Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch

Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MH...

Full description

Autores:
Posada Contreras, Johnny
Núñez-Rodríguez, Rafael A.
Unsihuay-Vila, Clodomiro
Pinzón-Ardila, Omar
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/16206
Acceso en línea:
https://hdl.handle.net/10614/16206
https://doi.org/10.3390/en17164036
https://red.uao.edu.co/
Palabra clave:
AC/DC microgrid
Data-driven distributionally robust optimization
Duality-free decomposition
Meshed hybrid microgrids
Uncertainty
Smart transformer
Microrred CA/CC
Optimización distribucional robusta basada en datos
Descomposición sin dualidad
Microrredes híbridas en malla
Incertidumbre
Transformador inteligente
Rights
openAccess
License
Derechos reservados - MDPI, 2024
id REPOUAO2_fa77ec28dc0b300be7f76658907d96b3
oai_identifier_str oai:red.uao.edu.co:10614/16206
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
dc.title.translated.spa.fl_str_mv Optimización distribucional robusta basada en datos para la planificación de operaciones diarias de una microrred híbrida de CA/CC en malla basada en transformador inteligente, considerando el despacho óptimo de potencia reactiva
title Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
spellingShingle Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
AC/DC microgrid
Data-driven distributionally robust optimization
Duality-free decomposition
Meshed hybrid microgrids
Uncertainty
Smart transformer
Microrred CA/CC
Optimización distribucional robusta basada en datos
Descomposición sin dualidad
Microrredes híbridas en malla
Incertidumbre
Transformador inteligente
title_short Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
title_full Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
title_fullStr Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
title_full_unstemmed Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
title_sort Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch
dc.creator.fl_str_mv Posada Contreras, Johnny
Núñez-Rodríguez, Rafael A.
Unsihuay-Vila, Clodomiro
Pinzón-Ardila, Omar
dc.contributor.author.none.fl_str_mv Posada Contreras, Johnny
Núñez-Rodríguez, Rafael A.
Unsihuay-Vila, Clodomiro
Pinzón-Ardila, Omar
dc.subject.proposal.eng.fl_str_mv AC/DC microgrid
Data-driven distributionally robust optimization
Duality-free decomposition
Meshed hybrid microgrids
Uncertainty
Smart transformer
topic AC/DC microgrid
Data-driven distributionally robust optimization
Duality-free decomposition
Meshed hybrid microgrids
Uncertainty
Smart transformer
Microrred CA/CC
Optimización distribucional robusta basada en datos
Descomposición sin dualidad
Microrredes híbridas en malla
Incertidumbre
Transformador inteligente
dc.subject.proposal.spa.fl_str_mv Microrred CA/CC
Optimización distribucional robusta basada en datos
Descomposición sin dualidad
Microrredes híbridas en malla
Incertidumbre
Transformador inteligente
description Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditions
publishDate 2024
dc.date.issued.none.fl_str_mv 2024
dc.date.accessioned.none.fl_str_mv 2025-07-08T16:36:21Z
dc.date.available.none.fl_str_mv 2025-07-08T16:36:21Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.eng.fl_str_mv Text
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
dc.type.redcol.eng.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.eng.fl_str_mv Posada Contreras, J.; Núñez-Rodríguez, R. A.; Unsihuay-Vila, C. y Pinzón-Ardila, O. (2024). Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch. Energies, 17(16). https://doi.org/10.3390/en17164036
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/16206
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/en17164036
dc.identifier.eissn.spa.fl_str_mv 19961073
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
identifier_str_mv Posada Contreras, J.; Núñez-Rodríguez, R. A.; Unsihuay-Vila, C. y Pinzón-Ardila, O. (2024). Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch. Energies, 17(16). https://doi.org/10.3390/en17164036
19961073
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
url https://hdl.handle.net/10614/16206
https://doi.org/10.3390/en17164036
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 25
dc.relation.citationissue.spa.fl_str_mv 16
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 17
dc.relation.ispartofjournal.eng.fl_str_mv Energies
dc.relation.references.none.fl_str_mv 1. Judge, M.A.; Khan, A.; Manzoor, A.; Khattak, H.A. Overview of smart grid implementation: Frameworks, impact, performance and challenges. J. Energy Storage 2022, 49, 104056. [CrossRef]
2. Chicco, G.; Ciocia, A.; Colella, P.; Di Leo, P.; Mazza, A.; Musumeci, S.; Spertino, F. Introduction—Advances and Challenges in Active Distribution Systems. In Planning and Operation of Active Distribution Networks; Lecture Notes in Electrical Engineering; Springer Science and Business Media Deutschland GmbH: Cham, Switzerland, 2022; Volume 826, pp. 1–42.
3. Sahoo, S.K.; Sinha, A.K.; Kishore, N.K. Control Techniques in AC, DC, and Hybrid AC-DC Microgrid: A Review. IEEE J. Emerg. Sel. Top. Power Electron. 2018, 6, 738–759. [CrossRef]
4. Debouza, M.; Al-Durra, A.; EL-Fouly, T.H.M.; Zeineldin, H.H. Survey on microgrids with flexible boundaries: Strategies, applications, and future trends. Electr. Power Syst. Res. 2022, 205, 107765. [CrossRef]
5. Jia, L.; Zhu, Y.; Wang, Y. Architecture design for new AC-DC hybrid micro-grid. In Proceedings of the 2015 IEEE 1st International Conference on Direct Current Microgrids, ICDCM, Atlanta, GA, USA, 7–10 June 2015; pp. 113–118. [CrossRef]
6. Zhu, R.; Liserre, M.; Langwasser, M.; Kumar, C. Operation and Control of Smart Transformer in Meshed and Hybrid Grids. IEEE Ind. Electron. Mag. 2020, 15, 43–57. [CrossRef]
7. Kumar, C.; Gao, X.; Liserre, M. Smart Transformer Based Loop Power Controller in Radial Power Distribution Grid. In Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, 21–25 October 2018. [CrossRef]
8. Das, D.; Hrishikesan, V.M.; Kumar, C.; Liserre, M. Smart Transformer-Enabled Meshed Hybrid Distribution Grid. IEEE Trans. Ind. Electron. 2021, 68, 282–292. [CrossRef]
9. Zhu, R.; de Carne, G.; Andresen, M.; Liserre, M. Control of Smart Transformer in Different Electric Grid Configurations. In Proceedings of the 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019-ECCE Asia), Busan, Republic of Korea, 27–30 May 2019; pp. 1668–1675.
