Derechos de autor 2021 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Metodología computacionalmente eficiente para resolver el despacho económico multiperiodo estocástico con restricciones de seguridad
Corresponding Author(s) : Jesús María López Lezama
Investigación e Innovación en Ingenierías,
Vol. 9 Núm. 2 (2021): Julio - Diciembre
Resumen
Objetivo: Validar una metodología computacionalmente eficiente para resolver el problema de despacho económico multiperiodo estocástico con restricciones de seguridad. Metodología: Se utilizan factores lineales de sensibilidad para calcular flujos de carga de forma rápida y precisa. También se usa un método iterativo, que identifica y agrega como cortes de usuario, las restricciones de seguridad activas. Estas restricciones establecen la región factible de un modelo embebido dentro de un algoritmo Progressive Hedging, el cual descompone el problema principal en un conjunto de sub-problemas computacionalmente más tratables, al relajar las restricciones de acoplamiento entre escenarios. Resultados: Los resultados numéricos sobre el sistema IEEE RTS96, muestran que la estrategia propuesta entrega soluciones de alta calidad en bajos tiempos de cálculo. Conclusiones: La metodología propuesta permite solucionar el despacho económico multiperiodo estocástico seguro hasta 50 veces más rápido cuando se compara con su formulación extensiva.
Palabras clave
Descargar cita
Endnote/Zotero/Mendeley (RIS)BibTeX
- D. A. Tejada-Arango, P. Sánchez-Martın, y A. Ramos, “Security Constrained Unit Commitment Using Line Outage Distribution Factors”, IEEE Transactions on Power Systems, vol. 33, núm. 1, pp. 329–337, ene. 2018. DOI: https://doi.org/10.1109/TPWRS.2017.2686701.
- R. Lu, T. Ding, B. Qin, J. Ma, X. Fang, y Z. Dong, “Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy”, IEEE Transactions on Sustainable Energy, vol. 11, núm. 3, pp. 1140–1151, jul. 2020. DOI: https://doi.org/10.1109/TSTE.2019.2918269.
- H. Wu, X. Guan, Q. Zhai, y H. Ye, “A Systematic Method for Constructing Feasible Solution to SCUC Problem With Analytical Feasibility Conditions”, IEEE Transactions on Power Systems, vol. 27, núm. 1, pp. 526–534, feb. 2012. DOI: https://doi.org/10.1109/TPWRS.2011.2165087.
- A. J. Ardakani y F. Bouffard, “Identification of umbrella constraints in DC-based security-constrained optimal power flow”, en 2014 IEEE PES General Meeting textbar Conference & Exposition, jul. 2014, pp. 1–6.
- Allen J. Wood, Bruce F. Wollenberg, y Gerald B. Sheble, Power Generation, Operation, and Control., 3rd ed. Wiley, 2013.
- C. C. Marín-Cano, J. E. Sierra-Aguilar, J. M. López-Lezama, Á. Jaramillo-Duque, y W. M. Villa-Acevedo, “Implementation of User Cuts and Linear Sensitivity Factors to Improve the Computational Performance of the Security-Constrained Unit Commitment Problem”, Energies, vol. 12, núm. 7, p. 1399, ene. 2019. DOI: https://doi.org/10.3390/en12071399
- Á. S. Xavier, F. Qiu, F. Wang, y P. R. Thimmapuram, “Transmission Constraint Filtering in Large-Scale Security-Constrained Unit Commitment”, IEEE Transactions on Power Systems, vol. 34, núm. 3, pp. 2457–2460, may 2019. DOI: https://doi.org/10.1109/TPWRS.2019.2892620
- T. G. Hlalele, R. M. Naidoo, J. Zhang, y R. C. Bansal, “Dynamic Economic Dispatch With Maximal Renewable Penetration Under Renewable Obligation”, IEEE Access, vol. 8, pp. 38794–38808, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2975674.
- P. Shinde, M. R. Hesamzadeh, P. Date, y D. W. Bunn, “Optimal Dispatch in a Balancing Market with Intermittent Renewable Generation”, IEEE Transactions on Power Systems, pp. 1–1, 2020. DOI: https://doi.org/10.1109/TPWRS.2020.3014515.
- L. Wu, M. Shahidehpour, y Z. Li, “Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC”, IEEE Transactions on Power Systems, vol. 27, núm. 2, pp. 913–921, may 2012. DOI: https://doi.org/10.1109/TPWRS.2011.2164947
- O. Alizadeh-Mousavi y M. Nick, “Stochastic Security Constrained Unit Commitment with variable-speed pumped-storage Hydropower Plants”, en 2016 Power Systems Computation Conference (PSCC), jun. 2016, pp. 1–7.
