolfs, P. J. (2016). Optimal Operation of Distributed Energy Storage Systems to Improve Distribution Network Load and Generation Hosting Capability. IEEE Transactions on Sustainable Energy, 7(1), 250-261. doi:10.1109/tste.2015.2487360

Mehrjerdi, H., & Hemmati, R. (2019). Modeling and optimal scheduling of battery energy storage systems in electric power distribution networks. Journal of Cleaner Production, 234, 810-821. doi:10.1016/j.jclepro.2019.06.195

Macedo, L. H., Franco, J. F., Rider, M. J., & Romero, R. (2015). Optimal Operation of Distribution Networks Considering Energy Storage Devices. IEEE Transactions on Smart Grid, 6(6), 2825-2836. doi:10.1109/tsg.2015.2419134

Lunci Hua, Jia Wang, & Chi Zhou. (2014). Adaptive Electric Vehicle Charging Coordination on Distribution Network. IEEE Transactions on Smart Grid, 5(6), 2666-2675. doi:10.1109/tsg.2014.2336623

Guo, X., Guo, X., & Su, J. (2013). Improved Support Vector Machine Short-term Power Load Forecast Model Based on Particle Swarm Optimization Parameters. Journal of Applied Sciences, 13(9), 1467-1472. doi:10.3923/jas.2013.1467.1472

Bordin, C., Anuta, H. O., Crossland, A., Gutierrez, I. L., Dent, C. J., & Vigo, D. (2017). A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration. Renewable Energy, 101, 417-430. doi:10.1016/j.renene.2016.08.066

IEEE PES AMPS DSAS Test Feeder Working Grouphttps://site.ieee.org/pes-testfeeders/resources/

Lotero, R. C., & Contreras, J. (2011). Distribution System Planning With Reliability. IEEE Transactions on Power Delivery, 26(4), 2552-2562. doi:10.1109/tpwrd.2011.2167990

Munoz-Delgado, G., Contreras, J., & Arroyo, J. M. (2015). Joint Expansion Planning of Distributed Generation and Distribution Networks. IEEE Transactions on Power Systems, 30(5), 2579-2590. doi:10.1109/tpwrs.2014.2364960

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem