A BANDIT ONLINE CONVEX OPTIMIZATION APPROACH TO DISTRIBUTED ENERGY MANAGEMENT IN NETWORKED SYSTEMS

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 O(T^(3/4)) regret bound and O(T^(3/4)) 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 the state-of-the-art methods.