# Sharing Energy Storage Between Transmission and Distribution

## TL;DR

This paper presents a unified framework for coordinating energy storage between transmission and distribution levels, improving congestion relief, reducing costs, and enhancing renewable energy integration through case studies and a real-world demonstration.

## Contribution

It introduces a novel communication and optimization framework for energy storage coordination across grid levels, validated through simulations and a live demonstration.

## Key findings

- The scheduling method effectively relieves congestion and cuts costs.
- The approach enhances renewable energy integration.
- Demonstration confirmed real-world feasibility.

## Abstract

This paper addresses the problem of how best to coordinate, or `stack,' energy storage services in systems that lack centralized markets. Specifically, its focus is on how to coordinate transmission-level congestion relief with local, distribution-level objectives. We describe and demonstrate a unified communication and optimization framework for performing this coordination. The congestion relief problem formulation employs a weighted $\ell_{1}$-norm objective. This approach determines a set of corrective actions, i.e., energy storage injections and conventional generation adjustments, that minimize the required deviations from a planned schedule. To exercise this coordination framework, we present two case studies. The first is based on a 3-bus test system, and the second on a realistic representation of the Pacific Northwest region of the United States. The results indicate that the scheduling methodology provides congestion relief, cost savings, and improved renewable energy integration. The large-scale case study informed the design of a live demonstration carried out in partnership with the University of Washington, Doosan GridTech, Snohomish County PUD, and the Bonneville Power Administration. The goal of the demonstration was to test the feasibility of the scheduling framework in a production environment with real-world energy storage assets. The demonstration results were consistent with computational simulations.