Fast security constraint optimal power flow using parallel and heterogenous computing

ilustraciones, gráficas, tablas

Autores:
Rodríguez Medina, Diego Fernando
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2021
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/80849
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/80849
https://repositorio.unal.edu.co/
Palabra clave:
530 - Física::537 - Electricidad y electrónica
Power system security
Power distribution networks
Análisis de redes eléctricas
Security Constrained Optimal Power Flow (SCOPF)
Optimal Power Flow (OPF)
Parallel Computing (PC)
Complex Power Networks
Real-Time SCOPF
Graphical Processing Unit (GPU)
Flujo de Potencia Óptimo
Seguridad en Sistemas de Potencia
Computación Paralela
Redes de Potencia Complejas
Operación en Tiempo Real
Unidad de Procesamiento Gráfico
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_6b87291f3f65fdaa24158fbc0d9e1b9b
oai_identifier_str oai:repositorio.unal.edu.co:unal/80849
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv Fast security constraint optimal power flow using parallel and heterogenous computing
dc.title.translated.spa.fl_str_mv Cálculo rápido de flujo de potencia óptimo con restricciones de seguridad utilizando computación paralela y heterogénea
title Fast security constraint optimal power flow using parallel and heterogenous computing
spellingShingle Fast security constraint optimal power flow using parallel and heterogenous computing
530 - Física::537 - Electricidad y electrónica
Power system security
Power distribution networks
Análisis de redes eléctricas
Security Constrained Optimal Power Flow (SCOPF)
Optimal Power Flow (OPF)
Parallel Computing (PC)
Complex Power Networks
Real-Time SCOPF
Graphical Processing Unit (GPU)
Flujo de Potencia Óptimo
Seguridad en Sistemas de Potencia
Computación Paralela
Redes de Potencia Complejas
Operación en Tiempo Real
Unidad de Procesamiento Gráfico
title_short Fast security constraint optimal power flow using parallel and heterogenous computing
title_full Fast security constraint optimal power flow using parallel and heterogenous computing
title_fullStr Fast security constraint optimal power flow using parallel and heterogenous computing
title_full_unstemmed Fast security constraint optimal power flow using parallel and heterogenous computing
title_sort Fast security constraint optimal power flow using parallel and heterogenous computing
dc.creator.fl_str_mv Rodríguez Medina, Diego Fernando
dc.contributor.advisor.none.fl_str_mv Rivera, Sergio
dc.contributor.author.none.fl_str_mv Rodríguez Medina, Diego Fernando
dc.contributor.referee.none.fl_str_mv Pinzón, Jaime
Elizondo, Marcelo
Wu, Di
dc.subject.ddc.spa.fl_str_mv 530 - Física::537 - Electricidad y electrónica
topic 530 - Física::537 - Electricidad y electrónica
Power system security
Power distribution networks
Análisis de redes eléctricas
Security Constrained Optimal Power Flow (SCOPF)
Optimal Power Flow (OPF)
Parallel Computing (PC)
Complex Power Networks
Real-Time SCOPF
Graphical Processing Unit (GPU)
Flujo de Potencia Óptimo
Seguridad en Sistemas de Potencia
Computación Paralela
Redes de Potencia Complejas
Operación en Tiempo Real
Unidad de Procesamiento Gráfico
dc.subject.other.none.fl_str_mv Power system security
Power distribution networks
dc.subject.lemb.none.fl_str_mv Análisis de redes eléctricas
dc.subject.proposal.eng.fl_str_mv Security Constrained Optimal Power Flow (SCOPF)
Optimal Power Flow (OPF)
Parallel Computing (PC)
Complex Power Networks
Real-Time SCOPF
Graphical Processing Unit (GPU)
dc.subject.proposal.spa.fl_str_mv Flujo de Potencia Óptimo
Seguridad en Sistemas de Potencia
Computación Paralela
Redes de Potencia Complejas
Operación en Tiempo Real
Unidad de Procesamiento Gráfico
description ilustraciones, gráficas, tablas
publishDate 2021
dc.date.issued.none.fl_str_mv 2021
dc.date.accessioned.none.fl_str_mv 2022-02-01T22:31:59Z
dc.date.available.none.fl_str_mv 2022-02-01T22:31:59Z
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TD
format http://purl.org/coar/resource_type/c_db06
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/80849
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/80849
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Q. Wang, Risk-based Security-Constrained Optimal Power Flow: Mathematical Fundamentals, Computational Strategies, Validation, and use within Electricity Markets. PhD thesis, Iowa State University, 2013.
D. Page, A Practical Introduction to Computer Architecture. Springer, 2009.
NVIDIA, “NVIDIA Turing GPU,” White Paper, 2018.
S. Rennich, “Cuda c/c++ streams and concurrency,” tech. rep., 2012.
G. Wood, A; Wollenberg, B; Shebl´e, Power Generation, Operation and Control. Wiley, 2014.
F. Garcia, N. D. Sarma, V. Kanduri, G. Nissankala, K. Gopinath, J. Polusani, T. Mortensen, and I. Flores, “ERCOT control center experience in using real-time contingency analysis in the new nodal market,” IEEE Power and Energy Society General Meeting, pp. 1–8, 2012.
Z. Li and F. Yang, Advanced metering infrastructure and graphics processing unit technologies in electric distribution networks. No. 9789811070006, Springer Singapore, 2018.
NERC, “Standard TPL-001-4 — Transmission System Planning Performance Requirements,” vol. 2, 2014.
J. K. Debnath, W. K. Fung, A. M. Gole, and S. Filizadeh, “Simulation of large-scale electrical power networks on graphics processing units,” 2011 IEEE Electrical Power and Energy Conference, EPEC 2011, pp. 199–204, 2011.
J. Baranowski and D. J. French, “Operational use of contingency analysis at PJM,”IEEE Power and Energy Society General Meeting, pp. 13–16, 2012.
TOP500, “Home — TOP500 Supercomputer Sites,” 2020.
R. Gnanavignesh and U. J. Shenoy, “Parallel Sparse LU Factorization of Power Flow Jacobian using GPU,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2019-Octob, no. C, pp. 1857–1862, 2019.
I. Araújo, V. Tadaiesky, D. Cardoso, Y. Fukuyama, and A. Santana, “Simultaneous ´parallel power flow calculations using hybrid CPU-GPU approach,” International Journal of Electrical Power and Energy Systems, vol. 105, no. February 2018, pp. 229–236, 2019.
Q. Shi, C. Yuan, W. Feng, G. Liu, R. Dai, Z. Wang, and F. Li, “Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement with Graph Computing-Based Power Flow Calculation,” IEEE Access, vol. 7, pp. 167010–167018, 2019.
P. Duan, S. Xu, H. Chen, X. Yang, S. Wang, and E. Hu, “High Performance Computing (HPC)for Advanced Power System Studies,” 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings, pp. 1–9, 2018.
K. Tang, S. Dong, B. Zhu, Q. Ni, and Y. Song, “GPU-Based Real-time N-1 AC Power Flow Algorithm with Preconditioned Iterative Method,” IEEE Power and Energy Society General Meeting, vol. 2018-Augus, pp. 1–5, 2018.
G. Ruetsch and B. Oster, “Getting Started with CUDA What is CUDA ?,” Materials, vol. 17, no. 4, pp. 223–224, 2008.
T. Soyata, GPU Parallel Program Development Using CUDA. 2018.
L. Platbrood, H. Crisciu, F. Capitanescu, and L. Wehenkel, “Solving very large-scale security-constrained optimal power flow problems by combining iterative contingency selection and network compression,” 17th Power Systems Computation Conference, PSCC 2011, 2011.
F. Capitanescu, M. Glavic, D. Ernst, and L. Wehenkel, “Applications of security constrained optimal power flows,” Modern Electric Power Systems Symposium, MEPS06, p. 7, 2006.
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.
O. Alsa¸c, J. Bright, M. Prais, and B. Stott, “Further developments in lp-based optimal power flow,” IEEE Transactions on Power Systems, vol. 5, no. 3, pp. 697–711, 1990.
D. Rodriguez-Medina, D. Gomez, S. Rivera, and J. Gers, “A fast decomposition method to solve a security-constrained optimal power flow (scopf) empowered by heterogeneous and parallel computing (hpc) (under review),” PES General Meeting, pp. 52812–52824, 2022.
D. Rodriguez, D. Alvarez, D. Gomez, J. Gers, and S. Rivera, “Low-cost analysis of load flow computing using embedded computer empowered by gpu,” Proceedings - IEEE PES ISGT NA 2021: “Technology Solutions for an Evolving Grid”, 02 2021.
D. Rodriguez, D. Gomez, D. Alvarez, and S. Rivera, “A review of parallel heterogeneous computing algorithms in power systems,” Algorithms, vol. 14, no. 10, 2021.
D. Rodriguez, A. Angulo, D. F. Gomez, A. David, J. Gil, and S. Rivera, “Smart Microgrids Operation Considering Expert Knowledge and Ensembled Based Metaheuristic Optimization Algorithms (Under Review),” International Journal of Electrical and Computer Science, vol. 12, 2021.
T. Valencia-Zuluaga, D. Agudelo-Martinez, D. Arango-Angarita, C. Acosta-Urrego, S. Rivera, D. Rodriguez-Medina, and J. Gers, “A Fast Decomposition Method to Solve a Security-Constrained Optimal Power Flow (SCOPF) Problem through Constraint Handling,” IEEE Access, vol. 9, pp. 52812–52824, 2021.
A. Angulo, D. Rodr´ıguez, W. Garz´on, D. F. G´omez, A. Al Sumaiti, and S. Rivera, “Algorithms for bidding strategies in local energy markets: Exhaustive search through parallel computing and metaheuristic optimization,” Algorithms, vol. 14, no. 9, 2021.
