Optimization in a Multi-Microgrid Peer-To-Peer Scenario with Replicator Dynamics

Vista sencilla de ítem
dc.creatorChacón, Sofia
dc.creatorBenavides, Edinson
dc.creatorPantoja, Andrés
dc.creatorObando, Germán
dc.date2024-07-02
dc.date.accessioned2025-10-01T23:53:12Z
dc.descriptionOptimization plays a crucial role in the planning and operation of energy management systems, reducing costs and avoiding losses in generation while also decreasing carbon emissions. This is achieved by balancing supply and demand and leveraging distributed energy resources (DER). This study aimed to propose a generalized energy community scheme, where the generators within a microgrid meet the demand of their own or neighboring microgrids. It is important to consider that each energy generator has an associated cost function, and there is a penalty or transmission cost when a DER, located in a specific microgrid, sends energy to a neighboring microgrid. To address these constraints, a solution methodology based on population games was proposed, in conjunction with the Lagrangian relaxation technique, was proposed. The results obtained included the application of the model and solution method in three different scenarios. Additionally, the performance of the proposed solution was compared with the response of a conventional optimization method, achieving similar dispatches and minimal errors compared to the traditional technique. The research demonstrated that the combination of population game concepts and Lagrangian relaxation techniques can handle constraints that are challenging for replicator dynamics. Finally, it is concluded that the model is an effective tool for addressing energy management problems that involve meeting regional demand in a peer-to-peer scenario.en-US
dc.descriptionLa optimización desempeña un papel crucial en la planificación y operación de los sistemas de gestión de energía, reduciendo costos y evitando pérdidas en su generación, disminuyendo, además, las emisiones de carbono. Lo anterior se da teniendo en cuenta el equilibrio entre oferta y demanda y el aprovechamiento de los recursos energéticos distribuidos (DER, por sus siglas en inglés). Este trabajo tuvo como objetivo proponer un esquema generalizado de comunidad energética, donde los generadores que componen una microrred suplen la demanda de esta o de las microrredes vecinas. Es de considerar que cada generador de energía tiene una función de costos asociada a esta, y existe una penalización, o costo de transmisión, cuando un DER, ubicado en una microrred definida, envía energía a la microrred vecina. Con el fin de abordar las restricciones, se propuso, como metodología de solución, un enfoque basado en juegos poblacionales, en conjunto con la técnica de relajación lagrangiana. Los resultados obtenidos fueron la aplicación del modelo y método de solución en tres diferentes escenarios. Además, se comparó el desempeño de la solución propuesta con la respuesta de un método de optimización convencional, logrando despachos similares y errores mínimos en comparación con la técnica tradicional. La investigación demostró que la combinación de conceptos de juegos poblacionales y técnicas de relajación lagrangiana permiten asumir restricciones que son de difícil manejo para la dinámica de replicadores. Finalmente, se concluye que el modelo es una buena herramienta para abordar problemas de gestión de energía que implican cumplir con la demanda por región en un escenario peer to peer.es-ES
dc.formatapplication/pdf
dc.formattext/xml
dc.formatapplication/zip
dc.formattext/html
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2992
dc.identifier10.22430/22565337.2992
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7900
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2992/3291
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2992/3421
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2992/3542
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2992/3544
dc.relation/*ref*/N. Razzaghi-Asl, J. Tanha, M. Nabatian, and N. Samadi, “Smart Grid based decentralized Peer-to-Peer Energy Trading Using Whale Optimization Algorithm,” in 2021 7th International Conference on Signal Processing and Intelligent Systems, Tehran, Iran, Islamic Republic of, 2021, pp. 01-05. https://doi.org/10.1109/ICSPIS54653.2021.9729347
