Deep Multi-Agent Reinforcement Learning for Cost Efficient Distributed Load Frequency Control

TL;DR

This paper introduces a distributed load frequency control method using multi-agent deep reinforcement learning, enabling microgrid systems to operate efficiently without central control.

Contribution

It applies MADDPG to power system control, demonstrating a novel, distributed RL approach for load balancing in microgrids.

Findings

RL-based control achieves cost efficiency

Distributed agents effectively restore frequency

Method outperforms traditional control approaches

Abstract

The rise of microgrid-based architectures is heavily modifying the energy control landscape in distribution systems making distributed control mechanisms necessary to ensure reliable power system operations. In this paper, we propose the use of Reinforcement Learning techniques to implement load frequency control without requiring a central authority. To this end, we approximate the optimal solution of the primary, secondary, and tertiary control with the use of the Multi- Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Generation units are characterised as agents that learn how to maximise their long-term performance by acting and interacting with the environment to balance generation and load in a cost efficient way. Network effects are also modelled in our framework for the restoration of frequency to the nominal value. We validate our Reinforcement Learning methodology through numerical results and show that it can be used to implement the load frequency control in a distributed and cost efficient way.