QuESt is a Python-based, open source energy storage software suite developed by Sandia.

Analytics is an important component of Sandia’s energy storage research. We perform research that develops and analyzes storage-based solutions to a variety of technical challenges for the electrical grid such as improving grid reliability and resilience and enhancing renewable energy integration. Our research includes developing/validating models and simulations such as QuESt, a free, open source, Python-based application suite for energy storage simulation and analysis developed to bring Sandia energy storage analytics research tools to your desktop. Sandia also evaluates the economic and technical benefits of energy storage systems and develops optimal control schemes and energy management systems.

The key elements of energy storage analytics at Sandia include:

Modeling and simulation are core elements of energy storage analytics at Sandia. Our research in this area includes: 1) developing and validating energy storage models using large sets of testing and operational data of ESSs, 2) incorporating energy storage systems in the existing model-based frameworks in power systems such as production cost modeling and resource adequacy planning.
To realize the potential benefits of ESSs for grid and customer services, it is crucial to evaluate their overall economic value, considering their technical benefits to the grid along with their limits in performance. Sandia performs techno-economic studies that comprehensively investigate the economic and technical benefits of ESSs in a variety of applications including grid ancillary services (e.g., frequency regulation, forward capacity) and behind-the-meter services (e.g., power quality, demand charge reduction, time-of-use management).

For the efficient operation of large scale energy storage systems, there are two main engineering challenges that need to be adequately addressed: 1) optimal control of grid energy storage to guarantee safe operation while delivering the maximum benefit, and 2) coordination of multiple grid energy storage systems that vary in size and technology.

Our research in this area focuses on addressing these challenges by developing the core functions of ESMS software (e.g., optimal dispatch, state estimation and visualization) and core capabilities of ESMS hardware (e.g., sensoring, control and communication).

We build our analytical frameworks into software tools that are made publicly available. The intent is to support all stakeholders in analyzing energy storage systems thereby removing the technical barriers in integrating energy storage. One example of such software tools is QuESt, a software application suite for energy storage valuation. It helps users quantify the value of energy storage systems (ESS) in different applications including grid and customer services. Using optimization techniques and techno-economic models to evaluate ESS’s potential revenue, QuESt provides answers regarding economic viability of energy storage projects.

QuESt incorporates the technical expertise and institutional knowledge developed over many years by the energy storage analytics team at Sandia. The software incorporates mathematical algorithms and models from studies conducted by the Sandia team. QuESt also has a suite of innovative features such as built-in support and acquisition tools for the most widely used online data resources and automated report generation to summarize analysis in a comprehensive document.

QuESt was developed out of an initiative to help reduce the barriers for the widespread adoption of energy storage. It empowers the public at large with the told to make informed decisions about investments in energy storage. By doing so, the way can be paved for potentially transformative technologies to enhance the energy sector and, consequently, the general population’s way of life.

R&D 100 Entry: QuESt

Tu Nguyen

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