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REVIEW 2 major objections 1 minor 37 references

Threshold Optimization and Dynamic Adaptation of Distributed Optimal Power Flow in 5G Networks

T0 review · 2 major / 1 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read A dynamic threshold policy in real 5G testbed cuts DOPF convergence time by 26.42 percent over static optimum.

desk verdict The paper reports concrete 26% convergence-time gains from a dynamic threshold policy in a real 5G ADMM DOPF testbed, but the attribution to the policy itself is weakened by missing controls for network and compute variability. read the letter →

arxiv 2606.27542 v1 pith:D7TJC6TY submitted 2026-06-25 eess.SY cs.SYeess.SP

Biswajit Kumar Dash , Garrett Thomas , Adedoyin Inaolaji , Filippo Malandra This is my paper · ORCID
classification eess.SYcs.SYeess.SP
keywords ADMMdistributedoptimalpowerflow5GnetworksthresholdoptimizationdynamicadaptationconvergencetimesmartgridIEEE123-bus
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper runs ADMM-based distributed optimal power flow on an IEEE 123-bus feeder split into five areas, each controlled by a Raspberry Pi linked through commercial 5G. It introduces a delay threshold to skip late messages and a policy that raises or lowers the threshold according to measured communication and computation times. Experiments show the fixed threshold shortens convergence by 7.75 percent versus no threshold, while the dynamic rule adds a further 26.42 percent improvement over the best fixed value. The work therefore tests whether communication-aware adaptation can keep distributed grid control practical when network conditions vary.

What carries the argument

The delay threshold mechanism together with the dynamic policy that recomputes the threshold from current communication and computation conditions.

What would settle it

Repeating the identical hardware runs with the dynamic update policy turned off while keeping all other settings fixed and obtaining no measurable change in convergence time would falsify the central claim.

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Extended reading notes

Core claim

A delay threshold mechanism applied to ADMM iterations on the subdivided IEEE 123-bus system reduces convergence time by 7.75 percent relative to the no-threshold baseline under commercial 5G. Replacing the fixed threshold with a policy that continuously recomputes the value from observed communication and computation conditions produces an additional 26.42 percent reduction relative to the best static threshold.

Load-bearing premise

Measured reductions in convergence time result from the threshold rules rather than from particular choices of test conditions or unmeasured 5G performance factors.

Editorial extensions

If this is right

  • ADMM-based DOPF on the five-area IEEE 123-bus feeder converges faster when late messages are dropped at a chosen delay threshold.
  • Dynamically recomputing the threshold from real-time communication and computation measurements outperforms any single fixed threshold.
  • Adaptive threshold control demonstrates feasibility for communication-aware smart-grid operation over commercial 5G links.
  • Hardware results with Raspberry Pi controllers confirm that the approach works on an unbalanced distribution feeder without requiring perfect network timing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same threshold adaptation logic could be tested on other distributed optimization algorithms that exchange iterative messages over variable networks.
  • Scaling the testbed to more areas or to 5G slices with higher latency variance would reveal whether the reported gains persist.
  • Combining the dynamic threshold with local computation throttling might produce further reductions in total solution time.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper reports an experimental evaluation of ADMM-based distributed optimal power flow (DOPF) on the IEEE 123-bus feeder partitioned into five areas, each controlled by a Raspberry Pi over commercial 5G links. It introduces a delay-threshold mechanism claimed to reduce convergence time by 7.75% versus a no-threshold baseline and a dynamic threshold-update policy claimed to reduce convergence time by 26.42% versus the static optimal threshold, attributing the gains to communication-aware adaptation in a real-time smart-grid testbed.

Significance. A fully experimental platform using commercial 5G and embedded controllers provides concrete evidence on how network variability affects distributed optimization; if the attribution of the reported speed-ups is substantiated, the work offers practical guidance for deploying communication-aware DOPF in variable 5G environments.

major comments (2)
  1. [Abstract / Results] Abstract and experimental-results section: the 26.42% convergence-time reduction is presented as the effect of the dynamic policy, yet the manuscript provides no trial count, standard deviation, or controlled replay of identical delay traces. Without these, the observed delta cannot be isolated from run-to-run 5G channel fluctuations or Raspberry Pi compute jitter, undermining the central experimental claim.
  2. [Experimental Setup] Experimental-setup description: the five-area partitioning and ADMM iteration counts are stated, but the paper does not report how area boundaries or penalty parameters were chosen or whether they remained fixed across all compared runs; any implicit change would confound the threshold-policy comparison.
minor comments (1)
  1. [Method] Notation for the delay threshold and the dynamic-update rule should be introduced with explicit equations rather than prose descriptions only.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of experimental rigor and transparency. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core contributions.

