Výsledky vyhledávání - multiobjective security-constrained optimal generation dispatch

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    Popis souboru: 9 páginas; application/pdf

    Relation: Volumen 15, número 6 (2020); 492; Número 6; 484; Volumen 15; Moreno Chuquen, R., Cantillo Luna, S. (2020). Assessment of a multiperiod optimal power flow for power system operation. International Review of Electrical Engineering. (Vol. 15 (6), pp. 484-492. DOI: https://doi.org/10.15866/iree.v15i6.18304; International Review of Electrical Engineering; [1] Al Hasibi, R., Hadi, S., Sarjiya, S., Multi-Objective Optimization of Integrated Power System Expansion Planning with Renewable Energy-Based Distributed Generation, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 19-31. doi: https://doi.org/10.15866/iree.v14i1.16082; [2] Sangwato, S., Oonsivilai, A., Optimal Power Flow with Interline Power Flow Controller Using Hybrid Genetic Algorithm, (2015) International Review of Electrical Engineering (IREE), 10 (6), pp. 727-733. doi: https://doi.org/10.15866/iree.v10i6.7568; [3] Lakdja, F., Abdeslam, D., Gherbi, F., Optimal Location of Thyristor-Controlled Series Compensator for Optimal Power Flows, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 465-472.; [4] R.A. Jabr. Adjustable robust OPF with renewable energy sources. IEEE Trans Power Syst, vol. 28 n. 4, 2013, pp 4742–4751.; [5] A. Castillo, X. Jiang, D.F. Gayme. Lossy DCOPF for optimizing congested grids with renewable energy and storage. Proceedings of the American Control Conference. Institute of Electrical and Electronics Engineers Inc. June 4-6, 2014, Portland, OR, United States.; [6] T. Geetha, V. Jayashankar. Generation dispatch with storage and renewables under availability-based tariff. IEEE Region 10 Annual International Conference TENCON. 2008.; [7] Gourma, A., Berdai, A., Reddak, M., Tytiuk, V., Reliability and Optimization Strategy in an Interconnected Network at a Wind Farm, (2018) International Review on Modelling and Simulations (IREMOS), 11 (2), pp. 76-83. doi: https://doi.org/10.15866/iremos.v11i2.13596; [8] Varanasi, J., Tripathi, M., Performance Comparison of Generalized Regression Network, Radial Basis Function Network and Support Vector Regression for Wind Power Forecasting, (2019) International Review on Modelling and Simulations (IREMOS), 12 (1), pp. 16-23. doi: https://doi.org/10.15866/iremos.v12i1.15781; [9] Srivastava, A., Bajpai, R., An Efficient Maximum Power Extraction Algorithm for Wind Energy Conversion System Using Model Predictive Control, (2019) International Journal on Energy Conversion (IRECON), 7 (3), pp. 93-107. doi: https://doi.org/10.15866/irecon.v7i3.17403; [10] A. Castillo, D. F. Gayme. Evaluating the effects of real power losses in optimal power flow-based storage integration. IEEE Transactions on Control of Network Systems, vol 5, n. 3. Sep 2018, pp 1132–1145.; [11] Sharifzadeh H, Amjady N, Zareipour H. Multi-period stochastic security-constrained OPF considering the uncertainty sources of wind power, load demand and equipment unavailability. Electric Power Systems Research, vol. 146, n. 5. May 2017, pp. 33–42.; [12] Boonchuay, K. Tomsovic, F. Li, W. Ongsakul. Robust optimization-based DC optimal power flow for managing wind generation uncertainty. AIP Conference Procedings, vol 1499, n. 1. May 2014, pp 31–35.; [13] Rahmat Azami MSJ and GH. Economic load Dispatch and DCOptimal Power Flow Problem-PSO versus LR. International Journal of Multidisciplinary Sciences and Engineering, vol. 2, n. 9. Dec 2011, pp 8–13.; [14] A. Soroudi. Power System Optimization Modeling in GAMS. (Springer International Publishing, 2017).; [15] R. A. Jabr, S. Karaki, J. A. Korbane. Robust