Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power
The variability and uncertainty of renewable resources impose new challenges in the operational planning related to the unit commitment of generation units. The development of day‐ahead multi‐period optimal power flow, under integration of wind power, requires modelling of multiple scenarios in orde...
- Autores:
-
Zuluaga, Jorge
Murillo Sánchez, Carlos E.
Moreno Chuquen, Ricardo
Chamorro, Harold R.
Sood, Vijay K.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/14748
- Acceso en línea:
- https://hdl.handle.net/10614/14748
https://red.uao.edu.co/
- Palabra clave:
- Energía eólica
Day‐ahead dispatch
Hydro power
Progressive hedging
Wind power
- Rights
- openAccess
- License
- Derechos reservados - Institution of Engineering and Technology (IET), 2022
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Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| title |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| spellingShingle |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power Energía eólica Day‐ahead dispatch Hydro power Progressive hedging Wind power |
| title_short |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| title_full |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| title_fullStr |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| title_full_unstemmed |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| title_sort |
Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power |
| dc.creator.fl_str_mv |
Zuluaga, Jorge Murillo Sánchez, Carlos E. Moreno Chuquen, Ricardo Chamorro, Harold R. Sood, Vijay K. |
| dc.contributor.author.none.fl_str_mv |
Zuluaga, Jorge Murillo Sánchez, Carlos E. Moreno Chuquen, Ricardo Chamorro, Harold R. Sood, Vijay K. |
| dc.subject.armarc.spa.fl_str_mv |
Energía eólica |
| topic |
Energía eólica Day‐ahead dispatch Hydro power Progressive hedging Wind power |
| dc.subject.proposal.eng.fl_str_mv |
Day‐ahead dispatch Hydro power Progressive hedging Wind power |
| description |
The variability and uncertainty of renewable resources impose new challenges in the operational planning related to the unit commitment of generation units. The development of day‐ahead multi‐period optimal power flow, under integration of wind power, requires modelling of multiple scenarios in order to ensure an optimal power flow minimising the generation cost. A progressive hedging approach has been proposed and developed to solve efficiently the unit commitment problem as a two‐stage stochastic programming problem to update each stage in parallel. The performance of progressive hedging is compared with a standard mixed‐integer linear programming problem. The results indicate that the computation time is 50 times faster than standard mixed‐integer linear program ming. The test case system is based on a reduced version of the interconnected Colombian system. The comparative results indicate an important reduction in computational time |
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2022 |
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2022 |
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2023-05-16T19:50:24Z |
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2023-05-16T19:50:24Z |
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Universidad Autónoma de Occidente |
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Repositorio Educativo Digital UAO |
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Zuluaga, J., Murillo Sánchez, C.E., Moreno Chuquen, R., Chamorro, H.R., Sood, V.K. (2022). Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power. IET Renewable Power Generation, 5(2), 1-9. https://hdl.handle.net/10614/14748 |
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IET Energy Systems Integration |