10. Singh, A.; Maulik, A.; Maheshwari, A. Probabilistic multi-objective energy management of a distribution system considering reactive power injection by voltage source converters. Electr. Eng. 2023, 105, 2107–2136. [CrossRef]
11. Guo, H.;Wang, F.; Luo, J.; Zhang, L. Review of energy routers applied for the energy internet integrating renewable energy. In Proceedings of the 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Hefei, China, 22–26 May 2016; pp. 1997–2003. [CrossRef]
12. Gao, H.; Wang, R.; Liu, Y.; Wang, L.; Xiang, Y.; Liu, J. Data-driven distributionally robust joint planning of distributed energy resources in active distribution network. IET Gener. Transm. Distrib. 2020, 14, 1653–1662. [CrossRef]
13. Wang, X.; Yang, W.; Liang, D. Multi-Objective Robust Optimization of Hybrid AC/DC Distribution Networks Considering Flexible Interconnection Devices. IEEE Access 2021, 9, 166048–166057. [CrossRef]
14. Rangu, S.K.; Lolla, P.R.; Dhenuvakonda, K.R.; Singh, A.R. Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review. Int. J. Energy Res. 2020, 44, 9889–9911. [CrossRef]
15. Qiu, H.; Gu, W.; Liu, P.; Sun, Q.; Wu, Z.; Lu, X. Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective. Energy 2022, 251, 123942. [CrossRef]
16. Yang, J.; Su, C. Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty. Energy 2021, 223, 120043. [CrossRef]
17. Claeys, S.; Vanin, M.; Geth, F.; Deconinck, G. Applications of optimization models for electricity distribution networks. Wiley Interdiscip. Rev. Energy Environ. 2021, 10, e401. [CrossRef]
18. Zhang, Y.; Le, J.; Zheng, F.; Zhang, Y.; Liu, K. Two-stage distributionally robust coordinated scheduling for gas-electricity integrated energy system considering wind power uncertainty and reserve capacity configuration. Renew. Energy 2019, 135, 122–135. [CrossRef]
19. Zhang, Y.; Liu, Y.; Shu, S.; Zheng, F.; Huang, Z. A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources. Energy 2021, 216, 119171. [CrossRef]
20. Zhao, C.; Guan, Y. Data-Driven Stochastic Unit Commitment for Integrating Wind Generation. IEEE Trans. Power Syst. 2016, 31, 2587–2596. [CrossRef]
21. Li, P.; Song, L.; Qu, J.; Huang, Y.; Wu, X.; Lu, X.; Xia, S. A Two-Stage Distributionally Robust Optimization Model for Wind Farms and Storage Units Jointly Operated Power Systems. IEEE Access 2021, 9, 111132–111142. [CrossRef]
22. Pinto, R.S.; Unsihuay-Vila, C.; Tabarro, F.H. Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties. Appl. Energy 2021, 297, 117108. [CrossRef]
23. Mieth, R.; Dvorkin, Y. Data-Driven Distributionally Robust Optimal Power Flow for Distribution Systems. IEEE Control. Syst. Lett. 2018, 2, 363–368. [CrossRef]
24. Zeng, B.; Zhao, L. Solving two-stage robust optimization problems using a column-and-constraint generation method. Oper. Res. Lett. 2013, 41, 457–461. [CrossRef]
25. Paredes, M.; Martins, L.S.A.; Soares, S.; Ye, H. Benders’ decomposition of the unit commitment problem with semidefinite relaxation of AC power flow constraints. Electr. Power Syst. Res. 2021, 192, 106965. [CrossRef]
26. Wu, T.; Xie, K.; Hu, B.; Lu, H.; Ding, J.; Shen, W. Data-Driven Approach based Tri-Level Generation and Transmission Expansion Planning Model with High Wind Penetration. In Proceedings of the iSPEC 2019—2019 IEEE Sustainable Power and Energy Conference, Beijing, China, 21–23 November 2019; Volume 58, pp. 2607–2618. [CrossRef]
27. Ding, T.; Yang, Q.; Liu, X.; Huang, C.; Yang, Y.; Wang, M.; Blaabjerg, F. Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment. IEEE Trans. Sustain. Energy 2019, 10, 82–93. [CrossRef]
28. Wang, L.; Jiang, C.; Gong, K.; Si, R.; Shao, H.; Liu, W. Data-driven distributionally robust economic dispatch for distribution network with multiple microgrids. IET Gener. Transm. Distrib. 2020, 14, 5816–5822. [CrossRef]
29. Ning, C.; You, F. Data-driven adaptive robust unit commitment under wind power uncertainty: A Bayesian nonparametric approach. IEEE Trans. Power Syst. 2019, 34, 2409–2418. [CrossRef]
30. Shi, Z.; Zhang, T.; Liu, Y.; Feng, Y.;Wang, R.; Huang, S. Optimal design and operation of islanded multi-microgrid system with distributionally robust optimization. Electr. Power Syst. Res. 2023, 221, 109437. [CrossRef]
31. Qiu, H.; Gu, W.; Pan, J.; Xu, B.; Xu, Y.; Fan, M.; Wu, Z. Multi-interval-uncertainty constrained robust dispatch for AC/DC hybrid microgrids with dynamic energy storage degradation. Appl. Energy 2018, 228, 205–214. [CrossRef]
32. Syed, I.; Khadkikar, V.; Zeineldin, H.H. Loss Reduction in Radial Distribution Networks Using a Solid-State Transformer. IEEE Trans. Ind. Appl. 2018, 54, 5474–5482. [CrossRef]
33. de Carne, G.; Buticchi, G.; Zou, Z.; Liserre, M. Reverse Power Flow Control in a ST-Fed Distribution Grid. IEEE Trans. Smart Grid 2018, 9, 3811–3819. [CrossRef]
34. Zhang, Z.; Chen, Y.; Ma, J.; Liu, X.; Wang, W. Two-Stage Robust Security Constrained Unit Commitment Considering the Spatiotemporal Correlation of Uncertainty Prediction Error. IEEE Access 2019, 7, 22891–22901. [CrossRef]
35. Zhang, Z.; Chen, Y.; Liu, X.; Wang, W. Two-Stage Robust Security-Constrained Unit Commitment Model Considering Time Autocorrelation of Wind/Load Prediction Error and Outage Contingency Probability of Units. IEEE Access 2019, 7, 25398–25408. [CrossRef]