Multi-Period OPF with Storage and Renewables. IEEE Transactions on Power Systems, vol. 30 n. 5. Sep 2015, pp. 2790–2799.; [16] B. Eldridge, R. O’Neill, A. Castillo. An Improved Method for the DCOPF with Losses. IEEE Transactions on Power Systems vol. 33, n. 4. July 2018, pp. 3779–3788.; [17] P. Maghouli, A. Soroudi, A. Keane. Robust computational framework for mid-term techno-economical assessment of energy storage. IET Generation, Transmission & Distribution, vol. 10 n. 3. Feb 2016, pp. 822–831.; [18] Hafez, A., AlSadi, S., Nassar, Y., Chaotic Optimization Versus Genetic Algorithm for Optimal Tuning of Static Synchronous Series Compensator Stabilizing Controller, (2019) International Review of Electrical Engineering (IREE), 14 (3), pp. 159-172. doi: https://doi.org/10.15866/iree.v14i3.16163; [19] Mmary, E., Marungsri, B., Multiobjective Optimization of Renewable Distributed Generations in Radial Distribution Networks with Optimal Power Factor, (2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 297-304. doi: https://doi.org/10.15866/iree.v13i4.15069; [20] Adam, K., Miyauchi, H., Optimization of a Photovoltaic Hybrid Energy Storage System Using Energy Storage Peak Shaving, (2019) International Review of Electrical Engineering (IREE), 14 (1), pp. 8-18. doi: https://doi.org/10.15866/iree.v14i1.16162; [21] Oloulade, A., Moukengue, A., Vianou, A., Multi-Criteria Optimization of the Functionning of a Distribution Network in Normal Operating Regime, (2018) International Review of Electrical Engineering (IREE), 13 (4), pp. 290-296. doi: https://doi.org/10.15866/iree.v13i4.14401; [22] Hassoune, A., Khafallah, M., Mesbahi, A., Benaaouinate, L., ouragba, T., Control Strategies of a Smart Topology of EVs Charging Station Based Grid Tied RES-Battery, (2018) International Review of Electrical Engineering (IREE), 13 (5), pp. 385-396. doi: https://doi.org/10.15866/iree.v13i5.15520; [23] Moreno, R. Identification of Topological Vulnerabilities for Power Systems Networks. In 2018 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5. doi: https://doi.org/10.1109/PESGM.2018.8586143; [24] Moreno-Chuquen, R., Obando-Ceron, J., Network Topological Notions for Power Systems Security Assessment, (2018) International Review of Electrical Engineering (IREE), 13 (3), pp. 237-245. doi: https://doi.org/10.15866/iree.v13i3.14210; [25] Khemmook, P., Khomfoi, S., Transient Stability Improvement Using Coordinated Control of Solar PVs and Solid State Transformers, (2018) International Review of Electrical Engineering (IREE), 13 (6), pp. 486-494. doi: https://doi.org/10.15866/iree.v13i6.15869; [26] Omar, A., Ali, Z., Abdel Aleem, S., Abou-El-Zahab, E., Sharaf, A., A Dynamic Switched Compensation Scheme for Grid- Connected Wind Energy Systems Using Cuckoo Search Algorithm, (2019) International Journal on Energy Conversion (IRECON), 7 (2), pp. 64-74. doi: https://doi.org/10.15866/irecon.v7i2.16895; [27] Mauledoux, M., Valencia, A., Avilés, O., Genetic Algorithm Optimization for DC Micro Grid Design, a Case of Study, (2017) International Review of Electrical Engineering (IREE), 12 (4), pp. 318-323. doi: https://doi.org/10.15866/iree.v12i4.11544; [28] Jabri, M., Aloui, H., Genetic Lagrangian Relaxation Selection Method for the Solution of Unit Commitment Problem, (2019) International Journal on Engineering Applications (IREA), 7 (2), pp. 59-64. doi: https://doi.org/10.15866/irea.v7i2.17022; [29] Prodromidis, G., Tsiaras, E., Coutelieris, F., Autonomous Buildings with Electricity by Renewables, (2018) International Journal on Energy Conversion (IRECON), 6 (5), pp. 153-159. doi: https://doi.org/10.15866/irecon.v6i5.15919; [30] Rizk-Allah, R., Abdel Mageed, H., El-Sehiemy, R., Abdel Aleem, S., El Shahat, A., A