dc.relation/*ref*/N. Ghorbani-Renani, and P. Odonkor, “An Energy Cost Optimization Model for Electricity Trading in Community Microgrids,” in 2022 IEEE International Smart Cities Conference, Pafos, Cyprus, 2022, pp. 1-7. https://doi.org/10.1109/ISC255366.2022.9922504
dc.relation/*ref*/G. Vieira, and J. Zhang, “Peer-to-peer energy trading in a microgrid leveraged by smart contracts,” Renewable and Sustainable Energy Reviews, vol. 143, p. 110900, Jun. 2021. https://doi.org/10.1016/j.rser.2021.110900
dc.relation/*ref*/Y. Xia, Q. Xu, S. Li, R. Tang, and P. Du, “Reviewing the peer-to-peer transactive energy market: Trading environment, optimization methodology, and relevant resources,” J. Cleaner Prod., vol. 383, p. 135441, Jan. 2022. https://doi.org/10.1016/j.jclepro.2022.135441
dc.relation/*ref*/S. Suthar, S. H. C. Cherukuri, and N. M. Pindoriya, “Peer-to-peer energy trading in smart grid: Frameworks, implementation methodologies, and demonstration projects,” Electric Power Syst. Res., vol. 214, p. 108907, Jan. 2023. https://doi.org/10.1016/j.epsr.2022.108907
dc.relation/*ref*/T. Capper et al., “Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models,” Renewable Sustain. Energy Rev., vol. 162, p. 112403, Jul. 2022. https://doi.org/10.1016/j.rser.2022.112403
dc.relation/*ref*/A. L. Bukar et al., “Peer-to-peer electricity trading: A systematic review on current developments and perspectives,” Renew. Energy Focus., vol. 44, pp. 317–333, 2023. https://doi.org/10.1016/j.ref.2023.01.008
dc.relation/*ref*/A. Timilsina, and S. Silvestri, “Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing,” in 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 4836-4841. https://doi.org/10.1109/GLOBECOM48099.2022.10001173
dc.relation/*ref*/M. Vieira, R. Faia, T. Pinto, and Z. Vale, “Schedule Peer-to-Peer Transactions of an Energy Community Using Particle Swarm,” in 2022 18th International Conference on the European Energy Market, Ljubljana, Slovenia, 2022, pp. 1-6. https://doi.org/10.1109/EEM54602.2022.9921094
dc.relation/*ref*/S. Cui, W. Yan-Wu, and X. Jiang-Wen, “Peer-to-peer energy sharing among smart energy buildings by distributed transaction,” IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6491–6501, Nov. 2019. https://doi.org/10.1109/TSG.2019.2906059
dc.relation/*ref*/Y. Sharifian, and H. Abdi, “Multi-area economic dispatch problem: Methods, uncertainties, and future directions,” Renewable Sustain. Energy Rev., vol. 191, p. 114093, Mar. 2024. https://doi.org/10.1016/j.rser.2023.114093
dc.relation/*ref*/A. B. Kunya, A. S. Abubakar, and S. S. Yusuf, “Review of economic dispatch in multi-area power system: State-of-the-art and future prospective,” Electric Power Syst. Res., vol. 217, p. 109089, Apr. 2023. https://doi.org/10.1016/j.epsr.2022.109089
dc.relation/*ref*/S. Xuanyue et al., “Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach,” Int. J. Electr. Power Energy Syst., vol. 138, p. 107892, Jun. 2022. https://doi.org/10.1016/j.ijepes.2021.107892
dc.relation/*ref*/T. Alskaif, J. L. Crespo-Vazquez, M. Sekuloski, G. V. Leeuwen, and J. P. Catalao, “Blockchain-based fully peer-to-peer energy trading strategies for residential energy systems,” IEEE Transactions on Industrial Informatics, vol. 18, no. 1, pp. 231–241, Jan. 2022. https://doi.org/10.1109/TII.2021.3077008
dc.relation/*ref*/K. Anoh, S. Maharjan, A. Ikpehai, Y. Zhang, and B. Adebisi, “Energy peer-to-peer trading in virtual microgrids in smart grids: A game- theoretic approach,” IEEE Transactions on Smart Grid, vol. 11, no 2, pp.1264–1275, Mar. 2020. https://doi.org/10.1109/TSG.2019.2934830
dc.relation/*ref*/Y. Cui, Y. Xu, Y. Wang, Y. Zhao, H. Zhu, and D. Cheng, “Peer-to-peer energy trading with energy trading consistency in interconnected multi-energy microgrids: A multi-agent deep reinforcement learning approach,” Int. J. Elect. Power & Energy Syst., vol. 156, p. 109753, Feb. 2024. https://doi.org/10.1016/j.ijepes.2023.109753
dc.relation/*ref*/I. Quintas-Pereira, “Implementación del algoritmo del replicador dinámico en lenguaje R,” Política y Cultura, no. 39, pp. 251–261, Jun. 2013. https://www.redalyc.org/articulo.oa?id=26727013013
dc.relation/*ref*/J. Rychtář, and M. Broom, Game-Theoretical Models in Biology, 2nd ed. New York, NY, USA: Chapman and Hall/CRC, 2022. https://doi.org/10.1201/9781003024682
dc.relation/*ref*/E. Baron-Prada, and E. Mojica-Nava, “A population games transactive control for distributed energy resources,” Int. J. Elect. Power & Energy Syst., vol. 130, p. 106874, Sep. 2021. https://doi.org/10.1016/j.ijepes.2021.106874
