QuESt 2.0 – Open-source platform for Energy Storage Analytics

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. For example:

• QuESt Data Manager manages the acquisition of data.
• QuESt Valuation estimates the potential revenue generated by energy storage systems when providing ancillary services in the electricity markets.
• QuESt BTM (Behind-The-Meter) calculates the cost savings for time-of-use and net energy metering customers utilizing behind-the-meter energy storage systems.
• QuESt Technology Selection supports in selecting the appropriate energy storage technology based on specific applications and requirements.
• QuESt Performance evaluates the performance of energy storage systems in different climatic conditions.
• QuESt Microgrid supports microgrid design and simulation considering energy storage as a key component.
• QuESt Equity calculates the costs and distributional health impacts of replacing powerplants with energy storage and PV.

It has been designed with key features to improve user experience and application management:

• User-Friendly Access: Users can easily find and install applications that suit their specific needs.
• Isolated Environments: Upon installation, each application creates an isolated environment. This ensures that applications run independently, preventing conflicts, and enhancing stability.
• Simultaneous Operation: Multiple applications can be installed and operated simultaneously, allowing users to leverage different tools without interference.

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 several mechanisms:

• Integration of Applications: Users can create work processes that integrate multiple apps by assembling pipelines using plugin extensions. This modular approach allows for the flexible composition of analytics workflows tailored to specific needs.
• Workflow Management: The workspace supports the selection, assembly, connection, and post-processing of data and tools. This structured approach streamlines the analytics process, from data preparation to visualization, making it easier to manage and understand.

QuESt GPT represents a leap forward in data analytics within the platform, utilizing generative AI (specifically Large Language Models, or LLM) for data characterization and visualization:

• Data Insights: 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.
• Utilization of Open-source LLMs: By leveraging advanced open-source LLMs such as GPT4All, QuESt GPT can perform sophisticated data analytics tasks, such as characterizing and visualizing large datasets. This enables users to gain deeper insights from their data, supporting more informed decision-making at no costs.

At its core, QuESt 2.0 revolutionizes how energy storage analytics are performed by providing a seamless, user-friendly platform that integrates multiple applications developed by independent developers. This allows for a more cohesive and efficient user experience, significantly lowering the learning curve for users at various levels of expertise.

The incorporation of QuESt GPT, utilizing Large Language Models (LLM), represents a significant technological leap forward. This feature enables users to perform more sophisticated data analytics, providing deeper insights from diverse data sources. It allows users to interact with data in an intuitive way, asking questions and receiving insights, which democratizes access to complex data analysis.

The QuESt Workspace and the QuESt App Hub enhance the platform’s capability to support complex analytical workflows. Users can integrate multiple applications into a single process, creating efficient pipelines for data analysis. This not only streamlines operations but also opens new possibilities for exploring and understanding energy storage systems.