An integrated OPF dispatching model with wind power and demand response for day-ahead markets
In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resourcesare dispatched together with conventional generation. The uncertainty and volatility associated torenewable resources represents a new paradigm to be faced for power system operation. M...
- Autores:
-
Moreno-Chuquen, Ricardo
González Palomino, Gabriel
Obando Ceron, Johan Samir
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/11522
- Palabra clave:
- Recursos energéticos renovables
Renewable energy sources
Demand response
Electricity markets
Monte-Carlo simulations
Optimal power flow (OPF)
Wind power
- Rights
- openAccess
- License
- Derechos Reservados - Universidad Autónoma de Occidente
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An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| title |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| spellingShingle |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets Recursos energéticos renovables Renewable energy sources Demand response Electricity markets Monte-Carlo simulations Optimal power flow (OPF) Wind power |
| title_short |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| title_full |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| title_fullStr |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| title_full_unstemmed |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| title_sort |
An integrated OPF dispatching model with wind power and demand response for day-ahead markets |
| dc.creator.fl_str_mv |
Moreno-Chuquen, Ricardo González Palomino, Gabriel Obando Ceron, Johan Samir |
| dc.contributor.author.none.fl_str_mv |
Moreno-Chuquen, Ricardo González Palomino, Gabriel Obando Ceron, Johan Samir |
| dc.subject.armarc.spa.fl_str_mv |
Recursos energéticos renovables |
| topic |
Recursos energéticos renovables Renewable energy sources Demand response Electricity markets Monte-Carlo simulations Optimal power flow (OPF) Wind power |
| dc.subject.armarc.eng.fl_str_mv |
Renewable energy sources |
| dc.subject.proposal.eng.fl_str_mv |
Demand response Electricity markets Monte-Carlo simulations Optimal power flow (OPF) Wind power |
| description |
In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resourcesare dispatched together with conventional generation. The uncertainty and volatility associated torenewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow tofind a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposedintegrated OPFmodel is tested on the standard IEEE 39-bus system |
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2019 |
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2019-11-18T20:19:34Z |
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2019-11-18T20:19:34Z |
| dc.date.issued.none.fl_str_mv |
2019 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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2088-8708 |
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http://hdl.handle.net/10614/11522 |
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http://doi.org/10.11591/ijece.v9i4.pp2794-2802 |
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2088-8708 |
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eng |
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2802 |
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4 |
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2794 |
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9 |
| dc.relation.cites.eng.fl_str_mv |
Moreno, R., Obando, J., & Gonzalez, G. (2019). An integrated OPF dispatching model with wind power and demand response for day-ahead markets. International Journal of Electrical & Computer Engineering . 9(4), 2794-2802. DOI: 10.11591/ijece.v9i4.pp2794-2802 |
| dc.relation.ispartofjournal.eng.fl_str_mv |
International Journal of Electrical and Computer Engineering (IJECE) |
| dc.relation.references.none.fl_str_mv |
