Carbon-Aware Intelligent Electrical Distribution Network Using Digital Twins and Edge Artificial Intelligence
Article Sidebar
Main Article Content
Abstract
The escalating integration of distributed energy resources and the urgency of decarbonization targets have exposed the limitations of conventional, centrally managed electrical distribution networks. This paper synthesizes findings from twenty-three studies to examine how digital twin technology and edge artificial intelligence can be combined to form a carbon-aware intelligent distribution network. The problem addressed concerns the inability of legacy supervisory control and data acquisition architectures to deliver the millisecond-scale responsiveness, emission transparency, and adaptive reconfiguration required by high-penetration renewable grids. A layered conceptual framework is proposed, integrating field sensing, edge inference, digital twin synchronisation, and carbon-weighted reinforcement learning control. Evidence drawn from the literature indicates that hybrid edge-cloud architectures reduce control-loop latency from several hundred milliseconds to below 100 milliseconds relative to cloud-only deployment, while safe deep reinforcement learning controllers for Volt-VAR optimisation converge within 300 to 500 training episodes and reduce voltage violations substantially. Carbon emission flow modelling combined with temporally shifted, carbon-aware scheduling is shown to yield emission reductions ranging from approximately 10 per cent to more than 25 per cent when co-optimised with digital twin state estimation. The synthesis further identifies bandwidth reduction of up to 82 per cent and inference accuracy gains of nearly 5 percentage points for hybrid configurations. Implications include improved grid resilience, measurable decarbonization, and a pathway toward regulatory-compliant, self-optimising distribution networks, with future work directed toward standardised digital twin interoperability and federated edge learning.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] M. Ibrar et al., “A Machine Learning-Based Model for Stability Prediction of Decentralized Power Grid Linked with Renewable Energy Resources,” Wirel. Commun. Mob. Comput., vol. 2022, p. 2697303, 2022, doi: 10.1155/2022/2697303.
[2] N. Bazmohammadi et al., “Microgrid Digital Twins: Concepts, Applications, and Future Trends,” IEEE Access, vol. 10, pp. 2284–2302, 2022, doi: 10.1109/ACCESS.2021.3138990.
[3] C. Feng, Y. Wang, Q. Chen, Y. Ding, G. Strbac, and C. Kang, “Smart Grid Encounters Edge Computing: Opportunities and Applications,” Adv. Appl. Energy, vol. 1, p. 100006, 2021, doi: 10.1016/j.adapen.2020.100006.
[4] H. B. Gooi, T. Wang, and Y. Tang, “Edge Intelligence for Smart Grid: A Survey on Application Potentials,” CSEE J. Power Energy Syst., vol. 9, no. 5, pp. 1623–1640, 2023, doi: 10.17775/CSEEJPES.2022.02210.
[5] J. B. Heluany and V. Gkioulos, “A Review on Digital Twins for Power Generation and Distribution,” Int. J. Inf. Secur., vol. 23, no. 2, pp. 1171–1195, 2024, doi: 10.1007/s10207-023-00784-x.
[6] M. Jafari, A. Kavousi-Fard, T. Chen, and M. Karimi, “A Review on Digital Twin Technology in Smart Grid, Transportation System, and Smart City: Challenges and Future,” IEEE Access, vol. 11, pp. 17471–17484, 2023, doi: 10.1109/ACCESS.2023.3241588.
[7] Z. Song et al., “Digital Twins for the Future Power System: An Overview and a Future Perspective,” Sustainability, vol. 15, no. 6, p. 5259, 2023, doi: 10.3390/su15065259.
[8] P. Moutis and O. Alizadeh-Mousavi, “Digital Twin of Distribution Power Transformer for Real-Time Monitoring of Medium Voltage From Low Voltage Measurements,” IEEE Trans. Power Deliv., vol. 36, no. 4, pp. 1952–1963, 2021, doi: 10.1109/TPWRD.2020.3017355.
[9] M. M. H. Sifat, S. K. Das, and S. M. Choudhury, “Design, Development, and Optimization of a Conceptual Framework of Digital Twin Electric Grid Using Systems Engineering Approach,” Electr. Power Syst. Res., vol. 226, p. 109958, 2024, doi: 10.1016/j.epsr.2023.109958.
[10] X. Xie, J. Merino, N. Moretti, P. Pauwels, J. Y. Chang, and A. Parlikad, “Digital Twin Enabled Fault Detection and Diagnosis Process for Building HVAC Systems,” Autom. Constr., vol. 146, p. 104695, 2023, doi: 10.1016/j.autcon.2022.104695.
[11] A. Bourechak, O. Zedadra, M. N. Kouahla, A. Guerrieri, H. Seridi, and G. Fortino, “At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives,” Sensors, vol. 23, no. 3, p. 1639, 2023, doi: 10.3390/s23031639.
[12] Y. Himeur, A. N. Sayed, A. Alsalemi, F. Bensaali, and A. Amira, “Edge AI for Internet of Energy: Challenges and Perspectives,” Internet of Things, vol. 25, p. 101035, 2024, doi: 10.1016/j.iot.2023.101035.
[13] S. Liang, S. Jin, and Y. Chen, “A Review of Edge Computing Technology and Its Applications in Power Systems,” Energies, vol. 17, no. 13, p. 3230, 2024, doi: 10.3390/en17133230.
[14] Q. N. Minh, V.-H. Nguyen, V. K. Quy, L. A. Ngoc, A. Chehri, and G. Jeon, “Edge Computing for IoT-Enabled Smart Grid: The Future of Energy,” Energies, vol. 15, no. 17, p. 6140, 2022, doi: 10.3390/en15176140.
[15] C. Kang et al., “Carbon Emission Flow From Generation to Demand: A Network-Based Model,” IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2386–2394, 2015, doi: 10.1109/TSG.2015.2388695.
[16] A. Radovanović et al., “Carbon-Aware Computing for Datacenters,” IEEE Trans. Power Syst., vol. 38, no. 2, pp. 1270–1280, 2023, doi: 10.1109/TPWRS.2022.3173250.
[17] S. Yang and R. Zheng, “Edge Computing Helps the Development of Smart Grid,” J. Comput. Electron. Inf. Manag., vol. 10, no. 3, pp. 69–71, 2023, doi: 10.54097/jceim.v10i3.8704.
[18] R. Henry and D. Ernst, “Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systems,” Energy AI, vol. 5, p. 100092, 2021, doi: 10.1016/j.egyai.2021.100092.
[19] H. Li and H. He, “Learning to Operate Distribution Networks With Safe Deep Reinforcement Learning,” IEEE Trans. Smart Grid, vol. 13, no. 3, pp. 1860–1872, 2022, doi: 10.1109/TSG.2022.3142961.
[20] H. Liu and W. Wu, “Two-Stage Deep Reinforcement Learning for Inverter-Based Volt-VAR Control in Active Distribution Networks,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 2037–2047, 2021, doi: 10.1109/TSG.2020.3041620.
[21] Y. Zhang, X. Wang, J. Wang, and Y. Zhang, “Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems,” IEEE Trans. Smart Grid, vol. 12, no. 1, pp. 361–371, 2021, doi: 10.1109/TSG.2020.3010130.
[22] S. H. Oh, Y. T. Yoon, and S. W. Kim, “Online Reconfiguration Scheme of Self-Sufficient Distribution Network Based on a Reinforcement Learning Approach,” Appl. Energy, vol. 280, p. 115900, 2020, doi: 10.1016/j.apenergy.2020.115900.
[23] W. Wang, N. Yu, Y. Gao, and J. Shi, “Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems,” IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3008–3018, 2020, doi: 10.1109/TSG.2019.2962625.