Quantification of operating reserves with high penetration of wind power considering extreme values

The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore,the quantification...

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Autores:
Obando Ceron, Johan Samir
González Palomino, Gabriel
Moreno-Chuquen, Ricardo
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/13249
Acceso en línea:
https://hdl.handle.net/10614/13249
Palabra clave:
Energía eólica
Extreme values
Montecarlo simulation
Operating reserves
Optimal power flow
Wind power
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openAccess
License
Derechos reservados - International Journal of Electrical and Computer Engineering (IJECE), 2020
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oai_identifier_str oai:red.uao.edu.co:10614/13249
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Quantification of operating reserves with high penetration of wind power considering extreme values
title Quantification of operating reserves with high penetration of wind power considering extreme values
spellingShingle Quantification of operating reserves with high penetration of wind power considering extreme values
Energía eólica
Extreme values
Montecarlo simulation
Operating reserves
Optimal power flow
Wind power
title_short Quantification of operating reserves with high penetration of wind power considering extreme values
title_full Quantification of operating reserves with high penetration of wind power considering extreme values
title_fullStr Quantification of operating reserves with high penetration of wind power considering extreme values
title_full_unstemmed Quantification of operating reserves with high penetration of wind power considering extreme values
title_sort Quantification of operating reserves with high penetration of wind power considering extreme values
dc.creator.fl_str_mv Obando Ceron, Johan Samir
González Palomino, Gabriel
Moreno-Chuquen, Ricardo
dc.contributor.author.none.fl_str_mv Obando Ceron, Johan Samir
González Palomino, Gabriel
Moreno-Chuquen, Ricardo
dc.subject.armarc.spa.fl_str_mv Energía eólica
topic Energía eólica
Extreme values
Montecarlo simulation
Operating reserves
Optimal power flow
Wind power
dc.subject.proposal.eng.fl_str_mv Extreme values
Montecarlo simulation
Operating reserves
Optimal power flow
Wind power
description The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore,the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty inwind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated asa mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-04
dc.date.accessioned.none.fl_str_mv 2021-09-23T16:02:27Z
dc.date.available.none.fl_str_mv 2021-09-23T16:02:27Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.issn.none.fl_str_mv 20888708
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dc.language.iso.spa.fl_str_mv eng
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dc.relation.citationedition.spa.fl_str_mv Volumen 10, número 2 (2020)
dc.relation.citationendpage.spa.fl_str_mv 1700
dc.relation.citationissue.spa.fl_str_mv Número 2
dc.relation.citationstartpage.spa.fl_str_mv 1693
dc.relation.citationvolume.spa.fl_str_mv Volumen 10
dc.relation.cites.eng.fl_str_mv Obando, J. S., González, G., Moreno, R.(abril, 2020). Quantification of operating reserves with high penetration of wind power considering extreme values. International Journal of Electrical and Computer Engineering (IJECE), (Vol.10 (2), pp.1693-1700. DOI: http://doi.org/10.11591/ijece.v10i2.pp. 1693-1700
dc.relation.ispartofjournal.eng.fl_str_mv International Journal of Electrical and Computer Engineering (IJECE)
dc.relation.references.spa.fl_str_mv [1] S. S. Sakthi, R.K. Santhi, N. M. Krishman, S. Ganesan, S. Subramanian, “Wind Integrated Thermal Unit Commitment Solution using Grey Wolf Optimizer,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2309-2320, Oct. 2017.
[2] R. A. Jabr and B. C. Pal, “Intermittent wind generation in optimal power flow dispatching,” IET Gener. Transm. Distrib, vol. 3, no. 1, pp. 66–74, Jan 2009. http://doi.org/10.1049/iet-gtd:20080273
[3] S. Reddy, “Multi-objetive based optimal energy and reactive power dispatch in deregulated electricity markets,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 5, pp. 3427-3435, Oct. 2018.
[4] H. Zhang and P. Li, “Probabilistic analysis for optimal power flow under uncertainty,” IET Gener. Transm. Distrib, vol. 4, no. 5, pp. 553–561, May 2010. http://doi.org/10.1049/iet-gtd.2009.0374
[5] R. Entriken, A. Tuohy, and D. Brooks, “Stochastic optimal power flow in systems with wind power,” USA, Jul. 2011, pp. 1–5. http://doi.org/10.1109/PES.2011.6039581
[6] B. Banhthasit, C. Jamroen, S. Dechanupaprittha, “Optimal generation schedeluing of power system for maximum renewable energy harvesting and power losses minimization,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 4, pp. 1954-1966, Aug. 2018.
