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What is new in this version?

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 App Hub

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 Workspace

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

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’s GPT4 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.

What are the key innovations of QuESt 2.0?

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:

1. Integration and Usability: 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.
2. AI-powered Data Analytics: 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.
3. Complex Workflows: 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. The users can run their work flows locally or schedule them to run on cloud services (e.g., AWS, Azure..)

• Two new applications have been added: QuESt Performance and QuESt Technology Selection

QuESt Performance

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.

QuESt Technology Selection

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.

• NSRDB weather data can now be accessed using QuESt Data Manager

Patch 1.2.f

QuESt

• A quick-start tour is now available from the home index screen of QuESt.
  • 
  • This tour consists of a sequence of slides explaining the general usage of QuESt and is aimed to help new or returning users get oriented.
  • 
• Help carousel modal views (or "help" for short) are now available throughout QuESt. They may be accessed from the top navigation bar whenever available.
  • 
  • Opening the help carousel displays a sequence of slides similar to those from the quick-start tour. The content of the help carousel is relevant to wherever it is accessed. For example, opening the help carousel while in QuESt Data Manager will show help related to that application.
  • 
• Interfaces for users to import custom data have been implemented ("data importer").
  • The data importer is designed to accept time series data in substitution for similar data acquired through QuESt Data Manager.
  • Currently, data importers are available for the QuESt BTM Time-of-Use Cost Savings wizard for load profile and photovoltaic power profile.
  • The data importer is accessible directly within the relevant application when the corresponding data is selected. Instead of selecting from the QuESt data bank, the data importer may be used.
  • 
  • Upon opening the data importer, a series of prompts will appear. The file chooser can be used to select the desired CSV file to use.
    • The drive displayed in the file chooser is based on where QuESt is launched from. Only folders and .csv files will be displayed.
    • 
    • Folders can be expanded by clicking the dropdown arrow to the left of the folder name. Folders can be entered by clicking on the folder name. Clicking the ".." at the top of the current directory listing will navigate up one directory level.
    • To finalize file selection, double-click on the desired file name or click on the file name and click on the 'Select' button.
    • 
    • Next, the data column must be specified to determine the time series that will be used. The requirements for the data will vary among the different data importers, but generally, for hourly data a time series of length 8,760 (for one standard year) is expected. Please refer to the accompoanying description in the data importer window for specific details.
    • If the selected time series passes validation, the data importer will report a successful import. Upon returning to the current application, the data importer button will change appearance to indicate that time series data was imported and will be used in the current application.
    • 
    • Note that any data imported will be made available for future use in the QuESt data bank. The data may be denoted by an "imported/" tag (in the case of load profile data) and the name will be based on the originating CSV file name and the data column specified during the import process.
    • Selecting from the data bank after import will override the imported data.
    • Opening the data importer and completing the data import process again will override any previous selections or imports.

Resolved issues

• An issue where Inf or NaN values in data would create infeasible models that could not be solved. Specifically, for PJM data where mileage ratios are computed, division by zero would result in Inf values. QuESt will use a "fill forward" interpolation method to attempt to correct these instances.

Known issues

• An issue where dragging through slides in help carousels or similar widgets instead of using the arrow navigation buttons does not update the "progress indicator" bubbles for the slides.
  • Clicking the navigation buttons will properly update the bubbles.
• An issue where graphics/charts for HTML reports generated in application wizards would not save and render properly. A blank or incomplete figure would be saved and displayed in the report. We have increased the time for rendering and saving each figure during the report generation process in order to address this issue.

Patch 1.2.e

QuESt

• Reports (HTML) generated through wizards such as those in QuESt Valuation and QuESt BTM will now be separated appropriately.
  • Previously: reports were generated in place using the same name if of the same type. For example, if two reports for different wizard runs in QuESt BTMs were generated, the newer one would overwrite the older one because they shared the same name.
  • Previously: figures for reports of the same type would overwrite older figures. For example, a new report generated in QuESt BTM would overwrite the figures of a previous report. However, if the HTML file for the report was not overwritten (e.g., the file name was changed), then the previous report could still be opened. Unfortunately, since the image links in that older report refer to the same path, they would then refer to the newer figures with the same filenames that most likely refer to the incorrect simulation run. An analogous issue occurs in QuESt Valuation. This caused considerable confusion.
  • Now: reports and their figures are saved in separate folders which are timestamped when they are generated. Due to the nature of HTML files, we still encourage users to save the report to a more permanent format (such as PDF) when moving or distributing these reports.
• Errors during optimization solving are now reported more explicitly.
  • Previously: A generic error message would be displayed that would obfuscate underlying causes for errors. For example, an exception from the optimization solver being unable to be found and an exception from mismatched time series data would be reported identically. The user would be unable to know that his or her selected solver was unable to be located or was not installed.
  • Now: Specific errors that are identified are enumerated in the completion popup window. For example, in a batch run with multiple months selected, months for which the data is mismatched are specifically listed.

