An official website of the United States government
Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL’s performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL’s effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability.
more »
« less
Deep reinforcement learning based voltage control revisited
Abstract Deep Reinforcement Learning (DRL) has shown promise for voltage control in power systems due to its speed and model‐free nature. However, learning optimal control policies through trial and error on a real grid is infeasible due to the mission‐critical nature of power systems. Instead, DRL agents are typically trained on a simulator, which may not accurately represent the real grid. This discrepancy can lead to suboptimal control policies and raises concerns for power system operators. In this paper, we revisit the problem of RL‐based voltage control and investigate how model inaccuracies affect the performance of the DRL agent. Extensive numerical experiments are conducted to quantify the impact of model inaccuracies on learning outcomes. Specifically, techniques that enable the DRL agent are focused on learning robust policies that can still perform well in the presence of model errors. Furthermore, the impact of the agent's decisions on the overall system loss are analyzed to provide additional insight into the control problem. This work aims to address the concerns of power system operators and make DRL‐based voltage control more practical and reliable.
more »
« less
- Award ID(s):
- 2301938
- PAR ID:
- 10570699
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- IET Generation, Transmission & Distribution
- Volume:
- 17
- Issue:
- 21
- ISSN:
- 1751-8687
- Format(s):
- Medium: X Size: p. 4826-4835
- Size(s):
- p. 4826-4835
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)While Deep Reinforcement Learning has emerged as a de facto approach to many complex experience-driven networking problems, it remains challenging to deploy DRL into real systems. Due to the random exploration or half-trained deep neural networks during the online training process, the DRL agent may make unexpected decisions, which may lead to system performance degradation or even system crash. In this paper, we propose PnP-DRL, an offline-trained, plug and play DRL solution, to leverage the batch reinforcement learning approach to learn the best control policy from pre-collected transition samples without interacting with the system. After being trained without interaction with systems, our Plug and Play DRL agent will start working seamlessly, without additional exploration or possible disruption of the running systems. We implement and evaluate our PnP-DRL solution on a prevalent experience-driven networking problem, Dynamic Adaptive Streaming over HTTP (DASH). Extensive experimental results manifest that 1) The existing batch reinforcement learning method has its limits; 2) Our approach PnP-DRL significantly outperforms classical adaptive bitrate algorithms in average user Quality of Experience (QoE); 3) PnP-DRL, unlike the state-of-the-art online DRL methods, can be off and running without learning gaps, while achieving comparable performances.more » « less
-
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee the satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe Voltage Regulator, which is composed of an RL learner and a specifically designed, computationally efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages are within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm.more » « less
-
This paper describes how domain knowledge of power system operators can be integrated into reinforcement learning (RL) frameworks to effectively learn agents that control the grid's topology to prevent thermal cascading. Typical RL-based topology controllers fail to perform well due to the large search/optimization space. Here, we propose an actor-critic-based agent to address the problem's combinatorial nature and train the agent using the RL environment developed by RTE, the French TSO. To address the challenge of the large optimization space, a curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using network physics for enhanced agent learning. Further, a parallel training approach on multiple scenarios is employed to avoid biasing the agent to a few scenarios and make it robust to the natural variability in grid operations. Without these modifications to the training procedure, the RL agent failed for most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL learning. The agent was tested by RTE for the 2019 learning to run the power network challenge and was awarded the 2nd place in accuracy and 1st place in speed. The developed code is open-sourced for public use. Analysis of a simple system proves the enhancement in training RL-agents using the curriculum.more » « less
-
Deep reinforcement learning (DRL) has demonstrated impressive success in solving complex control tasks by synthesizing control policies from data. However, the safety and stability of applying DRL to safety-critical systems remain a primary concern and challenging problem. To address the problem, we propose the Phy-DRL: a novel physics-model regulated deep reinforcement learning framework. The Phy-DRL is novel in two architectural designs: a physics-model-regulated reward and residual control, which integrates physics-model-based control and data-driven control. The concurrent designs enable the Phy-DRL to mathematically provable safety and stability guarantees. Finally, the effectiveness of the Phy-DRL is validated by an inverted pendulum system. Additionally, the experimental results demonstrate that the Phy-DRL features remarkably accelerated training and enlarged reward.more » « less
