Optimal design and control of cool thermal energy storage as a distributed energy resource

Abstract

The electric grid of the future is envisioned to be a smart grid, where electricity ismanaged in coordinated manner between suppliers and users. To achieve this, a high level of electric demand flexibility must be integrated into our building infrastructure. In the U.S., 9% of all electricity generated is used to cool buildings, making this end-use an ideal target for active management through cool thermal energy storage (CTES) technologies. Historic uses for CTES are designed around central chilled water plants, but these systems cool less than 25% of U.S. commercial floorspace. Emerging technologies are under development to serve the many smaller distributed cooling systems, such as rooftop units (RTUs), and have the potential to add CTES to an additional 66% of cooled commercial floorspace. However, these unitary thermal storage systems (UTSS) lack the modeling and analysis tools to evaluate them in the future interactive grid context. The purpose of this research is to develop the modeling and optimization tools necessary to not only examine UTSS within specific building applications, but also within the multibuilding, connected community context. To do so, new simulation tools for the U.S. Department of Energy’s OpenStudio energy modeling platform are developed, and a novel mixed-integer linear program is devised to optimize the design and dispatch of multiple CTES technologies, both UTSS and central systems. An integrated simulation-optimization workflow is created to allow for rapid customized analysis. Several case studies are presented illustrating the benefits of community-scale optimization and the impacts of storage costs and utility rate structure on CTES design and dispatch. CTES modeling results, demonstrating the energy and flexibility tradeoffs of various implementations are presented. Optimization results are described in terms of minimum cost, optimal design, and optimal dispatch, with analysis on the CTES impacts on the aggregated community electricity use. It is further demonstrated that community-scale optimization yields greater cost savings potential than an individual-building approach and that certain CTES technologies are more appropriate for different demand-response cost signals.