89 research outputs found
Detection of False Data Injection Attacks Using the Autoencoder Approach
State estimation is of considerable significance for the power system
operation and control. However, well-designed false data injection attacks can
utilize blind spots in conventional residual-based bad data detection methods
to manipulate measurements in a coordinated manner and thus affect the secure
operation and economic dispatch of grids. In this paper, we propose a detection
approach based on an autoencoder neural network. By training the network on the
dependencies intrinsic in 'normal' operation data, it effectively overcomes the
challenge of unbalanced training data that is inherent in power system attack
detection. To evaluate the detection performance of the proposed mechanism, we
conduct a series of experiments on the IEEE 118-bus power system. The
experiments demonstrate that the proposed autoencoder detector displays robust
detection performance under a variety of attack scenarios.Comment: 6 pages, 5 figures, 1 table, conferenc
A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
The optimal dispatch of energy storage systems (ESSs) presents formidable
challenges due to the uncertainty introduced by fluctuations in dynamic prices,
demand consumption, and renewable-based energy generation. By exploiting the
generalization capabilities of deep neural networks (DNNs), deep reinforcement
learning (DRL) algorithms can learn good-quality control models that adaptively
respond to distribution networks' stochastic nature. However, current DRL
algorithms lack the capabilities to enforce operational constraints strictly,
often even providing unfeasible control actions. To address this issue, we
propose a DRL framework that effectively handles continuous action spaces while
strictly enforcing the environments and action space operational constraints
during online operation. Firstly, the proposed framework trains an action-value
function modeled using DNNs. Subsequently, this action-value function is
formulated as a mixed-integer programming (MIP) formulation enabling the
consideration of the environment's operational constraints. Comprehensive
numerical simulations show the superior performance of the proposed MIP-DRL
framework, effectively enforcing all constraints while delivering high-quality
dispatch decisions when compared with state-of-the-art DRL algorithms and the
optimal solution obtained with a perfect forecast of the stochastic variables.Comment: This paper has been submitted to a publication in a journal. This
corresponds to the submitted version. After acceptance, it may be removed
depending on the journal's requirements for copyrigh
Quantum Neural Networks for Power Flow Analysis
This paper explores the potential application of quantum and hybrid
quantum-classical neural networks in power flow analysis. Experiments are
conducted using two small-size datasets based on the IEEE 4-bus and 33-bus test
systems. A systematic performance comparison is also conducted among quantum,
hybrid quantum-classical, and classical neural networks. The comparison is
based on (i) generalization ability, (ii) robustness, (iii) training dataset
size needed, (iv) training error. (v) training computational time, and (vi)
training process stability. The results show that the developed
quantum-classical neural network outperforms both quantum and classical neural
networks, and hence can improve deep learning-based power flow analysis in the
noisy-intermediate-scale quantum (NISQ) era.Comment: 7 pages, 15 figure
Protection Testing for Multiterminal High-Voltage dc Grid:Procedures and Procedures and Assessment
eAssessment The application of multiterminal (MT), high-voltage dc (HVdc) (MTdc) grid technology requires test procedures for the operation and implementation of the protection solutions. The test procedures are usually derived from experience and from extensive measurement data, which, at present, are still not widely available. Based on a hardware-inthe- loop (HIL) method, advanced dc protection testing strategies, utilizing existing experience for ac grids and requirements for MTdc grids, may overcome this gap
Protection testing for multiterminal high-voltage dc grid : procedures and assessment
The application of multiterminal (MT), high-voltage dc (HVdc) (MTdc) grid technology requires test procedures for the operation and implementation of the protection solutions. The test procedures are usually derived from experience and from extensive measurement data, which, at present, are still not widely available. Based on a hardware-in-the-loop (HIL) method, advanced dc protection testing strategies, utilizing existing experience for ac grids and requirements for MTdc grids, may overcome this gap.
This article proposes procedures and guidelines for testing system-level dc protection based on the functionality of MTdc grids for both primary and backup dc protection. Specific performance criteria have been defined, based on multicase testing and statistical analysis, with the considerations of related critical testing parameters for each functional requirement of the dc protection. Accordingly, procedures for a dc protection testing environment and various fault scenarios are defined. The proposed algorithm test procedures will contribute to the standardization of dc protection system design and testing.The European Commission through the Horizon 2020 program and was supported by the EUDP project Voltage Control and Protection for a Grid Toward 100% Power Electronics and Cable Network.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4154573hj2021Electrical, Electronic and Computer Engineerin
The competition and equilibrium in power markets under decarbonization and decentralization
Equilibrium analysis has been widely studied as an effective tool to model gaming interactions and predict market results. However, as competition modes are fundamentally changed by the decarbonization and decentralization of power systems, analysis techniques must evolve. This article comprehensively reviews recent developments in modelling methods, practical settings and solution techniques in equilibrium analysis. Firstly, we review equilibrium in the evolving wholesale power markets which feature new entrants, novel trading products and multi-stage clearing. Secondly, the competition modes in the emerging distribution market and distributed resource aggregation are reviewed, and we compare peer-to-peer clearing, cooperative games and Stackelberg games. Furthermore, we summarize the methods to treat various information acquisition degrees, risk preferences and rationalities of market participants. To deal with increasingly complex market settings, this review also covers refined analytical techniques and agent-based models used to compute the equilibrium. Finally, based on this review, this paper summarizes key issues in the gaming and equilibrium analysis in power markets under decarbonization and decentralization
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