Decentralized convex optimization under affine constraints for power systems control

TL;DR

This paper develops a decentralized convex optimization method for power systems that handles affine constraints without extensive information exchange, reformulating the problem as a saddle point problem and analyzing algorithm complexity.

Contribution

It introduces a novel reformulation of constrained optimization as a saddle point problem for decentralized power system control.

Findings

Provides a complexity analysis for saddle point problem algorithms.

Reformulates affine constraints using consensus techniques.

Enables distributed optimization without extensive data sharing.

Abstract

Modern power systems are now in continuous process of massive changes. Increased penetration of distributed generation, usage of energy storage and controllable demand require introduction of a new control paradigm that does not rely on massive information exchange required by centralized approaches. Distributed algorithms can rely only on limited information from neighbours to obtain an optimal solution for various optimization problems, such as optimal power flow, unit commitment etc. As a generalization of these problems we consider the problem of decentralized minimization of the smooth and convex partially separable function $f = \sum_{k=1}^l f^k(x^k,\tilde x)$ under the coupled $\sum_{k=1}^l (A^k x^k - b^k) \leq 0$ and the shared $\tilde{A} \tilde{x} - \tilde{b} \leq 0$ affine constraints, where the information about $A^k$ and $b^k$ is only available for the $k$-th node of the computational network. One way to handle the coupled constraints in a distributed manner is to rewrite them in a distributed-friendly form using the Laplace matrix of the communication graph and auxiliary variables (Khamisov, CDC, 2017). Instead of using this method we reformulate the constrained optimization problem as a saddle point problem (SPP) and utilize the consensus constraint technique to make it distributed-friendly. Then we provide a complexity analysis for state-of-the-art SPP solving algorithms applied to this SPP.