What is QuESt?
QuESt 3.0 is an evolved version of the original QuESt, an open-source Python software designed for energy storage (ES) analytics. It transforms into a platform providing centralized access to multiple tools and improved data analytics, aiming to simplify ES analysis and democratize access to these tools.
- 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.
- Progress is a Python-based open-source tool for assessing the resource adequacy of the evolving electric power grid integrated with energy storage systems.
- 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 Agent is the AI-assisted workflow companion built into QuESt Workspace. It uses the active canvas, pinned messages, attached files, available QuESt tools, and saved reusable skills to help users understand, build, validate, and refine Workspace flows.
- Workspace-aware assistance: The agent can inspect the current flow, summarize data nodes, Python nodes, connections, missing parts, and validation facts, then use that context when answering questions or planning edits.
- Tool and skill matching: The agent ranks relevant QuESt tools and saved skills for a request. Tool-specific skills are gated by high-confidence tool matches, while general Workspace skills and workflow templates can be reused when they directly match the task.
- Reusable workflow templates: When a saved skill includes a strong matching workflow JSON template, the agent can prefer loading that template before building from scratch, then validate the resulting flow and suggest follow-up fixes only when real gaps remain.
- Preview-first canvas actions: Before changing the canvas, the agent proposes an explicit operation plan, such as creating data nodes, updating Python wrappers, connecting ports, loading a matched template, or validating the current flow. Users can apply the next step, review the plan, cancel it, or revise the request.
- Flexible LLM providers: The agent supports OpenAI, Anthropic Claude, and local Ollama/Gemma models. Cloud models can provide stronger semantic review and skill reranking, while local models support lower-cost or local-first workflows.
What are the key innovations of QuESt 3.0?
QuESt 3.0 advances QuESt from a collection of energy-storage applications into a workflow-centered analytics platform. It combines installable domain tools, an executable visual Workspace, and an AI-assisted QuESt Agent so users can move from data, to model setup, to workflow execution, to interpretation inside one environment.
The QuESt Workspace lets users assemble energy-storage studies as connected flows rather than one-off scripts. Data nodes, Python nodes, notebooks, subflows, application-specific processing steps, inputs, and outputs can be organized on the same canvas, making the logic of a study easier to inspect, rerun, revise, and share.
The QuESt Agent works with the active Workspace context. It can review the current canvas, reason over pinned notes and attached files, identify relevant tools or reusable skills, suggest workflow actions, preview canvas changes, and help troubleshoot node outputs or flow-run results.
QuESt Agent supports OpenAI, Anthropic Claude, and local Ollama/Gemma models. Users can choose between cloud-hosted frontier models and local models depending on cost, privacy, availability, and task complexity.
QuESt 3.0 strengthens support for capturing workflow knowledge as reusable skills and action templates. This helps preserve successful study patterns and makes repeat analyses easier for both new and experienced users.
The App Hub provides specialized tools such as BTM, Valuation, Performance, Technology Selection, Planning, Progress, Microgrid, and Data Manager, while the Workspace provides the place to connect those capabilities into larger analyses.
How is QuESt 3.0 different from the other tools in Energy Storage Analytics?
Many energy-storage tools focus on a single analysis domain, such as valuation, performance modeling, planning, data acquisition, or microgrid simulation. QuESt 3.0 is different because it treats these capabilities as parts of a larger study environment. Users can install domain tools from the App Hub, connect them through the Workspace, and use QuESt Agent to understand and modify the workflow as it evolves.
This makes QuESt 3.0 especially useful for analyses that cross tool boundaries. A study can combine data preparation, technology assumptions, storage sizing, optimization, scenario execution, and result export in one repeatable workflow. Instead of forcing users to move manually between disconnected applications, scripts, and notes, QuESt provides a shared structure for building and revisiting the analysis.
QuESt also remains open-source and Python-based, which makes it inspectable and extensible. Users can see how workflows are assembled, edit generated node code, attach notebooks, and adapt the platform to local research needs.
Key Competitive Advantages of QuESt 3.0:
- Unified energy-storage platform: QuESt brings multiple energy-storage analytics applications into one platform while still allowing each tool to remain independently installable and maintainable.
- Workflow-first analysis: The Workspace gives users a visual, executable representation of a study. This improves transparency, repeatability, and collaboration compared with isolated scripts or single-purpose graphical tools.
- AI assistance grounded in the active workflow: QuESt Agent uses Workspace context, attachments, pinned messages, tool metadata, and reusable skills to provide more targeted assistance than a standalone chatbot.
- Model choice and deployment flexibility: Users can select OpenAI, Anthropic Claude, or local Ollama/Gemma models, allowing the agent experience to be tuned for performance, privacy, cost, or offline/local workflows.
- Extensible Python foundation: Because QuESt is Python-based and open-source, researchers and developers can inspect, customize, and extend workflows, app integrations, node logic, and agent skills.
- Repeatable study patterns: Reusable skills, workflow templates, node notebooks, saved flow files, and explicit input cases help teams preserve analysis methods and rerun studies with changed assumptions.
For more information, contact Tu Nguyen.