New Sine Cosine Optimization Algorithm for Solving Combined Non-Convex Economic and Emission Power Dispatch Problems, (2017) International Journal on Energy Conversion (IRECON), 5 (6), pp. 180-192. doi: https://doi.org/10.15866/irecon.v5i6.14291; [31] Syahputra, R., Robandi, I., Ashari, M., Performance Improvement of Radial Distribution Network with Distributed Generation Integration Using Extended Particle Swarm Optimization Algorithm, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 293-304. doi: https://doi.org/10.15866/iree.v10i2.5410; [32] Muthukumar, K., Jayalalitha, S., Ramaswamy, M., PSO Embedded Artificial Bee Colony Algorithm for Optimal Shunt Capacitor Allocation and Sizing in Radial Distribution Networks with Voltage Dependent Load Models, (2015) International Review of Electrical Engineering (IREE), 10 (2), pp. 305-320. doi: https://doi.org/10.15866/iree.v10i2.5481; [33] Moreno, R., Obando, J., Gonzalez, G., An integrated OPF dispatching model with wind power and demand response for day-ahead markets, (2019) International Journal of Electrical and Computer Engineering (IJECE), 4 (4), pp. 2794-2802. doi: http://doi.org/10.11591/ijece.v9i4.pp2794-2802; [34] Wongdet, P., Leeton, U., Marungsri, B., Line Loss Reduction by Optimal Location of Battery Energy Storage System for the Daily Operation in Microgrid with Distributed Generations, (2018) International Journal on Energy Conversion (IRECON), 6 (3), pp. 83-89. doi: https://doi.org/10.15866/irecon.v6i3.15095; [35] Obando, J., Gonzalez, G., Moreno, R., Quantification of operating reserves with high penetration of wind power considering extreme values, (2020) International Journal of Electrical and Computer Engineering (IJECE), 10 (2), pp. 1693-1700. doi: http://doi.org/10.11591/ijece.v10i2.pp1693-1700; [36] J. Yi-Xiong, C. Hao-Zhong, Y. Jian-yong, Z. Li, New discrete method for particle swarm optimization and its application in transmission network expansion planning, Electric Power Systems Research, vol. 77 n. 3-4, 2007, pp. 227-233. doi. http://dx.doi.org/10.1016/j.epsr.2006.02.016; [37] COIN-OR Branch and Cut Interface Julia package. Accessed on Aug. 4, 2019. [Online]. Available: https://github.com/JuliaOpt/Cbc.jl; [38] COIN-OR Linear Programming Interface Julia package. Accessed on Aug. 5, 2019. [Online]. Available: https://github.com/JuliaOpt/Clp.jl; [39] IBM ILOG CPLEX Optimization Studio V12.9.0 documentation. Accessed on Aug. 4, 2019. [Online]. Available: https://www.ibm.com/support/knowledgecenter/SSSA5P_12.9.0/i log.odms.studio.help/Optimization_Studio/topics/COS_home.html; [40] The GUROBI Manual. Accessed on August 5, 2019. [Online]. Available: https://www.gurobi.com/documentation/8.1/refman/index.html; [41] Julia GNU Linear Programming Kit (GLPK) package. Accessed on August 5, 2019. [Online]. Available: https://github.com/JuliaOpt/GLPK.jl; [42] Julia for Mathematical Optimization (JuMP) package. Accessed on Aug. 4, 2019. [Online]. Available: http://www.juliaopt.org/JuMP.jl/v0.19.0; https://hdl.handle.net/10614/13288

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    Popis souboru: xxiv, 175 páginas; application/pdf

    Relation: J. Lin and F. H. Magnago, “Desing, structure and operation of an electricity market,” in Electricity markets: Theories and applications, I. Press, Ed. Wiley, 2017, ch. 7, pp. 173–209.; P. Pinson, “Wind energy: Forecasting challenges for its operational management,” Statist. Sci., vol. 28, no. 4, pp. 564–585, Nov. 2013.; Y. Wang, Z. Zhou, C. Liu, and A. Botterud, “Systematic evaluation of stochastic methods in power system scheduling and dispatch with renewable energy.” [Online]. Available: https://www.osti.gov/biblio/1307654; E. Ela, C. Wang, S. Moorty, K. Ragsdale, J. O’Sullivan, M. Rothleder, and