Stor4Build Modeling Tool

Table of Contents

• About
• Installation
• Command Line Usage
• VS Code/Hatch Dev Environment
• Web API
• Prototype Coverage
• License

About

The stor4build package is a modeling tool for evaluating building-integrated thermal energy storage systems using EnergyPlus(R) and OpenStudio(R). It provides a Python package, command line interface, and web API for running baseline building models, adding TES technologies, sizing storage systems, and comparing simulation outputs.

The package is intended to support studies of how thermal energy storage can shift cooling loads, reduce peak demand, and interact with building type, climate zone, vintage, utility rates, and control strategy. stor4build builds on standard OpenStudio measures, EnergyPlus simulations, JSON inputs, and CSV outputs so that studies can be reproduced and extended with familiar tools.

Installation

pip install stor4build

Command Line Usage

The stor4build command includes three subcommands at this time:

• run-icetank - Runs the chiller-based ice tank Python plugin case with a specified capacity. Using --chw will use the chilled water version.
• size-icetank - Runs the chiller-based ice tank Python plugin case with a size based on the baseline model and discharge window parameters. Using --chw will use the chilled water version.
• run-dxcoil - Runs the native E+ DX coil packaged ice TES system with autosizing.

Further information is available with the --help option.

VS Code/Hatch Dev Environment

To set up a Visual Studio Code development environment, first install Python. Then install Visual Studio code and the Python extension(s) from Microsoft. Next, install hatch with

pip install hatch

Clone the repository to the location of your choice and open the directory with Visual Studio Code. In the root folder of the repo, execute the following to generate an environment that has everything that is needed:

hatch env create

To point Visual Studio Code at the created environment, find the environment with

hatch run python -c "import sys;print(sys.executable)"

and copy the result. In Visual Studio Code, hit ctrl-shift-P to bring up the command palette, select "Python: Select Interpreter", and paste in the result from above. Any warnings (yellow squiqqly underlines) in the source files should go away. To make sure that everything has worked, run

hatch shell

to enter the environment that was created, and then execute

stor4build --help

You should see the help output from the tool.

Web API

A simple flask-based web api is included. To run it, a PostgreSQL database storing the baseline models and weather is required. That setup is not described here yet. The following four environment variables need to be set:

FLASK_TIMESCALE_HOST
FLASK_TIMESCALE_DB
FLASK_TIMESCALE_USERNAME
FLASK_TIMESCALE_PASSWORD

Standard password rules apply. To launch the back end (that does the calculation) run

flask --app stor4build.api run

This will start up the flask development server and output will appear on the console. The API accepts JSON inputs in the form described in the schema directory in the file stor4build.json. Example inputs and scripts to send them to the API are in the resources and scripts directories.

Prototype Coverage

The tool now supports the majority of the OpenStudio prototypes. Three models (Laboratory, LargeDataCenterLowITE, and LargeDataCenterHighITE) are only available for vintages 2004 and later, so these models are not available before 2004.

License

stor4build is distributed under the terms of the BSD-3-Clause license.