J. Ramírez-Romero, D. Medina, and S. Rivera, “Teaching using a synchronous machine virtual laboratory,” Global Journal of Engineering Education, vol. 22, pp. 123–130, 06 2020.
J. Garcia-Guarin, D. Rodriguez, D. Alvarez, S. Rivera, C. Cortes, A. Guzman, A. Bretas, J. R. Aguero, and N. Bretas, “Smart microgrids operation considering a variable neighborhood search: The differential evolutionary particle swarm optimization algorithm,” Energies, vol. 12, no. 16, pp. 1–13, 2019.
S. Vargas, D. Rodriguez, and S. Rivera, “Mathematical Formulation and Numerical Validation of Uncertainty Costs for Controllable Loads,” Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, vol. 12, 2019.
S. Rivera, D. Rodriguez, and I. Erlich, “2018 Grid Optimization Competition Evaluating the Performance of Modern Heuristic Optimizers on Stochastic Optimization Problems applied to Smart Grids Test bed A : Stochastic OPF in Presence of Renewable Energy and Controllable Loads,” Intelligent Systems Subcommittee Power System Analysis, Computing, and Economic Committee, no. January, 2018.
J. Arevalo, D. Medina, J. Rueda, and S. Rivera, “2018 competition on operational planning of sustainable power systems: Testsbeds and results,” WSEAS Transactions on Power Systems, vol. 14, pp. 98–106, 08 2019.
D. Rodriguez, D. Gomez, W. Garzon, D. Alvarez, S. Rivera, and J. Gers, “Posicionamiento Optimo de cuadrillas basado en estadísticas de Tránsito de Google Maps e Indicadores de Confiabilidad,” 2018.
D. Rodriguez, J. M. Gers, T. Valencia, C. Acosta, D. Agudelo, and D. Arango, “Ensembled Method: Constraints Relaxation With Analytical Optimization With Combined Heuristic Method,” tech. rep., 2020.
D. Rodriguez and T. Valencia, “Posicionamiento Optimo de cuadrillas basado en estadísticas de Tránsito de Google Maps e Indicadores de Confiabilidad,” 2018.
J. García, D. Rodríguez, and S. Rivera, “Herramienta para la Programación de Redes Inteligentes con Recursos Energéticos de Alta Incertidumbre,” 2019.
NERC, Reliability Assessment Guidebook. 1 ed., 2010.
N. Garcia, “Parallel power flow solutions using a biconjugate gradient algorithm and a Newton method: A GPU-based approach,” IEEE PES General Meeting, PES 2010, no. 5, pp. 1–4, 2010.
ARPA, “About the Competition — Grid Optimization Competition,” 2019.
S. Huang and V. Dinavahi, “Fast Batched Solution for Real-Time Optimal Power Flow with Penetration of Renewable Energy,” IEEE Access, vol. 6, pp. 13898–13910, 2018.
V. H. Hinojosa and F. Gonzalez-Longatt, “Preventive security-constrained DCOPF formulation using power transmission distribution factors and line outage distribution factors,” Energies, vol. 11, no. 6, pp. 1–13, 2018.
Y. Yu and P. Luh, “Scalable corrective security-constrained economic dispatch considering conflicting contingencies,” International Journal of Electrical Power and Energy Systems, vol. 98, no. December 2017, pp. 269–278, 2018.
Y. Yang and Y. Feng, “Large-scale preventive security constrained optimal power flow based on compensation method,” IEEE Power and Energy Society General Meeting, vol. 2015-Septe, 2015.
F. Capitanescu, J. L. Martinez Ramos, P. Panciatici, D. Kirschen, A. Marano Marcolini, L. Platbrood, and L. Wehenkel, “State-of-the-art, challenges, and future trends in security constrained optimal power flow,” Electric Power Systems Research, vol. 81, no. 8, pp. 1731–1741, 2011.
H. Harsan, N. Hadjsaid, and P. Pruvot, “Cyclic Security Analysis for Security Constrained Optimal Power Flow,” IEEE Transactions on Power Systems, vol. 12, no. 2, pp. 948–953, 1997.
Q. Wang, J. D. McCalley, T. Zheng, and E. Litvinov, “Solving corrective risk based security-constrained optimal power flow with Lagrangian relaxation and Benders decomposition,” International Journal of Electrical Power and Energy Systems, vol. 75, pp. 255–264, 2016.
Y. Li and J. D. McCalley, “Decomposed SCOPF for improving efficiency,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 494–495, 2009.
Q. Wang, J. D. McCalley, T. Zheng, and E. Litvinov, “A computational strategy to solve preventive risk-based security-constrained OPF,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1666–1675, 2013.
V. H. Hinojosa, “Comparative Corrective and Preventive Security-Constrained DCOPF Problems Using Linear Shift-Factors,” Energies, pp. 1–16, 2020.
J. Mohammadi, G. Hug, and S. Kar, “A benders decomposition approach to corrective security constrained OPF with power flow control devices,” IEEE Power and Energy Society General Meeting, 2013.
S. Huang and V. Dinavahi, “Performance analysis of GPU-accelerated fast decoupled power flow using direct linear solver,” 2017 IEEE Electrical Power and Energy Conference, EPEC 2017, vol. 2017-Octob, no. 1, pp. 1–6, 2017.
M. Wang, Y. Chen, and S. Huang, “GPU-based Power Flow Analysis with Continuous Newton ’ s Method,” IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–5, 2017.
L. Y. Kyaw and S. Phyu, “Scheduling Methods in HPC System: Review,” 2020 IEEE Conference on Computer Applications, ICCA 2020, pp. 1–6, 2020.
L. R, P. M, and B. C, Computational Physics. 2007.
P. Marksteiner, “High-performance computing - An overview,” Computer Physics Communications, vol. 97, no. 1-2, pp. 16–35, 1996.
H. Andrade and I. Crnkovic, “A Review on Software Architectures for Heterogeneous Platforms,” Proceedings - Asia-Pacific Software Engineering Conference, APSEC, vol. 2018-December, pp. 209–218, 2018.
P. N. Glaskowsky, “NVIDIA ’ s Fermi : The First Complete GPU Computing Architecture,” no. September, 2009.
M. Marin, G.-e. P. Flow, and A. Distributed, GPU-Enhanced power flow analysis. PhD thesis, UNIVERSITE DE PERPIGNAN VIA DOMITIA-UNIVERSITY COLLEGE DUBLIN, 2016.
Z. Feng, X. Zhao, and Z. Zeng, “Robust parallel preconditioned power grid simulation on gpu with adaptive runtime performance modeling and optimization,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 30, no. 4, pp. 562–573, 2011.
Z. Li, J. Zhu, and F. Yang, “How far is the GPU technology from practical power system applications?,” IEEE Power and Energy Society General Meeting, vol. 2014-Octob, no. October, 2014.
X. Li, F. Li, and S. Member, “GPU-based Fast Decoupled Power Flow with Preconditioned Iterative Solver and Inexact Newton Method,” IEEE Power & Energy Society General Meeting, vol. 8950, no. c, pp. 1–1, 2017.
V. Roberge, M. Tarbouchi, and F. Okou, “Parallel power flow on graphics processing units for concurrent evaluation of many networks,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1639–1648, 2017.
V. Jalili-Marandi and V. Dinavahi, “Simd-based large-scale transient stability simulation on the graphics processing unit,” IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1589–1599, 2010.
V. Jalili-Marandi, Z. Zhou, and V. Dinavahi, “Large-scale transient stability simulation of electrical power systems on parallel gpus,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 7, pp. 1255–1266, 2012.
Z. Zhou and V. Dinavahi, “Parallel massive-thread electromagnetic transient simulation on gpu,” IEEE Transactions on Power Delivery, vol. 29, no. 3, pp. 1045–1053, 2014.
Y. Song, Y. Chen, S. Huang, Y. Xu, Z. Yu, and J. R. Marti, “Fully gpu-based electromagnetic transient simulation considering large-scale control systems for system-level studies,” IET Generation, Transmission Distribution, vol. 11, no. 11, pp. 2840–2851, 2017.
N. Lukac and B. Zalik, “Gpu-based roofs’ solar potential estimation using lidar data,” Computers Geosciences, vol. 52, pp. 34 – 41, 2013.
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.
G. Capizzi, G. Lo Sciuto, C. Napoli, and E. Tramontana, “Advanced and adaptive dispatch for smart grids by means of predictive models,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6684–6691, 2018.
G. Zhou, X. Zhang, Y. Lang, R. Bo, Y. Jia, J. Lin, and Y. Feng, “A novel GPU accelerated strategy for contingency screening of static security analysis,” International Journal of Electrical Power and Energy Systems, vol. 83, pp. 33–39, 2016.
H. Karimipour and V. Dinavahi, “Extended kalman filter-based parallel dynamic state estimation,” IEEE Transactions on Smart Grid, vol. 6, no. 3, pp. 1539–1549, 2015.
W. Qiu, Q. Tang, J. Liu, Z. Teng, and W. Yao, “Power quality disturbances recognition using modified s transform and parallel stack sparse auto-encoder,” Electric Power Systems Research, vol. 174, p. 105876, 2019.
Z. Liu, X. Li, L. Wu, S. Zhou, and K. Liu, “Gpu-accelerated parallel coevolutionary algorithm for parameters identification and temperature monitoring in permanent magnet synchronous machines,” IEEE Transactions on Industrial Informatics, vol. 11, no. 5, pp. 1220–1230, 2015.
V. Schwarzer and R. Ghorbani, “New opportunities for large-scale design optimization of electric vehicles using gpu technology,” in 2011 IEEE Vehicle Power and Propulsion Conference, pp. 1–6, 2011.