B. Hobbs, “Electricity markets and renewables: A survey of potential design changes and their consequences,” IEEE Power and Energy Magazine, vol. 15, no. 6, pp. 70–82, 2017.; P. Denholm, E. Ela, B. Kirby, and M. Milligan, “The role of energy storage with electricity renewable generation,” NREL, Tech. Rep, Tech. Rep., Jan. 2010. [Online]. Available: http://www.nrel.gov/docs/fy10osti/47187.pdf; M. Kefayati and R. Baldick, “Harnessing demand flexibility to match renewable pro duction using localized policies,” in 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2012, pp. 1105–1109.; E. Karangelos and F. Bouffard, “Towards full integration of demand-side resources in joint forward energy/reserve electricity markets,” IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 280–289, 2012; M. Motalleb, M. Thornton, E. Reihani, and R. Ghorbani, “A nascent market for contingency reserve services using demand response,” Applied Energy, vol. 179, pp. 985 –995, 2016.; Y. Degeilh and G. Gross, “Stochastic simulation of utility-scale storage resources in power systems with integrated renewable resources,” IEEE Transactions on Power Systems, vol. 30, no. 3, pp. 1424–1434, 2015; E. Litvinov, F. Zhao, and T. Zheng, “Electricity markets in the United States: Power industry restructuring processes for the present and future,” IEEE Power and Energy Magazine, vol. 17, no. 1, pp. 32–42, 2019; G. Martínez, J. Liu, B. Li, J. L. Mathieu, and C. L. Anderson, “Enabling renewable resource integration: The balance between robustness and flexibility,” in 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2015, pp. 195–202.; A. Papavasiliou and S. S. Oren, “Large-scale integration of deferrable demand and renewable energy sources,” IEEE Transactions on Power Systems, vol. 29, no. 1, pp. 489–499, 2014.; D. Kourounis, A. Fuchs, and O. Schenk, “Toward the next generation of multiperiod optimal power flow solvers,” IEEE Transactions on Power Systems, vol. 33, no. 4, pp. 4005–4014, 2018.; W. A. Bukhsh, C. Zhang, and P. Pinson, “An integrated multiperiod OPF model with demand response and renewable generation uncertainty,” IEEE Transactions on Smart Grid, vol. 7, no. 3, pp. 1495–1503, 2016.; C. J. López-Salgado, A. Helseth, O. Añó, and D. M. Ojeda-Esteybar, “Stochastic daily hydrothermal scheduling based on decomposition and parallelization,” International Journal of Electrical Power Energy Systems, vol. 118, p. 105700, 2020; I. Gomes, H. Pousinho, R. Melício, and V. Mendes, “Stochastic coordination of joint wind and photovoltaic systems with energy storage in day-ahead market,” Energy, vol. 124, pp. 310 – 320, 2017; A. Banshwar, N. K. Sharma, Y. R. Sood, and R. Shrivastava, “Market based procurement of energy and ancillary services from renewable energy sources in deregulated environment,” Renewable Energy, vol. 101, pp. 1390 – 1400, 2017.; F. Bouffard and F. D. Galiana, “Stochastic security for operations planning with significant wind power generation,” IEEE Transactions on Power Systems, vol. 23, no. 2, pp. 306–316, 2008.; F. Liu, Z. Bie, S. Liu, and T. Ding, “Day-ahead optimal dispatch for wind integrated power system considering zonal reserve requirements,” Applied Energy, vol. 188, pp. 399–408, feb 2017.; H. Sharifzadeh, N. Amjady, and H. Zareipour, “Multi-period stochastic security-constrained OPF considering the uncertainty sources of wind power, load demand and equipment unavailability,” Electric Power Systems Research, vol. 146, pp. 33–42, may 2017.; A. J. Lamadrid, T. Mount, R. Zimmerman, C. E. Murillo-Sanchez, and L. Anderson, “Alternate mechanisms for integrating renewable sources of energy into electricity markets,” in 2012 IEEE Power and