# OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation

arXiv: https://arxiv.org/abs/2203.04410 · HTML: https://mlsanity.com/paper/2203.04410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Exact Convex Relaxation of Optimal Power Flow in Radial Networks](https://mlsanity.com/paper/1311.7170/) (2013)
- [Fairness-Regularized DLMP-Based Bilevel Transactive Energy Mechanism in Distribution Systems](https://mlsanity.com/paper/1810.05275/) (2018)

## Abstract (arXiv)

This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms. We present the architecture and design choice for the proposed framework, elaborate on how users interact with OpenGridGym, and highlight its value by providing multiple cases to demonstrate its use. Four modules are used in any simulation: (1) the physical grid, (2) market mechanisms, (3) a set of trainable agents which interact with the former two modules, and (4) environment module that connects and coordinates the above three. We provide templates for each of those four, but they are easily interchangeable with custom alternatives. Several case studies are presented to illustrate the capability and potential of this toolkit in helping researchers address key design and operational questions in distribution electricity markets.