36. Qiu, H.; Gu, W.; Xu, Y.; Wu, Z.; Zhou, S.; Wang, J. Interval-Partitioned Uncertainty Constrained Robust Dispatch for AC/DC Hybrid Microgrids with Uncontrollable Renewable Generators. IEEE Trans. Smart Grid 2019, 10, 4603–4614. [CrossRef]
37. Yuan, S.; Dai, C.; Guo, A.; Chen,W. A novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers. Electr. Power Syst. Res. 2019, 169, 115–123. [CrossRef]
38. Dehghan, S.; Nakiganda, A.; Aristidou, P. A data-driven two-stage distributionally robust planning tool for sustainable Microgrids. In Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada, 2–6 August 2020. [CrossRef]
39. Kumar, C.; Manojkumar, R.; Ganguly, S.; Liserre, M. Power Loss Minimization in Smart Transformer Based Meshed Hybrid Distribution Network. In Proceedings of the IECON 2020—The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 18–21 October 2020; pp. 1670–1675. [CrossRef]
40. Le, L.; Fang, J.; Zhang, M.; Zeng, K.; Ai, X.; Wu, Q.; Wen, J. Data-driven stochastic unit commitment considering commercial air conditioning aggregators to provide multi-function demand response. Int. J. Electr. Power Energy Syst. 2021, 129, 106790. [CrossRef]
41. Zhang, C.; Liu, L.; Cheng, H.; Liu, D.; Zhang, J.; Li, G. Data-driven distributionally robust transmission expansion planning considering contingency-constrained generation reserve optimization. Int. J. Electr. Power Energy Syst. 2021, 131, 106973. [CrossRef]
42. Wang, C.; Gong, Z.; He, C.; Gao, H.; Bi, T. Data-Driven Adjustable Robust Unit Commitment of Integrated Electric-Heat Systems. IEEE Trans. Power Syst. 2021, 36, 1385–1398. [CrossRef]
43. Hrishikesan, V.M.; Kumar, C. Operation of Meshed Hybrid Microgrid During Adverse Grid Conditions with Storage Integrated Smart Transformer. IEEE Open J. Ind. Electron. Soc. 2021, 2, 315–325. [CrossRef]
44. Kumar, C.; Manojkumar, R.; Ganguly, S.; Liserre, M. Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network. IEEE Access 2021, 9, 140268–140280. [CrossRef]
45. Hrishikesan, V.M.; Kumar, C.; Liserre, M. An MVDC Based Meshed Hybrid Microgrid Enabled Using Smart Transformers. IEEE Trans. Ind. Electron. 2021, 69, 3722–3731. [CrossRef]
46. Das, D.; Manojkumar, R.; Kumar, C.; Ganguly, S. Optimal Power Management for Islanded Operation of ST-based Meshed Hybrid LV Microgrid. In Proceedings of the 2021 IEEE 12th Energy Conversion Congress & Exposition—Asia (ECCE-Asia), Singapore, 24–27 May 2021; pp. 183–188. [CrossRef]
47. Kumar, C.; Manojkumar, R.; Ganguly, S. Optimal Placement of Smart Transformer Low Voltage Converter in Meshed Hybrid Distribution Network. In Proceedings of the 2021 IEEE 12th Energy Conversion Congress & Exposition—Asia (ECCE-Asia), Singapore, 24–27 May 2021; pp. 1795–1800. [CrossRef]
48. De Lara Filho, M.O.; Pinto, R.S.; De Campos, A.C.; Vila, C.U.; Tabarro, F.H. Day-ahead robust operation planning of microgrids under uncertainties considering DERs and demand response. In Proceedings of the 2021 IEEE PES Innovative Smart Grid Technologies Conference—Latin America, ISGT Latin America 2021, Lima, Peru, 15–17 September 2021; Volume 15, pp. 1–5. [CrossRef]
49. Feizi, M.R.; Khodayar, M.E.; Li, J. Data-driven distributionally robust unbalanced operation of distribution networks with high penetration of photovoltaic generation and electric vehicles. Electr. Power Syst. Res. 2022, 210, 108001. [CrossRef]
50. Gupta, S.; Maulik, A.; Das, D.; Singh, A. Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers. Renew. Sustain. Energy Rev. 2022, 155, 111861. [CrossRef]
51. Huang, Z.; Zhang, Y.; Xie, S. Data-adaptive robust coordinated optimization of dynamic active and reactive power flow in active distribution networks. Renew. Energy 2022, 188, 164–183. [CrossRef]
52. Das, D.; Kumar, C. Partial Startup Scheme for Smart Transformer in Meshed Hybrid Islanded Grid Operation. IEEE Trans. Ind. Appl. 2022, 58, 142–151. [CrossRef]
53. Singh, A.; Maulik, A. Energy Management of an Active Distribution Network Considering Correlation Between Uncertain Input Variables. Arab. J. Sci. Eng. 2023, 48, 6377–6398. [CrossRef]
54. Zhao, Q.; García-González, J.; Gomis-Bellmunt, O.; Prieto-Araujo, E.; Echavarren, F.M. Impact of converter losses on the optimal power flow solution of hybrid networks based on VSC-MTDC. Electr. Power Syst. Res. 2017, 151, 395–403. [CrossRef]
55. Mohammadi, F.; Nazri, G.-A.; Saif, M. An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems. Appl. Sci. 2019, 10, 297. [CrossRef]
56. Núñez-Rodríguez, R.A.; Unsihuay-Vila, C.; Posada, J.; Pinzón-Ardila, O.; Aoki, A.R.; Bueno-Otto, R. Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation. Energies 2024, 17, 1950. [CrossRef]
57. Lachovicz, F.J.; Fernandes, T.S.P.; Junior, J.A.V. Impacts of PV-STATCOM Reactive Power Dispatch in the Allocation of Capacitors Bank and Voltage Regulators on Active Distribution Networks. J. Control. Autom. Electr. Syst. 2023, 34, 796–807. [CrossRef]
58. Jeyapradha, R.B.; Rajini, V. Investigations on Service Extensions of Solid State Transformer. In Proceedings of the 2019 Fifth International Conference on Electrical Energy Systems (ICEES), Chennai, India, 21–22 February 2019; pp. 1–6. [CrossRef]
