A Bandit Online Convex Optimization Approach To Distributed Energy Management In Networked Systems
Ioannis Tsetis, Xiaotong Cheng, Setareh Maghsudi
Abstract
Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, due to the inherent unreliability of renewable energy, it is imperative to develop effective algorithms for DER management. In this work, 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, where the constraints correspond to each node's limitations for energy production. We propose a distributed decision-making policy to solve the formulated problem, that achieves ${\mathcal{O}}\left( {{T^{\frac{3}{4}}}} \right)$ regret bound and ${\mathcal{O}}\left( {{T^{\frac{3}{4}}}} \right)$ constraint violations. To reduce the constraint violations, we suggest two decision-making variations. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.
BibTeX
@inproceedings{icassp2023_abanditonlinecon,
title = {A Bandit Online Convex Optimization Approach To Distributed Energy Management In Networked Systems},
author = {Ioannis Tsetis and Xiaotong Cheng and Setareh Maghsudi},
booktitle = {ICASSP 2023},
year = {2023}
}