G. Zhou, R. Bo, L. Chien, X. Zhang, S. Yang, and D. Su, “Gpu-accelerated algorithm for online probabilistic power flow,” IEEE Transactions on Power Systems, vol. 33, pp. 1132–1135, Jan 2018
Z. Chen, L. Shen, Y. Zhao, and C. Yang, “Parallel algorithm for real-time contouring from grid dem on modern gpus,” Science China Technological Sciences, vol. 53, pp. 33–37, May 2010.
T. He, K. Meng, Z.-Y. Dong, Y.-T. Oh, and Y. Xu, “Use of high-performance graphics processing units for power system demand forecasting,” Journal of Electrical Engineering and Technology, vol. 5, p. 363–370, Jan 2010.
F. Milano, “Small-signal stability analysis of large power systems with inclusion of multiple delays,” IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3257–3266, 2016.
B. Shang, Y. Xu, C. Zhang, Y. Chen, Z. Liu, L. Lin, C. Xu, and J. Yu, “Gpu-accelerated batch solution for short-circuit current calculation of large-scale power systems,” in 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), pp. 1743–1748, 2019.
J. S. Chai, N. Zhu, A. Bose, and D. J. Tylavsky, “Parallel newton type methods for power system stability analysis using local and shared memory multiprocessors,” IEEE Transactions on Power Systems, vol. 6, no. 4, pp. 1539–1545, 1991.
J. Shu, Wei Xue, and Weimin Zheng, “A parallel transient stability simulation for power systems,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 1709–1717, 2005.
P. Aristidou, D. Fabozzi, and T. Van Cutsem, “Dynamic simulation of large-scale power systems using a parallel schur-complement-based decomposition method,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 10, pp. 2561–2570, 2014.
S. K. Khaitan, J. D. McCalley, and A. Somani, “Proactive task scheduling and stealing in master-slave based load balancing for parallel contingency analysis,” Electric Power Systems Research, vol. 103, pp. 9 – 15, 2013.
S. K. Khaitan and J. D. McCalley, “Scale: A hybrid mpi and multithreading based work stealing approach for massive contingency analysis in power systems,” Electric Power Systems Research, vol. 114, pp. 118 – 125, 2014.
Jun Qiang Wu and A. Bose, “Parallel solution of large sparse matrix equations and parallel power flow,” IEEE Transactions on Power Systems, vol. 10, no. 3, pp. 1343– 1349, 1995.
X. Wang, S. G. Ziavras, C. Nwankpa, J. Johnson, and P. Nagvajara, “Parallel solution of newton’s power flow equations on configurable chips,” International Journal of Electrical Power Energy Systems, vol. 29, no. 5, pp. 422 – 431, 2007.
J. Baek, Q. H. Vu, J. K. Liu, X. Huang, and Y. Xiang, “A secure cloud computing based framework for big data information management of smart grid,” IEEE Transactions on Cloud Computing, vol. 3, no. 2, pp. 233–244, 2015.
J. Soares, M. A. F. Ghazvini], Z. Vale, and P. [de Moura Oliveira], “A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads,” Applied Energy, vol. 162, pp. 1074 – 1088, 2016.
G. N. Korres, A. Tzavellas, and E. Galinas, “A distributed implementation of multi-area power system state estimation on a cluster of computers,” Electric Power Systems Research, vol. 102, pp. 20 – 32, 2013.
Y. Fukuyama and Hsaio-Dong Chiang, “A parallel genetic algorithm for generation expansion planning,” IEEE Transactions on Power Systems, vol. 11, no. 2, pp. 955– 961, 1996.
A. Rami, A. Zeblah, H. Hamdaoui, Y. Massim, and F. Harrou, “An efficient artificial immune algorithm for power system reliability optimisation,” International Journal of Power and Energy Conversion, vol. 1, no. 2-3, pp. 178–197, 2009. cited By 7.
C. Dufour, V. Jalili-Marandi, and J. Bélanger, “Real-time simulation using transient stability, electromagnetic transient and fpga-based high-resolution solvers,” in 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, pp. 283– 288, 2012.
J. Ma, K. L. Man, S.-U. Guan, T. O. Ting, and P. W. H. Wong, “Parameter estimation of photovoltaic model via parallel particle swarm optimization algorithm,” International Journal of Energy Research, vol. 40, no. 3, pp. 343–352, 2016.
F. Sato, A. Garcia, A. Monticelli, and A. B. Alves], “Distributed short-circuit analysis in heterogeneous computer networks,” International Journal of Electrical Power Energy Systems, vol. 22, no. 2, pp. 129 – 136, 2000.
A. K. Zadeh, K. M. Nor, and H. Zeynal, “Multi-thread security constraint economic dispatch with exact loss formulation,” in 2010 IEEE International Conference on Power and Energy, pp. 864–869, 2010.
T. Cui and F. Franchetti, “A multi-core high performance computing framework for probabilistic solutions of distribution systems,” in 2012 IEEE Power and Energy Society General Meeting, pp. 1–6, 2012.
J. Zhang, S. Lin, H. Liu, Y. Chen, M. Zhu, and Y. Xu, “A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints,” Energy, vol. 123, pp. 538 – 554, 2017.
V. Roberge, M. Tarbouchi, and F. Okou, “Optimal power flow based on parallel metaheuristics for graphics processing units,” Electric Power Systems Research, vol. 140, pp. 344–353, 2016.
G. Geng, Q. Jiang, and Y. Sun, “Parallel transient stability-constrained optimal power flow using gpu as coprocessor,” IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1436–1445, 2017.
B. Kim, “A fast distributed implementation of optimal power flow,” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 858–864, 1999. cited By 169.
H. R. Cai, C. Y. Chung, and K. P. Wong, “Application of differential evolution algorithm for transient stability constrained optimal power flow,” IEEE Transactions on Power Systems, vol. 23, no. 2, pp. 719–728, 2008.
M. Abedini, “A novel algorithm for load flow analysis in island microgrids using an improved evolutionary algorithm,” International Transactions on Electrical Energy Systems, vol. 26, pp. 2727–2743, dec 2016.
Taufik, M. A. Guevara, A. Shaban, and A. Nafisi, “Modeling and Load Flow Analysis of a Microgrid Laboratory.,” International Journal of Smart Grid and Sustainable Energy Technologies, vol. 3, pp. 103–111, dec 2019.
J. I. Giraldez Miner, F. Flores-Espino, S. MacAlpine, and P. Asmus, “Phase i microgrid cost study: Data collection and analysis of microgrid costs in the united states,” National Renewable Energy Laboratory, 10 2018.
V. Kloh, D. Yokoyama, A. Yokoyama, G. Silva, M. Ferro, and B. Schulze, “Performance and Energy Efficiency Evaluation for HPC Applications in Heterogeneous Architectures,” in 2018 Symposium on High Performance Computing Systems (WSCAD), pp. 162–169, IEEE, oct 2018.
S. K. Khaitan, “A survey of high-performance computing approaches in power systems,” in 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1– 5, IEEE, jul 2016.
L. Rakai and W. Rosehart, “GPU-accelerated solutions to optimal power flow problems,” Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 2511–2516, 2014.
D. Greenwood, K. Lim, C. Patsios, P. Lyons, Y. Lim, and P. Taylor, “Frequency response services designed for energy storage,” Applied Energy, vol. 203, pp. 115 – 127, 2017.
D. Carreira, G. D. Marques, and D. M. Sousa, “Hybrid energy storage system with a low cost digital control,” in 2015 9th International Conference on Compatibility and Power Electronics (CPE), pp. 185–190, 2015.
D. Wu, T. Dragicevic, J. C. Vasquez, J. M. Guerrero, and Y. Guan, “Secondary coordinated control of islanded microgrids based on consensus algorithms,” in 2014 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 4290–4297, 2014.
A. R. Brodtkorb, T. R. Hagen, and M. L. Sætra, “Graphics processing unit (GPU) programming strategies and trends in GPU computing,” Journal of Parallel and Distributed Computing, vol. 73, pp. 4–13, jan 2013.
H. H. Holm, A. R. Brodtkorb, and M. L. Sætra, “GPU Computing with Python: Performance, Energy Efficiency and Usability,” Computation, vol. 8, p. 4, jan 2020.
S. K. Lam, A. Pitrou, and S. Seibert, “Numba: A LLVM-Based Python JIT Compiler,” in Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, LLVM ’15, (New York, NY, USA), Association for Computing Machinery, 2015.
P. Virtanen, R. Gommers, and et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nature Methods, vol. 17, pp. 261–272, mar 2020.
R. D. Zimmerman, C. E. Murillo-Sanchez, 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, pp. 12–19, feb 2011.
Q. Wang, J. D. McCalley, T. Zheng, and E. Litvinov, “A computational strategy to solve preventive risk-based security-constrained OPF,” IEEE Transactions on Power Systems, vol. 28, pp. 1666–1675, may 2013.
S. A. Sadat, D. Haralson, and M. Sahraei-Ardakani, “Security versus computation time in IV-ACOPF with SOCP initialization,” in 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE, jun 2018.
F. Capitanescu, J. M. Ramos, P. Panciatici, D. Kirschen, A. M. Marcolini, L. Platbrood, and L. Wehenkel, “State-of-the-art, challenges, and future trends in security constrained optimal power flow,” Electric Power Systems Research, vol. 81, pp. 1731–1741, Aug 2011.
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, jul 2016.
D. Phan and J. Kalagnanam, “Some efficient optimization methods for solving the security-constrained optimal power flow problem,” IEEE Transactions on Power Systems, vol. 29, pp. 863–872, March 2014.
J. Mohammadi, G. Hug, and S. Kar, “A benders decomposition approach to corrective security constrained OPF with power flow control devices,” in 2013 IEEE Power & Energy Society General Meeting, IEEE, 2013.
D. T. Phan and X. A. Sun, “Minimal impact corrective actions in security-constrained optimal power flow via sparsity regularization,” IEEE Transactions on Power Systems, vol. 30, pp. 1947–1956, jul 2015.