59. Jakka, V.N.; Acharya, S.; Anurag, A.; Prabowo, Y.; Kumar, A.; Parashar, S.; Bhattacharya, S. Protection design considerations of a 10 kV SiC MOSFET enabled mobile utilities support equipment based solid state transformer (muse-SST). In Proceedings of the IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018; pp. 5559–5565. [CrossRef]
60. Zhu, R.; Andresen, M.; Langwasser, M.; Liserre, M.; Lopes, J.P.; Moreira, C.; Rodrigues, J.; Couto, M. Smart transformer/large flexible transformer. CES Trans. Electr. Mach. Syst. 2021, 4, 264–274. [CrossRef]
61. Zhu, R.; Buticchi, G.; Liserre, M. Investigation on Common-Mode Voltage Suppression in Smart Transformer-Fed Distributed Hybrid Grids. IEEE Trans. Power Electron. 2018, 33, 8438–8448. [CrossRef]
62. González-Castellanos, A.; Pozo, D.; Bischi, A. Distribution System Operation with Energy Storage and Renewable Generation Uncertainty. In Handbook of Optimization in Electric Power Distribution Systems; Resener, M., Rebennack, S., Pardalos, P.M., Haffner, S., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 69–120.
63. Pinto, R.S. Computational Models for Expansion Planning of Electric Power Distribution Systems Containing Multiple Microgrids and Distributed Energy Resources under Uncertainties. Ph.D. Thesis, Universidade Federal do Paraná, Curitiba, Brazil, 2022.
64. Bernal, D.E.; Chen, Q.; Gong, F.; Grossmann, I.E. Mixed-Integer Nonlinear Decomposition Toolbox for Pyomo (MindtPy). Comput. Aided Chem. Eng. 2018, 44, 895–900. [CrossRef]
65. XM. Curva de Demanda Promedio en Colombia. 2019. Available online: https://sinergox.xm.com.co/dmnd/Paginas/Informes/ DemandaRealPerdidas.aspx (accessed on 15 April 2023).
66. SolarCast Historical and TMY. Time Series. 2022. Available online: https://toolkit.solcast.com.au/historical (accessed on 15 April 2023).
67. Strunz, K.; Abbey, C.; Andrieu, C.; Campbell, R.C.; Fletcher, R. Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources—TF C6.04.02. 2014. Available online: https://www.e-cigre.org/publications/detail/575- benchmark-systems-for-network-integration-of-renewable-and-distributed-energy-resources.html (accessed on 15 April 2023).
68. Zhang, M.; Ai, X.; Fang, J.; Yao, W.; Zuo, W.; Chen, Z.; Wen, J. A systematic approach for the joint dispatch of energy and reserve incorporating demand response. Appl. Energy 2018, 230, 1279–1291. [CrossRef]
69. Goerigk, M.; Khosravi, M. Optimal scenario reduction for one- and two-stage robust optimization with discrete uncertainty in the objective. Eur. J. Oper. Res. 2023, 310, 529–551. [CrossRef]
dc.rights.spa.fl_str_mv Derechos reservados - MDPI, 2024
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.uri.eng.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.eng.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
rights_invalid_str_mv Derechos reservados - MDPI, 2024
https://creativecommons.org/licenses/by-nc-nd/4.0/
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv 25 páginas
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.eng.fl_str_mv MDPI
dc.publisher.place.eng.fl_str_mv Basel, Switzerland
institution Universidad Autónoma de Occidente
bitstream.url.fl_str_mv https://red.uao.edu.co/bitstreams/4094b777-feac-4c18-854f-652bd346733e/download
https://red.uao.edu.co/bitstreams/52abbebd-1e60-4b0a-a57d-b10fb7b6d4ed/download
https://red.uao.edu.co/bitstreams/e9edc03e-e0eb-4648-a71f-75d936a6243f/download
https://red.uao.edu.co/bitstreams/eaeca3b2-6fca-4f89-9fd0-cac06750b1ba/download
bitstream.checksum.fl_str_mv 782da1fa498639ec7d572d083aafcc4e
6987b791264a2b5525252450f99b10d1
94c82304bb280baa5208e0c639eadac8
ddb9055d554352f9eeeba34ff35562e9
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Digital Universidad Autonoma de Occidente
repository.mail.fl_str_mv repositorio@uao.edu.co
_version_ 1851053210660241408
spelling Posada Contreras, Johnnyvirtual::6096-1Núñez-Rodríguez, Rafael A.Unsihuay-Vila, ClodomiroPinzón-Ardila, Omar2025-07-08T16:36:21Z2025-07-08T16:36:21Z2024Posada Contreras, J.; Núñez-Rodríguez, R. A.; Unsihuay-Vila, C. y Pinzón-Ardila, O. (2024). Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatch. Energies, 17(16). https://doi.org/10.3390/en17164036https://hdl.handle.net/10614/16206https://doi.org/10.3390/en1716403619961073Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Smart Transformer (ST)-based Meshed Hybrid AC/DC Microgrids (MHMs) present a promising solution to enhance the efficiency of conventional microgrids (MGs) and facilitate higher integration of Distributed Energy Resources (DERs), simultaneously managing active and reactive power dispatch. However, MHMs face challenges in resource management under uncertainty and control of electronic converters linked to the ST and DERs, complicating the pursuit of optimal system performance. This paper introduces a Data-Driven Distributionally Robust Optimization (DDDRO) approach for day-ahead operation planning in ST-based MHMs, focusing on minimizing network losses, voltage deviations, and operational costs by optimizing the reactive power dispatch of DERs. The approach accounts for uncertainties in photovoltaic generator (PVG) output and demand. The Column-and-Constraint Generation (C&CG) algorithm and the