Online Stochastic Optimization of Networked Distributed Energy Resources

TL;DR

This paper develops an online stochastic optimization algorithm for coordinating distributed energy resources in power grids, addressing device dynamics, discrete decisions, and real-time operational challenges.

Contribution

It introduces a novel distributed stochastic dual gradient algorithm extended for online real-time control of networked DERs with nonlinear power flows and asynchronous updates.

Findings

Algorithm converges analytically under realistic conditions.

Numerical evaluations demonstrate effective coordination of DERs.

Reduces communication overhead while maintaining operational objectives.

Abstract

This paper investigates distributed control and incentive mechanisms to coordinate distributed energy resources (DERs) with both continuous and discrete decision variables as well as device dynamics in distribution grids. We formulate a multi-period social welfare maximization problem, and based on its convex relaxation propose a distributed stochastic dual gradient algorithm for managing DERs. We further extend it to an online realtime setting with time-varying operating conditions, asynchronous updates by devices, and feedback being leveraged to account for nonlinear power flows as well as reduce communication overhead. The resulting algorithm provides a general online stochastic optimization algorithm for coordinating networked DERs with discrete power setpoints and dynamics to meet operational and economic objectives and constraints. We characterize the convergence of the algorithm analytically and evaluate its performance numerically.