dc.relation/*ref*/B. Xin, and M. Zhang, “Evolutionary game on international energy trade under the russia-ukraine conflict,” Energy Economics, vol. 125, p. 106827, 2023. https://doi.org/10.1016/j.eneco.2023.106827
dc.relation/*ref*/A. Paudel, K. Chaudhari, C. Long, and H. B. Gooi, “Peer-to-peer energy trading in a prosumer-based community microgrid: A game-theoretic model,” IEEE Transactions on Industrial Electronics, vol. 66, no. 8, pp. 6087–6097, Aug. 2019. https://doi.org/10.1109/TIE.2018.2874578
dc.relation/*ref*/L. Won-Poong, D. Han, and D. Won, “Grid-oriented coordination strategy of prosumers using game-theoretic peer-to-peer trading framework in energy community,” Applied Energy, vol. 326, p. 119980, Nov. 2022. https://doi.org/10.1016/j.apenergy.2022.119980
dc.relation/*ref*/M. Tofighi-Milani, S. Fattaheian-Dehkordi, M. Gholami, M. Fotuhi-Firuzabad, and M. Lehtonen, “A novel distributed paradigm for energy scheduling of islanded multiagent microgrids,” IEEE Access, vol. 10, pp. 83636–83649, Aug. 2022. https://doi.org/10.1109/ACCESS.2022.3197160
dc.relation/*ref*/J. Martinez-Piazuelo, W. Ananduta, C. Ocampo-Martinez, S. Grammatico, and N. Quijano, “Population Games With Replicator Dynamics Under Event-Triggered Payoff Provider and a Demand Response Application,” IEEE Control Systems Letters, vol. 7, pp. 3417-3422, Jun. 2023. https://doi.org/10.1109/LCSYS.2023.3285532
dc.relation/*ref*/A. Pantoja, G. Obando, and N. Quijano, “Distributed optimization with information-constrained population dynamics,” Journal of the Franklin Institute, vol. 356, no 1, pp. 209–236, Jan. 2019. https://doi.org/10.1016/j.jfranklin.2018.10.016
dc.relation/*ref*/S. Chacon, E. Benavides, A. Pantoja, and G. Obando, “Optimización de Costos en Transacciones de Energía Multi-Región Mediante Replicadores Dinámicos con Restricciones,” in 1º Congreso de Electrónica e Informática Aplicada “CEIA”, Pasto, Colombia, 2023. [Unpublished]
dc.relation/*ref*/J. Zhu, "Classic Economic Dispatch" In Optimization of Power System Operation, Hoboken, Ed., NJ, USA: Wiley, 2015, pp. 91-143. https://doi.org/10.1002/9781118887004
dc.relation/*ref*/A. Aguilar, and J. Díaz. “Una visión del mercado eléctrico colombiano,” Bogotá, Colombia: Unidad de Planeación Minero-Energética (UPME), 2004. http://www.upme.gov.co/Docs/Vision_Mercado_Electrico_Colombiano.pdf
dc.relation/*ref*/RESOLUCIÓN 174 DE 2021, 174, Comisión de Regulación de Energía y Gas, Colombia, 2021. [Online]. Available: https://gestornormativo.creg.gov.co/gestor/entorno/docs/resolucion_creg_0174_2021.htm#6
dc.relation/*ref*/R. H. Byrd, J. C. Gilbert, and J. Nocedal, “A trust region method based on interior point techniques for nonlinear programming,” Math. Program., vol. 89, no. 1, pp. 149–185, Nov. 2000. https://doi.org/10.1007/PL00011391
dc.relation/*ref*/
dc.rightsDerechos de autor 2024 TecnoLógicases-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0es-ES
dc.sourceTecnoLógicas; Vol. 27 No. 60 (2024); e2992en-US
dc.sourceTecnoLógicas; Vol. 27 Núm. 60 (2024); e2992es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectDinámicas de replicadoreses-ES
dc.subjectrecursos energéticos distribuidoses-ES
dc.subjectmercados de energíaes-ES
dc.subjectpeer to peeres-ES
dc.subjectrelajación lagrangianaes-ES
dc.subjectsistemas de energía eléctricaes-ES
dc.subjectReplicator dynamicsen-US
dc.subjectdistributed energy resourcesen-US
dc.subjectelectricity tradingen-US
dc.subjectpeer to peeren-US
dc.subjectLagrangian relaxationen-US
dc.subjectelectric power systemsen-US
dc.titleOptimization in a Multi-Microgrid Peer-To-Peer Scenario with Replicator Dynamicsen-US
dc.titleOptimización de costos en un escenario de mercado entre pares multimicrorred con dinámicas de replicadoreses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

Archivos

Bloque original

Mostrando 1 - 4 de 4
Miniatura
Nombre:
2992-MPU-VF_V2_1.pdf
Tamaño:
1.28 MB
Formato:
Adobe Portable Document Format
Descargar
Miniatura
Nombre:
2256-5337-teclo-27-60-e202.xml
Tamaño:
93.84 KB
Formato:
Extensible Markup Language
Descargar
Miniatura
Nombre:
344277854015.epub
Tamaño:
2.65 MB
Formato:
Electronic publishing
Descargar
Miniatura
Nombre:
3544.html
Tamaño:
137.33 KB
Formato:
Hypertext Markup Language
Descargar

Colecciones

Revista TecnoLógicas - Cosecha Portal de Revistas