arXiv AI
LLMs are Few-Shot Decision-Makers: Generalized Context-Aware Microgrid Frequency Control through Prompt Decision Transformer
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 2
Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
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 AI
Aug 21
Learning-Augmented Power System Operations: A Unified Optimization View
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.
arXiv AI
Aug 18
LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning
arXiv:2608. 15041v1 Announce Type: new Abstract: Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational information is heterogeneous, and low-level actions must satisfy strict constraints.
arXiv AI
5d ago
Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
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 Machine Learning
Aug 5
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
Hugging Face Trending Papers
Jul 14
Learning-enabled Acceleration of Scenario-based Model Predictive Control
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.