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This framework employs the Cauchy convergence sequence to extend the ADMM method from being applicable only to two\u2010separable convex optimization problems to multiseparable convex optimization problems. Compared with the alternating direction method of multipliers with Gaussian back substitution (ADMM\u2010G), ADMM\u2010Cauchy adapts the penalty parameters through simple vector norm computations, avoiding the high\u2010complexity steps of projection equation balancing and matrix inversion. Compared with the proximal Jacobian decomposition of the augmented Lagrangian method (PJD\u2010ALM), ADMM\u2010Cauchy exploits the latest variable information immediately through serial updates, whereas PJD\u2010ALM must wait for all parallel subproblems to complete before performing a unified update, incurring an inherent information delay. The simulation results further demonstrate that the ADMM\u2010Cauchy algorithm proposed in this paper outperforms the ADMM\u2010G and PJD\u2010ALM, which is consistent with the theoretical analysis and mutually verifies this superiority.<\/jats:p>","DOI":"10.1155\/jece\/4478795","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:14:42Z","timestamp":1765278882000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Decentralized ADMM\u2010Cauchy Framework for Enhancing Dynamic Economic Dispatch in Power 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