Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures

• URL: http://arxiv.org/abs/2205.02108v1
• Date: Wed, 4 May 2022 15:09:50 GMT
• Title: Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures
• Authors: Muhammad Usman Awais
• Abstract summary: Two classical algorithms have been presented to solve the Optimal Power Flow (OPF) problem. The drawbacks of the vanilla DRL application are discussed, and an algorithm is suggested to improve the performance. A reward function for the OPF problem is presented that enables the solution of inherent issues in DRL.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep Reinforcement Learning (DRL) is being used in many domains. One of the biggest advantages of DRL is that it enables the continuous improvement of a learning agent. Secondly, the DRL framework is robust and flexible enough to be applicable to problems of varying nature and domain. Presented work is evidence of using the DRL technique to solve an Optimal Power Flow (OPF) problem. Two classical algorithms have been presented to solve the OPF problem. The drawbacks of the vanilla DRL application are discussed, and an algorithm is suggested to improve the performance. Secondly, a reward function for the OPF problem is presented that enables the solution of inherent issues in DRL. Reasons for divergence and degeneration in DRL are discussed, and the correct strategy to deal with them with respect to OPF is presented.

Related papers

• Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II [52.083337333478674]
  This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW) The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL.
  arXiv  Detail & Related papers  (2024-07-18T02:46:06Z)
• Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases [60.30995339585003]
  Deep reinforcement learning (DRL) has been widely applied across various fields and has achieved remarkable accomplishments. DRL faces certain limitations, including low sample efficiency and poor generalization. We present how to leverage generative AI (GAI) to address these issues and enhance the performance of DRL algorithms.
  arXiv  Detail & Related papers  (2024-05-31T01:25:40Z)
• Federated Reinforcement Learning for Resource Allocation in V2X Networks [46.6256432514037]
  Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning. In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning.
  arXiv  Detail & Related papers  (2023-10-15T15:26:54Z)
• Decoupled Prioritized Resampling for Offline RL [120.49021589395005]
  We propose Offline Prioritized Experience Replay (OPER) for offline reinforcement learning. OPER features a class of priority functions designed to prioritize highly-rewarding transitions, making them more frequently visited during training. We show that this class of priority functions induce an improved behavior policy, and when constrained to this improved policy, a policy-constrained offline RL algorithm is likely to yield a better solution.
  arXiv  Detail & Related papers  (2023-06-08T17:56:46Z)
• Efficient Diffusion Policies for Offline Reinforcement Learning [85.73757789282212]
  Diffsuion-QL significantly boosts the performance of offline RL by representing a policy with a diffusion model. We propose efficient diffusion policy (EDP) to overcome these two challenges. EDP constructs actions from corrupted ones at training to avoid running the sampling chain.
  arXiv  Detail & Related papers  (2023-05-31T17:55:21Z)
• Open Problems and Modern Solutions for Deep Reinforcement Learning [0.0]
  We review two publications that investigate the mentioned issues of DRL and propose effective solutions. One designs the reward for human-robot collaboration by combining the manually designed extrinsic reward with a parameterized intrinsic reward function. The other one applies selective attention and particle filters to rapidly and flexibly attend to and select crucial pre-learned features for DRL using approximate inference instead of backpropagation.
  arXiv  Detail & Related papers  (2023-02-05T04:42:42Z)
• Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks [21.618559590818236]
  We discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms.
  arXiv  Detail & Related papers  (2022-09-16T04:50:49Z)
• Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling [0.0]
  Deep Reinforcement Learning (DRL) algorithms can deal with the increasing level of uncertainty due to the introduction of renewable-based generation. This paper presents a performance comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO. Results show DRL algorithms' capability of providing in real-time good-quality solutions, even in unseen operational scenarios.
  arXiv  Detail & Related papers  (2022-08-01T10:25:52Z)
• Maximum Entropy RL (Provably) Solves Some Robust RL Problems [94.80212602202518]
  We prove theoretically that standard maximum entropy RL is robust to some disturbances in the dynamics and the reward function. Our results suggest that MaxEnt RL by itself is robust to certain disturbances, without requiring any additional modifications.
  arXiv  Detail & Related papers  (2021-03-10T18:45:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.