IEEE Open Access Journal of Power and Energy最新文献

Guaranteed False Data Injection Attack Without Physical Model 无物理模型的保证虚假数据注入攻击

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-06-16 DOI: 10.1109/OAJPE.2025.3580108

Chenhan Xiao;Napoleon Costilla-Enriquez;Yang Weng

{"title":"Guaranteed False Data Injection Attack Without Physical Model","authors":"Chenhan Xiao;Napoleon Costilla-Enriquez;Yang Weng","doi":"10.1109/OAJPE.2025.3580108","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3580108","url":null,"abstract":"Smart grids are increasingly vulnerable to False Data Injection Attacks (FDIAs) due to their growing reliance on interconnected digital systems. Many existing FDIA techniques assume access to critical physical model information, such as grid topology, to successfully bypass Bad Data Detection (BDD). However, this assumption is often impractical, as utilities may restrict access to this data, or the evolving nature of distribution grids—particularly with the integration of renewable energy—can render this information unavailable. Current methods that address the absence of physical model lack formal guarantees for BDD evasion. To bridge this gap, we propose a novel physical-model-free FDIA framework that 1) bypasses BDD with formal guarantees and 2) maximizes the attack impact without requiring explicit physical model. Our approach leverages an autoencoder (AE) with a regularized latent space to enforce physical consistency, using historical measurements to replicate the residual error distribution, ensuring BDD evasion. Additionally, we integrate a Generative Adversarial Network (GAN) to explore the measurement manifold and induce the most significant state changes, enhancing the impact of the attack. The key innovation lies in the AE-GAN hybrid model’s ability to replicate the residual error distribution while maximizing attack efficacy, offering a performance guarantee that existing methods lack. We validate our method across 11 representative grid systems, using real power profiles simulated in MATPOWER, and demonstrate its consistent ability to bypass BDD by preserving the residual error distribution. The results highlight the robustness and generalizability of the proposed FDIA framework.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"429-441"},"PeriodicalIF":3.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medium- and Long-Term Optimal Stochastic Scheduling for Inter-Basin Hydro-Wind-Photovoltaic Complementary Systems Considering Wind and Solar Output Uncertainty 考虑风能和太阳能输出不确定性的流域间水光互补系统中长期最优随机调度

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-06-02 DOI: 10.1109/OAJPE.2025.3575734

Chengrui Du;Yuan Gao;Lili Wang;Xiang Li;Yichen Cui;Jian Gao

{"title":"Medium- and Long-Term Optimal Stochastic Scheduling for Inter-Basin Hydro-Wind-Photovoltaic Complementary Systems Considering Wind and Solar Output Uncertainty","authors":"Chengrui Du;Yuan Gao;Lili Wang;Xiang Li;Yichen Cui;Jian Gao","doi":"10.1109/OAJPE.2025.3575734","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3575734","url":null,"abstract":"With the large-scale integration of wind power and photovoltaic (PV) into the grid, dealing with their output uncertainties and formulating more reliable scheduling strategies has become a critical challenge for the efficient operation of hydropower-dominated inter-basin hydro-wind-PV complementary systems. To quantify the uncertainty associated with wind and PV power generation, this paper proposes a method for generating wind and PV power output scenarios, combining adaptive diffusion kernel density estimation with Copula theory. Scenario reduction is then carried out using the K-means clustering algorithm. Based on this, a medium- and long-term stochastic expectation model for the inter-basin hydro-wind-PV complementary system is developed. The model is subsequently solved using the Gurobi 11.0.3 optimization solver within the MATLAB environment. A case study is conducted based on a selected inter-basin hydro-wind-PV clean energy base in China. The results demonstrate that the proposed scheduling strategy effectively addresses the unpredictability of wind and solar power, improves the overall utilization of renewable energy sources, and facilitates more efficient water level regulation at each power station. Furthermore, it significantly enhances the overall performance and efficiency of the complementary system.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"404-416"},"PeriodicalIF":3.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11021448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection, and Mitigation 针对电网的负载改变攻击调查：攻击影响、检测和缓解

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-17 DOI: 10.1109/OAJPE.2025.3562052

Sajjad Maleki;Shijie Pan;Subhash Lakshminarayana;Charalambos Konstantinou

引用次数: 0

Characterization of the Long-Term Impedance Variations Due to Electric Vehicle Charging From 20 kHz to 500 kHz 电动汽车充电从20 kHz到500 kHz时的长期阻抗变化特性

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-17 DOI: 10.1109/OAJPE.2025.3562091

Jon González-Ramos;Itziar Angulo;Igor Fernández;Bernhard Grasel;Alexander Gallarreta;Amaia Arrinda;David de la Vega

