Welcome to the Demand Response Simulator’s documentation!
Overview
User Guide
API Reference
Examples
Welcome to Demand Response Events Simulator
Incentive Based DR Program: Overview
Incentive based Demand Response programs are voluntary programs offered to residential, commercial, and industrial customer. The participants are offered financial incentives if they voluntarily reduce loads during stressful times for the grid, which are notified as DR events. There are different flavors of these DR programs across the country, with different rules that constitute when the events are called, how often they are called, the duration of these calls and much more. The DR Simulator tool uses various program and simulation parameters to model these incentive-based demand response programs across the country. This enables the user to configure any DR programs from any ISOs and simulate DR events once they provide the simulation parameters based on historical distribution or based on a custom distribution.
Features
Use custom or historic distribution
Simulate Monte-Carlo samples
Customize and configure DR events using marimo app
Installation
Stable Release: pip install dr-simulator
Development Head:
pip install git+https://github.com/we3lab/dr-simulator.git
Documentation
For full package documentation please visit we3lab.github.io/dr-simulator.
Development
See CONTRIBUTING.rst for information related to developing the code.
The Commands You Need To Know
pip install -e .[dev]
This will install your package in editable mode with all the required development dependencies (i.e.
tox
).
Visualizing the DR Simulator using marimo notebook
You can visualize the DR Simulator using marimo notebook.
Install marimo using
pip install marimo
From the terminal, run
marimo run dr_events_simulator.py
. This will open a new tab in your browser with the marimo notebook in app mode.You can also run
marimo edit dr_events_simulator.py
to open the notebook in edit mode.
Upcoming release features
Watch out for the upcoming release features:
Optimization framework for the simulated DR events using
cvxpy
library and finding the optimal capacity bidInclude program parametes data for other ISO’s DR programs
Case study of using DR simulator for finding the optimal capacity bid of SVCW water resource recovery facility in participating in the PG&E’s Capacity Bidding Program