Distributed Dual Subgradient Methods with Averaging and Applications to Grid Optimization

Haitian Liu (), Subhonmesh Bose (), Hoa Dinh Nguyen (), Ye Guo (), Thinh T. Doan () and Carolyn L. Beck ()
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Haitian Liu: Tsinghua University
Subhonmesh Bose: University of Illinois Urbana Champaign
Hoa Dinh Nguyen: Kyushu University
Ye Guo: Tsinghua University
Thinh T. Doan: Virginia Tech
Carolyn L. Beck: University of Illinois Urbana Champaign

Journal of Optimization Theory and Applications, 2024, vol. 203, issue 2, No 34, 2024 pages

Abstract: Abstract We study finite-time performance of a recently proposed distributed dual subgradient (DDSG) method for convex-constrained multi-agent optimization problems. The algorithm enjoys performance guarantees on the last primal iterate, as opposed to those derived for ergodic means for standard DDSG algorithms. Our work improves the recently published convergence rate of $${{\mathcal {O}}}(\log T/\sqrt{T})$$ O ( log T / T ) with decaying step-sizes to $${{\mathcal {O}}}(1/\sqrt{T})$$ O ( 1 / T ) with constant step-size on a metric that combines sub-optimality and constraint violation. We then numerically evaluate the algorithm on three grid optimization problems. Namely, these are tie-line scheduling in multi-area power systems, coordination of distributed energy resources in radial distribution networks, and joint dispatch of transmission and distribution assets. The DDSG algorithm applies to each problem with various relaxations and linearizations of the power flow equations. The numerical experiments illustrate various properties of the DDSG algorithm–comparison with standard DDSG, impact of the number of agents, and why Nesterov-style acceleration can fail in DDSG settings.

Keywords: Distributed optimization; Power system examples (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10957-024-02385-7

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