[1] J. M. Morales, et al., “Short-term trading for a wind power producer,” IEEE Transactions on Power Systems, vol/issue: 25(1), pp. 554-564, 2010. [2] S. S. Sakthi, et al., “Wind Integrated Thermal Unit Commitment Solution using Grey Wolf Optimizer,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 7(5), pp. 2309-2320, 2017. [3] I. M. Wartana, et al., “Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 7(5), pp. 2329-2337, 2017. [4] C. L. Su and D. Kirschen, “Quantifying the effect of demand response on electricity markets,” IEEE Transactions on Power Systems, vol/issue: 24(3), pp. 1199-1207, 2009. [5] R. A. Jabr and B. C. Pal, “Intermittent wind generation in optimal power flow dispatching,” IET Gener. Transm. Distrib, vol/issue: 3(1), pp. 66-74, 2009. [6] H. Zhang and P. Li, “Probabilistic analysis for optimal power flow under uncertainty,” IET Gener. Transm. Distrib, vol/issue: 4(5), pp. 553-561, 2010. [7] R. Entriken, et al., “Stochastic optimal power flow in systems with wind power,” Proc. IEEE Power Energy Soc. Gen. Meeting, San Diego, CA, USA, pp. 1-5, 2011. [8] C. S. Saunders, “Point estimate method addressing correlated wind power for probabilistic optimal power flow,” IEEE Trans. Power Syst., vol/issue: 29(3), pp. 1045-1054, 2014. [9] A. Papavasiliou and S. S. Oren, “Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network,” Oper. Res., vol/issue: 61(3), pp. 578-592, 2013. [10] F. Bouffard and F. D. Galiana, “Stochastic security for operations planning with significant wind power generation,” IEEE Trans. Power Syst., vol/issue: 23(2), pp. 306-316, 2008. [11] J. M. Morales, et al., “Economic valuation of reserves in power systems with high penetration of wind power,” IEEE Trans. Power Syst., vol/issue: 24(2), pp. 900-910, 2009. [12] A. Papavasiliou, et al., “Reserve requirements for wind power integration: A scenario-based stochastic programming framework,” IEEE Trans. Power Syst., vol/issue: 26(4), pp. 2197-2206, 2011. [13] B. Banhtasit and C. S. Dechanupaprittha, “Optimal Generation Scheduling of Power System for Maximum Renewable Energy Harvesting and Power Losses Minimization,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 8(4), pp. 1954-1966, 2018. [14] S. Kim and S. R. Salkut, “Optimal power flow based congestion management using enhanced genetic algorithms,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 9(2), pp. 875-883, 2019. [15] M. Kefayati and R. Baldick, “Harnessing demand flexibility to match renewable production using localized policies,” Proc. 50th Annu. Allerton Conf. Commun. Control Comput. (Allerton), Monticello, IL, USA, pp. 1105-1109, 2012. [16] U.S. Energy Information Administration (EIA), “Estimated U.S. Residential Electricty Consumption by End-Use,” 2010. Available: http://www.eia.gov/tools/faqs/faq.cfm?id=96&t=3 [17] M. Arroyo and A. J. Conejo, “Multiperiod auction for a pool-based electricity market,” IEEE Trans. Power Syst., vol. 17, pp. 1225-1231, 2002. [18] J. Wang, et al., “Demand-side reserve offers in joint energy/reserve electricity markets,” IEEE Trans. Power Syst., vol. 18, pp. 1300-1306, 2003. [19] A. Borghetti, et al., “Auctions with explicit demand side bidding in competitive electricity markets,” The Next Generation of Electric Power Unit Commitment Models. Norwell, MA: Kluwer, pp. 53-74, 2001. [20] O. Ma, et al., “Demand Response for Ancillary Services,” IEEE Trans. Smart Grid, vol. 4, pp. 1988-1995, 2013. [21] U. Helman, et al., “Operational requirements and generation fleet capability at 20% RPS,” CAISO, 2010. Available: http://www.uwig.org/ [22] G. Lazaros, et al., “The role of aggregators in smart grid demand response markets,” IEEE Journal on Selected Areas in Communications, vol/issue: 31(7), pp. 1247-1257, 2013. [23] The GUROBI Manual, 2017. Available: https://www.gurobi.com/documentation/7.5/refman/index.html. [24] Matpower Optimal Scheduling Tool (MOST) package, 2017. Available: http://www.pserc.cornell.edu/ matpower/manual.pdf [25] R. Z. Miñano, et al., “An OPF Methodology to Ensure Small-Signal Stability,” IEEE Trans. Power System, vol/issue: 26(3), 2011. [26] T. Dai, et al., “Real-time Optimal Participation of Wind Power in an Electricity Market,” IEEE Innovative Smart Grid Technologies Conf., Tianjin, China, 2012. [27] S. Jang, et al., “A new network partition method using the sensitive of marginal cost under network congestion,” IEEE Power Engineering Society Summer Meeting, 2001 |
