A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders
This paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations t...
- Autores:
-
Montoya, O.D.
Gil-González, Walter
Hernández, J. C.
Giral-Ramírez, Diego Armando
Medina-Quesada, A.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/9943
- Palabra clave:
- Branch-to-node incidence matrix
Direct current networks
Mixed-integer nonlinear programming model
General algebraic modelling system
Optimal reconfiguration of distribution grids
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| title |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| spellingShingle |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders Branch-to-node incidence matrix Direct current networks Mixed-integer nonlinear programming model General algebraic modelling system Optimal reconfiguration of distribution grids LEMB |
| title_short |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| title_full |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| title_fullStr |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| title_full_unstemmed |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| title_sort |
A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feeders |
| dc.creator.fl_str_mv |
Montoya, O.D. Gil-González, Walter Hernández, J. C. Giral-Ramírez, Diego Armando Medina-Quesada, A. |
| dc.contributor.author.none.fl_str_mv |
Montoya, O.D. Gil-González, Walter Hernández, J. C. Giral-Ramírez, Diego Armando Medina-Quesada, A. |
| dc.subject.keywords.spa.fl_str_mv |
Branch-to-node incidence matrix Direct current networks Mixed-integer nonlinear programming model General algebraic modelling system Optimal reconfiguration of distribution grids |
| topic |
Branch-to-node incidence matrix Direct current networks Mixed-integer nonlinear programming model General algebraic modelling system Optimal reconfiguration of distribution grids LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
This paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations through a branch-to-node incidence matrix. The general algebraic modelling system (GAMS) is chosen as a solution tool, showing in tutorial form the implementation of the proposed MINLP model in a 6-nodes test feeder with 10 candidate lines. The validation of the MINLP formulation is performed in two classical 10-nodes DC test feeders. These are typically used for power flow and optimal power flow analyses. Numerical results demonstrate that power losses are reduced by about 16% when the optimal reconfiguration plan is found. The numerical validations are made in the GAMS software licensed by Universidad Tecnológica de Bolívar. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-08-27 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-08T15:18:44Z |
| dc.date.available.none.fl_str_mv |
2021-02-08T15:18:44Z |
| dc.date.submitted.none.fl_str_mv |
2021-02-03 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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publishedVersion |
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Montoya, O.D.; Gil-González, W.; Hernández, J.C.; Giral-Ramírez, D.A.; Medina-Quesada, A. A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders. Energies 2020, 13, 4440. https://doi.org/10.3390/en13174440 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/9943 |
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https://www.mdpi.com/1996-1073/13/17/4440 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/en13174440 |
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1996-1073 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Montoya, O.D.; Gil-González, W.; Hernández, J.C.; Giral-Ramírez, D.A.; Medina-Quesada, A. A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders. Energies 2020, 13, 4440. https://doi.org/10.3390/en13174440 10.3390/en13174440 1996-1073 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/9943 https://www.mdpi.com/1996-1073/13/17/4440 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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22 páginas |
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application/pdf |
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Cartagena de Indias |
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Energies 2020, 13(17), 4440 |
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Universidad Tecnológica de Bolívar |