M. Al-Saffar and P. Musilek, “Distributed optimal power flow for electric power systems with high penetration of distributed energy resources,” in 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), IEEE, may 2019.
R. Louca and E. Bitar, “Robust AC optimal power flow,” IEEE Transactions on Power Systems, vol. 34, pp. 1669–1681, may 2019.
Y. Li and J. McCalley, “Decomposed SCOPF for improving efficiency,” IEEE Transactions on Power Systems, vol. 24, pp. 494–495, feb 2009.
F. Capitanescu, M. Glavic, D. Ernst, and L. Wehenkel, “Applications of security constrained optimal power flows,” in In Proceedings of Modern Electric Power Systems Symposium, MEPS06, 2006.
S. Sojoudi and J. Lavaei, “Physics of power networks makes hard optimization problems easy to solve,” in 2012 IEEE Power and Energy Society General Meeting, IEEE, jul 2012.
V. Hinojosa and F. Gonzalez-Longatt, “Preventive security-constrained DCOPF formulation using power transmission distribution factors and line outage distribution factors,” Energies, vol. 11, p. 1497, jun 2018.
Y. Xu, H. Yang, R. Zhang, Z. Dong, M. Lai, and K. Wong, “A contingency partitioning approach for preventive-corrective security-constrained optimal power flow computation,” Electric Power Systems Research, vol. 132, pp. 132–140, 2016.
Y. Xu, Z. Y. Dong, R. Zhang, K. P. Wong, and M. Lai, “Closure to discussion on “solving preventive-corrective scopf by a hybrid computational strategy”,” IEEE Transactions on Power Systems, vol. 29, p. 3124–3125, Nov 2014.
M. Javadi, A. E. Nezhad, M. Gough, M. Lotfi, and J. P. Catalao, “Implementation of consensus-ADMM approach for fast DC-OPF studies,” in 2019 International Conference on Smart Energy Systems and Technologies (SEST), IEEE, sep 2019.
A. Attarha and N. Amjady, “Solution of security constrained optimal power flow for large-scale power systems by convex transformation techniques and taylor series,” IET Generation, Transmission & Distribution, vol. 10, p. 889–896, Mar 2016.
A. Werner, K. Duwadi, N. Stegmeier, T. M. Hansen, and J.-H. Kimn, “Parallel implementation of ac optimal power flow and time constrained optimal power flow using high performance computing,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, Jan 2019.
E. Karangelos and L. Wehenkel, “An iterative AC-SCOPF approach managing the contingency and corrective control failure uncertainties with a probabilistic guarantee,” IEEE Transactions on Power Systems, vol. 34, pp. 3780–3790, sep 2019.
S. Lee, W. Kim, and B. H. Kim, “Performance comparison of optimal power flow algorithms for lmp calculations of the full scale korean power system,” Journal of Electrical Engineering and Technology, vol. 10, p. 109–117, Jan 2015.
Y. Chen, Z. Zhang, Y. Lang, J. Ma, and S. Zheng, “Generalised-fast decoupled state estimator,” IET Generation, Transmission Distribution, vol. 12, pp. 5928–5938, may 2018.
J. Guo, G. Hug, and O. K. Tonguz, “A case for nonconvex distributed optimization in large-scale power systems,” IEEE Transactions on Power Systems, vol. 32, pp. 3842– 3851, sep 2017.
M. Granada Echeverri, M. J. Rider Flores, and J. R. S. Mantovani, “Dos técnicas de descomposición aplicadas al problema de flujo de potencia óptimo multi-areas,” DYNA, vol. 77, pp. 303 – 312, 06 2010.
J. Guo, G. Hug, and O. Tonguz, “Asynchronous admm for distributed non-convex optimization in power systems,” arXiv preprint arXiv:1710.08938, 2017.
M. Bazrafshan, K. Baker, and J. Mohammadi, “Computationally efficient solutions for large-scale security-constrained optimal power flow,” 2020.
S. Stankovic and L. Soder, “Optimal power flow based on genetic algorithms and clustering techniques,” in 2018 Power Systems Computation Conference (PSCC), IEEE, Jun 2018.
A. Zamzam and K. Baker, “Learning optimal solutions for extremely fast ac optimal power flow,” arXiv preprint arXiv:1910.01213, 2019.
I. Ghosh and P. K. Roy, “Application of earthworm optimization algorithm for solution of optimal power flow,” in 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), IEEE, Mar 2019.
“Optimal power flow using fuzzy-firefly algorithm,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), IEEE, Oct 2018.
F. Bouffard, F. D. Galiana, and J. M. Arroyo, “Umbrella contingencies in security-constrained optimal power flow,” in 15th Power systems computation conference, PSCC, vol. 5, 2005.
S. Eftekharnejad, “Selection of multiple credible contingencies for real time contingency analysis,” in 2015 IEEE Power Energy Society General Meeting, pp. 1–5, July 2015.
H. Bevrani, Robust Power System Frequency Control. Power Electronics and Power Systems, Springer US, 2008.
J. Zhao, H.-D. Chiang, H. Li, and P. Ju, “On pv-pq bus type switching logic in power flow computation,” in Proceedings of the 16th power systems computation conference, vol. 16, p. 7, jul 2008.
HSL, A collection of Fortran codes for large scale scientific computation, 2019 (accessed December 5th, 2019). Availabel in: http://www.hsl.rl.ac.uk/.
Y. Yuan, X. Wen, and K. Qian, “Preventive/corrective control for voltage stability based on primal-dual interior point method,” in 2006 International Conference on Power System Technology, pp. 1–5, 2006.
Xuelian Liu, Jiwen Li, Hongmei Li, and Hongxia Peng, “Fuzzy modeling and interior point algorithm of multi-objective opf with voltage security margin,” in 2005 IEEE/PES Transmission Distribution Conference Exposition: Asia and Pacific, pp. 1– 6, 2005.
Y. Chen, J. Ma, P. Zhang, F. Liu, and S. Mei, “Robust state estimator based on maximum exponential absolute value,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1537–1544, 2017.
R. D. Zimmerman and C. E. Murillo-Sánchez, “Matpower,” 2019.
A. W¨achter and L. T. Biegler, “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,” Mathematical Programming, vol. 106, pp. 25–57, apr 2005.
ARPA-E, “SCOPF Problem Formulation: Challenge 1,” tech. rep., Advanced Research Projects Agency–Energy), 04 2018.
ARPA-E, Grid Optimization (GO) Competition, 2019 (accessed December 5th, 2019). Available in: https://gocompetition.energy.gov.
R. D. Zimmerman and C. E. Murillo-Sánchez, “Matpower user’s manual,” 2019.
M. Bazrafshan, K. Baker, and J. Mohammadi, “Computationally efficient solutions for large-scale security-constrained optimal power flow,” 2020.
Zhang, Solving Large Security-Constrained Optimal Power Flow for Power Grid Planning and Operations. PhD thesis, Case Western Reserve University, 2020.
S. Huang and V. Dinavahi, “Real-time contingency analysis on massively parallel architectures with compensation method,” IEEE Access, vol. 6, pp. 44519–44530, 2018.
X. Su, C. He, T. Liu, and L. Wu, “Full Parallel Power Flow Solution: A GPU-CPU Based Vectorization Parallelization and Sparse Techniques for Newton-Raphson Implementation,” IEEE Transactions on Smart Grid, vol. PP, no. ii, pp. 1–1, 2019.
M. Bazrafshan, K. Baker, and J. Mohammadi, “Computationally efficient solutions for large-scale security-constrained optimal power flow,” arXiv, pp. 1–8, 2020.
Z. Huang et al., High-Performance Computing for Real-Time Grid Analysis and Operation, pp. 151–188. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
L. Balduino et al., “Parallel processing in a cluster of microcomputers with application in contingency analysis,” in 2004 IEEE/PES Transmision and Distribution Conference and Exposition: Latin America (IEEE Cat. No. 04EX956), pp. 285–290, 2004.
G. Angeline Ezhilarasi et al., “Parallel contingency analysis in a high performance computing environment,” in 2009 International Conference on Power Systems, pp. 1– 6, 2009.
W. Gao et al., “Distributed generation placement design and contingency analysis with parallel computing technology,” J. Comput., vol. 4, pp. 347–354, 2009.
Z. Huang et al., “Massive contingency analysis with high performance computing,” in 2009 IEEE Power Energy Society General Meeting, pp. 1–8, 2009.
Yousu Chen et al., “Performance evaluation of counter-based dynamic load balancing schemes for massive contingency analysis with different computing environments,” in IEEE PES General Meeting, pp. 1–6, 2010.
S. Jin et al., “A novel application of parallel betweenness centrality to power grid contingency analysis,” in 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS), pp. 1–7, 2010.
A. Mittal et al., “Real time contingency analysis for power grids,” in Euro-Par 2011 Parallel Processing (E. Jeannot, R. Namyst, and J. Roman, eds.), (Berlin, Heidelberg), pp. 303–315, Springer Berlin Heidelberg, 2011.
S. K. Khaitan et al., Dynamic Load Balancing and Scheduling for Parallel Power System Dynamic Contingency Analysis, pp. 189–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
S. K. Khaitan et al., “Parallelizing power system contingency analysis using d programming language,” in 2013 IEEE Power Energy Society General Meeting, pp. 1– 5, 2013.
S. K. Khaitan et al., “Proactive task scheduling and stealing in master-slave based load balancing for parallel contingency analysis,” Electric Power Systems Research, vol. 103, pp. 9 – 15, 2013.
G. Zhou et al., “The static security analysis in power system based on spark cloud computing platform,” in 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), pp. 1–6, 2015.