Energy Society General Meeting, 2012, pp. 1–8.; N. Amjady, J. Aghaei, and H. A. Shayanfar, “Stochastic multiobjective market clearing of joint energy and reserves auctions ensuring power system security,” IEEE Transactions on Power Systems, vol. 24, no. 4, pp. 1841–1854, 2009.; V. Virasjoki, P. Rocha, A. S. Siddiqui, and A. Salo, “Market impacts of energy storage in a transmission-constrained power system,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 4108–4117, 2016; C. E. Murillo-Sánchez, R. D. Zimmerman, C. L. Anderson, and R. J. Thomas, “A stochastic, contingency-based security-constrained optimal power flow for the procurement of energy and distributed reserve,” Decision Support Systems, vol. 56, pp. 1 –10, 2013.; J. Zhang, J. D. Fuller, and S. Elhedhli, “A stochastic programming model for a day-ahead electricity market with real-time reserve shortage pricing,” IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 703–713, 2010.; M. Parastegari, R.-A. Hooshmand, A. Khodabakhshian, and A.-H. Zare, “Joint operation of wind farm, photovoltaic, pump-storage and energy storage devices in energy and reserve markets,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 275 – 284, 2015; C. E. Murillo-Sánchez, R. D. Zimmerman, C. L. Anderson, and R. J. Thomas, “Secure planning and operations of systems with stochastic sources, energy storage, and active demand,” IEEE Transactions on Smart Grid, vol. 4, no. 4, pp. 2220–2229, 2013; A. J. Lamadrid, D. Muñoz-Alvarez, C. E. Murillo-Sánchez, R. D. Zimmerman, H. Shin, and R. J. Thomas, “Using the Matpower Optimal Scheduling Tool to test power system operation methodologies under uncertainty,” IEEE Transactions on Sustainable Energy, vol. 10, no. 3, pp. 1280–1289, 2019.; H. Wang, C. E. Murillo-Sánchez, R. D. Zimmerman, and R. J. Thomas, “On computational issues of market-based optimal power flow,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1185–1193, 2007.; F. Capitanescu, “Critical review of recent advances and further developments needed in ac optimal power flow,” Electric Power Systems Research, vol. 136, pp. 57 – 68, 2016; T. D. Mount, A. J. Lamadrid, W. Y. Jeon, R. D. Zimmerman, and C. E. Murillo-Sánchez, “How will customers pay for the smart-grid,” in 30th Annual Eastern Conference on Regulated Industries, Skytop, Jan. 2011.; A. J. Lamadrid, T. Mount, R. Zimmerman, and C. E. Murillo-Sánchez, “Harnessing the renewable generation potential,” in 30th USAEE/IAEE North American Conference, 2011.; A. J. Lamadrid, D. L. Shawhan, C. E. Murillo-Sánchez, R. D. Zimmerman, Y. Zhu, D. J. Tylavsky, A. G. Kindle, and Z. Dar, “Stochastically optimized, carbon-reducing dispatch of storage, generation, and loads,” IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 1064–1075, 2015; C. Küchler and S. Vigerske, Decomposition of Multistage Stochastic Programs with Recombining Scenraio Trees. Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik, 2007.; W. S. Sifuentes and A. Vargas, “Hydrothermal scheduling using benders decomposition: Accelerating techniques,” IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1351–1359, 2007.; M. d. P. Buitrago-Villada, S. García-Marín, J. E. Zuluaga-Orozco, and C. E. Murillo-Sánchez, “On the importance of using an ac or dc network model in the multi-period secure stochastic optimal power flow for settling a multidimensional day-ahead market,” IEEE Latin America Transactions, vol. 19, no. 12, pp. 2003–2010, May 2021. [Online]. Available: https://latamt.ieeer9.org/index.php/transactions/article/view/4794; A. Fuchs, J. Garrison, and T. Demiray, “A security-constrained multi-period OPF for the locational allocation of automatic reserves,” in 2017 IEEE Manchester PowerTech, 2017, pp. 