Optimization in a Multi-Microgrid Peer-To-Peer Scenario with Replicator Dynamics
Abstract
Optimization 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.
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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]
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
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
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
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
Downloads
Copyright (c) 2024 TecnoLógicas
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
| Article metrics | |
|---|---|
| Abstract views | |
| Galley vies | |
| PDF Views | |
| HTML views | |
| Other views | |