A. Haas, “Pypardisoproject,” 2013.
X. Chen, Y. Wang, and H. Yang, “Nicslu: An adaptive sparse matrix solver for parallel circuit simulation,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 32, no. 2, pp. 261–274, 2013.
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
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spelling Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rivera, Sergio1b580d04220f7fb7ad2d622638a1f932Rodríguez Medina, Diego Fernando40ec6280e6c559641426dae861a04d1fPinzón, JaimeElizondo, MarceloWu, Di2022-02-01T22:31:59Z2022-02-01T22:31:59Z2021https://repositorio.unal.edu.co/handle/unal/80849Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasOptimal and secure grid operation is paramount for modern power systems. However, the ever increasing system size, number of conventional and renewable sources, not to mention system loads and power system controllers, make the satisfaction of those requirements in on-line applications not an easy task. Different approaches have been applied to meet power system security criteria and reach optimal cost during real-time operation. Nevertheless, the strategies are mostly employed in small power systems, using strong assumptions or lack of advanced and efficient software-hardware interaction. That makes some of the applications infeasible in real operation or very costly in terms of hardware implementation. As a solution for those limitations, this research will address the problem of Security Constrained Optimal Power Flow (SCOPF) using the potential of Parallel and Heterogeneous Computing (PHC). By this approach, this research is looking to expand the application of advanced computing techniques for the solution of real-time power system problems that simultaneously involves security and optimal cost. The intention is to understand the strategies and principles for computer memory management, data structures and SCOPF re-formulation to optimally satisfy security and time response for proper power system operation.El funcionamiento óptimo y seguro de la red eléctrica es primordial para los sistemas de energía modernos. Sin embargo, el tamaño cada vez mayor de dichos sistemas, así como la cantidad de fuentes convencionales y renovables, sin mencionar las cargas del sistema y los controladores del sistema de energía, hacen que la satisfacción de esos requisitos en las aplicaciones en tiempo real no sean una tarea fácil. Se han aplicado diferentes enfoques para cumplir con los criterios de seguridad del sistema de energía y alcanzar un costo ´optimo durante la operación en tiempo real. Sin embargo, las estrategias se emplean principalmente en sistemas académicos de pequeñas dimensiones, utilizando fuertes suposiciones o falta de software-hardware avanzado y eficiente interacción. Eso hace que algunas de las aplicaciones sean inviables en operación real o muy costosas en términos de implementación de hardware. Como una solución para esas limitaciones, esta investigación abordará el problema del flujo de energía óptimo con restricciones de seguridad (SCOPF) utilizando el potencial de la computación paralela y heterogénea (PHC). Mediante este enfoque, esta investigación busca expandir la aplicación de técnicas informáticas avanzadas para la solución de problemas de sistemas de potencia en tiempo real que involucran simultáneamente seguridad y costo óptimo. La intención es comprender las estrategias y los principios para la gestión de la memoria de la computadora, las estructuras de datos y la reformulación de SCOPF para satisfacer de manera óptima la seguridad y el tiempo de respuesta para correcto funcionamiento del sistema de potencia.DoctoradoDoctorado en Ingeniería EléctricaOptimización de sistemas de potencia114 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Doctorado en Ingeniería - Ingeniería EléctricaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de IngenieríaUniversidad Nacional de Colombia - Sede Bogotá530 - Física::537 - Electricidad y electrónicaPower system securityPower distribution networksAnálisis de redes eléctricasSecurity Constrained Optimal Power Flow (SCOPF)Optimal Power Flow (OPF)Parallel Computing (PC)Complex Power NetworksReal-Time SCOPFGraphical Processing Unit (GPU)Flujo de Potencia ÓptimoSeguridad en Sistemas de PotenciaComputación ParalelaRedes de Potencia ComplejasOperación en Tiempo RealUnidad de Procesamiento GráficoFast security constraint optimal power flow using parallel and heterogenous computingCálculo rápido de flujo de potencia óptimo con restricciones de seguridad utilizando computación paralela y heterogéneaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDQ. Wang, Risk-based Security-Constrained Optimal Power Flow: Mathematical Fundamentals, Computational Strategies, Validation, and use within Electricity Markets. PhD thesis, Iowa State University, 2013.D. Page, A Practical Introduction to Computer Architecture. Springer, 2009.NVIDIA, “NVIDIA Turing GPU,” White Paper, 2018.S. Rennich, “Cuda c/c++ streams and concurrency,” tech. rep., 2012.G. Wood, A; Wollenberg, B; Shebl´e, Power Generation, Operation and Control. Wiley, 2014.F. Garcia, N. D. Sarma, V. Kanduri, G. Nissankala, K. Gopinath, J. Polusani, T. Mortensen, and I. Flores, “ERCOT control center experience in using real-time contingency analysis in the new nodal market,” IEEE Power and Energy Society General Meeting, pp. 1–8, 2012.Z. Li and F. Yang, Advanced metering infrastructure and graphics processing unit technologies in electric distribution networks. No. 9789811070006, Springer Singapore, 2018.NERC, “Standard TPL-001-4 — Transmission System Planning Performance Requirements,” vol. 2, 2014.J. K. Debnath, W. K. Fung, A. M. Gole, and S. Filizadeh, “Simulation of large-scale electrical power networks on graphics processing units,” 2011 IEEE Electrical Power and Energy Conference, EPEC 2011, pp. 199–204, 2011.J. Baranowski and D. J. French, “Operational use of contingency analysis at PJM,”IEEE Power and Energy Society General Meeting, pp. 13–16, 2012.TOP500, “Home — TOP500 Supercomputer Sites,” 2020.R. Gnanavignesh and U. J. Shenoy, “Parallel Sparse LU Factorization of Power Flow Jacobian using GPU,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2019-Octob, no. C, pp. 1857–1862, 2019.I. Araújo, V. Tadaiesky, D. Cardoso, Y. Fukuyama, and A. Santana, “Simultaneous ´parallel power flow calculations using hybrid CPU-GPU approach,” International Journal of Electrical Power and Energy Systems, vol. 105, no. February 2018, pp. 229–236, 2019.Q. Shi, C. Yuan, W. Feng, G. Liu, R. Dai, Z. Wang, and F. Li, “Enabling Model-Based LTI for Large-Scale Power System Security Monitoring and Enhancement with Graph Computing-Based Power Flow Calculation,” IEEE Access, vol. 7, pp. 167010–167018, 2019.P. Duan, S. Xu, H. Chen, X. Yang, S. Wang, and E. Hu, “High Performance Computing (HPC)for Advanced Power System Studies,” 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings, pp. 1–9, 2018.K. Tang, S. Dong, B. Zhu, Q. Ni, and Y. Song, “GPU-Based Real-time N-1 AC Power Flow Algorithm with Preconditioned Iterative Method,” IEEE Power and Energy Society General Meeting, vol. 2018-Augus, pp. 1–5, 2018.G. Ruetsch and B. Oster, “Getting Started with CUDA What is CUDA ?,” Materials, vol. 17, no. 4, pp. 223–224, 2008.T. Soyata, GPU Parallel Program Development Using CUDA. 2018.L. Platbrood, H. Crisciu, F. Capitanescu, and L. Wehenkel, “Solving very large-scale security-constrained optimal power flow problems by combining iterative contingency selection and network compression,” 17th Power Systems Computation Conference, PSCC 2011, 2011.F. Capitanescu, M. Glavic, D. Ernst, and L. Wehenkel, “Applications of security constrained optimal power flows,” Modern Electric Power Systems Symposium, MEPS06, p. 7, 2006.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.O. Alsa¸c, J. Bright, M. Prais, and B. Stott, “Further developments in lp-based optimal power flow,” IEEE Transactions on Power Systems, vol. 5, no. 3, pp. 697–711, 1990.D. Rodriguez-Medina, D. Gomez, S. Rivera, and J. Gers, “A fast decomposition method to solve a security-constrained optimal power flow (scopf) empowered by heterogeneous and parallel computing (hpc) (under review),” PES General Meeting, pp. 52812–52824, 2022.D. Rodriguez, D. Alvarez, D. Gomez, J. Gers, and S. Rivera, “Low-cost analysis of load flow computing using embedded computer empowered by gpu,” Proceedings - IEEE PES ISGT NA 2021: “Technology Solutions for an Evolving Grid”, 02 2021.D. Rodriguez, D. Gomez, D. Alvarez, and S. Rivera, “A review of parallel heterogeneous computing algorithms in power systems,” Algorithms, vol. 14, no. 10, 2021.D. Rodriguez, A. Angulo, D. F. Gomez, A. David, J. Gil, and S. Rivera, “Smart Microgrids Operation Considering Expert Knowledge and Ensembled Based Metaheuristic Optimization Algorithms (Under Review),” International Journal of Electrical and Computer Science, vol. 12, 2021.T. Valencia-Zuluaga, D. Agudelo-Martinez, D. Arango-Angarita, C. Acosta-Urrego, S. Rivera, D. Rodriguez-Medina, and J. Gers, “A Fast Decomposition Method to Solve a Security-Constrained Optimal Power Flow (SCOPF) Problem through Constraint Handling,” IEEE Access, vol. 9, pp. 52812–52824, 2021.A. Angulo, D. Rodr´ıguez, W. Garz´on, D. F. G´omez, A. Al Sumaiti, and S. Rivera, “Algorithms for bidding strategies in local energy markets: Exhaustive search through parallel computing and metaheuristic