1–6.; A. Street, A. Brigatto, and D. M. Valladão, “Co-optimization of energy and ancillary services for hydrothermal operation planning under a general security criterion,” IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4914–4923, 2017.; J. Chen, T. D. Mount, J. S. Thorp, and R. J. Thomas, “Location-based scheduling and pricing for energy and reserves: a responsive reserve market proposal,” Decision Support Systems, vol. 40, no. 3, pp. 563 – 577, 2005, challenges of restructuring the power industry; F. D. Galiana, F. Bouffard, J. M. Arroyo, and J. F. Restrepo, “Scheduling and pricing of coupled energy and primary, secondary, and tertiary reserves,” Proceedings of the IEEE, vol. 93, no. 11, pp. 1970–1983, 2005; J. M. Arroyo and F. D. Galiana, “Energy and reserve pricing in security and network-constrained electricity markets,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 634–643, 2005.; J. Wang, M. Shahidehpour, and Z. Li, “Contingency-constrained reserve requirements in joint energy and ancillary services auction,” IEEE Transactions on Power Systems, vol. 24, no. 3, pp. 1457–1468, 2009.; W. Wei, F. Liu, S. Mei, and Y. Hou, “Robust energy and reserve dispatch under variable renewable generation,” IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 369–380, 2015; F. Bouffard, F. D. Galiana, and A. J. Conejo, “Market-clearing with stochastic security - part I: formulation,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 1818–1826, 2005; K. Van den Bergh and E. Delarue, “Energy and reserve markets: interdependency in electricity systems with a high share of renewables,” Electric Power Systems Research, vol. 189, p. 106537, 2020; Y. Z. Li, K. C. Li, P. Wang, Y. Liu, X. N. Lin, H. B. Gooi, G. F. Li, D. L. Cai, and Y. Luo, “Risk constrained economic dispatch with integration of wind power by multi-objective optimization approach,” Energy, vol. 126, pp. 810–820, 2017.; Unidad de Planeación Minero Energética- UPME, “Plan de expansión de referencia generación-transmisión 2017-2031,” pp. 1–381, 2018. [Online]. Available: https://www1.upme.gov.co; R. D. Zimmerman and C. E. Murillo-Sánchez, “ MATPOWER Optimal Scheduling Tool (MOST) User’s Manual.” 2020. [Online]. Available: https://matpower.org/docs/MOST-manual.pdf; Gurobi Optimization LLC, “Gurobi Optimizer Reference Manual version 9.0.0.” 2020. [Online]. Available: http://www.gurobi.com; A. Wächter and L. T. Biegler, “On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming,” Mathematical Programming, vol. 106, no. 1, pp. 25–57, 2006.; Generadora y comercializadora de energía del Caribe - GECELCA S.A. E.S.P. (2014) Informe de gestión 2014. Accessed 2020-08-06. [Online]. Available: https://www.gecelca.com.co/ Descargas/ publico/ Transparencia/INFORME%20DE%20GESTION.pdf; G. Cohen, Optimisation des Grands Systèmes. Cours du DEA MMME, Universitè de Paris I, 2004, pp. 1–116; M. A. Bazaraa, H. D. Sherali, and C. M. Shetty, Nonlinear Programming. Theory and algorithms, 3rd ed. Wiley-Interscience, 2006, vol. 1, pp. 257–298; D. P. Bertsekas, “Multipliers Methods: A Survey,” Automatica, vol. 12, no. 7, pp. 135–145, 1976; A. Ruszczynski, “On convergence of an augmented lagrangian decomposition method for sparse convex optimization,” Mathematics of operations research, vol. 20, pp. 634–656, 1995.; M. R. Hestenes, “Multipliers and gradient methods,” Journal of Optimization Theory and Applications, vol. 4, pp. 303–320, 1969.; M. J. D. Powell, “A method for nonlinear constraints in minimization problems,” Optimization (R.Fletchr, ed.), Academic Press, New York, vol. 4, pp. 283–298, 1969.; G. Cohen and D. Zhu, Decomposition Coordination Methods in Large Scale Optimization Problems: The Nondifferentiable Case and the Use of