Open-source platform eases energy storage analytics

A new version of the open-source QuEST Python software designed by U.S. Sandia National Laboratory researchers for energy storage analytics is now publicly available. QuEST 2.0 provides centralized access to multiple tools and improved data analytics, aiming to simplify energy storage analysis and democratize access to these tools. Source: U.S. Sandia National Laboratory

The updated platform supports integration and usability, artificial intelligence (AI) —powered data analytics and complex workflows through the inclusion of these new capabilities:

• The QuESt App Hub operates similarly to an app store, offering access points to a multitude of applications. Currently, various energy storage analytics tools have been available on QuESt App hub. It has been designed with key features to improve user experience and application management in terms of user-friendly access, isolated environments and simultaneous operation.

• The QuESt Workspace provides an integrated environment where users can create workflows by assembling multiple applications into a coherent process. It enhances the platform’s usability and efficiency through mechanisms pertaining to integration of applications and workflow management.

• QuESt GPT represents an advance in data analytics within the platform, utilizing generative AI and Large Language Models for data characterization and visualization. Users can select datasets and ask questions about the data, with QuESt GPT providing insights based on the data’s characteristics. This interaction model simplifies complex data analysis, making it accessible to users without deep technical expertise.

The platform is of value examining potential revenue generated by energy storage systems when providing ancillary services in electricity markets, and in calculating cost savings for time-of-use and net energy metering customers utilizing behind-the-meter energy storage systems. Program users can select an appropriate energy storage technology based on specific applications and requirements, and evaluate energy storage system performance in different climatic conditions. QuEST 2.0 also supports microgrid design and simulation considering energy storage as a key component.

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