The paper presents a reinforcement learning framework for strategic investment decisions in the energy transition, aiming to balance profit, CO2 social costs, and competitive advantage across oil & gas, renewables, and CO2 reduction sectors. A custom simulation environment models the dynamic energy landscape up to 2050, allowing the RL agent to learn optimal policies that adapt to uncertainties in production, prices, and costs. Benchmarking shows the RL strategy consistently outperforms manually defined baseline policies in adaptability and long-term value creation.
By Yasaman Cheraghi (Department of Energy and Petroleum Engineering, University of Stavanger, Norway), Reidar B. Bratvold (Department of Energy and Petroleum Engineering, University of Stavanger, Norway), Aojie Hong (Independent Researcher, Stavanger, Norway), Ressi B. Muhammad (Department of Energy and Petroleum Engineering, University of Stavanger, Norway), Sergey Alyaev (NORCE Norwegian Research Centre, Bergen, Norway)
arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer
This study evaluates four control strategies—rule-based, model predictive control (MPC), reinforcement learning without forecasts (RL‑NF), and reinforcement learning with forecasts (RL‑F)—for a renewable‑powered hydrogen supply chain. Using a unified, physically realistic simulation that includes electrolyzer constraints, storage dynamics, and grid limits, the authors find that MPC delivers the best economic performance by leveraging short‑term forecasts, while RL‑NF performs robustly without future information. RL‑F does not consistently outperform RL‑NF, indicating that forecast uncertainty and added state complexity can hinder forecast‑augmented learning.
By Mahammad Valiyev
arXiv:2606. 16051v1 Announce Type: cross Abstract: Residential battery energy storage systems (BESS) are increasingly deployed alongside photovoltaic (PV) generation to reduce household energy costs under volatile time-of-use (TOU) tariffs.
By Dawood Butt, Nandor Verba
This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and delayed completion feedback, designed to be extensible toward more complex scenarios.
arXiv:2505. 05203v3 Announce Type: replace-cross Abstract: With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces growing challenges in maintaining economic efficiency, security, and robustness.
By Wangkun Xu, Zhongda Chu, Fei Teng
arXiv:2606. 30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.
By Jan Stenner, Alexander Kilian, Sebastian Peitz, Hermann de Meer
The paper proposes a value‑oriented policy distillation framework that uses explainable boosting machines to create a transparent wind‑farm power control policy. By incorporating a value‑oriented perspective and expert knowledge, the resulting controller reduces imbalance costs by 82% on real Belgian grid data, outperforming both a price threshold controller and a decision‑tree baseline. This approach addresses the challenge of unknown settlement prices while maintaining interpretability for deployment on real wind farms.
By Max Bruninx, Timothy Verstraeten, Jan Decuyper, Ann Now\'e, Jan Helsen
arXiv:2609.23590v1 Announce Type: new
Abstract: Battery energy storage system (BESS) dispatch must preserve operational feasibility while declining price spreads reduce the margin available to pay fo...
By Kuanlin Chen, Chen-Wei Kuo, Cheng-En Ou
The paper presents a reinforcement learning framework for designing solar PV adoption policies under uncertainty, integrating RL with a stochastic agent‑based model to simulate yearly adoption over a 16‑year horizon. Policymakers can choose annual incentives such as grants, subsidised loans, and feed‑in tariffs, and the study evaluates three RL algorithms—PPO, SAC, and TD3—within a scalarised reward framework that balances adoption gains against costs. Results show clear trade‑off patterns, with TD3 yielding the highest adoption at higher cost, PPO achieving the lowest cost with fewer adopters, and a balanced PPO policy offering a middle ground, all outperforming static baseline policies.
By Iias Faiud, Jonaid Shianifar, Michael Schukat, Karl Mason
arXiv:2608. 01151v1 Announce Type: cross Abstract: In this paper, we consider stochastic optimal control problems with infinite-horizon joint chance constraints.
By Francesco Cordiano, Kanghui He, Bart De Schutter
Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing control actions over multiple predicted scenarios. However, its computational complexity increases rapidly with the number of scenarios and prediction horizon, limiting is applicability to real-time planning and control.