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Moreno-Chuquen, Ricardof36efacf1d947d7410ab7d332d414753González Palomino, Gabriel3d52c20631e564b1f043775aa5dbc9f3Obando Ceron, Johan Samirb8aaef71edc965de027f0670491e3948Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-11-18T20:19:34Z2019-11-18T20:19:34Z20192088-8708http://hdl.handle.net/10614/11522http://doi.org/10.11591/ijece.v9i4.pp2794-2802In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resourcesare dispatched together with conventional generation. The uncertainty and volatility associated torenewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow tofind a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposedintegrated OPFmodel is tested on the standard IEEE 39-bus systemapplication/pdf9 páginasengInstitute of Advanced Engineering and ScienceDerechos Reservados - Universidad Autónoma de Occidentehttps://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_abf2reponame:Repositorio Institucional UAOAn integrated OPF dispatching model with wind power and demand response for day-ahead marketsArtículo 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/ARTREFinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Recursos energéticos renovablesRenewable energy sourcesDemand responseElectricity marketsMonte-Carlo simulationsOptimal power flow (OPF)Wind power2802427949Moreno, R., Obando, J., & Gonzalez, G. (2019). An integrated OPF dispatching model with wind power and demand response for day-ahead markets. International Journal of Electrical & Computer Engineering . 9(4), 2794-2802. DOI: 10.11591/ijece.v9i4.pp2794-2802International Journal of Electrical and Computer Engineering (IJECE)[1] J. M. Morales, et al., “Short-term trading for a wind power producer,” IEEE Transactions on Power Systems, vol/issue: 25(1), pp. 554-564, 2010.[2] S. S. Sakthi, et al., “Wind Integrated Thermal Unit Commitment Solution using Grey Wolf Optimizer,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 7(5), pp. 2309-2320, 2017.[3] I. M. Wartana, et al., “Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 7(5), pp. 2329-2337, 2017.[4] C. L. Su and D. Kirschen, “Quantifying the effect of demand response on electricity markets,” IEEE Transactions on Power Systems, vol/issue: 24(3), pp. 1199-1207, 2009.[5] R. A. Jabr and B. C. Pal, “Intermittent wind generation in optimal power flow dispatching,” IET Gener. Transm. Distrib, vol/issue: 3(1), pp. 66-74, 2009.[6] H. Zhang and P. Li, “Probabilistic analysis for optimal power flow under uncertainty,” IET Gener. Transm. Distrib, vol/issue: 4(5), pp. 553-561, 2010.[7] R. Entriken, et al., “Stochastic optimal power flow in systems with wind power,” Proc. IEEE Power Energy Soc. Gen. Meeting, San Diego, CA, USA, pp. 1-5, 2011.[8] C. S. Saunders, “Point estimate method addressing correlated wind power for probabilistic optimal power flow,” IEEE Trans. Power Syst., vol/issue: 29(3), pp. 1045-1054, 2014.[9] A. Papavasiliou and S. S. Oren, “Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network,” Oper. Res., vol/issue: 61(3), pp. 578-592, 2013.[10] F. Bouffard and F. D. Galiana, “Stochastic security for operations planning with significant wind power generation,” IEEE Trans. Power Syst., vol/issue: 23(2), pp. 306-316, 2008.[11] J. M. Morales, et al., “Economic valuation of reserves in power systems with high penetration of wind power,” IEEE Trans. Power Syst., vol/issue: 24(2), pp. 900-910, 2009.[12] A. Papavasiliou, et al., “Reserve requirements for wind power integration: A scenario-based stochastic programming framework,” IEEE Trans. Power Syst., vol/issue: 26(4), pp. 2197-2206, 2011.[13] B. Banhtasit and C. S. Dechanupaprittha, “Optimal Generation Scheduling of Power System for Maximum Renewable Energy Harvesting and Power Losses Minimization,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 8(4), pp. 1954-1966, 2018.