# Online Stochastic Optimization of Networked Distributed Energy Resources

arXiv: https://arxiv.org/abs/1711.09953 · HTML: https://mlsanity.com/paper/1711.09953/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads](https://mlsanity.com/paper/2301.02593/) (2023)
- [Online Dynamic Submodular Optimization](https://mlsanity.com/paper/2306.10835/) (2023)
- [Evaluation of Prosumer Networks for Peak Load Management in Iran: A Distributed Contextual Stochastic Optimization Approach](https://mlsanity.com/paper/2409.00493/) (2024)
- [Learning to Price Electricity for Optimal Demand Response](https://mlsanity.com/paper/2610.00755/) (2026)

## Abstract (arXiv)

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.