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Large-scale Islanded Microgrids based on Multi-agent Reinforcement Learning Control Methods

  • Built by Dong Chen from Michigan State University
  • Started on Feb.25, 2020

Overview

We plan to develop a power grid simulation platform with large number of DERs. Instead of the conventional control methods, we want to try multi-agent reinforcement learning algorithms.

Problems we are targeting at are voltage and frequency stabilization and power sharing among DERs.

Code Structure

  • main.py: the main function used to run the whole project. In this file, you can choose the DER and system configurations.
  • DER_fn.py: This is the graph generation function and will be called by the main.py function.
  • configs/parameters_4.py: This is the configuration file for the 4 DER system.
  • configs/parameters_20.py: This is the configuration file for the 20 DER system.

In the implementation, we should build a similar configuration file and list the system configurations. We can claim our system design in the main function and keep the graph generation function unchanged.

Experiments & Analysis

  • The first experiment is the frequency and voltage control for 4-DER and 20-DER microgrid systems.

output_example
Fig.1 Frequency and voltage of 20 DER system

output_example
Fig.2 Frequency and voltage of 20 DER system

Reference

  1. Bidram, Ali, Ali Davoudi, and Frank L. Lewis. "A multiobjective distributed control framework for islanded AC microgrids." IEEE Transactions on industrial informatics 10.3 (2014): 1785-1798.

  2. Bidram, Ali, et al. "Distributed cooperative secondary control of microgrids using feedback linearization." IEEE Transactions on Power Systems 28.3 (2013): 3462-3470.

  3. Mustafa, Aquib, et al. "Detection and Mitigation of Data Manipulation Attacks in AC Microgrids." IEEE Transactions on Smart Grid (2019).

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A Inverter-based microgrid system with PID controller

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