REopt API Analysis using Python

Updated for Multi-Scenario and Multi-Tariff Inputs

We now have now have the ability to use multiple custom electric tariffs with the multi-scenario version of the program. In particular, it allows the addition of a column in the multi-scenario input file titled urdb_json_file that allows a specific electric tariff to be specified for each scenario. Below is an example of some inputs:

description	load_file	urdb_json_file	urdb_label
87104	87104_load_kw.csv	87104_electric_tariff.json
87106	87106_load_kw.csv	87106_electric_tariff.json
87106	87106_load_kw.csv		5cc090b85457a3a43667107e

The user can specify the location of a json file that is placed within ./electric_rates/ or a urdb_label as usual. This is useful for when users have scenarios looking at multiple different customer classes that may face different rate structures.

REopt is a techno-economic decision support model from NREL which is used for optimizing energy systems for buildings, campuses, communities, and microgrids. REopt Lite offers any user the capability to perform their own techno-economic analysis, but customizable analysis solutions are also available by reaching out to NREL's REopt team. REopt Lite also offers an application programming interface (API). This is a guide to use REopt’s Application Programming Interface for running REopt analysis programmatically.

Detailed documentation of REopt Lite API is available in this repository's wiki.

This repository includes example scripts and notebooks for calling the API.

Usage

There are two ways to setup an environment from which to call the API:

1. Use Docker to host a pre-configured Python environment
2. Install a Python environment natively on your operating system

See the instructions below for each method.

Prerequisites

1. Obtain API_key from here

Use Docker to host a pre-configured Python environment

1. Install Docker (https://www.docker.com/get-started)
2. Open a command line terminal (e.g. command prompt, bash terminal) and type cd path/to/cloned/repo
3. Type docker compose up --build depending on the version of Docker
4. If there is an error in the build process (likely pulling the base image), try first running docker pull jupyter/datascience-notebook and then do docker compose up
5. Click the provided URL (starting with http://127.0.0.1) to open Jupyter Lab in your browser
6. Click on the work folder in the left-hand project explorer and navigate to either of the notebooks (.ipynb files).
7. If running REopt locally, you will also need to spin up REopt Lite Docker containers (https://github.com/NREL/REopt_Lite_API)
8. To shut down, cntrl+c in the terminal or shutdown using the Jupyter Lab controls

After initial docker setup

1. To spin up again after you've already done --build in step 3. above, just type docker compose up and click the Jupyter URL

Setup a python environment

1. Install Python 3.6+ interpreter:

   • Ubuntu: sudo apt-get install python3-dev

   • Mac OSX: Download and install version 3.6+ from here

   • Windows: Download and install version 3.6+ from here

2. Install pip

   Recommended: use a virtual environment with venv

3. Add the required python packages:

   • If using venv: activate the environment

   • pip install -r requirements.txt

   NOTE: The requirements.txt does not include dependecies for the jupyter notebooks

4. (OPTIONAL) Install git: - https://git-scm.com/book/en/v2/Getting-Started-Installing-Git

Running the code

1. Clone, download, or fork the repository.
2. See single_scenario_example.ipynb and multi_scenario_example.ipynb