read point-by-point responses

  1. Referee: [Abstract / Results] Abstract and experimental-results section: the 26.42% convergence-time reduction is presented as the effect of the dynamic policy, yet the manuscript provides no trial count, standard deviation, or controlled replay of identical delay traces. Without these, the observed delta cannot be isolated from run-to-run 5G channel fluctuations or Raspberry Pi compute jitter, undermining the central experimental claim.

    Authors: We agree that statistical details are necessary to substantiate the reported improvements. The original manuscript omitted these elements. In the revision we will add the number of independent experimental trials performed, report mean convergence times together with standard deviations for each policy, and describe the extent to which delay traces were replayed or controlled to isolate the effect of the threshold policy from channel variability. revision: yes

  2. Referee: [Experimental Setup] Experimental-setup description: the five-area partitioning and ADMM iteration counts are stated, but the paper does not report how area boundaries or penalty parameters were chosen or whether they remained fixed across all compared runs; any implicit change would confound the threshold-policy comparison.

    Authors: We concur that full disclosure of these choices is required. The area boundaries were selected according to geographic and load-balance criteria on the IEEE 123-bus feeder, and the ADMM penalty parameter was fixed after preliminary tuning. We will insert a new paragraph in the experimental-setup section that explicitly states these selection criteria and confirms that both the partitioning and the penalty value remained unchanged across all compared runs. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Experimental measurements of convergence-time reductions contain no derivation chain that reduces to fitted inputs or self-citations by construction

full rationale

The manuscript is an experimental evaluation study using a physical testbed (IEEE 123-bus feeder, five Raspberry Pi controllers, commercial 5G). It reports measured convergence-time improvements from a delay-threshold mechanism (7.75% vs. no-threshold baseline) and a dynamic-update policy (26.42% vs. static optimal threshold). No equations, first-principles derivations, or predictions are presented that could reduce to their own inputs; the results are direct empirical observations from the described hardware/software platform. The central claims rest on experimental attribution rather than any self-definitional, fitted-input, or self-citation load-bearing structure.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract provides insufficient detail to identify any free parameters, axioms, or invented entities; the threshold values appear chosen based on experiments but no specifics given.

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Pith. "Pith review of Threshold Optimization and Dynamic Adaptation of Distributed Optimal Power Flow in 5G Networks." pith.science (2026). https://pith.science/paper/D7TJC6TY

@misc{pith2026260627542,
  author       = {Pith},
  title        = {Pith review of: Threshold Optimization and Dynamic Adaptation of Distributed Optimal Power Flow in 5G Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D7TJC6TY}},
  note         = {Machine review of arXiv:2606.27542}
}
read the original abstract

In this paper, we present an experimental evaluation study of the Alternating Direction Method of Multipliers (ADMM), which is a widely used technique in the distributed optimization of power distribution networks. The focus of this study is on how real 5G communication performance affects ADMM in a fully experimental platform that features commercial 5G connectivity and real-time control. The ADMM-based Distributed Optimal Power Flow (DOPF) problem is solved using the IEEE 123-bus unbalanced distribution feeder subdivided into five areas, each managed by a local controller implemented on a Raspberry Pi. To mitigate the impact of the communication network variability, we propose a delay threshold-based mechanism that yields a 7.75% reduction in convergence time compared to a no-threshold baseline. We also devised a policy to dynamically update the threshold value based on communication and computation conditions, achieving a 26.42% reduction in the convergence time compared with the static optimal threshold. These results demonstrate the potential of adaptive, communication-aware control strategies for real-world Smart Grid (SG) deployments.

Figures

Figures reproduced from arXiv: 2606.27542 by the authors.