QuESt Valuation

• The formulation for participating in frequency regulation in ISO-NE has been adjusted.
  • The mileage is now estimated using a simulated AGC signal. This signal is automatically downloaded when acquiring data for ISO-NE in QuESt Data Manager.
  • This mileage is used to obtain a mileage multiplier time series for use in performance credit calculations.

Resolved issues

• An issue where using a search bar to search by state for "VA" would not yield "Virginia".
• An issue where certain error codes returned by the CAISO API would not be handled properly.
• Several typographical errors in reports generated by the QuESt Valuation wizard have been corrected.
• An issue where using CBC as an optimization solver would result in fatal crashes if the mathematical program to be solved was infeasible.

Patch 1.2.d

QuESt

• Removed "id" property for NavigationButton(ActionButton) widgets to address deprecation warnings.
• Added files to repository for Travis CI. We will be implementing continuous integration and unit tests over the next few updates.

QuESt Data Manager

• [#12] PV profiles that are obtained using PVWatts now use the "TMY3" dataset instead of the default "NSRDB" dataset.
  • This is to maintain consistency with the load profiles obtained from OpenEI.org which are based on TMY3.

QuESt Valuation

• The "Single Run" mode in QuESt Valuation has been deprecated and removed from the GUI.
  • The functionality of the "Single Run" mode is already duplicated by the "Batch Run" mode.

Resolved issues

• [#10] Various issues due to Kivy v1.11.0 update. Backwards compatibility with Kivy v1.10.1 should be maintained.
• An issue where the settings for the QuESt BTM data management system were not respected.
• An issue where the wrong navigation bar was loaded upon entering QuESt BTM.
• [#8] An issue where attempting to start the QuESt Valuation Wizard (on the second screen) or QuESt Valuation Batch Runs (when selecting a market area) after a fresh or install or with no /data/ directory resulted in a fatal crash.

Patch 1.2.c

This patch incorporates a few bug fixes from previous minor revisions. It also introduces an experimental, packaged executable version of QuESt for easier use.

QuESt

• An executable version of QuESt has been released. We are in the process of testing compatibility with a variety of systems and environments, but these executables will allow you to circumvent most of the installation process.
  • No Python installation required; packaged with its own Python environment.
  • Third party solvers for Pyomo still required, but we will look into the possibility of packaging a solver such as GLPK in the archive.
  • Each executable is for a specific operating system. We will start with Windows 10 versions but are looking into OS X (Mojave) versions as well.
  • Packaged using PyInstaller.
  • Once we gather enough feedback that these packages are functional, they will become a staple of each release.
  • The package is available below as snl-quest-1.2.c-win10.zip (not labeled as source code)
  • To run it, just extract the archive and run the snl-quest-1.2.c.exe file. It should launch as if you ran the python main.py.

Resolved issues

• An issue where attempting to download ISO/RTO market data for ERCOT resulted in a fatal crash.
• An issue where attempting to export a PNG from a results viewer when solved models were selected but no plot had yet been drawn resulted in a fatal crash.

Patch 1.2

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.

QuESt

QuESt Data Manager

• A tool for downloading utility rate structure data has been implemented.
  • This tool is designed for use in behind-the-meter applications.
  • This tool leverages the OpenEI database through their API.
    • An API key, available through the OpenEI website, is required to use this tool.
  • Search for a utility in the U.S. Utility Rate Database and select a rate structure.
  • 
  • The time-of-use energy and demand rate schedules are retrieved from the database, in addition to the monthly flat demand schedule.
  • 
  • Modify the period rates or rate schedules as desired.
  • Specify the net-metering scheme.
  • Save the rate structure for use in other QuESt applications.
  • 
• A tool for downloading simulated hourly load profiles for commercial and residential buildings has been implemented.
  • This tool is designed for use in behind-the-meter applications.
  • For commercial buildings, search by city to determine the climate and select from one of 17 different commercial building types.
  • 
  • For residential buildings, specify the amount of load from "base", "low", and "high" and select the climate based on location.
  • 
• A tool for downloading simulated photovoltaic power profiles has been implemented.
  • This tool is designed for behind-the-meter applications.
  • This tool leverages the PVWatts software through their API.
    • An API key, the same as the one used for the OpenEI utility rate structure database, is required to use this tool.
  • Specify parameters such as the location of the system in latitude and longitude coordinates, the nameplate capacity, the module and array types, and the system efficiency.
  • 
  • Save the photovoltaic power profile for use in other QuESt applications.

QuESt Valuation

• The appearance of the Valuation Wizard has been slightly adjusted.
• Some rudimentary value checking for energy storage system parameters has been implemented.
• The terms for adjusting the allowable range of state of charge have been changed.
  • % of energy capacity to reserve for charging (default: 0) --> maximum state of charge (%) (default: 100)
  • % of energy capacity to reserve for discharging (default: 0) --> minimum state of charge (%) (default: 0)
• The default initial (and final) state of charge has been set to 50% (was: 0).