Duality-Free Decomposition (DFD) method are employed. The initial mixed-integer non-linear planning problem is also reformulated into a mixed-integer (MI) Second-Order Cone Programming (SOCP) problem using second-order cone relaxation and a positive octagonal constraint method. Simulation results on a connected MHM system validate the model’s efficacy and performance. The study also highlights the advantages of the meshed MG structure and the positive impact of integrating the ST into MHMs, leveraging the multi-stage converter’s flexibility for optimal energy management under uncertain conditionsLas microrredes CA/CC híbridas malladas (MHM) basadas en transformadores inteligentes (ST) presentan una solución prometedora para mejorar la eficiencia de las microrredes convencionales (MG) y facilitar una mayor integración de los recursos energéticos distribuidos (DER), gestionando simultáneamente el despacho de potencia activa y reactiva. Sin embargo, las MHM enfrentan desafíos en la gestión de recursos bajo incertidumbre y el control de los convertidores electrónicos vinculados a las ST y los DER, lo que dificulta la búsqueda de un rendimiento óptimo del sistema. Este documento presenta un enfoque de optimización robusta distribucional basada en datos (DDDRO) para la planificación de la operación diaria en MHM basadas en ST, centrándose en minimizar las pérdidas de red, las desviaciones de tensión y los costes operativos mediante la optimización del despacho de potencia reactiva de los DER. El enfoque considera las incertidumbres en la salida y la demanda de los generadores fotovoltaicos (PVG). Se emplean el algoritmo de generación de columnas y restricciones (C&CG) y el método de descomposición sin dualidad (DFD). El problema inicial de planificación no lineal de enteros mixtos se reformula en un problema de Programación de Cono de Segundo Orden (SOCP) de enteros mixtos (MI) mediante relajación de cono de segundo orden y un método de restricción octagonal positiva. Los resultados de la simulación en un sistema MHM conectado validan la eficacia y el rendimiento del modelo. El estudio también destaca las ventajas de la estructura MG mallada y el impacto positivo de integrar la ST en los MHM, aprovechando la flexibilidad del convertidor multietapa para una gestión energética óptima en condiciones de incertidumbre25 páginasapplication/pdfengMDPIBasel, SwitzerlandDerechos reservados - MDPI, 2024https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Data-driven distributionally robust optimization for day-ahead operation planning of a smart transformer-based meshed hybrid ac/dc microgrid considering the optimal reactive power dispatchOptimización distribucional robusta basada en datos para la planificación de operaciones diarias de una microrred híbrida de CA/CC en malla basada en transformador inteligente, considerando el despacho óptimo de potencia reactivaArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a852516117Energies1. Judge, M.A.; Khan, A.; Manzoor, A.; Khattak, H.A. Overview of smart grid implementation: Frameworks, impact, performance and challenges. J. Energy Storage 2022, 49, 104056. [CrossRef]2. Chicco, G.; Ciocia, A.; Colella, P.; Di Leo, P.; Mazza, A.; Musumeci, S.; Spertino, F. Introduction—Advances and Challenges in Active Distribution Systems. In Planning and Operation of Active Distribution Networks; Lecture Notes in Electrical Engineering; Springer Science and Business Media Deutschland GmbH: Cham, Switzerland, 2022; Volume 826, pp. 1–42.3. Sahoo, S.K.; Sinha, A.K.; Kishore, N.K. Control Techniques in AC, DC, and Hybrid AC-DC Microgrid: A Review. IEEE J. Emerg. Sel. Top. Power Electron. 2018, 6, 738–759. [CrossRef]4. Debouza, M.; Al-Durra, A.; EL-Fouly, T.H.M.; Zeineldin, H.H. Survey on microgrids with flexible boundaries: Strategies, applications, and future trends. Electr. Power Syst. Res. 2022, 205, 107765. [CrossRef]5. Jia, L.; Zhu, Y.; Wang, Y. Architecture design for new AC-DC hybrid micro-grid. In Proceedings of the 2015 IEEE 1st International Conference on Direct Current Microgrids, ICDCM, Atlanta, GA, USA, 7–10 June 2015; pp. 113–118. [CrossRef]6. Zhu, R.; Liserre, M.; Langwasser, M.; Kumar, C. Operation and Control of Smart Transformer in Meshed and Hybrid Grids. IEEE Ind. Electron. Mag. 2020, 15, 43–57. [CrossRef]7. Kumar, C.; Gao, X.; Liserre, M. Smart Transformer Based Loop Power Controller in Radial Power Distribution Grid. In Proceedings of the 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, 21–25 October 2018. [CrossRef]8. Das, D.; Hrishikesan, V.M.; Kumar, C.; Liserre, M. Smart Transformer-Enabled Meshed Hybrid Distribution Grid. IEEE Trans. Ind. Electron. 2021, 68, 282–292. [CrossRef]9. Zhu, R.; de Carne, G.; Andresen, M.; Liserre, M. Control of Smart Transformer in Different Electric Grid Configurations. In Proceedings of the 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019-ECCE Asia), Busan, Republic of Korea, 27–30 May 2019; pp. 1668–1675.10. Singh, A.; Maulik, A.; Maheshwari, A. Probabilistic multi-objective energy management of a distribution system considering reactive power injection by voltage source converters. Electr. Eng. 2023, 105, 2107–2136. [CrossRef]11. Guo, H.;Wang, F.; Luo, J.; Zhang, L. Review of energy routers applied for the energy internet integrating renewable energy. In Proceedings of the 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Hefei, China, 22–26 May 2016; pp. 1997–2003. [CrossRef]12. Gao, H.; Wang, R.; Liu, Y.; Wang, L.; Xiang, Y.; Liu, J. Data-driven distributionally robust joint planning of distributed energy resources in active distribution network. IET Gener. Transm. Distrib. 