[7] C. S. Saunders, Point estimate method addressing correlated wind power for probabilistic optimal power flow, IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1045–1054, May 2014. http://doi.org/ 10.1109/TPWRS.2013.2288701
[8] A. Papavasiliou and S. S. Oren, “Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network,” Oper. Res., vol. 61, no. 3, pp. 578–592, 2013.
[9] F. Bouffard and F. D. Galiana, “Stochastic security for operations planning with significant wind power generation," IEEE Trans. Power Syst.,vol. 23, no. 2, pp. 306–316, May 2008. http://doi.org/10.1109/TPWRS.2008.919318
[10] J. M. Morales, A. J. Conejo, and J. “Perez-Ruiz, Economic valuation of reserves in power systems with high penetration of wind power,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 900–910, May 2009. http://doi.org/ 10.1109/TPWRS.2009.2016598
[11] A. Dalabeeh, A. Almofleh, A. Alzyoud, H. Ayman, “Economical and reliable expansion alternative of composite power system under restructuring,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6, pp. 4790-4799, Dec. 2018.
[12] T. Diep-Thanh, Q. Nguyen-Phung, H. Nguyen-Duc, “Stochastic control for optimal power flow in islanded microgrid,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 2, pp. 1045-1057, Apr. 2019.
[13] S. Kim, S. Reddy, “Optimal power flow based oncgestion management using enhanced genetic algorithms,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 2, pp. 875-883, Apr. 2019.
[14] R. Moreno, J. Obando, G. Gonzalez, “An integrated OPF dispatching model with wind power and demand response for day-ahead markets,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 4, pp. 2794-2802, Aug. 2019.
[15] E. Ela, B. Kirby, E. Lannoye, M. R. Milligan, D. Flynn, B. Zavadil, and M. O’ Malley, “Evolution of Operating Reserve Determination in Wind Power Integration Studies,” in Proceedings of the IEEE Power and Energy Society General Meeting, Minneapolis, MN, USA, July 25-29, 2010. http://doi.org/ 10.1109/PES.2010.5589272
[16] A. N. Afandi, A. P. Wibawa, S. Padmantara, G. Fujita, W. Triyana, Y. Sulistyorini, H. Miyauchi, N. Tutkun, M. EL-Shimy Mahmoud, X. Z. Gao, “Designed Operating Approach of Economic Dispatch for Java Bali Power Grid Areas Considered Wind Energy and Pollutant Emission Optimized Using Thunderstorm Algorithm Based on Forward Cloud Charge Mechanism,” International Review of Electrical Engineering (IREE), vol. 13 n. 1, February 2018, pp. 59-68. http://doi.org/10.15866/iree.v13i1.14687
[17] S. Surrender, “Optimal reactive power sceduling using cuckoo search algorithm,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2349-2356, Oct. 2017.
[18] D. Ganger, J. Zhang, and V. Vittal, “Statistical characterization of wind power ramps via extreme value analysis,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 3118–3119, Nov. 2014. http://doi.org/ 10.1109/TPWRS.2014.2315491
[19] Y. Wan, “Analysis of wind power ramping behavior in ERCOT,” Nat. Renewable Energy Lab., Golden, CO, USA, Tech. Rep. TP-5500-49218, Mar. 2011.
[20] Zhao, Jie, et al. “Quantifying risk of wind power ramps in ERCOT,” IEEE Transactions on Power Systems 32.6 (2017): 4970-4971. http://doi.org/10.1109/TPWRS.2017.2678761
[21] J. Pickands, III, “Statistical inference using extreme order statistics,” Ann. Statist., vol. 3, pp. 119-131, 1975.
[22] R. Zárate-Miñano, F. Milano and A. J. Conejo, “An OPF Methodology to Ensure Small-Signal Stability,” IEEE Trans. Power System, vol. 26, no. 3, Aug. 2011. http://doi.org/ 10.1109/TPWRS.2010.2076838
[23] T. Dai, W. Qiao and L. Qu, “Real-time Optimal Participation of Wind Power in an Electricity Market,” in IEEE Innovative Smart Grid Technologies Conf., Tianjin, China, 2012.
[24] S. Jang, H. Jung, J. Park, and S. King, “A new network partition method using the sensitive of marginal cost under network congestion,” IEEE Power Engineering Society Summer Meeting, 2001. http://doi.org/10.1109/PESS.2001.970326
[25] The GUROBI Manual. Accessed on May 5, 2017. [Online]. Available: https://www.gurobi.com/documentation/7.5/refman/index.html.
