ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

TL;DR

ACN-Sim is an open-source, modular simulation platform that enables realistic research and testing of electric vehicle charging algorithms, integrating datasets, grid simulators, and reinforcement learning tools.

Contribution

It introduces a comprehensive, extensible simulation environment for EV charging research, integrating datasets, grid tools, and reinforcement learning frameworks.

Findings

Provides realistic EV charging scenarios using ACN-Data

Supports testing of algorithms with ACN-Live and grid simulators

Facilitates reinforcement learning research with OpenAI Gym integration

Abstract

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.