2020, 14, 1653–1662. [CrossRef]13. Wang, X.; Yang, W.; Liang, D. Multi-Objective Robust Optimization of Hybrid AC/DC Distribution Networks Considering Flexible Interconnection Devices. IEEE Access 2021, 9, 166048–166057. [CrossRef]14. Rangu, S.K.; Lolla, P.R.; Dhenuvakonda, K.R.; Singh, A.R. Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review. Int. J. Energy Res. 2020, 44, 9889–9911. [CrossRef]15. Qiu, H.; Gu, W.; Liu, P.; Sun, Q.; Wu, Z.; Lu, X. Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective. Energy 2022, 251, 123942. [CrossRef]16. Yang, J.; Su, C. Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty. Energy 2021, 223, 120043. [CrossRef]17. Claeys, S.; Vanin, M.; Geth, F.; Deconinck, G. Applications of optimization models for electricity distribution networks. Wiley Interdiscip. Rev. Energy Environ. 2021, 10, e401. [CrossRef]18. Zhang, Y.; Le, J.; Zheng, F.; Zhang, Y.; Liu, K. Two-stage distributionally robust coordinated scheduling for gas-electricity integrated energy system considering wind power uncertainty and reserve capacity configuration. Renew. Energy 2019, 135, 122–135. [CrossRef]19. Zhang, Y.; Liu, Y.; Shu, S.; Zheng, F.; Huang, Z. A data-driven distributionally robust optimization model for multi-energy coupled system considering the temporal-spatial correlation and distribution uncertainty of renewable energy sources. Energy 2021, 216, 119171. [CrossRef]20. Zhao, C.; Guan, Y. Data-Driven Stochastic Unit Commitment for Integrating Wind Generation. IEEE Trans. Power Syst. 2016, 31, 2587–2596. [CrossRef]21. Li, P.; Song, L.; Qu, J.; Huang, Y.; Wu, X.; Lu, X.; Xia, S. A Two-Stage Distributionally Robust Optimization Model for Wind Farms and Storage Units Jointly Operated Power Systems. IEEE Access 2021, 9, 111132–111142. [CrossRef]22. Pinto, R.S.; Unsihuay-Vila, C.; Tabarro, F.H. Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties. Appl. Energy 2021, 297, 117108. [CrossRef]23. Mieth, R.; Dvorkin, Y. Data-Driven Distributionally Robust Optimal Power Flow for Distribution Systems. IEEE Control. Syst. Lett. 2018, 2, 363–368. [CrossRef]24. Zeng, B.; Zhao, L. Solving two-stage robust optimization problems using a column-and-constraint generation method. Oper. Res. Lett. 2013, 41, 457–461. [CrossRef]25. Paredes, M.; Martins, L.S.A.; Soares, S.; Ye, H. Benders’ decomposition of the unit commitment problem with semidefinite relaxation of AC power flow constraints. Electr. Power Syst. Res. 2021, 192, 106965. [CrossRef]26. Wu, T.; Xie, K.; Hu, B.; Lu, H.; Ding, J.; Shen, W. Data-Driven Approach based Tri-Level Generation and Transmission Expansion Planning Model with High Wind Penetration. In Proceedings of the iSPEC 2019—2019 IEEE Sustainable Power and Energy Conference, Beijing, China, 21–23 November 2019; Volume 58, pp. 2607–2618. [CrossRef]27. Ding, T.; Yang, Q.; Liu, X.; Huang, C.; Yang, Y.; Wang, M.; Blaabjerg, F. Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment. IEEE Trans. Sustain. Energy 2019, 10, 82–93. [CrossRef]28. Wang, L.; Jiang, C.; Gong, K.; Si, R.; Shao, H.; Liu, W. Data-driven distributionally robust economic dispatch for distribution network with multiple microgrids. IET Gener. Transm. Distrib. 2020, 14, 5816–5822. [CrossRef]29. Ning, C.; You, F. Data-driven adaptive robust unit commitment under wind power uncertainty: A Bayesian nonparametric approach. IEEE Trans. Power Syst. 2019, 34, 2409–2418. [CrossRef]30. Shi, Z.; Zhang, T.; Liu, Y.; Feng, Y.;Wang, R.; Huang, S. Optimal design and operation of islanded multi-microgrid system with distributionally robust optimization. Electr. Power Syst. Res. 2023, 221, 109437. [CrossRef]31. Qiu, H.; Gu, W.; Pan, J.; Xu, B.; Xu, Y.; Fan, M.; Wu, Z. Multi-interval-uncertainty constrained robust dispatch for AC/DC hybrid microgrids with dynamic energy storage degradation. Appl. Energy 2018, 228, 205–214. [CrossRef]32. Syed, I.; Khadkikar, V.; Zeineldin, H.H. Loss Reduction in Radial Distribution Networks Using a Solid-State Transformer. IEEE Trans. Ind. Appl. 2018, 54, 5474–5482. [CrossRef]33. de Carne, G.; Buticchi, G.; Zou, Z.; Liserre, M. Reverse Power Flow Control in a ST-Fed Distribution Grid. IEEE Trans. Smart Grid 2018, 9, 3811–3819. [CrossRef]34. Zhang, Z.; Chen, Y.; Ma, J.; Liu, X.; Wang, W. Two-Stage Robust Security Constrained Unit Commitment Considering the Spatiotemporal Correlation of Uncertainty Prediction Error. IEEE Access 2019, 7, 22891–22901. [CrossRef]35. Zhang, Z.; Chen, Y.; Liu, X.; Wang, W. Two-Stage Robust Security-Constrained Unit Commitment Model Considering Time Autocorrelation of Wind/Load Prediction Error and Outage Contingency Probability of Units. IEEE Access 2019, 7, 25398–25408. [CrossRef]36. Qiu, H.; Gu, W.; Xu, Y.; Wu, Z.; Zhou, S.; Wang, J. Interval-Partitioned Uncertainty