Augmented Lagrangians. Advances in Large Scale Systems. JAI Press Inc., 1984, vol. 1, pp. 203–266.; D. P. Bertsekas, Nonlinear programming, 2nd ed., 1999, ch. 4, pp. 201–205; A. J. Conejo, E. Castillo, R. Mínguez, and R. García-Bertrand, Decomposition Techniques in Mathematical Programming. Engineering and Science Applications. Springer, 2006, vol. 1, pp. 195–205.; N. Redondo and A. Conejo, “Short-term hydro-thermal coordination by lagrangian relaxation: solution of the dual problem,” IEEE Transactions on Power Systems, vol. 14, no. 1, pp. 89–95, 1999.; P. Bento, S. Mariano, M. Calado, and L. Ferreira, “A novel lagrangian multiplier update algorithm for short-term hydro-thermal coordination,” Energies, vol. 13, no. 24, pp. 728–742, 2020.; W. Ongsakul and N. Petcharaks, “Fast lagrangian relaxation for constrained generation scheduling in a centralized electricity market,” International Journal of Electrical Power and Energy Systems, vol. 30, no. 1, p. 46–59, 2008.; X. Feng and Y. Liao, “A new lagrangian multiplier update approach for lagrangian relaxation based unit commitment,” IEEE Transactions on Power Systems, vol. 34, no. 8, pp. 857–866, 2006; J. Benders, “Partitioning procedures for solving mixed-variables programming problems,” Numer. Math, vol. 4, p. 238–252, 1962.; A. Geoffrion, “Generalized Benders decomposition,” J Optim Theory Appl, vol. 10, no. 4, pp. 237––260, 1972.; H. Kim, S. Lee, S. Han, W. Kim, K. Ok, and S. Cho, “Integrated generation and transmission expansion planning using generalized Bender’s decomposition method,” in 2015 IEEE International Conference on Computational Intelligence Communication Technology, 2015, pp. 493–497; Z. Li, W. Wu, B. Zhang, and B. Wang, “Decentralized multi-area dynamic economic dispatch using modified generalized Benders decomposition,” IEEE Transactions on Power Systems, vol. 31, no. 1, pp. 526–538, 2016; N. Alguacil and A. J. Conejo, “Multiperiod optimal power flow using Benders decomposition,” IEEE Transactions on Power Systems, vol. 15, no. 1, pp. 196–201, 2000.; K. Chung, B. H. Kim, J. Lee, T. Oh, and J. Lee, “Transmission-security constrained optimal dispatch scheduling using generalized Benders decomposition,” in 2009 Transmission Distribution Conference Exposition: Asia and Pacific, 2009, pp. 1–4.; M. Majidi-Qadikolai and R. Baldick, “A generalized decomposition framework for large-scale transmission expansion planning,” IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 1635–1649, 2018; A. Moreira, A. Street, and J. M. Arroyo, “An adjustable robust optimization approach for contingency-constrained transmission expansion planning,” IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 2013–2022, 2015.; M. R. Ansari, N. Amjady, and B. Vatani, “Stochastic security-constrained hydrothermal unit commitment considering uncertainty of load forecast, inflows to reservoirs and unavailability of units by a new hybrid decomposition strategy,” IET Generation, Transmission Distribution, vol. 8, no. 12, pp. 1900–1915, 2014; R. Rahmaniani, T. G.Crainic, M. Gendreau, and W. Rei, “The Benders decomposition algorithm: A literature review,” Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation - CIRRELT, Tech. Rep., 2016.; D. W. Watkins and D. C. McKinney, “Decomposition methods for water resources optimization models with fixed costs,” Advances in Water Resources, vol. 21, no. 4, pp. 283 – 295, 1998; S. Trukhanov, L. Ntaimo, and A. Schaefer, “Adaptive multicut aggregation for two-stage stochastic linear programs with recourse,” European Journal of Operational Research, vol. 206, no. 2, pp. 395 – 406, 2010.; R. Rahmaniani, T. G. Crainic, M. Gendreau, and W. Rei, “The Benders decomposition algorithm: A literature