Figure 1
Figure 1. ExTODS two-layer architecture with the physical power system (top) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Timeline of an ADMM iteration 𝑘 ∈ K, starting from reception of the global variables broadcast by the coordinator at the end of iteration 𝑘 − 1. Stopping rule-1: Convergence is then assessed by evaluating the primal residual 𝑒(𝑘) :=  𝜎𝐴,𝑖(𝑘) − 𝜏𝑖(𝑘) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Performance of the ADMM algorithm under different static threshold [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Percentage of timely local updates per area controller across different [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 8
Figure 8. Figure 8: (a) shows that the static threshold configuration re￾quires 222 iterations, whereas dynamic threshold tuning re￾duces this to 157 iterations. Correspondingly, [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

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Works this paper leans on

37 extracted references · 1 canonical work pages

  1. [1]

    Slowly but Surely: Smart Grids Are Just Around the Corner [Guest Editorial],

    L. Ochoa, “Slowly but Surely: Smart Grids Are Just Around the Corner [Guest Editorial],”IEEE Power and Energy Magazine, vol. 15, no. 3, pp. 16 – 18, 2017

    2017

  2. [2]

    A Distributed Power System Control Architecture for Improved Distribution System Resiliency,

    K. P. Schneider, S. Laval, J. Hansen, R. B. Melton, L. Ponder, L. Fox, J. Hart, J. Hambrick, M. Buckner, M. Bagguet al., “A Distributed Power System Control Architecture for Improved Distribution System Resiliency,”IEEE Access, vol. 7, pp. 9957–9970, 2019

    2019

  3. [3]

    A fast distributed implementation of optimal power flow,

    R. Baldick, B. H. Kim, C. Chase, and Y . Luo, “A fast distributed implementation of optimal power flow,”IEEE Transactions on Power Systems, vol. 14, no. 3, pp. 858–864, 2002

    2002

  4. [4]

    A Fully Distributed Reactive Power Optimization and Control Method for Active Distribu- tion Networks,

    W. Zheng, W. Wu, B. Zhang, H. Sun, and Y . Liu, “A Fully Distributed Reactive Power Optimization and Control Method for Active Distribu- tion Networks,”IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 1021–1033, 2016

    2016

  5. [5]

    Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,

    S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Ecksteinet al., “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,”Foundations and Trends® in Machine learning, vol. 3, no. 1, pp. 1–122, 2011

    2011

  6. [6]

    Analyzing the Impact of Cellular Net- work Delay on Distributed Optimization of the Distribution Grid,

    A. Inaolaji and F. Malandra, “Analyzing the Impact of Cellular Net- work Delay on Distributed Optimization of the Distribution Grid,” in 2024 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, 2024, pp. 200–206

    2024

  7. [7]

    ExTODS: Exper- imental 5G-Enabled Testbed for Distributed Optimization of Distribution Systems,

    B. K. Dash, G. Thomas, A. Inaolaji, and F. Malandra, “ExTODS: Exper- imental 5G-Enabled Testbed for Distributed Optimization of Distribution Systems,” in2025 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGrid- Comm). IEEE, 2025, pp. 1–7

    2025

  8. [8]

    A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems,

    D. K. Molzahn, F. Dörfler, H. Sandberg, S. H. Low, S. Chakrabarti, R. Baldick, and J. Lavaei, “A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems,”IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2941–2962, 2017

    2017

Show all 37 references
  1. [9]

    Distributed Security-Constrained Unit Commitment for Large-Scale Power Systems,

    A. Kargarian, Y . Fu, and Z. Li, “Distributed Security-Constrained Unit Commitment for Large-Scale Power Systems,”IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 1925–1936, 2015

    1925

  2. [10]

    Distributed AC Optimal Power Flow using ALADIN,

    A. Engelmann, T. Mühlpfordt, Y . Jiang, B. Houska, and T. Faulwasser, “Distributed AC Optimal Power Flow using ALADIN,”IFAC- PapersOnLine, vol. 50, no. 1, pp. 5536–5541, 2017

    2017

  3. [11]

    The generalized unit commitment problem,

    R. Baldick, “The generalized unit commitment problem,”IEEE Trans- actions on Power Systems, vol. 10, no. 1, pp. 465–475, 1995

    1995

  4. [12]

    ADMM Enhancement Techniques for Distributed Optimal Power Flow,

    M. Hasanzadeh and A. Kargarian, “ADMM Enhancement Techniques for Distributed Optimal Power Flow,”IEEE Transactions on Power Systems, 2025

    2025

  5. [13]

    Convergence Analysis of the Incremental Cost Consensus Algorithm Under Different Communication Network Topologies in a Smart Grid,

