Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

Yue Chen, Andrey Bernstein, Adithya Devraj, Sean Meyn

Research output: Contribution to conferencePaperpeer-review

15 Scopus Citations


This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

Original languageAmerican English
Number of pages8
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5D00-76338 for preprint

NREL Publication Number

  • NREL/CP-5D00-77797


  • approximation algorithms
  • optimization
  • perturbation methods
  • power system stability
  • real-time systems
  • time-varying systems
  • trajectory


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