Cascading Failure Propagation Simulation in Integrated Electricity and Natural Gas Systems

Bao, Zhejing; Zhang, Qihong; Wu, Lei; Chen, Fan

doi:10.35833/mpce.2019.000455

Cited by 26 publications

(7 citation statements)

References 22 publications

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“…Illuminated by the recent advances in deep learning, the exploration of utilizing deep reinforcement learning methods to tackle routing problems has been emerging vigorously. Without the need of hand-crafted rules and domain prior knowledge Li et al (2021b), DRL methods can be adapted to solve varied and flexible routing scenarios Bao et al (2020);Yang et al (2022). We classify the DRL methods into two flavours according to the solution process.…”

Section: Reinforcement Learning-based Methodsmentioning

confidence: 99%

Large-scale power inspection: A deep reinforcement learning approach

Guan

Zhang²,

Xie³

et al. 2023

Front. Energy Res.

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Power inspection plays an important role in ensuring the normal operation of the power grid. However, inspection of transmission lines in an unoccupied area is time-consuming and labor-intensive. Recently, unmanned aerial vehicle (UAV) inspection has attracted remarkable attention in the space-ground collaborative smart grid, where UAVs are able to provide full converge of patrol points on transmission lines without the limitation of communication and manpower. Nevertheless, how to schedule UAVs to traverse numerous, dispersed target nodes in a vast area with the least cost (e.g., time consumption and total distance) has rarely been studied. In this paper, we focus on this challenging and practical issue which can be considered as a family of vehicle routing problems (VRPs) with regard to different constraints, and propose a Diverse Trajectory-driven Deep Reinforcement Learning (DT-DRL) approach with encoder-decoder scheme to tackle it. First, we bring in a threshold unit in our encoder for better state representation. Secondly, we realize that the already visited nodes have no impact on future decisions, and then devise a dynamic-aware context embedding which removes irrelevant nodes to trace the current graph. Finally, we introduce multiply decoders with identical structure but unshared parameters, and design a Kullback-Leibler divergence based regular term to enforce decoders to output diverse trajectories, which expands the search space and enhances the routing performance. Comprehensive experiments on five types of routing problems show that our approach consistently outperforms both DRL and heuristic methods by a clear margin.

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Section: Reinforcement Learning-based Methodsmentioning

confidence: 99%

Large-scale power inspection: A deep reinforcement learning approach

Guan

Zhang²,

Xie³

et al. 2023

Front. Energy Res.

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“…The reliable operation of the IEGS is threatened by multiple contingencies such as extreme weather [23], human error [5], and misoperation. With the developing coupling between subsystems of IEGS, the failure in one system may propagate to its interacted system [24]. Figure 1 illustrates the failure propagation from natural gas systems to power systems and its impact on reserve scheduling.…”

Section: A Impacts Of Random Failures In Natural Gas Systems On Power...mentioning

confidence: 99%

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

Hui

Bao

Ding

et al. 2022

Journal of Modern Power Systems and Clean Energy

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With the growing interdependence between the electricity system and the natural gas system, the operation uncertainties in either subsystem, such as wind fluctuations or component failures, could have a magnified impact on the reliability of the whole system due to energy interactions. A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system (IEGS). Therefore, this paper proposes a day-ahead security-constrained unit commitment (SCUC) model for the IEGS to schedule the operation and reserve simultaneously considering reliability requirements. Firstly, the multi-state models for generating units and gas wells are established. Based on the multi-state models, the expected unserved energy cost (EUEC) and the expected wind curtailment cost (EWC) criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS. Furthermore, the EUEC and EWC criteria are incorporated into the day-ahead SCUC model, which is nonconvex and mathematically reformulated into a solvable mixed-integer second-order cone programming (MISOCP) problem. The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system. Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation. Index Terms--Integrated electricity and natural gas system (IEGS), natural gas reserve, electric reserve, expected unserved energy cost, expected wind curtailment, multi-state model, operational reliability.

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“…Literature [11] proposes that the N-1 breaking accident of natural gas pipeline in a multi-energy flow system will affect the energy flow distribution of other energy flow systems through coupling energy hubs. Literature [12] establishes a steady-state AC power flow model. When disturbance and failure occur in the power-gas interconnection system, the two subsystems carry out instantaneous redistribution of energy flow through coupling components of energy hubs.…”

Section: Introductionmentioning

confidence: 99%

Load margin and stability analysis of power-gas interconnection system considering N-1 and power flow transfer prediction

Zhang

2023

Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023)

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In view of the influence of N-1 disconnection on the maximum load margin and system stability of power-gas interconnection system, a continuous power flow model of power system and natural gas system considering the dynamic piping characteristics of natural gas was established. Based on the increase of system load, the system N and N-1 load margin of power system and natural gas system is calculated by SRSM continuous hybrid power flow. The power flow transfer factor after N-1 branch of the electric-gas interconnection system is defined, and the power flow transfer is predicted for the system so as to obtain the off-limit branch of the system. The weak nodes and EH coupling degree of the power system, natural gas system and electric-gas coupling link under the electric-gas coupling are further evaluated.

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Cascading Failure Propagation Simulation in Integrated Electricity and Natural Gas Systems

Cited by 26 publications

References 22 publications

Large-scale power inspection: A deep reinforcement learning approach

Large-scale power inspection: A deep reinforcement learning approach

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

Load margin and stability analysis of power-gas interconnection system considering N-1 and power flow transfer prediction

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