This document provides recommended practices for applying reinforcement learning (RL) to power dispatch and energy management. It covers the establishment of RL models, training sample design, optimization implementation, and economic benefit evaluation, guided by the principles of safety, reliability, stability, and controllability. It offers guidance on integrating operational constraints into model design and on using simulated and actual data during training to help ensure that deployed RL agents do not compromise system safety. Applicable to power systems with diverse controllable devices (e.g., thermal units, flexible loads), this document supports the training, testing, and benchmarking of models. The objective is to enhance operational safety and reliability across regional grids, distribution networks, and microgrids.
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Status
- Active PAR
- PAR Approval
- 2025-11-04
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/AISC - Artificial Intelligence Standards Committee
- Working Group
-
RL-PDM - Reinforcement Learning Methods in Power Dispatching and Management
- IEEE Program Manager
- Christy Bahn
Contact Christy Bahn - Working Group Chair
- Kai Lv
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