DeepAI AI Chat
Log In Sign Up

Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control

02/15/2023
by   Anton R. Fuxjäger, et al.
0

The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly. In our prior work, we have introduced a congestion management approach consisting of a redispatching optimizer combined with a machine learning-based topology optimization agent. Compared to a typical redispatching-only agent, it was able to keep a simulated grid in operation longer while at the same time reducing operational cost. Our approach also ranked 1st in the L2RPN 2022 competition initiated by RTE, Europe's largest grid operator. The aim of this paper is to bring this promising technology closer to the real world of power grid operation. We deploy RL-based agents in two settings resembling established workflows, AI-assisted day-ahead planning and realtime control, in an attempt to show the benefits and caveats of this new technology. We then analyse congestion, redispatching and switching profiles, and elementary sensitivity analysis providing a glimpse of operation robustness. While there is still a long way to a real control room, we believe that this paper and the associated prototypes help to narrow the gap and pave the way for a safe deployment of RL agents in tomorrow's power grids.

READ FULL TEXT
11/10/2022

Power Grid Congestion Management via Topology Optimization with AlphaZero

The energy sector is facing rapid changes in the transition towards clea...
11/08/2019

Community Detection for Power Systems Network Aggregation Considering Renewable Variability

The increasing penetration of variable renewable energy (VRE) has brough...
11/26/2020

Exploring grid topology reconfiguration using a simple deep reinforcement learning approach

System operators are faced with increasingly volatile operating conditio...
04/24/2019

Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning

Modern power grids are experiencing grand challenges caused by the stoch...
06/29/2021

Action Set Based Policy Optimization for Safe Power Grid Management

Maintaining the stability of the modern power grid is becoming increasin...
03/27/2020

Robust and Deterministic Scheduling of Power Grid Actors

Modern power grids need to cope with increasingly decentralized, volatil...