optimization,” Algorithms, vol. 14, no. 9, 2021.J. Ramírez-Romero, D. Medina, and S. Rivera, “Teaching using a synchronous machine virtual laboratory,” Global Journal of Engineering Education, vol. 22, pp. 123–130, 06 2020.J. Garcia-Guarin, D. Rodriguez, D. Alvarez, S. Rivera, C. Cortes, A. Guzman, A. Bretas, J. R. Aguero, and N. Bretas, “Smart microgrids operation considering a variable neighborhood search: The differential evolutionary particle swarm optimization algorithm,” Energies, vol. 12, no. 16, pp. 1–13, 2019.S. Vargas, D. Rodriguez, and S. Rivera, “Mathematical Formulation and Numerical Validation of Uncertainty Costs for Controllable Loads,” Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, vol. 12, 2019.S. Rivera, D. Rodriguez, and I. Erlich, “2018 Grid Optimization Competition Evaluating the Performance of Modern Heuristic Optimizers on Stochastic Optimization Problems applied to Smart Grids Test bed A : Stochastic OPF in Presence of Renewable Energy and Controllable Loads,” Intelligent Systems Subcommittee Power System Analysis, Computing, and Economic Committee, no. January, 2018.J. Arevalo, D. Medina, J. Rueda, and S. Rivera, “2018 competition on operational planning of sustainable power systems: Testsbeds and results,” WSEAS Transactions on Power Systems, vol. 14, pp. 98–106, 08 2019.D. Rodriguez, D. Gomez, W. Garzon, D. Alvarez, S. Rivera, and J. Gers, “Posicionamiento Optimo de cuadrillas basado en estadísticas de Tránsito de Google Maps e Indicadores de Confiabilidad,” 2018.D. Rodriguez, J. M. Gers, T. Valencia, C. Acosta, D. Agudelo, and D. Arango, “Ensembled Method: Constraints Relaxation With Analytical Optimization With Combined Heuristic Method,” tech. rep., 2020.D. Rodriguez and T. Valencia, “Posicionamiento Optimo de cuadrillas basado en estadísticas de Tránsito de Google Maps e Indicadores de Confiabilidad,” 2018.J. García, D. Rodríguez, and S. Rivera, “Herramienta para la Programación de Redes Inteligentes con Recursos Energéticos de Alta Incertidumbre,” 2019.NERC, Reliability Assessment Guidebook. 1 ed., 2010.N. Garcia, “Parallel power flow solutions using a biconjugate gradient algorithm and a Newton method: A GPU-based approach,” IEEE PES General Meeting, PES 2010, no. 5, pp. 1–4, 2010.ARPA, “About the Competition — Grid Optimization Competition,” 2019.S. Huang and V. Dinavahi, “Fast Batched Solution for Real-Time Optimal Power Flow with Penetration of Renewable Energy,” IEEE Access, vol. 6, pp. 13898–13910, 2018.V. H. Hinojosa and F. Gonzalez-Longatt, “Preventive security-constrained DCOPF formulation using power transmission distribution factors and line outage distribution factors,” Energies, vol. 11, no. 6, pp. 1–13, 2018.Y. Yu and P. Luh, “Scalable corrective security-constrained economic dispatch considering conflicting contingencies,” International Journal of Electrical Power and Energy Systems, vol. 98, no. December 2017, pp. 269–278, 2018.Y. Yang and Y. Feng, “Large-scale preventive security constrained optimal power flow based on compensation method,” IEEE Power and Energy Society General Meeting, vol. 2015-Septe, 2015.F. Capitanescu, J. L. Martinez Ramos, P. Panciatici, D. Kirschen, A. Marano Marcolini, L. Platbrood, and L. Wehenkel, “State-of-the-art, challenges, and future trends in security constrained optimal power flow,” Electric Power Systems Research, vol. 81, no. 8, pp. 1731–1741, 2011.H. Harsan, N. Hadjsaid, and P. Pruvot, “Cyclic Security Analysis for Security Constrained Optimal Power Flow,” IEEE Transactions on Power Systems, vol. 12, no. 2, pp. 948–953, 1997.Q. Wang, J. D. McCalley, T. Zheng, and E. Litvinov, “Solving corrective risk based security-constrained optimal power flow with Lagrangian relaxation and Benders decomposition,” International Journal of Electrical Power and Energy Systems, vol. 75, pp. 255–264, 2016.Y. Li and J. D. McCalley, “Decomposed SCOPF for improving efficiency,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 494–495, 2009.Q. Wang, J. D. McCalley, T. Zheng, and E. Litvinov, “A computational strategy to solve preventive risk-based security-constrained OPF,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1666–1675, 2013.V. H. Hinojosa, “Comparative Corrective and Preventive Security-Constrained DCOPF Problems Using Linear Shift-Factors,” Energies, pp. 1–16, 2020.J. Mohammadi, G. Hug, and S. Kar, “A benders decomposition approach to corrective security constrained OPF with power flow control devices,” IEEE Power and Energy Society General Meeting, 2013.S. Huang and V. Dinavahi, “Performance analysis of GPU-accelerated fast decoupled power flow using direct linear solver,” 2017 IEEE Electrical Power and Energy Conference, EPEC 2017, vol. 2017-Octob, no. 1, pp. 1–6, 2017.M. Wang, Y. Chen, and S. Huang, “GPU-based Power Flow Analysis with Continuous Newton ’ s Method,” IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–5, 2017.L. Y. Kyaw and S. Phyu, “Scheduling Methods in HPC System: Review,” 2020 IEEE Conference on Computer Applications, ICCA 2020, pp. 1–6, 2020.L. R, P. M, and B. C, Computational Physics. 2007.P. Marksteiner, “High-performance computing - An overview,” Computer Physics Communications, vol. 97, no. 1-2, pp. 16–35, 1996.H. Andrade and I. Crnkovic, “A Review on Software Architectures for Heterogeneous Platforms,” Proceedings - Asia-Pacific Software Engineering Conference, APSEC, vol. 2018-December, pp. 209–218, 2018.P. N. Glaskowsky, “NVIDIA ’ s Fermi : The First Complete GPU Computing Architecture,” no. September, 2009.M. Marin, G.-e. P. Flow, and A. Distributed, GPU-Enhanced power flow analysis. PhD thesis, UNIVERSITE DE PERPIGNAN VIA DOMITIA-UNIVERSITY COLLEGE DUBLIN, 2016.Z. Feng, X. Zhao, and Z. Zeng, “Robust parallel preconditioned power grid simulation on gpu with adaptive runtime performance modeling and optimization,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 30, no. 4, pp. 562–573, 2011.Z. Li, J. Zhu, and F. Yang, “How far is the GPU technology from practical power system applications?,” IEEE Power and Energy Society General Meeting, vol. 2014-Octob, no. October, 2014.X. Li, F. Li, and S. Member, “GPU-based Fast Decoupled Power Flow with Preconditioned Iterative Solver and Inexact Newton Method,” IEEE Power & Energy Society General Meeting, vol. 8950, no. c, pp. 1–1, 2017.V. Roberge, M. Tarbouchi, and F. Okou, “Parallel power flow on graphics processing units for concurrent evaluation of many networks,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1639–1648, 2017.V. Jalili-Marandi and V. Dinavahi, “Simd-based large-scale transient stability simulation on the graphics processing unit,” IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1589–1599, 2010.V. Jalili-Marandi, Z. Zhou, and V. Dinavahi, “Large-scale transient stability simulation of electrical power systems on parallel gpus,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 7, pp. 1255–1266, 2012.Z. Zhou and V. Dinavahi, “Parallel massive-thread electromagnetic transient simulation on gpu,” IEEE Transactions on Power Delivery, vol. 29, no. 3, pp. 1045–1053, 2014.Y. Song, Y. Chen, S. Huang, Y. Xu, Z. Yu, and J. R. Marti, “Fully gpu-based electromagnetic transient simulation considering large-scale control systems for system-level studies,” IET Generation, Transmission Distribution, vol. 11, no. 11, pp. 2840–2851, 2017.N. Lukac and B. Zalik, “Gpu-based roofs’ solar potential estimation using lidar data,” Computers Geosciences, vol. 52, pp. 34 – 41, 2013.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.G. Capizzi, G. Lo Sciuto, C. Napoli, and E. Tramontana, “Advanced and adaptive dispatch for smart grids by means of predictive models,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6684–6691, 2018.G. Zhou, X. Zhang, Y. Lang, R. Bo, Y. Jia, J. Lin, and Y. Feng, “A novel GPU accelerated strategy for contingency screening of static security analysis,” International Journal of Electrical Power and Energy Systems, vol. 83, pp. 33–39, 2016.H. Karimipour and V. Dinavahi, “Extended kalman filter-based parallel dynamic state estimation,” IEEE Transactions on Smart Grid, vol. 6, no. 3, pp. 1539–1549, 2015.W. Qiu, Q. Tang, J. Liu, Z. Teng, and W. Yao, “Power quality disturbances recognition using modified s transform and parallel stack sparse auto-encoder,” Electric Power Systems Research, vol. 174, p. 105876, 2019.Z. Liu, X. Li, L. Wu, S. Zhou, and K. Liu, “Gpu-accelerated parallel coevolutionary algorithm for parameters identification and temperature monitoring in permanent magnet synchronous machines,” IEEE Transactions on Industrial Informatics, vol. 11, no. 5, pp. 1220–1230, 2015.V. Schwarzer and R. Ghorbani, “New opportunities for large-scale design optimization of electric vehicles using gpu technology,” in 2011 IEEE Vehicle Power and Propulsion Conference, pp. 1–6, 2011.G. Zhou, R. Bo, L. Chien, X. Zhang, S. Yang, and D. Su, “Gpu-accelerated algorithm for online probabilistic power flow,” IEEE Transactions on Power Systems, vol. 33, pp. 1132–1135, Jan 2018Z. Chen, L. Shen, Y. Zhao, and C. Yang, “Parallel algorithm for real-time contouring from grid dem on modern gpus,” Science China Technological Sciences, vol. 53, pp. 33–37, May 2010.T. He, K. Meng, Z.-Y. Dong, Y.