Safe Reinforcement Learning for Power Grid Scheduling

The presentation begins with an overview of Safe Reinforcement Learning (SafeRL) and its relevance to power grid dispatch, outlining five main sections: SafeRL introduction, power grid scheduling basics, SafeRL applications in grid dispatch, a dual decision‑engine development platform, and a Q&A.

SafeRL is built on deep reinforcement learning (DRL), which combines reinforcement learning, deep learning, and Markov Decision Processes (MDP) to find optimal policies through trial‑and‑error, high‑dimensional perception, and mathematical modeling of expected returns.

Typical reinforcement‑learning successes such as personalized recommendation, ride‑hailing dispatch, and 3D bin packing are highlighted, with safety concerns emphasized for critical domains like autonomous driving and robotics.

To incorporate safety, the standard reward is augmented with safety metrics, leading to a constrained optimization problem. The constrained MDP (CMDP) framework formalizes safety as a hard constraint, and two main solution families are described: Local Policy Search, which refines a known safe policy within a trust region, and Lagrange Multiplier methods, which convert constrained problems into unconstrained dual forms.

State‑wise safety ensures that each decision step satisfies safety constraints via a state‑wise Lagrange relaxation, using neural‑network dual variables and projection. Worst‑case safety is achieved through adversarial learning, where an attacker agent simulates N‑1 line failures and the defender agent learns to remain safe under these attacks.

For online deployment, a safety‑layer model corrects the learned policy in real time, projecting actions onto a feasible set defined by linear constraints, thereby improving both safety and computational efficiency.

Training efficiency is enhanced by exploiting the sparsity of power‑system linear equations, using symbolic analysis and sparse numerical techniques to accelerate large‑scale simulations involving thousands of generators and thousands of network nodes.

The dual decision‑engine platform, MindOpt Studio, integrates reinforcement‑learning and mathematical‑optimization tools (MindOpt APL, Solver, and Tuner). It supports distributed training, model management, resource monitoring, and seamless switching between data‑driven and model‑driven approaches, enabling rapid, high‑quality solutions for grid dispatch.

A real‑world case study demonstrates the platform handling a grid with over 4,000 nodes and 1,000 generators, achieving sub‑second decision times. The session concludes with a Q&A discussing the advantages of RL for handling uncertainty in grid scheduling and the need for hard‑constraint guarantees.