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

EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and Benchmarking

As electric vehicle (EV) numbers rise, concerns about the capacity of current charging and power grid infrastructure grow, necessitating the development of smart charging solutions. While many smart charging simulators have been developed in recent years, only a few support the development of Reinforcement Learning (RL) algorithms in the form of a Gym environment, and those that do usually lack depth in modeling Vehicle-to-Grid (V2G) scenarios. To address the aforementioned issues, this paper introduces the EV2Gym, a realistic simulator platform for the development and assessment of small and large-scale smart charging algorithms within a standardized platform. The proposed simulator is populated with comprehensive EV, charging station, power transformer, and EV behavior models validated using real data. EV2Gym has a highly customizable interface empowering users to choose from pre-designed case studies or craft their own customized scenarios to suit their specific requirements. Moreover, it incorporates a diverse array of RL, mathematical programming, and heuristic algorithms to speed up the development and benchmarking of new solutions. By offering a unified and standardized platform, EV2Gym aims to provide researchers and practitioners with a robust environment for advancing and assessing smart charging algorithms.Comment: 10 pages, 9 figures, and 6 table

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