- H. Park, Y. G. Jin, y J.-K. Park, “Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating”, International Journal of Electrical Power & Energy Systems, vol. 102, pp. 211–222, nov. 2018. DOI: https://doi.org/10.1016/j.ijepes.2018.04.026
- C. Wang y Y. Fu, “Fully Parallel Stochastic Security-Constrained Unit Commitment”, IEEE Transactions on Power Systems, vol. 31, núm. 5, pp. 3561–3571, sep. 2016. DOI: https://doi.org/10.1109/TPWRS.2015.2494590
- N. Yang, D. Ye, Z. Zhou, Y. Huang, y B. Dong, “Research on Solving Method of Security Constrained Unit Commitment Based on Improved Stochastic Constrained Ordinal Optimization”, en 2018 3rd International Conference on Intelligent Green Building and Smart Grid (IGBSG), abr. 2018, pp. 1–4.
- H. Pandžić, Ting Qiu, y D. S. Kirschen, “Comparison of state-of-the-art transmission constrained unit commitment formulations”, en 2013 IEEE Power & Energy Society General Meeting, jul. 2013, pp. 1–5.
- H. Pandzic, Y. Dvorkin, T. Qiu, Y. Wang, y D. Kirschen, Unit Commitment under Uncertainty - GAMS Models. University of Washington.
- S. Ryan, R. J.-B. Wets, D. Woodruff, C. Silva-Monroy, y J.-P. Watson, Toward scalable, parallel progressive hedging for stochastic unit commitment, en Proceedings of the 2013 IEEE Power Energy Society General
- Meeting, Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5.
- R. T. Rockafellar y R. J.-B. Wets, “Scenarios and Policy Aggregation in Optimization Under Uncertainty”, Math. Oper. Res., vol. 16, núm. 1, pp. 119–147, feb. 1991. DOI: https://doi.org/10.1287/moor.16.1.119.
- D. Gade, G. Hackebeil, S. M. Ryan, J.-P. Watson, R. J.-B. Wets, y D. L. Woodruff, “Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs”, Mathematical Programming, vol. 157, núm. 1, pp. 47–67, may 2016. DOI: https://doi.org/10.1007/s10107-016-1000-z
- R. E. C. Gonçalves, E. C. Finardi, y E. L. da Silva, “Applying different decomposition schemes using the progressive hedging algorithm to the operation planning problem of a hydrothermal system”, Electric Power Systems Research, vol. 83, núm. 1, pp. 19–27, feb. 2012. DOI: https://doi.org/10.1016/j.epsr.2011.09.006
- WashU__. University of Washington, 2018.
- C. Ordoudis, P. Pinson, M. Zugno, y J. M. Morales, “Stochastic unit commitment via Progressive Hedging; extensive analysis of solution methods”, en 2015 IEEE Eindhoven PowerTech, jun. 2015, pp. 1–6.
- J.-P. Watson y D. L. Woodruff, “Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems”, Computational Management Science, vol. 8, núm. 4, pp. 355–370, nov. 2011. DOI: https://doi.org/10.1007/s10287-010-0125-4
- Li Chao, Zhang Muhong, y Hedman Kory W., “N-1 Reliable Unit Commitment via Progressive Hedging”, Journal of Energy Engineering, vol. 141, núm. 1, p. B4014004, mar. 2015. DOI: https://doi.org/10.1061/(ASCE)EY.1943-7897.0000187
Referencias
D. A. Tejada-Arango, P. Sánchez-Martın, y A. Ramos, “Security Constrained Unit Commitment Using Line Outage Distribution Factors”, IEEE Transactions on Power Systems, vol. 33, núm. 1, pp. 329–337, ene. 2018. DOI: https://doi.org/10.1109/TPWRS.2017.2686701.
R. Lu, T. Ding, B. Qin, J. Ma, X. Fang, y Z. Dong, “Multi-Stage Stochastic Programming to Joint Economic Dispatch for Energy and Reserve With Uncertain Renewable Energy”, IEEE Transactions on Sustainable Energy, vol. 11, núm. 3, pp. 1140–1151, jul. 2020. DOI: https://doi.org/10.1109/TSTE.2019.2918269.
H. Wu, X. Guan, Q. Zhai, y H. Ye, “A Systematic Method for Constructing Feasible Solution to SCUC Problem With Analytical Feasibility Conditions”, IEEE Transactions on Power Systems, vol. 27, núm. 1, pp. 526–534, feb. 2012. DOI: https://doi.org/10.1109/TPWRS.2011.2165087.
A. J. Ardakani y F. Bouffard, “Identification of umbrella constraints in DC-based security-constrained optimal power flow”, en 2014 IEEE PES General Meeting textbar Conference & Exposition, jul. 2014, pp. 1–6.
Allen J. Wood, Bruce F. Wollenberg, y Gerald B. Sheble, Power Generation, Operation, and Control., 3rd ed. Wiley, 2013.