{"title":"Characterization of the Long-Term Impedance Variations Due to Electric Vehicle Charging From 20 kHz to 500 kHz","authors":"Jon González-Ramos;Itziar Angulo;Igor Fernández;Bernhard Grasel;Alexander Gallarreta;Amaia Arrinda;David de la Vega","doi":"10.1109/OAJPE.2025.3562091","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3562091","url":null,"abstract":"This paper aims to empirically characterize the long-term grid impedance variations due to Electric Vehicle Charging Processes (EVCPs) in the frequency range from 20 kHz to 500 kHz. The study is supported by a measurement campaign performed in a controlled Low Voltage (LV) grid in Austria, composed of a Secondary Substation (SS) and four houses, which statistically represents the public LV grids in Austria. The results show that different impedance states (with different spectral patterns and amplitudes) can be identified during the charging processes of all the EVs under analysis. Additionally, time variability within each impedance state is also registered. The findings, which cover the still uncharacterized frequency band from 20 kHz to 500 kHz, have important implications for the performance of Narrowband Power Line Communications (NB-PLC), the propagation of Non-Intentional Emissions (NIEs) and the definition of a reference impedance in this frequency band.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"235-244"},"PeriodicalIF":3.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10967552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soft Actor-Critic-Based MPPT Control of Solar PV Systems Under Partial Shading Conditions 部分遮阳条件下基于软因子临界的太阳能光伏系统MPPT控制

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-15 DOI: 10.1109/OAJPE.2025.3560626

Sampson E. Nwachukwu;Komla A. Folly;Kehinde O. Awodele

{"title":"Soft Actor-Critic-Based MPPT Control of Solar PV Systems Under Partial Shading Conditions","authors":"Sampson E. Nwachukwu;Komla A. Folly;Kehinde O. Awodele","doi":"10.1109/OAJPE.2025.3560626","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3560626","url":null,"abstract":"This paper presents a soft actor-critic (SAC)-based method for solving the solar photovoltaic (PV) Maximum Power Point Tracking (MPPT) control problem under partial shading conditions (PSCs). The MPPT method optimizes the solar PV power and ensures that it constantly operates at its “maximum power point (MPP),” regardless of the dynamics of weather conditions. Traditional MPPT methods, such as the perturb and observe (P&O) method, are commonly employed to solve the MPPT control problem. However, they often suffer from a slower convergence rate, significant oscillation near the MPP, drift problems. Additionally, in the presence of partial shading, they frequently fail to track the solar PV global maximum power point (GMPP). These problems were addressed using the deep Q-network (DQN) method. However, DQN cannot be applied to continuous action spaces. It also uses inefficient experience replay and suffers from Q-value overestimation. Thus, under PSCs and certain environmental conditions, DQN produces fluctuations of power close to the MPP or GMPP, resulting in power loss. To solve the MPPT control task, mathematical models of the Markov Decision Process, solar PV system, and boost converter were developed. Key hyperparameters affecting the SAC algorithm’s performance were also investigated. Furthermore, the P&O method was developed for comparison. Simulation results show that the SAC-based MPPT method achieved better tracking accuracy than the DQN method under standard testing conditions, varying irradiance levels, and PSCs. Also, it is shown that both the DQN and SAC methods have superior tracking performance compared to the P&O method under similar environmental conditions tested.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"194-208"},"PeriodicalIF":3.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10965705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting 基于多任务学习的多能源系统负荷预测新方法

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-14 DOI: 10.1109/OAJPE.2025.3559336

Zain Ahmed;Mohsin Jamil;Ashraf Ali Khan

引用次数: 0

Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks 部分单调神经网络用于电力系统保稳定自适应最优一次频率控制的强化学习

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-04-02 DOI: 10.1109/OAJPE.2025.3556142

Hamad Alduaij;Yang Weng

{"title":"Reinforcement Learning for Stability-Guaranteed Adaptive Optimal Primary Frequency Control of Power Systems Using Partially Monotonic Neural Networks","authors":"Hamad Alduaij;Yang Weng","doi":"10.1109/OAJPE.2025.3556142","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556142","url":null,"abstract":"Deepening the deployment of distributed energy resources requires the large-scale integration of inverter-based resources, which can deteriorate the frequency stability. Recent studies propose using neural Lyapunov-based reinforcement learning for control. While this method can be trained offline with performance guarantees, it is only optimal for specific values of system parameters, as it omits critical modeling factors like decreasing inertia and damping variation over time. To maintain the performance at varying operation points, we consider an adaptive neural Lyapunov framework that adapts the controller’s output in the presence of varying parameters. Neural networks require flexibility to maximize adaptive control performance, while stability demands monotonicity, creating an inherent conflict. In this paper, we design a partially monotonic controller that maintains stability with maximal representation capacity for parameter adaptation. Stability is ensured by having monotonicity retained for frequency while non-monotonicity is allowed for the system parameters, such as damping and inertia. The structural form of partially monotonic neural networks is used for the controller design to that end. Flexibility is allowed by the design when adaptation to changes to the system parameters is made, while the Lyapunov stability guarantee is retained. The non-monotonic layers are chosen through an adaptive layer that is designed for damping and inertia based on their relationship to control in the system equation, by which optimized output for different operating conditions is allowed.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"259-269"},"PeriodicalIF":3.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Learning-Based Power System Health Assessment Model 基于图学习的电力系统健康评估模型

