Search Results for author:
Found 6 papers, 1 papers with code
Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents
At stage one, centralized soft actor-critic (SAC) agent is trained to control generator active power outputs in a wide area to control transmission line flows against specified security limits.
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process.
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid.
Optimization and Control Systems and Control Systems and Control
Evaluating Load Models and Their Impacts on Power Transfer Limits
Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods.
Optimization and Control
Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning
Modern power grids are experiencing grand challenges caused by the stochastic and dynamic nature of growing renewable energy and demand response.
