dr-simulator 0.3.2

Demand Response Simulator enables you to sample DR events based on a distribution and optimize your energy flexible process

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

1. 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.

1. Install marimo using pip install marimo

2. 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.

3. 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 bid

• Include 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