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-31 DOI: 10.1109/OAJPE.2025.3556004

Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu

{"title":"Graph Learning-Based Power System Health Assessment Model","authors":"Koji Yamashita;Nanpeng Yu;Evangelos Farantatos;Lin Zhu","doi":"10.1109/OAJPE.2025.3556004","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3556004","url":null,"abstract":"As the power transmission system’s energy sources become increasingly diversified, the grid stability is experiencing increased fluctuations, thereby necessitating more frequent and near real-time monitoring by grid operators. The power system security has been monitored through real-time contingency analysis and dynamic security assessment framework, both of which are typically based on time-domain simulations or power flow calculations. Achieving higher accuracy in grid health level prediction often requires time-consuming simulation and analysis. To improve computational efficiency, this paper develops machine learning models with phasor measurement unit (PMU) data to monitor the power system health index, focusing on rotor angle stability and frequency stability. The proposed machine learning models accurately predict frequency and angle stability indicators, essential for evaluating grid health considering various contingencies, even when dealing with limited PMU deployment in transmission grids. The proposed framework leverages a physics-informed graph convolution network and graph attention network with ordinal encoders, which are benchmarked with multi-layer perceptron models. These models are trained on dataset derived from an augmented IEEE 118-bus system with different demand levels and fuel mix, including tailored dynamic generator models, generator controller models, and grid protection models. The numerical studies explored the performance of the proposed and baseline machine learning models under both full PMU coverage and various partial PMU coverage conditions, where different data imputation methods are used for substations without PMUs. The findings from this study offer valuable insights, such as machine learning model selection and critical PMU locations regarding power equipment, into the design of data-driven grid health index prediction models for power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"181-193"},"PeriodicalIF":3.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Two-Timescale Coordination of Discretely and Continuously Adjustable Devices in ADNs With DRL and Physical Convex Optimization 基于DRL和物理凸优化的ADNs中离散和连续可调器件的双时间尺度协调

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-27 DOI: 10.1109/OAJPE.2025.3573961

Jian Zhang;Yigang He

{"title":"Two-Timescale Coordination of Discretely and Continuously Adjustable Devices in ADNs With DRL and Physical Convex Optimization","authors":"Jian Zhang;Yigang He","doi":"10.1109/OAJPE.2025.3573961","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3573961","url":null,"abstract":"High penetration of electrical vehicles (EVs) and renewable distributed generators (DGs) into active distribution networks (ADNs) lead to frequent, rapid and fierce voltages magnitudes violations. A novel two-timescale coordination scheme for different types of adjustable devices in ADNs is put forward in this article by organically integrating data-driven deep reinforce-ment learning (DRL) into physical convex model. A Markov Decision Process (MDP) is formulated on slow timescale, in which ratios/statuses of on load transformer changers (OLTCs) and switchable capacitors reactors (SCRs) and ESSs charging/ discharging power are set hourly to optimize network losses while regulating voltages magnitudes. An improved DRL with relaxation-prediction-correction strategies is proposed for eradicating discrete action components dimension curses. Whereas, on fast timescale (e.g., several seconds or minutes), the optimal reactive power of DGs inverters and static VAR compensators (SVCs) in balanced and unbalanced ADNs are set with physical convex optimization to minimize network losses while respecting physical constraints. Five simulations cases with IEEE 33-node balanced and 123-node unbalanced feeders are carried out to verify capabilities of put forward method.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"391-403"},"PeriodicalIF":3.3,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11016130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications 开放电力系统数据集和开放仿真引擎：机器学习应用综述

IF 3.3

IEEE Open Access Journal of Power and Energy Pub Date : 2025-03-26 DOI: 10.1109/OAJPE.2025.3573958

Ignacio Aravena;Chih-Che Sun;Ranyu Shi;Subir Majumder;Weihang Yan;Jhi-Young Joo;Le Xie;Jiyu Wang

{"title":"Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications","authors":"Ignacio Aravena;Chih-Che Sun;Ranyu Shi;Subir Majumder;Weihang Yan;Jhi-Young Joo;Le Xie;Jiyu Wang","doi":"10.1109/OAJPE.2025.3573958","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3573958","url":null,"abstract":"A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"12 ","pages":"353-365"},"PeriodicalIF":3.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11015807","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0