Distributed Optimization under Edge Agreement with Application in Battery Network Management

TL;DR

This paper introduces a novel distributed optimization framework based on edge agreements, allowing flexible heterogeneous coordination among agents with local constraints, and applies it to battery network management.

Contribution

It proposes a discrete-time algorithm for edge agreement-based optimization and links it to distributed model predictive control in battery networks.

Findings

Algorithm converges under specified conditions

Framework enables heterogeneous network coordination

Application demonstrates practical effectiveness

Abstract

This paper investigates a distributed optimization problem under edge agreements, where each agent in the network is also subject to local convex constraints. Generalized from the concept of consensus, a group of edge agreements represents the constraints defined for neighboring agents, with each pair of neighboring agents required to satisfy one edge agreement constraint. Edge agreements are defined locally to allow more flexibility than a global consensus, enabling heterogeneous coordination within the network. This paper proposes a discrete-time algorithm to solve such problems, providing a theoretical analysis to prove its convergence. Additionally, this paper illustrates the connection between the theory of distributed optimization under edge agreements and distributed model predictive control through a distributed battery network energy management problem. This approach enables a new perspective to formulate and solve network control and optimization problems.