mlsanityData to 14 Oct 2026

Electric Arc Furnaces Scheduling under Electricity Price Volatility with Reinforcement Learning

arXiv 2512.09293 ↗First posted 10 Dec 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

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.

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.

Builds on

4 of 21 references.

References · 4

  1. 2017Optimal Battery Participation in Frequency Regulation Marketscited by 4
  2. 2022Energy Storage State-of-Charge Market Modelcited by 2
  3. 2023Socially Optimal Energy Usage via Adaptive Pricingcited by 2
  4. 2025Locational Energy Storage Bid Bounds for Facilitating Social Welfare Convergencecited by 1

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