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Meraz, P., et al.: Renewable Energy and Economic Dispatch Integration Within the Honduras Electricity Market, pp. 1–34 (2021). [Online]. http://link.springer.com/chapter/10.1007/978-981-33-6753-1 Wood, A.J., Wollenberg, B.F., Sheblé, G.B.: Power Generation, Opera tion, and Control, 3rd ed. John Wiley & Sons Ltd. (2014) Padhy, N.P.: Unit commitment—a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004). https://doi.org/10.1109/tpwrs. 2003.821611 Papavasiliou, A.: Coupling renewable energy supply with deferrable de mand. ProQuest Dissertations and Theses 3499039, 100 (20 Murillo‐Sánchez, C.E., et al.: Secure planning and operations of systems with stochastic sources, energy storage, and active demand. IEEE Trans. Smart Grid 4(4), 2220–2229 (2013). https://doi.org/10.1109/tsg.2013. 2281001 Lowery, C., O'Malley, M.: Reserves in stochastic unit commitment: an Irish system case study. IEEE Trans. Sustain. Energy 6(3), 1029–1038 (2015). https://doi.org/10.1109/tste.2014.2364520 Condren, J., Gedra, T.W.: Expected‐security‐cost optimal power flow with small‐signal stability constraints. IEEE Trans. Power Syst. 21(4), 1736–1743 (2006). https://doi.org/10.1109/tpwrs.2006.882453 Condren, J., Gedra, T.W., Damrongkulkamjorn, P.: Optimal power flow with expected security costs. IEEE Trans. Power Syst. 21(2), 541–547 (2006). https://doi.org/10.1109/tpwrs.2006.873114 Morales, J.M., et al.: Pricing electricity in pools with wind producers. IEEE Trans. Power Syst. 27(3), 1366–1376 (2012). https://doi.org/10. 1109/tpwrs.2011.2182622 Alqunun, K., et al.: Stochastic unit commitment problem, incorporating wind power and an energy storage system. Sustainability 12(23), 10100 (2020). https://doi.org/10.3390/su122310100 Zhao, N., You, F.: Unit commitment under uncertainty using data‐driven optimization with clustering techniques. Chem. Eng. Trans. 88, 1195–1200 (2021) Gollmer, R., et al.: Unit commitment in power generation – a basic model and some extensions. Ann. Oper. Res. 96(1/4), 167–189 (2000). https://doi.org/10.1023/a:1018947401538 Rockafellar, R., Wets, R.J.‐B.: Scenarios and policy aggregation in opti mization under uncertainty. Math. Oper. Res. 16(1), 119–147 (1991). https://doi.org/10.1287/moor.16.1.119 Haugen, K.K., Løkketangen, A., Woodruff, D.L.: Progressive hedging as a meta‐heuristic applied to stochastic lot‐sizing. Eur. J. Oper. Res. 132(1), 116–122 (2001). https://doi.org/10.1016/s0377-2217(00)00116-8 Mulvey, J.M., Vladimirou, H.: Applying the progressive hedging algo rithm to stochastic generalized networks. Oper. Res. 31(1), 399–424 (1991). https://doi.org/10.1007/bf02204860 Zimmerman, R.D., Murillo‐Sánchez, C.E.: Matpower (version 7.1). (2020)[Online]. https://matpower.org |
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Derechos reservados - Institution of Engineering and Technology (IET), 2022 |
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Zuluaga, Jorged840305c35d9f214ee9e1c37f126923aMurillo Sánchez, Carlos E.9b723855016901df080d51dd343607d0Moreno Chuquen, Ricardo1ba55dc18211144016950d3899f50db9Chamorro, Harold R.4b08e14f56fb217874cde66f1d371352Sood, Vijay K.be7ffe4ade6c8866a466709543e7b5262023-05-16T19:50:24Z2023-05-16T19:50:24Z2022https://hdl.handle.net/10614/1474825168401Universidad Autónoma de OccidenteRepositorio Educativo Digital UAOhttps://red.uao.edu.co/The variability and uncertainty of renewable resources impose new challenges in the operational planning related to the unit commitment of generation units. The development of day‐ahead multi‐period optimal power flow, under integration of wind power, requires modelling of multiple scenarios in order to ensure an optimal power flow minimising the generation cost. A progressive hedging approach has been proposed and developed to solve efficiently the unit commitment problem as a two‐stage stochastic programming problem to update each stage in parallel. The performance of progressive hedging is compared with a standard mixed‐integer linear programming problem. The results indicate that the computation time is 50 times faster than standard mixed‐integer linear program ming. The test case system is based on a reduced version of the interconnected Colombian system. The comparative results indicate an important reduction in computational time 9 páginasapplication/pdfengInstitution of Engineering and Technology (IET)Tianjin UniversityDerechos reservados - Institution of Engineering and Technology (IET), 2022https://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_abf2Day-ahead unit commitment for hydro-thermal coordination with high participation of wind powerArtículo 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 eólicaDay‐ahead dispatchHydro powerProgressive hedgingWind power9215Zuluaga, J., Murillo Sánchez, C.E., Moreno Chuquen, R., Chamorro, H.R., Sood, V.K. (2022). Day-ahead unit commitment for hydro-thermal coordination with high participation of wind power. IET Renewable Power Generation, 5(2), 1-9. https://hdl.handle.net/10614/14748IET Energy Systems IntegrationMeraz, P., et al.: Renewable Energy and Economic Dispatch Integration Within the Honduras Electricity Market, pp. 1–34 (2021). [Online]. http://link.springer.com/chapter/10.1007/978-981-33-6753-1Wood, A.J., Wollenberg, B.F., Sheblé, G.B.: Power Generation, Opera tion, and Control, 3rd ed. John Wiley & Sons Ltd. (2014)Padhy, N.P.: Unit commitment—a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004). https://doi.org/10.1109/tpwrs. 