Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

• URL: http://arxiv.org/abs/2405.05521v1
• Date: Thu, 9 May 2024 03:19:20 GMT
• Title: Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency
• Authors: Yuqi Zhou, Hao Zhu,
• Abstract summary: An optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios. We propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses.
• Score: 6.201026565902282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

Related papers

• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow [20.078717680640214]
  Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems.
  arXiv  Detail & Related papers  (2023-11-29T20:36:35Z)
• DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities [5.78463306498655]
  Electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and jeopardized stability. Modern power grids require coordinated or smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits.
  arXiv  Detail & Related papers  (2023-05-18T14:03:47Z)
• Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks [80.37827344656048]
  We develop a deep learning (DL)-guided hybrid beam and power allocation approach for millimeter-wave (mmWave) networks. We exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation.
  arXiv  Detail & Related papers  (2023-05-08T05:40:54Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory [0.0]
  This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events. The proposed framework is developed by effectively employing the complex network theory.
  arXiv  Detail & Related papers  (2022-08-24T13:41:37Z)
• Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operations [4.922268203017287]
  This work puts forth an innovative learning-for-OLS approach by constructing the optimal decision rules of load shedding under a variety of potential contingency scenarios. The proposed NN-based OLS decisions are fully decentralized, enabling individual load centers to quickly react to the specific contingency. Numerical studies on the IEEE 14-bus system have demonstrated the effectiveness of our scalable OLS design for real-time responses to severe grid emergency events.
  arXiv  Detail & Related papers  (2021-11-23T16:14:58Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow [0.0]
  Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drastically reduce the computation time and generate a good estimate of computationally intensive processes in power systems. Such neural networks can be applied in safety-critical applications in power systems and build a high level of trust among power system operators.
  arXiv  Detail & Related papers  (2021-06-28T10:45:22Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)
• High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow [49.2540510330407]
  AC-OPF is a key building block in many power system applications. Motivated by increased penetration of renewable sources, this paper explores deep learning to deliver efficient approximations to the AC-OPF.
  arXiv  Detail & Related papers  (2020-06-29T20:22:16Z)

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.