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Montoya, O.D.27ff4177-1725-4ebd-bfb1-60814364e669Gil-González, Walter59bfddb4-d5c7-4bd3-8cbe-49b131a07e1cHernández, J. C.2e683005-2088-49f9-a56f-335ed84362e7Giral-Ramírez, Diego Armandoe3e7c1cc-5d21-4f99-9629-3d575ca931e2Medina-Quesada, A.c9388225-c3a2-431f-b3b0-be73d13e9d452021-02-08T15:18:44Z2021-02-08T15:18:44Z2020-08-272021-02-03Montoya, O.D.; Gil-González, W.; Hernández, J.C.; Giral-Ramírez, D.A.; Medina-Quesada, A. A Mixed-Integer Nonlinear Programming Model for Optimal Reconfiguration of DC Distribution Feeders. Energies 2020, 13, 4440. https://doi.org/10.3390/en13174440https://hdl.handle.net/20.500.12585/9943https://www.mdpi.com/1996-1073/13/17/444010.3390/en131744401996-1073Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis paper deals with the optimal reconfiguration problem of DC distribution networks by proposing a new mixed-integer nonlinear programming (MINLP) formulation. This MINLP model focuses on minimising the power losses in the distribution lines by reformulating the classical power balance equations through a branch-to-node incidence matrix. The general algebraic modelling system (GAMS) is chosen as a solution tool, showing in tutorial form the implementation of the proposed MINLP model in a 6-nodes test feeder with 10 candidate lines. The validation of the MINLP formulation is performed in two classical 10-nodes DC test feeders. These are typically used for power flow and optimal power flow analyses. Numerical results demonstrate that power losses are reduced by about 16% when the optimal reconfiguration plan is found. The numerical validations are made in the GAMS software licensed by Universidad Tecnológica de Bolívar.22 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Energies 2020, 13(17), 4440A mixed-integer nonlinear programming model for optimal reconfiguration of DC distribution feedersinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Branch-to-node incidence matrixDirect current networksMixed-integer nonlinear programming modelGeneral algebraic modelling systemOptimal reconfiguration of distribution gridsLEMBCartagena de IndiasInvestigadoresSarkar, M.N.I.; Meegahapola, L.G.; Datta, M. Reactive Power Management in Renewable Rich Power Grids: A Review of Grid-Codes, Renewable Generators, Support Devices, Control Strategies and Optimization Algorithms. IEEE Access 2018, 6, 41458–41489.Jia, K.; Yang, Z.; Fang, Y.; Bi, T.; Sumner, M. Influence of Inverter-Interfaced Renewable Energy Generators on Directional Relay and an Improved Scheme. IEEE Trans. Power Electron. 2019, 34, 11843–11855.Montoya, O.D.; Gil-González, W. Dynamic active and reactive power compensation in distribution networks with batteries: A day-ahead economic dispatch approach. Comput. Electr. Eng. 2020, 85, 106710.Serra, F.M.; Fernández, L.M.; Montoya, O.D.; Gil-González, W.; Hernández, J.C. Nonlinear Voltage Control for Three-Phase DC-AC Converters in Hybrid Systems: An Application of the PI-PBC Method. Electronics 2020, 9, 847.Simiyu, P.; Xin, A.; Wang, K.; Adwek, G.; Salman, S. Multiterminal Medium Voltage DC Distribution Network Hierarchical Control. Electronics 2020, 9, 506.Sechilariu, M.; Wang, B.; Locment, F. Power management and optimization for isolated DC microgrid. In Proceedings of the 2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion, Ischia, Italy, 18–20 June 2014; pp. 1284–1289Garcés, A. On the Convergence of Newton’s Method in Power Flow Studies for DC Microgrids. IEEE Trans. Power Syst. 2018, 33, 5770–5777.Montoya, O.D.; Grisales-Noreña, L.F.; Gil-González, W.; Alcalá, G.; Hernandez-Escobedo, Q. Optimal Location and Sizing of PV Sources in DC Networks for Minimizing Greenhouse Emissions in Diesel Generators. Symmetry 2020, 12, 322.Gil-González, W.; Montoya, O.D.; Grisales-Noreña, L.F.; Cruz-Peragón, F.; Alcalá, G. Economic Dispatch of Renewable Generators and BESS in DC Microgrids Using Second-Order Cone Optimization. Energies 2020, 13, 1703.Sharip, M.R.M.; Haidar, A.M.A.; Jimel, A.C. Optimum Configuration of Solar PV Topologies for DC Microgrid Connected to the Longhouse Communities in Sarawak, Malaysia. Int. J. Photoenergy 2019, 2019, 1–13.Grisales-Noreña, L.; Montoya, O.D.; Ramos-Paja, C.A. An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm. J. Energy Storage 2020, 29, 101488.Kamran, M.; Bilal, M.; Mudassar, M. DC Home Appliances for DC Distribution System. Mehran Univ. Res. J. Eng. Technol. 