    Z. Zhang and M.-Y . Chow, “Convergence Analysis of the Incremental Cost Consensus Algorithm Under Different Communication Network Topologies in a Smart Grid,”IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1761–1768, 2012

    2012

  6. [14]

    Role of communication on the convergence rate of fully distributed DC optimal power flow,

    J. Mohammadi, G. Hug, and S. Kar, “Role of communication on the convergence rate of fully distributed DC optimal power flow,” in 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2014, pp. 43–48

    2014

  7. [15]

    Distributed consensus and optimization under communication delays,

    K. I. Tsianos and M. G. Rabbat, “Distributed consensus and optimization under communication delays,” in2011 49th Annual Allerton Conference on Communication, Control, and Computing, 2011, pp. 974–982

    2011

  8. [16]

    Distributed V oltage Control in Distribu- tion Networks: Online and Robust Implementations,

    H. J. Liu, W. Shi, and H. Zhu, “Distributed V oltage Control in Distribu- tion Networks: Online and Robust Implementations,”IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6106–6117, 2018

    2018

  9. [17]

    Frequency-constrained microgrid- distribution network coordinated load restoration: A distributed carbon- aware optimization approach,

    Y . Tian, Y . Xu, H. Sun, and N. Tai, “Frequency-constrained microgrid- distribution network coordinated load restoration: A distributed carbon- aware optimization approach,”Applied Energy, vol. 395, p. 126221, 2025

    2025

  10. [18]

    Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication,

    H. Li, C. Huang, G. Chen, X. Liao, and T. Huang, “Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication,”IEEE Transactions on Cybernetics, vol. 47, no. 8, pp. 2044–2057, 2017

  11. [19]

    On the Role of Communications Plane in Distributed Optimization of Power Systems,

    J. Guo, G. Hug, and O. K. Tonguz, “On the Role of Communications Plane in Distributed Optimization of Power Systems,”IEEE Transac- tions on Industrial Informatics, vol. 14, no. 7, pp. 2903–2913, 2018

    2018

  12. [20]

    Distributed Online V AR Control for Unbalanced Distribution Networks With Photovoltaic Generation,

    J. Li, C. Liu, M. E. Khodayar, M.-H. Wang, Z. Xu, B. Zhou, and C. Li, “Distributed Online V AR Control for Unbalanced Distribution Networks With Photovoltaic Generation,”IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4760–4772, 2020

    2020

  13. [21]

    ADMM-Based Distributed OPF Problem Meets Stochastic Communication Delay,

    J. Xu, H. Sun, and C. J. Dent, “ADMM-Based Distributed OPF Problem Meets Stochastic Communication Delay,”IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 5046–5056, 2019

    2019

  14. [22]

    EPOCHS: A Platform for Agent-Based Electric Power and Communication Simulation Built From Commercial Off-the-Shelf Components,

    K. Hopkinson, X. Wang, R. Giovanini, J. Thorp, K. Birman, and D. Coury, “EPOCHS: A Platform for Agent-Based Electric Power and Communication Simulation Built From Commercial Off-the-Shelf Components,”IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 548–558, 2006

    2006

  15. [23]

    GECO: Global Event-Driven Co-Simulation Framework for Interconnected Power Sys- tem and Communication Network,

    H. Lin, S. S. Veda, S. S. Shukla, L. Mili, and J. Thorp, “GECO: Global Event-Driven Co-Simulation Framework for Interconnected Power Sys- tem and Communication Network,”IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1444–1456, 2012

    2012

  16. [24]

    Attack Detection in Power Distribution Systems Using a Cyber-Physical Real-Time Refer- ence Model,

    M. M. S. Khan, J. A. Giraldo, and M. Parvania, “Attack Detection in Power Distribution Systems Using a Cyber-Physical Real-Time Refer- ence Model,”IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1490–1499, 2022

    2022

  17. [25]

    Real-Time Cyber-Physical Co-Simulation for Re- silient Wide-Area Damping Control Against FDIAs and Communication Disruptions,

    K. Kumar, P. Saini, A. Prakash, S. Parida, K. Al Jaafari, H. H. Zeineldin, and E. F. El-Saadany, “Real-Time Cyber-Physical Co-Simulation for Re- silient Wide-Area Damping Control Against FDIAs and Communication Disruptions,”IEEE Transactions on Industry Applications, 2025

    2025

  18. [26]

    Experimental End-To-End Delay Analysis of LTE Cat-M With High-Rate Synchrophasor Communica- tions,

