# Electric Arc Furnaces Scheduling under Electricity Price Volatility with Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.09293 · HTML: https://mlsanity.com/paper/2512.09293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## 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, 4 of 4)

- [Optimal Battery Participation in Frequency Regulation Markets](https://mlsanity.com/paper/1710.10514/) (2017)
- [Energy Storage State-of-Charge Market Model](https://mlsanity.com/paper/2207.07221/) (2022)
- [Socially Optimal Energy Usage via Adaptive Pricing](https://mlsanity.com/paper/2310.13254/) (2023)
- [Locational Energy Storage Bid Bounds for Facilitating Social Welfare Convergence](https://mlsanity.com/paper/2502.18598/) (2025)

## Abstract (arXiv)

This paper proposes a reinforcement learning-based framework for optimizing the operation of electric arc furnaces (EAFs) under volatile electricity prices. We formulate the deterministic version of the EAF scheduling problem into a mixed-integer linear programming (MILP) formulation, and then develop a Q-learning algorithm to perform real-time control of multiple EAF units under real-time price volatility and shared feeding capacity constraints. We design a custom reward function for the Q-learning algorithm to smooth the start-up penalties of the EAFs. Using real data from EAF designs and electricity prices in New York State, we benchmark our algorithm against a baseline rule-based controller and a MILP benchmark, assuming perfect price forecasts. The results show that our reinforcement learning algorithm achieves around 90% of the profit compared to the perfect MILP benchmark in various single-unit and multi-unit cases under a non-anticipatory control setting.