2017, 36, 881–890.Wong, C.; Liu, C.; Hou, K. DC power supply system for intelligent server. In Proceedings of the 2012 International Symposium on Intelligent Signal Processing and Communications Systems, Taipei, Taiwan, 4–7 November 2012; pp. 245–249.Christakou, K. A unified control strategy for active distribution networks via demand response and distributed energy storage systems. Sustain. Energy Grids Netw. 2016, 6, 1–6.Satpathi, K.; Ukil, A.; Nag, S.S.; Pou, J.; Zadrodnik, M.A. Comparison of Current-Only Directional Protection in AC and DC Power Systems. In Proceedings of the 2018 IEEE Innovative Smart Grid Technologies—Asia (ISGT Asia), Singapore, 22–25 May 2018; pp. 133–138.Xue, S.; Chen, C.; Jin, Y.; Li, Y.; Li, B.; Wang, Y. Protection for DC Distribution System with Distributed Generator. J. Appl. Math. 2014, 2014, 1–12.Opiyo, N.N. A comparison of DC- versus AC-based minigrids for cost-effective electrification of rural developing communities. Energy Rep. 2019, 5, 398–408.Alluhaidan, M.; Almutairy, I. Modeling and Protection for Low-Voltage DC Microgrids Riding Through Short Circuiting. Procedia Comput. Sci. 2017, 114, 457–464.Gil-González, W.; Montoya, O.D.; Garces, A. Direct power control for VSC-HVDC systems: An application of the global tracking passivity-based PI approach. Int. J. Electr. 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Second-order cone AC optimal power flow: Convex relaxations and feasible solutions. J. Mod Power Syst. Clean Energy 2018, 7, 268–280.Kronqvist, J.; Bernal, D.E.; Lundell, A.; Grossmann, I.E. A review and comparison of solvers for convex MINLP. Optim. Eng. 2018, 20, 397–455.Chew, B.S.H.; Xu, Y.; Wu, Q. Voltage Balancing for Bipolar DC Distribution Grids: A Power Flow Based Binary Integer Multi-Objective Optimization Approach. IEEE Trans. Power Syst. 2019, 34, 28–39.Lavorato, M.; Franco, J.F.; Rider, M.J.; Romero, R. Imposing Radiality Constraints in Distribution System Optimization Problems. IEEE Trans. Power Syst. 2012, 27, 172–180.Shen, T.; Li, Y.; Xiang, J. A Graph-Based Power Flow Method for Balanced Distribution Systems. Energies 2018, 11, 511.The, T.T.; Ngoc, D.V.; Anh, N.T. Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm. Complexity 2020, 2020, 1–15.Montoya, O.D.; Gil-González, W.; Grisales-Noreña, L. An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach. Ain Shams Eng. J. 2019.Pfitscher, L.; Bernardon, D.; Canha, L.; Montagner, V.; Garcia, V.; Abaide, A. Intelligent system for automatic reconfiguration of distribution network in real time. Electr. Power Syst. Res. 2013, 97, 84–92.Bernardon, D.; de Mello, A.P.C.; Pfitscher, L. Real-Time Reconfiguration of Distribution Network with Distributed Generation. In Real-Time Systems; InTech: London, UK, 2016.Soroudi, A. Power System Optimization Modeling in GAMS; Springer International Publishing: Berlin/Heidelberg, Germany, 2017.Montoya, O.D.; Gil-González, W.; Rivas-Trujillo, E. Optimal Location-Reallocation of Battery Energy Storage Systems in DC Microgrids. Energies 2020, 13, 2289.Amin, W.T.; Montoya, O.D.; Grisales-Noreña, L.F. Determination of the Voltage Stability Index in DC Networks with CPLs: A GAMS Implementation. In Communications in Computer and Information Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 552–564.Skworcow, P.; Paluszczyszyn, D.; Ulanicki, B.; Rudek, R.; Belrain, T. Optimisation of Pump and Valve Schedules in Complex Large-scale Water Distribution Systems Using GAMS Modelling Language. Procedia Eng. 2014, 70, 1566–1574.Naghiloo, A.; Abbaspour, M.; Mohammadi-Ivatloo, B.; Bakhtari, K. GAMS based approach for optimal design and sizing of a pressure retarded osmosis power plant in Bahmanshir river of Iran. Renew. Sustain. Energy Rev. 2015, 52, 1559–1565.Tartibu, L.; Sun, B.; Kaunda, M. Multi-objective optimization of the stack of a thermoacoustic engine using GAMS. Appl. Soft Comput. 2015, 28, 30–43.Montoya, O.D. Solving a Classical Optimization ProblemUsing GAMS Optimizer Package: Economic Dispatch ProblemImplementation. Ingeniería y Ciencia 2017, 13, 39–63.Tartibu, L.; Sun, B.; Kaunda, M. Optimal Design of A Standing Wave Thermoacoustic Refrigerator Using GAMS. Procedia Comput. Sci. 2015, 62, 611–618Garces, A. Uniqueness of the power flow solutions in low voltage direct current grids. Electr. Power Syst. Res. 2017, 151, 149–153.Enel-Codensa. Connection Voltage Levels of Customer Loads; Technical Report; Enel-Codensa (Diseño de red); ENEL: Bogotá, Colombia, 2018. (In Spanish)Montoya, O.D.; Gil-González, W.; Garces, A. Power flow approximation for DC networks with constant power loads via logarithmic transform of voltage magnitudes. Electr. Power Syst. Res. 2019, 175, 105887.Huang, Y.C. Enhanced-genetic-algorithm-based fuzzy multi-objective approach to distribution network reconfiguration. IEE Proc. Gener. Transm. 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