@inproceedings{c33f37f0b4794e428ad9a8f6ab1afce3,
title = "Enhanced Distributed Self-Healing System for Electrical Distribution Networks Using ADMM",
abstract = "Unlike centralized versions, a distributed self-healing system (SHS) for electrical distributed systems is less vulnerable to single-point failures (or attacks), requires less information from the agents, and is more scalable. However, optimality is challenging to achieve because binary variables are used in the modelling of the distributed service restoration problem. To deal with this challenge, this paper proposes an enhanced alternating direction method of multipliers (ADMM)based algorithm used to developed a fully distributed SHS in electrical distribution networks. Hereby, three ADMM-based heuristics are executed in parallel to improve the chances of obtaining a feasible solution. However, if none of the heuristics converge within given reasonable time, the proposed distributed SHS uses a basic restoration plan that is feasible in terms of topology and operational constraints. Results using the IEEE 123node system show that the proposed distributed SHS is reliable and it always provides a feasible solution.",
keywords = "ADMM, Distributed service restoration, Electrical distribution networks, Self-healing system, 2023 OA procedure",
author = "L{\'o}pez, {Juan Camilo} and Gerards, {Marco E.T.} and Hurink, {Johann L.} and Rider, {Marcos J.}",
note = "Funding Information: In this paper, an enhanced distributed self-healing system (SHS), for electrical distribution networks, is developed using alternating direction method of multipliers (ADMM)-based heuristics. In this case, a feasible solution is always guaranteed, even if none of the distributed optimization algorithms converges. Moreover, a new heuristic adaptation of ADMM is proposed to deal with the discrete nature of the restoration Fig. 4. Convergence process of each ADMM-based heuristic. problem in a distributed fashion. Results show that the proposed distributed SHS is reliable and that it always provides a feasible solution, even if the heuristics fail to converge. ACKNOWLEDGMENTS This work was supported by the S{\~a}o Paulo Research Funding Agency - FAPESP. Grants 2021/11310-7, 2019/01906-0, and thematic project 2021/11380-5. REFERENCES Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Power and Energy Society General Meeting, PESGM 2023, PESGM ; Conference date: 16-07-2023 Through 20-07-2023",
year = "2023",
doi = "10.1109/PESGM52003.2023.10253390",
language = "English",
isbn = "978-1-6654-6442-0",
series = "IEEE Power and Energy Society General Meeting",
publisher = "IEEE",
booktitle = "2023 IEEE Power and Energy Society General Meeting, PESGM 2023",
address = "United States",
}