-T. Oh, and Y. Xu, “Use of high-performance graphics processing units for power system demand forecasting,” Journal of Electrical Engineering and Technology, vol. 5, p. 363–370, Jan 2010.F. Milano, “Small-signal stability analysis of large power systems with inclusion of multiple delays,” IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3257–3266, 2016.B. Shang, Y. Xu, C. Zhang, Y. Chen, Z. Liu, L. Lin, C. Xu, and J. Yu, “Gpu-accelerated batch solution for short-circuit current calculation of large-scale power systems,” in 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC), pp. 1743–1748, 2019.J. S. Chai, N. Zhu, A. Bose, and D. J. Tylavsky, “Parallel newton type methods for power system stability analysis using local and shared memory multiprocessors,” IEEE Transactions on Power Systems, vol. 6, no. 4, pp. 1539–1545, 1991.J. Shu, Wei Xue, and Weimin Zheng, “A parallel transient stability simulation for power systems,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 1709–1717, 2005.P. Aristidou, D. Fabozzi, and T. Van Cutsem, “Dynamic simulation of large-scale power systems using a parallel schur-complement-based decomposition method,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 10, pp. 2561–2570, 2014.S. K. Khaitan, J. D. McCalley, and A. Somani, “Proactive task scheduling and stealing in master-slave based load balancing for parallel contingency analysis,” Electric Power Systems Research, vol. 103, pp. 9 – 15, 2013.S. K. Khaitan and J. D. McCalley, “Scale: A hybrid mpi and multithreading based work stealing approach for massive contingency analysis in power systems,” Electric Power Systems Research, vol. 114, pp. 118 – 125, 2014.Jun Qiang Wu and A. Bose, “Parallel solution of large sparse matrix equations and parallel power flow,” IEEE Transactions on Power Systems, vol. 10, no. 3, pp. 1343– 1349, 1995.X. Wang, S. G. Ziavras, C. Nwankpa, J. Johnson, and P. Nagvajara, “Parallel solution of newton’s power flow equations on configurable chips,” International Journal of Electrical Power Energy Systems, vol. 29, no. 5, pp. 422 – 431, 2007.J. Baek, Q. H. Vu, J. K. Liu, X. Huang, and Y. Xiang, “A secure cloud computing based framework for big data information management of smart grid,” IEEE Transactions on Cloud Computing, vol. 3, no. 2, pp. 233–244, 2015.J. Soares, M. A. F. Ghazvini], Z. Vale, and P. [de Moura Oliveira], “A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads,” Applied Energy, vol. 162, pp. 1074 – 1088, 2016.G. N. Korres, A. Tzavellas, and E. Galinas, “A distributed implementation of multi-area power system state estimation on a cluster of computers,” Electric Power Systems Research, vol. 102, pp. 20 – 32, 2013.Y. Fukuyama and Hsaio-Dong Chiang, “A parallel genetic algorithm for generation expansion planning,” IEEE Transactions on Power Systems, vol. 11, no. 2, pp. 955– 961, 1996.A. Rami, A. Zeblah, H. Hamdaoui, Y. Massim, and F. Harrou, “An efficient artificial immune algorithm for power system reliability optimisation,” International Journal of Power and Energy Conversion, vol. 1, no. 2-3, pp. 178–197, 2009. cited By 7.C. Dufour, V. Jalili-Marandi, and J. Bélanger, “Real-time simulation using transient stability, electromagnetic transient and fpga-based high-resolution solvers,” in 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, pp. 283– 288, 2012.J. Ma, K. L. Man, S.-U. Guan, T. O. Ting, and P. W. H. Wong, “Parameter estimation of photovoltaic model via parallel particle swarm optimization algorithm,” International Journal of Energy Research, vol. 40, no. 3, pp. 343–352, 2016.F. Sato, A. Garcia, A. Monticelli, and A. B. Alves], “Distributed short-circuit analysis in heterogeneous computer networks,” International Journal of Electrical Power Energy Systems, vol. 22, no. 2, pp. 129 – 136, 2000.A. K. Zadeh, K. M. Nor, and H. Zeynal, “Multi-thread security constraint economic dispatch with exact loss formulation,” in 2010 IEEE International Conference on Power and Energy, pp. 864–869, 2010.T. Cui and F. Franchetti, “A multi-core high performance computing framework for probabilistic solutions of distribution systems,” in 2012 IEEE Power and Energy Society General Meeting, pp. 1–6, 2012.J. Zhang, S. Lin, H. Liu, Y. Chen, M. Zhu, and Y. Xu, “A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints,” Energy, vol. 123, pp. 538 – 554, 2017.V. Roberge, M. Tarbouchi, and F. Okou, “Optimal power flow based on parallel metaheuristics for graphics processing units,” Electric Power Systems Research, vol. 140, pp. 344–353, 2016.G. Geng, Q. Jiang, and Y. Sun, “Parallel transient stability-constrained optimal power flow using gpu as coprocessor,” IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1436–1445, 2017.B. Kim, “A fast distributed implementation of optimal power flow,” IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 858–864, 1999. cited By 169.H. R. Cai, C. Y. Chung, and K. P. Wong, “Application of differential evolution algorithm for transient stability constrained optimal power flow,” IEEE Transactions on Power Systems, vol. 23, no. 2, pp. 719–728, 2008.M. Abedini, “A novel algorithm for load flow analysis in island microgrids using an improved evolutionary algorithm,” International Transactions on Electrical Energy Systems, vol. 26, pp. 2727–2743, dec 2016.Taufik, M. A. Guevara, A. Shaban, and A. Nafisi, “Modeling and Load Flow Analysis of a Microgrid Laboratory.,” International Journal of Smart Grid and Sustainable Energy Technologies, vol. 3, pp. 103–111, dec 2019.J. I. Giraldez Miner, F. Flores-Espino, S. MacAlpine, and P. Asmus, “Phase i microgrid cost study: Data collection and analysis of microgrid costs in the united states,” National Renewable Energy Laboratory, 10 2018.V. Kloh, D. Yokoyama, A. Yokoyama, G. Silva, M. Ferro, and B. Schulze, “Performance and Energy Efficiency Evaluation for HPC Applications in Heterogeneous Architectures,” in 2018 Symposium on High Performance Computing Systems (WSCAD), pp. 162–169, IEEE, oct 2018.S. K. Khaitan, “A survey of high-performance computing approaches in power systems,” in 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1– 5, IEEE, jul 2016.L. Rakai and W. Rosehart, “GPU-accelerated solutions to optimal power flow problems,” Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 2511–2516, 2014.D. Greenwood, K. Lim, C. Patsios, P. Lyons, Y. Lim, and P. Taylor, “Frequency response services designed for energy storage,” Applied Energy, vol. 203, pp. 115 – 127, 2017.D. Carreira, G. D. Marques, and D. M. Sousa, “Hybrid energy storage system with a low cost digital control,” in 2015 9th International Conference on Compatibility and Power Electronics (CPE), pp. 185–190, 2015.D. Wu, T. Dragicevic, J. C. Vasquez, J. M. Guerrero, and Y. Guan, “Secondary coordinated control of islanded microgrids based on consensus algorithms,” in 2014 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 4290–4297, 2014.A. R. Brodtkorb, T. R. Hagen, and M. L. Sætra, “Graphics processing unit (GPU) programming strategies and trends in GPU computing,” Journal of Parallel and Distributed Computing, vol. 73, pp. 4–13, jan 2013.H. H. Holm, A. R. Brodtkorb, and M. L. Sætra, “GPU Computing with Python: Performance, Energy Efficiency and Usability,” Computation, vol. 8, p. 4, jan 2020.S. K. Lam, A. Pitrou, and S. Seibert, “Numba: A LLVM-Based Python JIT Compiler,” in Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, LLVM ’15, (New York, NY, USA), Association for Computing Machinery, 2015.P. Virtanen, R. Gommers, and et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python,” Nature Methods, vol. 17, pp. 261–272, mar 2020.R. D. Zimmerman, C. E. Murillo-Sanchez, 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, pp. 12–19, feb 2011.Q. Wang, J. D. McCalley, T. Zheng, and E. Litvinov, “A computational strategy to solve preventive risk-based security-constrained OPF,” IEEE Transactions on Power Systems, vol. 28, pp. 1666–1675, may 2013.S. A. Sadat, D. Haralson, and M. Sahraei-Ardakani, “Security versus computation time in IV-ACOPF with SOCP initialization,” in 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE, jun 2018.F. Capitanescu, J. M. Ramos, P. Panciatici, D. Kirschen, A. M. Marcolini, L. Platbrood, and L. Wehenkel, “State-of-the-art, challenges, and future trends in security constrained optimal power flow,” Electric Power Systems Research, vol. 81, pp. 1731–1741, Aug 2011.