TY - JOUR
T1 - Optimization Algorithms for Distributed Model Predictive Control: Benchmarking Dual Decomposition Approaches
AU - Patrick O’Connor
AU - Sean Murphy
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2024
VL - 3
IS - 2
SP - 87
N2 - This paper introduces a benchmark comparison of distributed optimization algorithms based on dual decomposition for model predictive control problems with constraint coupling. Such problems can be viewed as multiple subsystems linked through constraints on shared, limited resources. In a dual decomposition approach, the allocation and use of these resources are coordinated by repeatedly calculating their associated prices and distributing those values to the participating subsystems. After a short overview of model predictive control, various architectures and communication structures suitable for distributed implementations are outlined. The centralized control problem is then decomposed into several subproblems by introducing dual variables. A range of distributed optimization methods is examined, including the recently developed quasi-Newton dual ascent algorithm. Additionally, an epigraph reformulation of the bundle, combined with a line search technique, is proposed for the quasi-Newton dual ascent method; these enhancements improve its numerical stability and accelerate convergence relative to the earlier trust-region approach. Lastly, the quasi-Newton dual ascent algorithm is benchmarked against the subgradient method, the bundle trust method, and the alternating direction method of multipliers across a broad set of test problems. All benchmark instances are openly accessible on GitHub. 
UR - https://isespub.com/f816344580
ER -