  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.