[26] Matpower Optimal Scheduling Tool (MOST) package. Accessed on Apr. 3, 2017. [Online]. Available: http://www.pserc.cornell.edu/ matpower/manual.pdf
[27] Black, M., Strbac, G. Value of bulk energy storage for managing wind power fluctuations, IEEE Trans. Energy Convers., 2007, 22, (1), pp. 197–205. http://doi.org/10.1109/TEC.2006.889619
dc.rights.spa.fl_str_mv Derechos reservados - International Journal of Electrical and Computer Engineering (IJECE), 2020
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spelling Obando Ceron, Johan Samirb8aaef71edc965de027f0670491e3948González Palomino, Gabriel3d52c20631e564b1f043775aa5dbc9f3Moreno-Chuquen, Ricardof36efacf1d947d7410ab7d332d4147532021-09-23T16:02:27Z2021-09-23T16:02:27Z2020-0420888708https://hdl.handle.net/10614/13249The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore,the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty inwind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated asa mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching8 páginasapplication/pdfengInternational Journal of Electrical and Computer Engineering (IJECE)Derechos reservados - International Journal of Electrical and Computer Engineering (IJECE), 2020https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Quantification of operating reserves with high penetration of wind power considering extreme valuesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Energía eólicaExtreme valuesMontecarlo simulationOperating reservesOptimal power flowWind powerVolumen 10, número 2 (2020)1700Número 21693Volumen 10Obando, J. S., González, G., Moreno, R.(abril, 2020). Quantification of operating reserves with high penetration of wind power considering extreme values. International Journal of Electrical and Computer Engineering (IJECE), (Vol.10 (2), pp.1693-1700. DOI: http://doi.org/10.11591/ijece.v10i2.pp. 1693-1700International Journal of Electrical and Computer Engineering (IJECE)[1] S. S. Sakthi, R.K. Santhi, N. M. Krishman, S. Ganesan, S. Subramanian, “Wind Integrated Thermal Unit Commitment Solution using Grey Wolf Optimizer,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2309-2320, Oct. 2017.[2] R. A. Jabr and B. C. Pal, “Intermittent wind generation in optimal power flow dispatching,” IET Gener. Transm. Distrib, vol. 3, no. 1, pp. 66–74, Jan 2009. http://doi.org/10.1049/iet-gtd:20080273[3] S. Reddy, “Multi-objetive based optimal energy and reactive power dispatch in deregulated electricity markets,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 5, pp. 3427-3435, Oct. 2018.[4] H. Zhang and P. Li, “Probabilistic analysis for optimal power flow under uncertainty,” IET Gener. Transm. Distrib, vol. 4, no. 5, pp. 553–561, May 2010. http://doi.org/10.1049/iet-gtd.2009.0374[5] R. Entriken, A. Tuohy, and D. Brooks, “Stochastic optimal power flow in systems with wind power,” USA, Jul. 2011, pp. 1–5. http://doi.org/10.1109/PES.2011.6039581[6] B. Banhthasit, C. Jamroen, S. Dechanupaprittha, “Optimal generation schedeluing of power system for maximum renewable energy harvesting and power losses minimization,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 4, pp. 1954-1966, Aug. 2018.[7] C. S. Saunders, Point estimate method addressing correlated wind power for probabilistic optimal power flow, IEEE Trans. Power Syst., vol. 29, no. 3, pp. 1045–1054, May 2014. http://doi.org/ 10.1109/TPWRS.2013.2288701[8] A. Papavasiliou and S. S. Oren, “Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network,” Oper. Res., vol. 61, no. 3, pp. 578–592, 2013.[9] F. Bouffard and F. D. Galiana, “Stochastic security for operations planning with significant wind power generation," IEEE Trans. Power Syst.,vol. 23, no. 2, pp. 306–316, May 2008. http://doi.org/10.1109/TPWRS.2008.919318[10] J. M. Morales, A. J. Conejo, and J. “Perez-Ruiz, Economic valuation of reserves in power systems with high penetration of wind power,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 900–910, May 2009. http://doi.org/ 10.1109/TPWRS.2009.2016598[11] A. Dalabeeh, A. Almofleh, A. Alzyoud, H. Ayman, “Economical and reliable expansion alternative of composite power system under restructuring,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6, pp. 4790-4799, Dec. 2018.[12] T. Diep-Thanh, Q. Nguyen-Phung, H. Nguyen-Duc, “Stochastic control for optimal power flow in islanded microgrid,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 2, pp. 1045-1057, Apr. 2019.[13] S. Kim, S. Reddy, “Optimal power flow based oncgestion management using enhanced genetic algorithms,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 2, pp. 875-883, Apr. 2019.