Constrained Robust Dispatch for AC/DC Hybrid Microgrids with Uncontrollable Renewable Generators. IEEE Trans. Smart Grid 2019, 10, 4603–4614. [CrossRef]37. Yuan, S.; Dai, C.; Guo, A.; Chen,W. A novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers. Electr. Power Syst. Res. 2019, 169, 115–123. [CrossRef]38. Dehghan, S.; Nakiganda, A.; Aristidou, P. A data-driven two-stage distributionally robust planning tool for sustainable Microgrids. In Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada, 2–6 August 2020. [CrossRef]39. Kumar, C.; Manojkumar, R.; Ganguly, S.; Liserre, M. Power Loss Minimization in Smart Transformer Based Meshed Hybrid Distribution Network. In Proceedings of the IECON 2020—The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 18–21 October 2020; pp. 1670–1675. [CrossRef]40. Le, L.; Fang, J.; Zhang, M.; Zeng, K.; Ai, X.; Wu, Q.; Wen, J. Data-driven stochastic unit commitment considering commercial air conditioning aggregators to provide multi-function demand response. Int. J. Electr. Power Energy Syst. 2021, 129, 106790. [CrossRef]41. Zhang, C.; Liu, L.; Cheng, H.; Liu, D.; Zhang, J.; Li, G. Data-driven distributionally robust transmission expansion planning considering contingency-constrained generation reserve optimization. Int. J. Electr. Power Energy Syst. 2021, 131, 106973. [CrossRef]42. Wang, C.; Gong, Z.; He, C.; Gao, H.; Bi, T. Data-Driven Adjustable Robust Unit Commitment of Integrated Electric-Heat Systems. IEEE Trans. Power Syst. 2021, 36, 1385–1398. [CrossRef]43. Hrishikesan, V.M.; Kumar, C. Operation of Meshed Hybrid Microgrid During Adverse Grid Conditions with Storage Integrated Smart Transformer. IEEE Open J. Ind. Electron. Soc. 2021, 2, 315–325. [CrossRef]44. Kumar, C.; Manojkumar, R.; Ganguly, S.; Liserre, M. Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network. IEEE Access 2021, 9, 140268–140280. [CrossRef]45. Hrishikesan, V.M.; Kumar, C.; Liserre, M. An MVDC Based Meshed Hybrid Microgrid Enabled Using Smart Transformers. IEEE Trans. Ind. Electron. 2021, 69, 3722–3731. [CrossRef]46. Das, D.; Manojkumar, R.; Kumar, C.; Ganguly, S. Optimal Power Management for Islanded Operation of ST-based Meshed Hybrid LV Microgrid. In Proceedings of the 2021 IEEE 12th Energy Conversion Congress & Exposition—Asia (ECCE-Asia), Singapore, 24–27 May 2021; pp. 183–188. [CrossRef]47. Kumar, C.; Manojkumar, R.; Ganguly, S. Optimal Placement of Smart Transformer Low Voltage Converter in Meshed Hybrid Distribution Network. In Proceedings of the 2021 IEEE 12th Energy Conversion Congress & Exposition—Asia (ECCE-Asia), Singapore, 24–27 May 2021; pp. 1795–1800. [CrossRef]48. De Lara Filho, M.O.; Pinto, R.S.; De Campos, A.C.; Vila, C.U.; Tabarro, F.H. Day-ahead robust operation planning of microgrids under uncertainties considering DERs and demand response. In Proceedings of the 2021 IEEE PES Innovative Smart Grid Technologies Conference—Latin America, ISGT Latin America 2021, Lima, Peru, 15–17 September 2021; Volume 15, pp. 1–5. [CrossRef]49. Feizi, M.R.; Khodayar, M.E.; Li, J. Data-driven distributionally robust unbalanced operation of distribution networks with high penetration of photovoltaic generation and electric vehicles. Electr. Power Syst. Res. 2022, 210, 108001. [CrossRef]50. Gupta, S.; Maulik, A.; Das, D.; Singh, A. Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers. Renew. Sustain. Energy Rev. 2022, 155, 111861. [CrossRef]51. Huang, Z.; Zhang, Y.; Xie, S. Data-adaptive robust coordinated optimization of dynamic active and reactive power flow in active distribution networks. Renew. Energy 2022, 188, 164–183. [CrossRef]52. Das, D.; Kumar, C. Partial Startup Scheme for Smart Transformer in Meshed Hybrid Islanded Grid Operation. IEEE Trans. Ind. Appl. 2022, 58, 142–151. [CrossRef]53. Singh, A.; Maulik, A. Energy Management of an Active Distribution Network Considering Correlation Between Uncertain Input Variables. Arab. J. Sci. Eng. 2023, 48, 6377–6398. [CrossRef]54. Zhao, Q.; García-González, J.; Gomis-Bellmunt, O.; Prieto-Araujo, E.; Echavarren, F.M. Impact of converter losses on the optimal power flow solution of hybrid networks based on VSC-MTDC. Electr. Power Syst. Res. 2017, 151, 395–403. [CrossRef]55. Mohammadi, F.; Nazri, G.-A.; Saif, M. An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems. Appl. Sci. 2019, 10, 297. [CrossRef]56. Núñez-Rodríguez, R.A.; Unsihuay-Vila, C.; Posada, J.; Pinzón-Ardila, O.; Aoki, A.R.; Bueno-Otto, R. Real-Time Testing Optimal Power Flow in Smart-Transformer-Based Meshed Hybrid Microgrids: Design and Validation. Energies 2024, 17, 1950. [CrossRef]57. Lachovicz, F.J.; Fernandes, T.S.P.; Junior, J.A.V. Impacts of PV-STATCOM Reactive Power Dispatch in the Allocation of Capacitors Bank and Voltage Regulators on Active Distribution Networks. J. Control. Autom. Electr. Syst. 2023, 34, 796–807. [CrossRef]58. Jeyapradha, R.B.; Rajini, V. Investigations on Service Extensions of Solid State Transformer. In Proceedings of the 2019 Fifth International Conference on Electrical Energy Systems (ICEES), Chennai, India, 21–22 February 2019; pp. 1–6. [CrossRef]59. Jakka, V.N.; Acharya, S.; Anurag, A.; Prabowo, Y.; Kumar, A.; Parashar, S.; Bhattacharya, S. Protection design considerations of a 10 kV SiC MOSFET enabled mobile utilities support equipment based solid state transformer (muse-SST). In Proceedings of the IECON 2018—44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 21–23 October 2018; pp. 5559–5565. [CrossRef]60. Zhu, R.; Andresen, M.; Langwasser, M.; Liserre, M.; Lopes, J.P.; Moreira, C.; Rodrigues, J.; Couto, M. Smart transformer/large flexible transformer. CES Trans. Electr. Mach. Syst. 2021, 4, 264–274. [CrossRef]61. Zhu, R.; Buticchi, G.; Liserre, M. Investigation on Common-Mode Voltage Suppression in Smart Transformer-Fed Distributed Hybrid Grids. IEEE Trans. Power Electron. 