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, jul 2016.D. Phan and J. Kalagnanam, “Some efficient optimization methods for solving the security-constrained optimal power flow problem,” IEEE Transactions on Power Systems, vol. 29, pp. 863–872, March 2014.J. Mohammadi, G. Hug, and S. Kar, “A benders decomposition approach to corrective security constrained OPF with power flow control devices,” in 2013 IEEE Power & Energy Society General Meeting, IEEE, 2013.D. T. Phan and X. A. Sun, “Minimal impact corrective actions in security-constrained optimal power flow via sparsity regularization,” IEEE Transactions on Power Systems, vol. 30, pp. 1947–1956, jul 2015.M. Al-Saffar and P. Musilek, “Distributed optimal power flow for electric power systems with high penetration of distributed energy resources,” in 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), IEEE, may 2019.R. Louca and E. Bitar, “Robust AC optimal power flow,” IEEE Transactions on Power Systems, vol. 34, pp. 1669–1681, may 2019.Y. Li and J. McCalley, “Decomposed SCOPF for improving efficiency,” IEEE Transactions on Power Systems, vol. 24, pp. 494–495, feb 2009.F. Capitanescu, M. Glavic, D. Ernst, and L. Wehenkel, “Applications of security constrained optimal power flows,” in In Proceedings of Modern Electric Power Systems Symposium, MEPS06, 2006.S. Sojoudi and J. Lavaei, “Physics of power networks makes hard optimization problems easy to solve,” in 2012 IEEE Power and Energy Society General Meeting, IEEE, jul 2012.V. Hinojosa and F. Gonzalez-Longatt, “Preventive security-constrained DCOPF formulation using power transmission distribution factors and line outage distribution factors,” Energies, vol. 11, p. 1497, jun 2018.Y. Xu, H. Yang, R. Zhang, Z. Dong, M. Lai, and K. Wong, “A contingency partitioning approach for preventive-corrective security-constrained optimal power flow computation,” Electric Power Systems Research, vol. 132, pp. 132–140, 2016.Y. Xu, Z. Y. Dong, R. Zhang, K. P. Wong, and M. Lai, “Closure to discussion on “solving preventive-corrective scopf by a hybrid computational strategy”,” IEEE Transactions on Power Systems, vol. 29, p. 3124–3125, Nov 2014.M. Javadi, A. E. Nezhad, M. Gough, M. Lotfi, and J. P. Catalao, “Implementation of consensus-ADMM approach for fast DC-OPF studies,” in 2019 International Conference on Smart Energy Systems and Technologies (SEST), IEEE, sep 2019.A. Attarha and N. Amjady, “Solution of security constrained optimal power flow for large-scale power systems by convex transformation techniques and taylor series,” IET Generation, Transmission & Distribution, vol. 10, p. 889–896, Mar 2016.A. Werner, K. Duwadi, N. Stegmeier, T. M. Hansen, and J.-H. Kimn, “Parallel implementation of ac optimal power flow and time constrained optimal power flow using high performance computing,” in 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, Jan 2019.E. Karangelos and L. Wehenkel, “An iterative AC-SCOPF approach managing the contingency and corrective control failure uncertainties with a probabilistic guarantee,” IEEE Transactions on Power Systems, vol. 34, pp. 3780–3790, sep 2019.S. Lee, W. Kim, and B. H. Kim, “Performance comparison of optimal power flow algorithms for lmp calculations of the full scale korean power system,” Journal of Electrical Engineering and Technology, vol. 10, p. 109–117, Jan 2015.Y. Chen, Z. Zhang, Y. Lang, J. Ma, and S. Zheng, “Generalised-fast decoupled state estimator,” IET Generation, Transmission Distribution, vol. 12, pp. 5928–5938, may 2018.J. Guo, G. Hug, and O. K. Tonguz, “A case for nonconvex distributed optimization in large-scale power systems,” IEEE Transactions on Power Systems, vol. 32, pp. 3842– 3851, sep 2017.M. Granada Echeverri, M. J. Rider Flores, and J. R. S. Mantovani, “Dos técnicas de descomposición aplicadas al problema de flujo de potencia óptimo multi-areas,” DYNA, vol. 77, pp. 303 – 312, 06 2010.J. Guo, G. Hug, and O. Tonguz, “Asynchronous admm for distributed non-convex optimization in power systems,” arXiv preprint arXiv:1710.08938, 2017.M. Bazrafshan, K. Baker, and J. Mohammadi, “Computationally efficient solutions for large-scale security-constrained optimal power flow,” 2020.S. Stankovic and L. Soder, “Optimal power flow based on genetic algorithms and clustering techniques,” in 2018 Power Systems Computation Conference (PSCC), IEEE, Jun 2018.A. Zamzam and K. Baker, “Learning optimal solutions for extremely fast ac optimal power flow,” arXiv preprint arXiv:1910.01213, 2019.I. Ghosh and P. K. Roy, “Application of earthworm optimization algorithm for solution of optimal power flow,” in 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), IEEE, Mar 2019.“Optimal power flow using fuzzy-firefly algorithm,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), IEEE, Oct 2018.F. Bouffard, F. D. Galiana, and J. M. Arroyo, “Umbrella contingencies in security-constrained optimal power flow,” in 15th Power systems computation conference, PSCC, vol. 5, 2005.S. Eftekharnejad, “Selection of multiple credible contingencies for real time contingency analysis,” in 2015 IEEE Power Energy Society General Meeting, pp. 1–5, July 2015.H. Bevrani, Robust Power System Frequency Control. Power Electronics and Power Systems, Springer US, 2008.J. Zhao, H.-D. Chiang, H. Li, and P. Ju, “On pv-pq bus type switching logic in power flow computation,” in Proceedings of the 16th power systems computation conference, vol. 16, p. 7, jul 2008.HSL, A collection of Fortran codes for large scale scientific computation, 2019 (accessed December 5th, 2019). Availabel in: http://www.hsl.rl.ac.uk/.Y. Yuan, X. Wen, and K. Qian, “Preventive/corrective control for voltage stability based on primal-dual interior point method,” in 2006 International Conference on Power System Technology, pp. 1–5, 2006.Xuelian Liu, Jiwen Li, Hongmei Li, and Hongxia Peng, “Fuzzy modeling and interior point algorithm of multi-objective opf with voltage security margin,” in 2005 IEEE/PES Transmission Distribution Conference Exposition: Asia and Pacific, pp. 1– 6, 2005.Y. Chen, J. Ma, P. Zhang, F. Liu, and S. Mei, “Robust state estimator based on maximum exponential absolute value,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1537–1544, 2017.R. D. Zimmerman and C. E. Murillo-Sánchez, “Matpower,” 2019.A. W¨achter and L. T. Biegler, “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming,” Mathematical Programming, vol. 106, pp. 25–57, apr 2005.ARPA-E, “SCOPF Problem Formulation: Challenge 1,” tech. rep., Advanced Research Projects Agency–Energy), 04 2018.ARPA-E, Grid Optimization (GO) Competition, 2019 (accessed December 5th, 2019). Available in: https://gocompetition.energy.gov.R. D. Zimmerman and C. E. Murillo-Sánchez, “Matpower user’s manual,” 2019.M. Bazrafshan, K. Baker, and J. Mohammadi, “Computationally efficient solutions for large-scale security-constrained optimal power flow,” 2020.Zhang, Solving Large Security-Constrained Optimal Power Flow for Power Grid Planning and Operations. PhD thesis, Case Western Reserve University, 2020.S. Huang and V. Dinavahi, “Real-time contingency analysis on massively parallel architectures with compensation method,” IEEE Access, vol. 6, pp. 44519–44530, 2018.X. Su, C. He, T. Liu, and L. Wu, “Full Parallel Power Flow Solution: A GPU-CPU Based Vectorization Parallelization and Sparse Techniques for Newton-Raphson Implementation,” IEEE Transactions on Smart Grid, vol. PP, no. ii, pp. 1–1, 2019.M. Bazrafshan, K. Baker, and J. Mohammadi, “Computationally efficient solutions for large-scale security-constrained optimal power flow,” arXiv, pp. 1–8, 2020.Z. Huang et al., High-Performance Computing for Real-Time Grid Analysis and Operation, pp. 151–188. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.L. Balduino et al., “Parallel processing in a cluster of microcomputers with application in contingency analysis,” in 2004 IEEE/PES Transmision and Distribution Conference and Exposition: Latin America (IEEE Cat. No. 04EX956), pp. 285–290, 2004.G. Angeline Ezhilarasi et al., “Parallel contingency analysis in a high performance computing environment,” in 2009 International Conference on Power Systems, pp. 1– 6, 2009.W. Gao et al., “Distributed generation placement design and contingency analysis with parallel computing technology,” J. Comput., vol. 4, pp. 347–354, 2009.Z. Huang et al., “Massive contingency analysis with high performance computing,” in 2009 IEEE Power Energy Society General Meeting, pp. 1–8, 2009.Yousu Chen et al., “Performance evaluation of counter-based dynamic load balancing schemes for massive contingency analysis with different computing environments,” in IEEE PES General Meeting, pp. 1–6, 2010.S. Jin et al., “A novel application of parallel betweenness centrality to power grid contingency analysis,” in 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS), pp. 1–7, 2010.A. Mittal et al., “Real time contingency analysis for power grids,” in Euro-Par 2011 Parallel Processing (E. Jeannot, R. Namyst, and J. Roman, eds.), (Berlin, Heidelberg), pp. 303–315, Springer Berlin Heidelberg, 2011.S. K. Khaitan et al., Dynamic Load Balancing and Scheduling for Parallel Power System Dynamic Contingency Analysis, pp. 189–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.S. K. Khaitan et al., “Parallelizing power system contingency analysis using d programming language,” in 2013 IEEE Power Energy Society General Meeting, pp. 1– 5, 2013.S. K. Khaitan et al., “Proactive task scheduling and stealing in master-slave based load balancing for parallel contingency analysis,” Electric Power Systems Research, vol. 103, pp. 9 – 15, 2013.G. Zhou et al., “The static security analysis in power system based on spark cloud computing platform,” in 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA), pp. 1–6, 2015.A. Haas, “Pypardisoproject,” 2013.X. Chen, Y. Wang, and H. Yang, “Nicslu: An adaptive sparse matrix solver for parallel circuit simulation,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 32, no. 2, pp. 261–274, 2013.EstudiantesInvestigadoresMedios de comunicaciónORIGINAL1015409514.2021.pdf1015409514.2021.pdfTesis de Doctorado en Ingeniería Eléctricaapplication/pdf4007993https://repositorio.unal.edu.co/bitstream/unal/80849/1/1015409514.2021.pdf20093306f9f7194d24815cef4665d9e5MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80849/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52Licencia y autorización para publicación de obras en el repositorio institucional UN - Diego Fernando Rodriguez Medina.pdfLicencia y autorización para publicación de obras en el repositorio institucional UN - Diego Fernando Rodriguez 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