QuESt BTM

• A new application in the QuESt suite, QuESt BTM, has been implemented.
  • This application is designed to have a collection of tools for behind-the-meter energy storage.
• The time-of-use cost savings wizard has been added to QuESt BTM.
  • This tool is for estimating the cost savings provided by energy storage for time-of-use and net-energy-metering customers.
  • Select a utility rate structure and load profile both of which can be obtained using QuESt Data Manager.
  • 
  • 
  • Optionally, select a photovoltaic power profile which can also be obtained using QuEST Data Manager.
  • 
  • Describe the parameters of the energy storage system.
  • 
  • One year of operation will be simulated to minimize cost and the total bill with and without energy storage will be calculated. This includes energy charges, demand charges, and net metering charges and/or credits.
  • 
  • A summary report can be generated at the end of the wizard that includes more detail on the mathematical formulation, a summary of the scenario specified, and a summary of the results including figures.
  • 
  • The results of each individual month can be analyzed further in the results viewer. This includes time series of the adjusted total power draw and the energy storage system's state of charge.
  • 

Miscellaneous

• Multiset bar charts generated by, e.g., QuESt Valuation and QuESt BTM wizards, now annotate the maximum value for each set.
• The "solved model" list in results viewer tools now lists the models in reverse chronological order.

Resolved issues

• An issue where versions of the Pyomo (~5.6) package would cause QuESt to crash to desktop when trying to solve optimization models.
• An issue where non-numeric characters could not be entered into the "NodeID" field in the ISO/RTO market and operations data tool in QuESt Data Manager. Previously, this prevented you from entering in Node ID values with alphanumeric characters and downloading the corresponding data.

Known issues

• An issue where a blank image instead of the expected chart appears when generating a report from the QuESt Valuation wizard or QuESt BTM Time-of-Use Cost Savings wizard.
  • If this issue persists, we may consider using alternative means to generate the charts external to the GUI. Generating the report again may solve the issue.
• An issue where search filters, such as those in selection lists or in the utility rate structure tool in QuESt Data Manager, are too sensitive to queries.
  • We plan on implementing "fuzzy" search or similar concepts to ease the use of these search tools and filters.
• An issue where QuESt will be reported as "not responding" by the operating system during batch optimization processes such as those in the QuESt Valuation and QuESt BTM applications.
  • This is due to the processing requirements of solving the models preventing the GUI from being updated. We will consider restructuring how the computation is performed in order to minimize GUI freezes.

11/21/18

Patch 1.1

This patch provides a number of content and quality of life updates for QuESt Data Manager and QuESt Valuation.

QuESt

QuESt Data Manager

• Market and operations data for ISO-NE, NYISO, CAISO, and SPP market areas are now available.
• Added buttons that cancel the current data download requests.
  • The download/cancel buttons are independent for each tab.

QuESt Valuation

• (Arbitrage) and regulation market models for ISO-NE, NYISO, CAISO, and SPP are now available for use in all QuESt Valuation modes.

Resolved issues

• An issue where QuESt Valuation would say that you have no data downloaded even if you do and prevent you from proceeding.
• An issue where certain choices in QuESt Valuation wizard would cause the application to force quit upon attempting to solve.
• An issue where data scanning at QuESt Valuation startup would take an inconvenient amount of time to complete.
• An issue where newer versions of matplotlib (3.x) were incompatible, resulting in application crash at launch.
• An issue where background threads would persist until completion even when QuESt was closed.

Known issues

• An issue where older versions of 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.
• An issue where certain pricing node options in the SPP market area will not have LMP data for certain time periods, despite having the option to select it in QuESt Valuation menus. This is due to having a static list of pricing nodes for SPP. Previously, this would cause a full application crash if none of the selected months for valuation were solvable.

09/11/18

Release 1.0

The official release version of QuESt.

QuESt

QuESt Data Manager

An application for downloading data from RTO/ISO/market operators for use in other QuESt applications.

• Market and operations data for the following RTOs/ISOs are available using the download utility:
  • ERCOT
  • MISO
  • PJM

QuESt Valuation

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.

• Features
  • Wizard: a streamlined, guided interface for setting up sets of valuation runs
    • Pick a market area, pricing node, energy storage device, and historical dataset
    • Perform a valuation for the device each month in the set
    • Generate a pro forma report to summarize the findings in a convenient document
  • Single Run: a more flexible interface for setting up valuation runs for one month at a time
    • Offers more customization in terms of energy storage device and simulation parameters than the wizard
  • Batch Runs: an even more flexible interface for setting up large sets of valuation runs
    • Expands upon the wizard, allowing for the selection of multiple months for valuation
    • Perform parameter sweeps to look at the sensitivity of certain device or market parameters
  • Results viewer: a convenient tool for quickly checking your valuation results
    • Plot time series of revenue, state of charge, or scheduled charging/discharging actions
    • View the statistics of electricity prices for each month
    • Export .csv files for external viewing or processing

Known issues

• An issue where the solver does not exit successfully for certain optimization problems.
• An issue where certain downloaded data may not appear as options for use in QuESt Valuation.
• An issue where results for MISO valuations have constant objective functions leading to trivial optimization problems are obtained when they should be excluded.