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arXiv · 1909.13132

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

Yue Chen·Andrey Bernstein·Adithya Devraj·Sean Meyn
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Abstract

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 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.

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Yue Chen, Andrey Bernstein, Adithya Devraj, Sean Meyn. 2019-09-28. Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow. https://arxiv.org/abs/1909.13132

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