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

dc.creatorObando Ceron, Johan Samir
dc.creatorGonzález Palomino, Gabriel
dc.creatorMoreno-Chuquen, Ricardo
dc.date.accessioned2021-09-23T16:02:27Z
dc.date.accessioned2022-09-22T18:43:46Z
dc.date.available2021-09-23T16:02:27Z
dc.date.available2022-09-22T18:43:46Z
dc.date.created2021-09-23T16:02:27Z
dc.date.issued2020-04
dc.identifier20888708
dc.identifierhttps://hdl.handle.net/10614/13249
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3457378
dc.description.abstractThe 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
dc.languageeng
dc.publisherInternational Journal of Electrical and Computer Engineering (IJECE)
dc.relationVolumen 10, número 2 (2020)
dc.relation1700
dc.relationNúmero 2
dc.relation1693
dc.relationVolumen 10
dc.relationObando, 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.relationInternational Journal of Electrical and Computer Engineering (IJECE)
dc.relation[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.
dc.relation[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
dc.relation[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.
dc.relation[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
dc.relation[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
dc.relation[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.
dc.relation[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
dc.relation[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.
dc.relation[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
dc.relation[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
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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.
dc.relation[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
dc.relation[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
dc.relation[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.
dc.relation[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
dc.relation[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.
dc.relation[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
dc.relation[21] J. Pickands, III, “Statistical inference using extreme order statistics,” Ann. Statist., vol. 3, pp. 119-131, 1975.
dc.relation[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
dc.relation[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.
dc.relation[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
dc.relation[25] The GUROBI Manual. Accessed on May 5, 2017. [Online]. Available: https://www.gurobi.com/documentation/7.5/refman/index.html.
dc.relation[26] Matpower Optimal Scheduling Tool (MOST) package. Accessed on Apr. 3, 2017. [Online]. Available: http://www.pserc.cornell.edu/ matpower/manual.pdf
dc.relation[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.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightsDerechos reservados - International Journal of Electrical and Computer Engineering (IJECE), 2020
dc.titleQuantification of operating reserves with high penetration of wind power considering extreme values
dc.typeArtículo de revista


Este ítem pertenece a la siguiente institución