[14] R. Moreno, J. Obando, G. Gonzalez, “An integrated OPF dispatching model with wind power and demand response for day-ahead markets,” International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 4, pp. 2794-2802, Aug. 2019.[15] E. Ela, B. Kirby, E. Lannoye, M. R. Milligan, D. Flynn, B. Zavadil, and M. O’ Malley, “Evolution of Operating Reserve Determination in Wind Power Integration Studies,” in Proceedings of the IEEE Power and Energy Society General Meeting, Minneapolis, MN, USA, July 25-29, 2010. http://doi.org/ 10.1109/PES.2010.5589272[16] A. N. Afandi, A. P. Wibawa, S. Padmantara, G. Fujita, W. Triyana, Y. Sulistyorini, H. Miyauchi, N. Tutkun, M. EL-Shimy Mahmoud, X. Z. Gao, “Designed Operating Approach of Economic Dispatch for Java Bali Power Grid Areas Considered Wind Energy and Pollutant Emission Optimized Using Thunderstorm Algorithm Based on Forward Cloud Charge Mechanism,” International Review of Electrical Engineering (IREE), vol. 13 n. 1, February 2018, pp. 59-68. http://doi.org/10.15866/iree.v13i1.14687[17] S. Surrender, “Optimal reactive power sceduling using cuckoo search algorithm,” International Journal of Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2349-2356, Oct. 2017.[18] D. Ganger, J. Zhang, and V. Vittal, “Statistical characterization of wind power ramps via extreme value analysis,” IEEE Trans. Power Syst., vol. 29, no. 6, pp. 3118–3119, Nov. 2014. http://doi.org/ 10.1109/TPWRS.2014.2315491[19] Y. Wan, “Analysis of wind power ramping behavior in ERCOT,” Nat. Renewable Energy Lab., Golden, CO, USA, Tech. Rep. TP-5500-49218, Mar. 2011.[20] Zhao, Jie, et al. “Quantifying risk of wind power ramps in ERCOT,” IEEE Transactions on Power Systems 32.6 (2017): 4970-4971. http://doi.org/10.1109/TPWRS.2017.2678761[21] J. Pickands, III, “Statistical inference using extreme order statistics,” Ann. Statist., vol. 3, pp. 119-131, 1975.[22] R. Zárate-Miñano, F. Milano and A. J. Conejo, “An OPF Methodology to Ensure Small-Signal Stability,” IEEE Trans. Power System, vol. 26, no. 3, Aug. 2011. http://doi.org/ 10.1109/TPWRS.2010.2076838[23] T. Dai, W. Qiao and L. Qu, “Real-time Optimal Participation of Wind Power in an Electricity Market,” in IEEE Innovative Smart Grid Technologies Conf., Tianjin, China, 2012.[24] S. Jang, H. Jung, J. Park, and S. King, “A new network partition method using the sensitive of marginal cost under network congestion,” IEEE Power Engineering Society Summer Meeting, 2001. http://doi.org/10.1109/PESS.2001.970326[25] The GUROBI Manual. Accessed on May 5, 2017. [Online]. Available: https://www.gurobi.com/documentation/7.5/refman/index.html.[26] Matpower Optimal Scheduling Tool (MOST) package. Accessed on Apr. 3, 2017. [Online]. Available: http://www.pserc.cornell.edu/ matpower/manual.pdf[27] Black, M., Strbac, G. Value of bulk energy storage for managing wind power fluctuations, IEEE Trans. Energy Convers., 2007, 22, (1), pp. 197–205. http://doi.org/10.1109/TEC.2006.889619GeneralPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://dspace7-uao.metacatalogo.com/bitstreams/a4b94140-ed35-4188-a606-777edac6c678/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINAL00387_Quantification of operating reserves with high penetration of wind power considering extreme values.pdf00387_Quantification of operating reserves with high penetration of wind power considering extreme values.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf604535https://dspace7-uao.metacatalogo.com/bitstreams/ed8bf237-4c8a-4d03-82ba-f9e25e331d9a/downloada0ecf7e3565d66d5b69c8a602d687158MD53TEXT00387_Quantification of operating reserves with high penetration of wind power considering extreme values.pdf.txt00387_Quantification of operating reserves with high penetration of wind power considering extreme values.pdf.txtExtracted texttext/plain25902https://dspace7-uao.metacatalogo.com/bitstreams/e5fadb79-d444-4f41-b9b7-59acee150277/download7d6c9cf5bb5a8548b0dd279979c3b6d3MD54THUMBNAIL00387_Quantification of operating reserves with high penetration of wind power considering extreme values.pdf.jpg00387_Quantification of operating reserves with high penetration of wind power considering extreme values.pdf.jpgGenerated Thumbnailimage/jpeg14924https://dspace7-uao.metacatalogo.com/bitstreams/565a49c0-a16a-435c-9b23-dfcf4bb2b368/downloadc4a2162cb63fc2443122c9381d6ee302MD5510614/13249oai:dspace7-uao.metacatalogo.com:10614/132492024-01-19 16:59:03.576https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - International Journal of Electrical and Computer Engineering (IJECE), 2020open.accesshttps://dspace7-uao.metacatalogo.comRepositorio UAOrepositorio@uao.edu.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