[14] S. Kim and S. R. Salkut, “Optimal power flow based congestion management using enhanced genetic algorithms,” International Journal of Electrical and Computer Engineering (IJECE), vol/issue: 9(2), pp. 875-883, 2019.[15] M. Kefayati and R. Baldick, “Harnessing demand flexibility to match renewable production using localized policies,” Proc. 50th Annu. Allerton Conf. Commun. Control Comput. (Allerton), Monticello, IL, USA, pp. 1105-1109, 2012.[16] U.S. Energy Information Administration (EIA), “Estimated U.S. Residential Electricty Consumption by End-Use,” 2010. Available: http://www.eia.gov/tools/faqs/faq.cfm?id=96&t=3[17] M. Arroyo and A. J. Conejo, “Multiperiod auction for a pool-based electricity market,” IEEE Trans. Power Syst., vol. 17, pp. 1225-1231, 2002.[18] J. Wang, et al., “Demand-side reserve offers in joint energy/reserve electricity markets,” IEEE Trans. Power Syst., vol. 18, pp. 1300-1306, 2003.[19] A. Borghetti, et al., “Auctions with explicit demand side bidding in competitive electricity markets,” The Next Generation of Electric Power Unit Commitment Models. Norwell, MA: Kluwer, pp. 53-74, 2001.[20] O. Ma, et al., “Demand Response for Ancillary Services,” IEEE Trans. Smart Grid, vol. 4, pp. 1988-1995, 2013.[21] U. Helman, et al., “Operational requirements and generation fleet capability at 20% RPS,” CAISO, 2010. Available: http://www.uwig.org/[22] G. Lazaros, et al., “The role of aggregators in smart grid demand response markets,” IEEE Journal on Selected Areas in Communications, vol/issue: 31(7), pp. 1247-1257, 2013.[23] The GUROBI Manual, 2017. Available: https://www.gurobi.com/documentation/7.5/refman/index.html.[24] Matpower Optimal Scheduling Tool (MOST) package, 2017. Available: http://www.pserc.cornell.edu/ matpower/manual.pdf[25] R. Z. Miñano, et al., “An OPF Methodology to Ensure Small-Signal Stability,” IEEE Trans. Power System, vol/issue: 26(3), 2011.[26] T. Dai, et al., “Real-time Optimal Participation of Wind Power in an Electricity Market,” IEEE Innovative Smart Grid Technologies Conf., Tianjin, China, 2012.[27] S. Jang, et al., “A new network partition method using the sensitive of marginal cost under network congestion,” IEEE Power Engineering Society Summer Meeting, 2001PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://dspace7-uao.metacatalogo.com/bitstreams/e3815192-1f30-41e3-b05a-22adee04bdc0/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://dspace7-uao.metacatalogo.com/bitstreams/f135e4a1-742e-4793-b19d-0904910ebff1/download20b5ba22b1117f71589c7318baa2c560MD53ORIGINALAn integrated OPF dispatching model wind power and demand response for day-ahead markets.pdfAn integrated OPF dispatching model wind power and demand response for day-ahead markets.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf661408https://dspace7-uao.metacatalogo.com/bitstreams/5aaac111-086b-44e4-8df3-91debbc46013/download5da73938d14118ecd9b148ab8b561346MD54TEXTAn integrated OPF dispatching model wind power and demand response for day-ahead markets.pdf.txtAn integrated OPF dispatching model wind power and demand response for day-ahead markets.pdf.txtExtracted texttext/plain23739https://dspace7-uao.metacatalogo.com/bitstreams/be436989-941b-45eb-8d3e-f32225539802/download15931888e9823f05eb1c34ebd10b461bMD55THUMBNAILAn integrated OPF dispatching model wind power and demand response for day-ahead markets.pdf.jpgAn integrated OPF dispatching model wind power and demand response for day-ahead markets.pdf.jpgGenerated Thumbnailimage/jpeg14265https://dspace7-uao.metacatalogo.com/bitstreams/122afa02-13ca-4a8d-b770-6a0617b3b864/download22a34646e96e68882b464f8cac575406MD5610614/11522oai:dspace7-uao.metacatalogo.com:10614/115222024-01-19 16:28:48.058https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos Reservados - Universidad Autónoma de Occidenteopen.accesshttps://dspace7-uao.metacatalogo.comRepositorio UAOrepositorio@uao.edu.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 |