C. C. Marín-Cano, J. E. Sierra-Aguilar, J. M. López-Lezama, Á. Jaramillo-Duque, y W. M. Villa-Acevedo, “Implementation of User Cuts and Linear Sensitivity Factors to Improve the Computational Performance of the Security-Constrained Unit Commitment Problem”, Energies, vol. 12, núm. 7, p. 1399, ene. 2019. DOI: https://doi.org/10.3390/en12071399
Á. S. Xavier, F. Qiu, F. Wang, y P. R. Thimmapuram, “Transmission Constraint Filtering in Large-Scale Security-Constrained Unit Commitment”, IEEE Transactions on Power Systems, vol. 34, núm. 3, pp. 2457–2460, may 2019. DOI: https://doi.org/10.1109/TPWRS.2019.2892620
T. G. Hlalele, R. M. Naidoo, J. Zhang, y R. C. Bansal, “Dynamic Economic Dispatch With Maximal Renewable Penetration Under Renewable Obligation”, IEEE Access, vol. 8, pp. 38794–38808, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2975674.
P. Shinde, M. R. Hesamzadeh, P. Date, y D. W. Bunn, “Optimal Dispatch in a Balancing Market with Intermittent Renewable Generation”, IEEE Transactions on Power Systems, pp. 1–1, 2020. DOI: https://doi.org/10.1109/TPWRS.2020.3014515.
L. Wu, M. Shahidehpour, y Z. Li, “Comparison of Scenario-Based and Interval Optimization Approaches to Stochastic SCUC”, IEEE Transactions on Power Systems, vol. 27, núm. 2, pp. 913–921, may 2012. DOI: https://doi.org/10.1109/TPWRS.2011.2164947
O. Alizadeh-Mousavi y M. Nick, “Stochastic Security Constrained Unit Commitment with variable-speed pumped-storage Hydropower Plants”, en 2016 Power Systems Computation Conference (PSCC), jun. 2016, pp. 1–7.
H. Park, Y. G. Jin, y J.-K. Park, “Stochastic security-constrained unit commitment with wind power generation based on dynamic line rating”, International Journal of Electrical Power & Energy Systems, vol. 102, pp. 211–222, nov. 2018. DOI: https://doi.org/10.1016/j.ijepes.2018.04.026
C. Wang y Y. Fu, “Fully Parallel Stochastic Security-Constrained Unit Commitment”, IEEE Transactions on Power Systems, vol. 31, núm. 5, pp. 3561–3571, sep. 2016. DOI: https://doi.org/10.1109/TPWRS.2015.2494590
N. Yang, D. Ye, Z. Zhou, Y. Huang, y B. Dong, “Research on Solving Method of Security Constrained Unit Commitment Based on Improved Stochastic Constrained Ordinal Optimization”, en 2018 3rd International Conference on Intelligent Green Building and Smart Grid (IGBSG), abr. 2018, pp. 1–4.
H. Pandžić, Ting Qiu, y D. S. Kirschen, “Comparison of state-of-the-art transmission constrained unit commitment formulations”, en 2013 IEEE Power & Energy Society General Meeting, jul. 2013, pp. 1–5.
H. Pandzic, Y. Dvorkin, T. Qiu, Y. Wang, y D. Kirschen, Unit Commitment under Uncertainty - GAMS Models. University of Washington.
S. Ryan, R. J.-B. Wets, D. Woodruff, C. Silva-Monroy, y J.-P. Watson, Toward scalable, parallel progressive hedging for stochastic unit commitment, en Proceedings of the 2013 IEEE Power Energy Society General
Meeting, Vancouver, BC, Canada, 21–25 July 2013; pp. 1–5.
R. T. Rockafellar y R. J.-B. Wets, “Scenarios and Policy Aggregation in Optimization Under Uncertainty”, Math. Oper. Res., vol. 16, núm. 1, pp. 119–147, feb. 1991. DOI: https://doi.org/10.1287/moor.16.1.119.
D. Gade, G. Hackebeil, S. M. Ryan, J.-P. Watson, R. J.-B. Wets, y D. L. Woodruff, “Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs”, Mathematical Programming, vol. 157, núm. 1, pp. 47–67, may 2016. DOI: https://doi.org/10.1007/s10107-016-1000-z
R. E. C. Gonçalves, E. C. Finardi, y E. L. da Silva, “Applying different decomposition schemes using the progressive hedging algorithm to the operation planning problem of a hydrothermal system”, Electric Power Systems Research, vol. 83, núm. 1, pp. 19–27, feb. 2012. DOI: https://doi.org/10.1016/j.epsr.2011.09.006
WashU__. University of Washington, 2018.
C. Ordoudis, P. Pinson, M. Zugno, y J. M. Morales, “Stochastic unit commitment via Progressive Hedging; extensive analysis of solution methods”, en 2015 IEEE Eindhoven PowerTech, jun. 2015, pp. 1–6.
J.-P. Watson y D. L. Woodruff, “Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems”, Computational Management Science, vol. 8, núm. 4, pp. 355–370, nov. 2011. DOI: https://doi.org/10.1007/s10287-010-0125-4
Li Chao, Zhang Muhong, y Hedman Kory W., “N-1 Reliable Unit Commitment via Progressive Hedging”, Journal of Energy Engineering, vol. 141, núm. 1, p. B4014004, mar. 2015. DOI: https://doi.org/10.1061/(ASCE)EY.1943-7897.0000187