    S. Shah, S. Koley, and F. Malandra, “Experimental End-To-End Delay Analysis of LTE Cat-M With High-Rate Synchrophasor Communica- tions,”IEEE Internet of Things Journal, vol. 10, no. 22, pp. 19 839– 19 848, 2023

    2023

  19. [27]

    Network Performance Analysis of Smart Grid Communications Over LTE cat-M,

    B. K. Dash, S. Shah, and F. Malandra, “Network Performance Analysis of Smart Grid Communications Over LTE cat-M,” in2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE, 2023, pp. 1–6

    2023

  20. [28]

    Practical Considerations of DER Coordination with Distributed Optimal Power Flow,

    D. Gebbran, S. Mhanna, A. C. Chapman, W. Hardjawana, B. Vucetic, and G. Verbi ˇc, “Practical Considerations of DER Coordination with Distributed Optimal Power Flow,” in2020 International Conference on Smart Grids and Energy Systems (SGES). IEEE, 2020, pp. 209–214

    2020

  21. [29]

    Controller Hardware-in-the-Loop Testbed of a Distributed Consensus Multi-Agent System Control under Deception and Disruption Cyber-Attacks,

    I. Kharchouf and O. A. Mohammed, “Controller Hardware-in-the-Loop Testbed of a Distributed Consensus Multi-Agent System Control under Deception and Disruption Cyber-Attacks,”Energies, vol. 17, no. 7, p. 1669, 2024

    2024

  22. [30]

    A tutorial on spectral clustering,

    U. V on Luxburg, “A tutorial on spectral clustering,”Statistics and computing, vol. 17, no. 4, pp. 395–416, 2007

    2007

  23. [31]

    Scikit-learn: Machine Learning in Python,

    F. Pedregosa, G. Varoquaux, A. Gramfort, V . Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V . Dubourget al., “Scikit-learn: Machine Learning in Python,”Journal of Machine Learn- ing Research, vol. 12, pp. 2825–2830, 2011

    2011

  24. [32]

    A Linearized Power Flow Model for Optimization in Unbalanced Distribution Systems,

    M. D. Sankur, R. Dobbe, E. Stewart, D. S. Callaway, and D. B. Arnold, “A Linearized Power Flow Model for Optimization in Unbalanced Distribution Systems,”arXiv preprint arXiv:1606.04492, 2016

    2016 arXiv

  25. [33]

    Consensus ADMM and Proximal ADMM for Economic Dispatch and AC OPF with SOCP Relaxation,

    M. Ma, L. Fan, and Z. Miao, “Consensus ADMM and Proximal ADMM for Economic Dispatch and AC OPF with SOCP Relaxation,” in2016 North American power symposium (NAPS). IEEE, 2016, pp. 1–6

    2016

  26. [34]

    Measures to Improve the Accuracy and Reliability of Clock Synchronization in Time-Sensitive Networking,

    H. Zhu, K. Liu, Y . Yan, H. Zhang, and T. Huang, “Measures to Improve the Accuracy and Reliability of Clock Synchronization in Time-Sensitive Networking,”IEEE Access, vol. 8, pp. 192 368–192 378, 2020

    2020

  27. [35]

    Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders,

    K. P. Schneider, B. Mather, B. C. Pal, C.-W. Ten, G. J. Shirek, H. Zhu, J. C. Fuller, J. L. R. Pereira, L. F. Ochoa, L. R. de Araujoet al., “Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders,”IEEE Transactions on Power Systems, vol. 33, no. 3, pp....

    2018

  28. [36]

    Distributed Optimization in Distribution Systems: Use Cases, Limitations, and Research Needs,

    N. Patari, V . Venkataramanan, A. Srivastava, D. K. Molzahn, N. Li, and A. Annaswamy, “Distributed Optimization in Distribution Systems: Use Cases, Limitations, and Research Needs,”IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3469–3481, 2022

    2022

  29. [37]

    A Consensus ADMM-Based Distributed V olt-V Ar Optimization for Unbalanced Dis- tribution Networks,

    A. Inaolaji, A. Savasci, S. Paudyal, and S. Kamalasadan, “A Consensus ADMM-Based Distributed V olt-V Ar Optimization for Unbalanced Dis- tribution Networks,” in2022 IEEE Industry Applications Society Annual Meeting (IAS). IEEE, 2022, pp. 1–8

    2022

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