# Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes

arXiv: https://arxiv.org/abs/2410.17696 · HTML: https://mlsanity.com/paper/2410.17696/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 2 of 2)

- [PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2111.05969/) (2021)
- [A Novel MDP Based Decision Support Framework to Restore Earthquake Damaged Distribution Systems](https://mlsanity.com/paper/1911.05389/) (2019)

## Built on by (most cited first, 2 of 2)

- [Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation](https://mlsanity.com/paper/2501.02414/) (2025)
- [Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning](https://mlsanity.com/paper/2503.07340/) (2025)

## Abstract (arXiv)

Power grid load scheduling is a critical task that ensures the balance between electricity generation and consumption while minimizing operational costs and maintaining grid stability. Traditional optimization methods often struggle with the dynamic and stochastic nature of power systems, especially when faced with renewable energy sources and fluctuating demand. This paper proposes a reinforcement learning (RL) approach using a Markov Decision Process (MDP) framework to address the challenges of dynamic load scheduling. The MDP is defined by a state space representing grid conditions, an action space covering control operations like generator adjustments and storage management, and a reward function balancing economic efficiency and system reliability. We investigate the application of various RL algorithms, from basic Q-Learning to more advanced Deep Q-Networks (DQN) and Actor-Critic methods, to determine optimal scheduling policies. The proposed approach is evaluated through a simulated power grid environment, demonstrating its potential to improve scheduling efficiency and adapt to variable demand patterns. Our results show that the RL-based method provides a robust and scalable solution for real-time load scheduling, contributing to the efficient management of modern power grids.