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.
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.
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.
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.
The paper introduces DR‑Gym, an open‑source, Gymnasium‑compatible environment that simulates electric utility demand‑response programs at the market level. It uses a regime‑switching wholesale price model calibrated to real extreme events and physics‑based building demand profiles, providing a rich observational space and a configurable multi‑objective reward function for reinforcement learning. Baseline strategies and data snapshots demonstrate the simulator’s realism and learnability.
arXiv:2607. 16534v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved strong results in control, yet learned policies remain brittle to changes in dynamics, action spaces, observation spaces, or goals, a critical limitation for real-world deployment.
arXiv:2601.03679v2 Announce Type: replace-cross Abstract: Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-...
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.
arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.
The paper compares rule‑based and reinforcement‑learning (RL) pricing mechanisms for peer‑to‑peer electricity trading in residential photovoltaic communities. Rule‑based benchmarks—bill‑sharing, mid‑market rate, and supply‑demand‑ratio pricing—outperform the best RL policy in a PV‑only setup, while RL policies achieve higher community savings when battery storage is added. Across both configurations, SDR‑shaped pricing outperforms multiplier‑based parameterization, but benefit distribution remains heterogeneous among households.
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.
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.