Distributed Energy Trading: The Multiple-Microgrid Case

TL;DR

This paper presents a distributed convex optimization framework for energy trading among islanded microgrids, enabling scalable, privacy-preserving, and economically interpretable energy exchanges with proven convergence and practical efficiency.

Contribution

It introduces a subgradient-based algorithm for microgrid energy trading that ensures convergence, scalability, and preserves local information, with an intuitive market interpretation.

Findings

Algorithm converges in a practical number of iterations.

Achieved cost reductions across different network topologies.

Provides insights into supply-demand dynamics in microgrid trading.

Abstract

In this paper, a distributed convex optimization framework is developed for energy trading between islanded microgrids. More specifically, the problem consists of several islanded microgrids that exchange energy flows by means of an arbitrary topology. Due to scalability issues and in order to safeguard local information on cost functions, a subgradient-based cost minimization algorithm is proposed that converges to the optimal solution in a practical number of iterations and with a limited communication overhead. Furthermore, this approach allows for a very intuitive economics interpretation that explains the algorithm iterations in terms of "supply--demand model" and "market clearing". Numerical results are given in terms of convergence rate of the algorithm and attained costs for different network topologies.