Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems

TL;DR

This paper presents a distributed algorithm for managing renewable energy resources in power networks, optimizing energy sharing while addressing environmental variability and system constraints.

Contribution

It introduces a novel bandit convex optimization framework with dynamic regret minimization and constraint adjustment strategies for renewable energy management.

Findings

The proposed algorithm achieves lower dynamic regret compared to existing methods.

It effectively reduces constraint violations in non-stationary environments.

Numerical experiments demonstrate superior performance on real-world data.

Abstract

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.