Distributed Dynamic Economic Dispatch using Alternating Direction Method of Multipliers

TL;DR

This paper introduces a fully distributed ADMM algorithm for economic dispatch in power systems, enabling real-time demand adaptation and decentralized computation without relying on specific assumptions.

Contribution

It proposes a novel distributed ADMM framework incorporating dynamic average consensus for dual variable estimation, capturing real-time demand changes without special assumptions.

Findings

Successfully validated on IEEE 30-bus and 300-bus systems.

Demonstrated effective handling of real-time demand variations.

Achieved distributed optimization without reliance on specific assumptions.

Abstract

With the proliferation of distributed energy resources and the volume of data stored due to advancement in metering infrastructure, energy management in power system operation needs distributed computing. In this paper, we propose a fully distributed Alternating Direction Method of Multipliers (ADMM) algorithm to solve the distributed economic dispatch (ED) problem, where the optimization problem is fully decomposed between participating agents. In our proposed framework, each agent estimates the dual variable and the average of the total power mismatch of the network using dynamic average consensus, which replaces the dual updater in the traditional ADMM with a distributed alternative. Unlike other distributed ADMM, the proposed method does not rely on any specific assumption and captures the real-time demand change. The algorithm is validated successfully via case studies for IEEE 30-bus and 300-bus test systems with the penetration of solar photovoltaic.