An open source, Python-based software platform for energy storage simulation and analysis developed by Sandia National Laboratories.
OTHER License
QuESt 2.0 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.
Currently, QuESt 2.0 includes three main components:
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.
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 LLMs: By leveraging advanced open-source LLMs such as OpenAi’sGPT4 and Meta’s Llama2, 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.
QuESt 2.0 facilitates the advancement of energy storage technology by making powerful analytics tools accessible to all energy storage stake holders, aligning with DOE’s energy storage program goals. The platform standardizes data and program structures, integrates applications seamlessly, and utilizes generative AI for advanced analytics, simplifying user interaction and enabling deeper insights from diverse data sources. This positions QuESt 2.0 as a pioneering platform in the energy storage domain, with the potential to significantly impact both the field and the broader energy landscape. Specifically, the key innovations of QuESt 2.0 include:
Published by atribera about 2 years ago
An application for analyzing battery energy storage system performance due to parasitic heating, ventilation, and air conditioning loads. This tool leverages the building simulation tool EnergyPlus to model the energy consumption of a particular battery housing.
An application for identifying the energy storage technologies most suitable for a given project. This tool is based on multiple parameters that characterize each storage technology; the technologies that do not satisfy the minimum application requirements are filtered out and the remaining technologies are ranked to indicate their compatibility to the desired project.
Published by rconcep almost 5 years ago
Published by rconcep about 5 years ago
Published by rconcep over 5 years ago
/data/
directory resulted in a fatal crash.Published by rconcep over 5 years ago
This patch incorporates a few bug fixes from previous minor revisions. It also introduces an experimental, packaged executable version of QuESt for easier use.
snl-quest-1.2.c-win10.zip
(not labeled as source code)snl-quest-1.2.c.exe
file. It should launch as if you ran the python main.py
.Published by rconcep over 5 years ago
This patch is the official release of QuESt BTM, the application for behind-the-meter energy storage analysis tools. A number of fixes and quality of life improvements for QuESt have also been implemented.
11/21/18
This patch provides a number of content and quality of life updates for QuESt Data Manager and QuESt Valuation.
matplotlib
(3.x) were incompatible, resulting in application crash at launch.pandas
were incompatible, resulting in application crash during certain operations. A package version number has now been specified in the setup file. Please either rerun the setup.py install
or manually update your pandas
package.Published by rconcep about 6 years ago
09/11/18
The official release version of QuESt.
An application for downloading data from RTO/ISO/market operators for use in other QuESt applications.
An application for performing energy storage valuation. Using historical data from market area operators, set up and solve optimization problems to estimate an upper bound on the revenue that a energy storage device could have generated over the horizon of the data.