2018, 33, 8438–8448. [CrossRef]62. González-Castellanos, A.; Pozo, D.; Bischi, A. Distribution System Operation with Energy Storage and Renewable Generation Uncertainty. In Handbook of Optimization in Electric Power Distribution Systems; Resener, M., Rebennack, S., Pardalos, P.M., Haffner, S., Eds.; Springer International Publishing: Cham, Switzerland, 2020; pp. 69–120.63. Pinto, R.S. Computational Models for Expansion Planning of Electric Power Distribution Systems Containing Multiple Microgrids and Distributed Energy Resources under Uncertainties. Ph.D. Thesis, Universidade Federal do Paraná, Curitiba, Brazil, 2022.64. Bernal, D.E.; Chen, Q.; Gong, F.; Grossmann, I.E. Mixed-Integer Nonlinear Decomposition Toolbox for Pyomo (MindtPy). Comput. Aided Chem. Eng. 2018, 44, 895–900. [CrossRef]65. XM. Curva de Demanda Promedio en Colombia. 2019. Available online: https://sinergox.xm.com.co/dmnd/Paginas/Informes/ DemandaRealPerdidas.aspx (accessed on 15 April 2023).66. SolarCast Historical and TMY. Time Series. 2022. Available online: https://toolkit.solcast.com.au/historical (accessed on 15 April 2023).67. Strunz, K.; Abbey, C.; Andrieu, C.; Campbell, R.C.; Fletcher, R. Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources—TF C6.04.02. 2014. Available online: https://www.e-cigre.org/publications/detail/575- benchmark-systems-for-network-integration-of-renewable-and-distributed-energy-resources.html (accessed on 15 April 2023).68. Zhang, M.; Ai, X.; Fang, J.; Yao, W.; Zuo, W.; Chen, Z.; Wen, J. A systematic approach for the joint dispatch of energy and reserve incorporating demand response. Appl. Energy 2018, 230, 1279–1291. [CrossRef]69. Goerigk, M.; Khosravi, M. Optimal scenario reduction for one- and two-stage robust optimization with discrete uncertainty in the objective. Eur. J. Oper. Res. 2023, 310, 529–551. [CrossRef]AC/DC microgridData-driven distributionally robust optimizationDuality-free decompositionMeshed hybrid microgridsUncertaintySmart transformerMicrorred CA/CCOptimización distribucional robusta basada en datosDescomposición sin dualidadMicrorredes híbridas en mallaIncertidumbreTransformador inteligenteComunidad generalPublication11ddcf21-b409-4913-9535-44b2a15539d0virtual::6096-111ddcf21-b409-4913-9535-44b2a15539d0virtual::6096-1https://scholar.google.com/citations?user=icvmhSkAAAAJ&hl=es&authuser=6virtual::6096-10000-0001-7576-1021virtual::6096-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000193488virtual::6096-1ORIGINALData-driven_distributionally_robust_optimization_for_day-ahead_operation_planning_of_a_smart_transformer-based_meshed_hybrid_acdc_microgrid_considering_the_optimal_reactive_power_dispatch.pdfData-driven_distributionally_robust_optimization_for_day-ahead_operation_planning_of_a_smart_transformer-based_meshed_hybrid_acdc_microgrid_considering_the_optimal_reactive_power_dispatch.pdfArchivo texto completo del artículo de revista, PDFapplication/pdf5785904https://red.uao.edu.co/bitstreams/4094b777-feac-4c18-854f-652bd346733e/download782da1fa498639ec7d572d083aafcc4eMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81672https://red.uao.edu.co/bitstreams/52abbebd-1e60-4b0a-a57d-b10fb7b6d4ed/download6987b791264a2b5525252450f99b10d1MD52TEXTData-driven_distributionally_robust_optimization_for_day-ahead_operation_planning_of_a_smart_transformer-based_meshed_hybrid_acdc_microgrid_considering_the_optimal_reactive_power_dispatch.pdf.txtData-driven_distributionally_robust_optimization_for_day-ahead_operation_planning_of_a_smart_transformer-based_meshed_hybrid_acdc_microgrid_considering_the_optimal_reactive_power_dispatch.pdf.txtExtracted texttext/plain101415https://red.uao.edu.co/bitstreams/e9edc03e-e0eb-4648-a71f-75d936a6243f/download94c82304bb280baa5208e0c639eadac8MD53THUMBNAILData-driven_distributionally_robust_optimization_for_day-ahead_operation_planning_of_a_smart_transformer-based_meshed_hybrid_acdc_microgrid_considering_the_optimal_reactive_power_dispatch.pdf.jpgData-driven_distributionally_robust_optimization_for_day-ahead_operation_planning_of_a_smart_transformer-based_meshed_hybrid_acdc_microgrid_considering_the_optimal_reactive_power_dispatch.pdf.jpgGenerated Thumbnailimage/jpeg16341https://red.uao.edu.co/bitstreams/eaeca3b2-6fca-4f89-9fd0-cac06750b1ba/downloadddb9055d554352f9eeeba34ff35562e9MD5410614/16206oai:red.uao.edu.co:10614/162062025-07-10 03:02:01.59https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - MDPI, 2024open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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

Publicaciones similares