review,” European Journal of Operational Research, vol. 259, no. 3, pp. 801–817, 2017.; R. Pacqueau, F. Soumis, and L. Hoang, “A fast and accurate algorithm for stochastic integer programming, applied to stochastic shift scheduling,” Ècole Polytechnique de Montrèal, Tech. Rep. [Online]. Available: https://www.gerad.ca/en/papers/G-2012-29; J. R. Birge and F. Louveaux, Introduction to Stochastic Programming, 2nd ed. Springer-Verlag New York, 2011, vol. 1, pp. 198–202.; J. F. Bonnans, J. C. Gilbert, C. Lemarechal, and C. A. Sagastizabal, Numerical Optimization. Theoretical and Practical Aspects, 2nd ed. Springer, 2006, pp. 137–154; J. Linderoth and S. Wright, “Decomposition algorithms for stochastic programming on a computational grid,” Computational Optimization and Applications, no. 24, pp. 207 – 250, 2003.; S. Zaourar and J. Malik, “Quadratic stabilization of Benders decomposition,” HAL - archives-ouvertes, no. hal-01181273, pp. 1 – 27, 2014.; G. Cohen, Auxiliary problem principle and decomposition of optimization problems. J Optim Theory Appl, 1980, vol. 32, pp. 277–305; A. V. Fiacco, Introduction to Sensitivity and Stability Analysis in Nonlinear Programming, 1st ed., August 1983, vol. 165.; H. Ma and S. M. Shahidehpour, “Unit commitment with transmission security and voltage constraints,” IEEE Transactions on Power Systems, vol. 14, no. 2, pp. 757– 764, 1999; R. C. Green, L. Wang, and M. Alam, “Applications and trends of high performance computing for electric power systems: Focusing on smart grid,” IEEE Transactions on Smart Grid, vol. 4, no. 2, pp. 922–931, 2013.; R. C. Green, L. Wang, and M. Alam, “High performance computing for electric power systems: Applications and trends,” in 2011 IEEE Power and Energy Society General Meeting, 2011, pp. 1–8; S. K. Khaitan, “A survey of high-performance computing approaches in power systems,” in 2016 IEEE Power and Energy Society General Meeting (PESGM), 2016, pp. 1–5; MATLAB, Parallel Computing Toolbox, User’s guide, MatWorks, Inc., 2018; MATLAB, MATLAB Distributed Computing Server, System administrator’s guide, MatWorks, Inc., 2018.; R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “Matpower: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12–19, 2011; R. D. Zimmerman and C. E. Murillo-Sánchez, “Matpower User’s Manual version 7.0.” 2019. [Online]. Available: https://matpower.org/docs/MATPOWER-manual-7.1.pdf; Lawrence Livermore National Laboratory. (2021) Introduction to parallel computing tutorial. [Online]. Available: https://hpc.llnl.gov/training/tutorials/introduction-parallel-computing-tutorial; H. Falsafi, A. Zakariazadeh, and S. Jadid, “The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming,” Energy, vol. 64, pp. 853–867, 2014.; O. Alsac and B. Stott, “Optimal load flow with steady-state security,” IEEE Transactions on Power Apparatus and Systems, vol. PAS-93, no. 3, pp. 745–751, 1974; R. Ferrero, S. Shahidehpour, and V. Ramesh, “Transaction analysis in deregulated power systems using game theory,” IEEE Transactions on Power Systems, vol. 12, no. 3, pp. 1340–1347, 1997.; P. Hansen, Rank-Deficient and Discrete Ill-Posed Problems, 1998, ch. 1. Setting the Stage, pp. 1–17; J. Gondzio and A. Grothey, “Exploiting structure in parallel implementation of interior point methods for optimization,” Computational Management Science, vol. 6, no. 2, pp. 135–160, 2009.; R. D. Zimmerman and C. E. Murillo-Sánchez, “Multi-period SuperOPF (SuperOPF 2.0) User’s Manual.” 2013.; FERC, “FERC RTO Unit Commitment Test System,” Federal Energy Regulatory Commission, Tech. Rep; https://repositorio.unal.edu.co/handle/unal/81411; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

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