Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory

• URL: http://arxiv.org/abs/2208.11543v4
• Date: Thu, 6 Jul 2023 04:55:09 GMT
• Title: Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory
• Authors: Divyanshi Dwivedi, Pradeep Kumar Yemula, Mayukha Pal
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Electrical Distribution Systems are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time series data of active power monitored at nodes of the electrical network. For these correlated networks, computed the network parameters such as clustering coefficient, assortative coefficient, average degree and power law exponent for the anticipation; and percolation threshold for the determination of the network withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavourable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system.

Related papers

• SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids [55.35059657148395]
  We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for Graph Neural Networks (GNNs) in power systems (PS) operations. SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages.
  arXiv  Detail & Related papers  (2024-07-17T09:01:38Z)
• Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency [6.201026565902282]
  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.
  arXiv  Detail & Related papers  (2024-05-09T03:19:20Z)
• Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach [8.326834499339107]
  This paper introduces the simulated reality of communication networks (SRCON) framework. It harnesses live network data and employs a blend of machine learning (ML)- and expert-based models. Results show significant gains over a state-of-the art method used by a operator for network energy efficiency modeling.
  arXiv  Detail & Related papers  (2023-12-31T10:03:08Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Data-Driven Approach to form Energy Resilient Smart Microgrids with Identification of Vulnerable Nodes in Active Electrical Distribution Network [0.0]
  We propose a methodology for the optimal allocation of DERs with vulnerable node identification in electrical distribution networks. We partitioned the distribution system into optimal microgrids using graph theory and graph neural network (GNN) architecture. The placement of DERs on the vulnerable nodes enhanced network reliability and resilience.
  arXiv  Detail & Related papers  (2022-08-24T17:39:36Z)
• Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer [128.19212716007794]
  We propose a Transformer-based Multi-Agent Actor-Critic framework (T-MAAC) to stabilize voltage in power distribution networks. In addition, we adopt a novel auxiliary-task training process tailored to the voltage control task, which improves the sample efficiency.
  arXiv  Detail & Related papers  (2022-06-08T07:48:42Z)
• Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks [0.9809636731336702]
  We propose a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs) The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services.
  arXiv  Detail & Related papers  (2021-03-12T13:00:01Z)
• Resilient Identification of Distribution Network Topology [0.0]
  This paper develops a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Having a low computational burden, this approach is fast-track and can be applied in real-time applications.
  arXiv  Detail & Related papers  (2020-11-16T14:23:56Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)

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.