2003.821611Papavasiliou, A.: Coupling renewable energy supply with deferrable de mand. ProQuest Dissertations and Theses 3499039, 100 (20Murillo‐Sánchez, C.E., et al.: Secure planning and operations of systems with stochastic sources, energy storage, and active demand. IEEE Trans. Smart Grid 4(4), 2220–2229 (2013). https://doi.org/10.1109/tsg.2013. 2281001Lowery, C., O'Malley, M.: Reserves in stochastic unit commitment: an Irish system case study. IEEE Trans. Sustain. Energy 6(3), 1029–1038 (2015). https://doi.org/10.1109/tste.2014.2364520Condren, J., Gedra, T.W.: Expected‐security‐cost optimal power flow with small‐signal stability constraints. IEEE Trans. Power Syst. 21(4), 1736–1743 (2006). https://doi.org/10.1109/tpwrs.2006.882453Condren, J., Gedra, T.W., Damrongkulkamjorn, P.: Optimal power flow with expected security costs. IEEE Trans. Power Syst. 21(2), 541–547 (2006). https://doi.org/10.1109/tpwrs.2006.873114Morales, J.M., et al.: Pricing electricity in pools with wind producers. IEEE Trans. Power Syst. 27(3), 1366–1376 (2012). https://doi.org/10. 1109/tpwrs.2011.2182622Alqunun, K., et al.: Stochastic unit commitment problem, incorporating wind power and an energy storage system. Sustainability 12(23), 10100 (2020). https://doi.org/10.3390/su122310100Zhao, N., You, F.: Unit commitment under uncertainty using data‐driven optimization with clustering techniques. Chem. Eng. Trans. 88, 1195–1200 (2021)Gollmer, R., et al.: Unit commitment in power generation – a basic model and some extensions. Ann. Oper. Res. 96(1/4), 167–189 (2000). https://doi.org/10.1023/a:1018947401538Rockafellar, R., Wets, R.J.‐B.: Scenarios and policy aggregation in opti mization under uncertainty. Math. Oper. Res. 16(1), 119–147 (1991). https://doi.org/10.1287/moor.16.1.119Haugen, K.K., Løkketangen, A., Woodruff, D.L.: Progressive hedging as a meta‐heuristic applied to stochastic lot‐sizing. Eur. J. Oper. Res. 132(1), 116–122 (2001). https://doi.org/10.1016/s0377-2217(00)00116-8Mulvey, J.M., Vladimirou, H.: Applying the progressive hedging algo rithm to stochastic generalized networks. Oper. Res. 31(1), 399–424 (1991). https://doi.org/10.1007/bf02204860Zimmerman, R.D., Murillo‐Sánchez, C.E.: Matpower (version 7.1). (2020)[Online]. https://matpower.orgComunidad universitaria en generalPublicationORIGINALDay_ahead_unit_commitment_for_hydro_thermal_coordination_with_high_participation_of_wind_power.pdfDay_ahead_unit_commitment_for_hydro_thermal_coordination_with_high_participation_of_wind_power.pdftexto completo del artículoapplication/pdf1125381https://red.uao.edu.co/bitstreams/b2b1cbf0-cc7e-471c-a06a-da2f5dcd8bd3/download51e0b09396e04712880581a1088dca97MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/b3b88920-b568-448e-bc52-ab220b883b23/download20b5ba22b1117f71589c7318baa2c560MD52TEXTDay_ahead_unit_commitment_for_hydro_thermal_coordination_with_high_participation_of_wind_power.pdf.txtDay_ahead_unit_commitment_for_hydro_thermal_coordination_with_high_participation_of_wind_power.pdf.txtExtracted texttext/plain42958https://red.uao.edu.co/bitstreams/dbfcf7c1-d6e2-4e07-8626-ca96dc20e6fa/download40eaa909cab71c22a5a92ec8a322b2a1MD53THUMBNAILDay_ahead_unit_commitment_for_hydro_thermal_coordination_with_high_participation_of_wind_power.pdf.jpgDay_ahead_unit_commitment_for_hydro_thermal_coordination_with_high_participation_of_wind_power.pdf.jpgGenerated Thumbnailimage/jpeg16716https://red.uao.edu.co/bitstreams/e009e7c0-97c9-4c13-8d8e-7c8372ce710e/download37faa29e6d40b759260a656ce8de24d0MD5410614/14748oai:red.uao.edu.co:10614/147482024-04-05 09:49:52.787https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - Institution of Engineering and Technology (IET), 2022open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |