Sustainable Energy Grids & Networks最新文献_第8页

Multivariate physics-informed convolutional autoencoder for anomaly detection in power distribution systems with widespread deployment of distributed energy resources 基于多变量物理信息的卷积自编码器在分布式能源广泛部署的配电系统中的异常检测

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-24 DOI: 10.1016/j.segan.2025.102022

Mehdi Jabbari Zideh, Sarika Khushalani Solanki

Despite the relentless progress of deep learning models in analyzing the system conditions under cyber-physical events, their abilities are limited in the power system domain due to data availability issues, cost of data acquisition, and lack of interpretation and extrapolation of the data beyond the training windows. In addition, the integration of distributed energy resources (DERs) such as wind and solar generations increases the complexities and nonlinear nature of power systems. Therefore, an interpretable and reliable methodology is of utmost importance to increase the confidence of power system operators and their situational awareness for making reliable decisions. This has led to the development of physics-informed neural network (PINN) models as more interpretable, trustworthy, and robust models where the underlying principled laws are integrated into the training process of neural network models to achieve improved performance. This paper proposes a multivariate physics-informed convolutional autoencoder (PIConvAE) model to detect cyber anomalies in power distribution systems with unbalanced configurations and high penetration of DERs. The physical laws are integrated through a customized loss function that embeds the underlying complex nodal power balance equation into the training process of the autoencoder. The performance of the multivariate PIConvAE model is evaluated on two unbalanced power distribution grids, IEEE 123-bus system and a real-world feeder in Riverside, CA. The results show the exceptional performance of the proposed method in detecting various cyber anomalies in both systems. In addition, the model’s effectiveness is evaluated in data scarcity scenarios with different training data ratios. Finally, the model’s performance is compared with existing machine learning models where the PIConvAE model surpasses other models with considerably higher detection metrics.

尽管深度学习模型在分析网络物理事件下的系统条件方面取得了不懈的进步，但由于数据可用性问题、数据获取成本以及缺乏对训练窗口之外数据的解释和外推，它们在电力系统领域的能力受到限制。此外，分布式能源（如风能和太阳能发电）的集成增加了电力系统的复杂性和非线性性质。因此，一个可解释和可靠的方法对于提高电力系统运营商的信心和态势感知能力以做出可靠的决策至关重要。这导致了物理信息神经网络（PINN）模型的发展，作为更具可解释性、可信赖性和鲁棒性的模型，其中基本原则定律被集成到神经网络模型的训练过程中，以实现改进的性能。本文提出了一种基于多变量物理信息的卷积自编码器（PIConvAE）模型，用于检测配置不平衡且der渗透率高的配电系统中的网络异常。物理定律通过定制的损失函数集成，该损失函数将底层复杂节点功率平衡方程嵌入到自编码器的训练过程中。在两个不平衡配电网，IEEE 123总线系统和加利福尼亚州Riverside的现实馈线上评估了多变量PIConvAE模型的性能。结果表明，所提出的方法在检测两个系统中的各种网络异常方面具有优异的性能。此外，在不同训练数据比例的数据稀缺性场景下，对模型的有效性进行了评估。最后，将该模型的性能与现有的机器学习模型进行比较，其中PIConvAE模型优于其他具有更高检测指标的模型。

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引用次数: 0

A bi-level hybrid game framework for Stochastic Robust optimization in multi-integrated energy microgrids 多集成能源微电网随机鲁棒优化的双层混合博弈框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-24 DOI: 10.1016/j.segan.2025.102024

Yuan Gao , Mustafa Tahir , Pierluigi Siano , Sheharyar Hussain , Weiqiang Sun , Ying He , Qinglin Meng

Multi-Integrated Energy Microgrids (IEMs) facilitate efficient renewable energy utilization through coordinated scheduling across interconnected systems. However, the dynamic interactions between renewable energy sources and multi-energy loads result in complex benefit couplings and significant operational uncertainties, creating substantial challenges for scheduling optimization. To tackle these issues, this paper introduces an innovative hybrid game framework tailored for IEMs. Specifically, the framework employs a bi-level hybrid game structure, where IEMs act as leaders and multi-energy loads as followers. Additionally, profit allocation among IEMs is regulated through a Nash game mechanism, ensuring equitable and efficient resource distribution. This framework employs Two-Stage Stochastic Robust Optimization (TSSRO) to address uncertainties, including variations in renewable energy generation, multi-energy loads, and electricity pricing. Initial scenarios for uncertain variables are generated using Spectrally Normalized Conditional Generative Adversarial Networks (SNCGAN). Leveraging the Karush-Kuhn-Tucker (KKT) conditions, the bi-level game is transformed into a single-level optimization problem, enhancing computational efficiency. The solution approach combines the Alternating Direction Method of Multipliers (ADMM) with a Column and Constraint Generation Algorithm using an Alternating Iteration Strategy (C&CG-AIS), effectively optimizing IEM operational performance. Numerical validation demonstrates that the proposed framework significantly improves collaborative optimization in multi-IEM systems, showcasing enhanced stability and adaptability over conventional models.

多集成能源微电网（IEMs）通过互联系统间的协调调度促进可再生能源的高效利用。然而，可再生能源与多能负荷之间的动态相互作用导致了复杂的效益耦合和显著的运行不确定性，给调度优化带来了巨大挑战。为了解决这些问题，本文介绍了一种为集成电路设备量身定制的创新混合游戏框架。具体来说，该框架采用了一个双层混合游戏结构，其中IEMs作为领导者，多能负载作为追随者。此外，通过纳什博弈机制调节企业间的利润分配，确保资源的公平高效分配。该框架采用两阶段随机稳健优化（TSSRO）来解决不确定性，包括可再生能源发电、多能负荷和电价的变化。使用频谱归一化条件生成对抗网络（SNCGAN）生成不确定变量的初始场景。利用Karush-Kuhn-Tucker （KKT）条件，将双层博弈转化为单层优化问题，提高了计算效率。该方法将乘法器的交替方向法（ADMM）与使用交替迭代策略（C&CG-AIS）的列约束生成算法相结合，有效地优化了IEM的运行性能。数值验证表明，该框架显著改善了多iem系统的协同优化，比传统模型表现出更强的稳定性和适应性。

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引用次数: 0

Day ahead optimization of insular systems considering future interconnection: The case of Ikaria island 考虑未来互联的岛屿系统的提前日优化：以伊卡利亚岛为例

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-24 DOI: 10.1016/j.segan.2025.102018

Kyriaki-Nefeli D. Malamaki , Maria Fotopoulou , Diego Martinez-Lopez , Georgios Lampsidis , Pablo Ferrer-Fernandez , Nikolaos Andriopoulos , Nikolaos Tzanis , Konstantinos Kaousias , Efthimia Chassioti , Ioannis Moraitis

The energy transition of remote islands is a major challenge, but it also offers a great opportunity for sustainable growth in society and the local economy. Recently, Greek islands have shown promise in this area, gradually incorporating Renewable Energy Sources (RES) into their energy mix, as well as Energy Storage Systems (ESS), which are critical especially in non-interconnected power systems or in systems with high RES curtailments. In this context, the need to have an advanced day-ahead optimization system emerges, tailored to the specific needs of each island. This paper presents a day-ahead optimal dispatch strategy for Ikaria island, which is currently a non-interconnected Greek island, leading Greece’s journey towards decarbonization and clean energy. Ikaria features a Hybrid Power Plant (HPP) that includes three wind turbines and a special Hydro-Pumped Storage System (HPSS) with three reservoirs. Since, the use of the HPP is still limited by its current, non-optimized, operating mode, the scope of this paper is to propose an optimization framework that leads towards its full exploitation, taking into account both operational factors and CO2 emissions. Moreover, considering the future island interconnection plans, with the neighboring island of Samos, an expansion of the proposed optimization framework is also proposed, aiming to remove operational barriers, boost the RES penetration and optimize the management of island interconnections, ultimately transforming Ikaria into a model insular system of the current transition.

偏远岛屿的能源转型是一个重大挑战，但它也为社会和当地经济的可持续增长提供了一个巨大的机会。最近，希腊岛屿在这一领域表现出了希望，逐步将可再生能源（RES）纳入其能源结构，以及储能系统（ESS），这对于非互联电力系统或高RES削减的系统至关重要。在这种情况下，需要有一个先进的提前优化系统，以适应每个岛屿的具体需求。本文提出了伊卡利亚岛的日前最优调度策略，该岛目前是一个非互联的希腊岛屿，引领希腊走向脱碳和清洁能源的旅程。伊卡利亚岛的特色是一个混合发电厂（HPP），包括三个风力涡轮机和一个特殊的水力抽水蓄能系统（HPSS），有三个水库。由于HPP的使用仍然受到其当前未优化的运行模式的限制，因此本文的范围是提出一个优化框架，使其能够充分利用，同时考虑到运行因素和二氧化碳排放。此外，考虑到未来岛屿互联规划，与邻近的萨摩斯岛（Samos）一起，还提出了对所提出的优化框架的扩展，旨在消除运营障碍，提高RES渗透率，优化岛屿互联管理，最终将伊卡利亚岛转变为当前转型的典型岛屿系统。

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引用次数: 0

A novel framework for operational infeasibility assessment of active distribution systems using improved quantile polynomial chaos expansion 基于改进分位数多项式混沌展开的有源配电系统运行不可行性评估新框架

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-23 DOI: 10.1016/j.segan.2025.102006

Sel Ly , Kapil Chauhan , Tan Minh Nguyen , Franz-Erich Wolter , Hung Dinh Nguyen

This article presents an advanced two-stage statistical framework for operational infeasibility analysis (OIA) in active distribution systems operating under high uncertainties. In Stage-I, enhanced Mean and Multiple Quantile Lite-Polynomial Chaos Expansions (IMQ-Lite-PCEs) are proposed as robust meta-modeling tools for uncertainty quantification. In Stage-II, the IMQ-Lite-PCEs are leveraged to extract comprehensive statistical insights, enabling accurate estimations of key metrics such as means, variances, confidence intervals, and conditional distributions of system states, facilitating informed decision-making. The efficacy of the proposed method (PM) is rigorously validated through comparisons with state-of-the-art PCE variants for uncertainty quantification in renewable energy resource (RES)- and electric vehicle (EV)-dominated power systems. The results underline the superior accuracy of the PM, with L1 -relative errors as low as 0.22 %, 0.19 %, 0.16 %, 0.12 %, and 0.43 % for state estimations on the IEEE 33-, −69, −85, 141-, and unbalanced three-phase 37-bus systems, respectively. Moreover, the PM demonstrates exceptional capabilities in probabilistic and classification analyses, achieving 98.27 %, 98.72 %, 98.63 %, and 98.95 % classification accuracy for identifying nodal voltage violations and 91.06 %, 99.58 %, 92.94 %, and 93.11 % accuracy for detecting overloaded line power flows in the IEEE −33, −69, −85, and 141-bus networks, respectively. Additionally, comparative analysis against low-rank approximation methods, Gaussian Process Regression (GPR), and Deep Sparse GPR underscores the PM’s robust performance in handling complex probabilistic computations and classification tasks.

本文提出了一种先进的两阶段统计框架，用于高不确定性条件下主动配电系统的运行不可行性分析。在第一阶段，提出了增强的均值和多分位数生命多项式混沌展开（imq - lite - pce）作为不确定性量化的鲁棒元建模工具。在第二阶段，利用imq - lite - pce提取全面的统计见解，能够准确估计关键指标，如平均值、方差、置信区间和系统状态的条件分布，从而促进明智的决策。通过与最先进的PCE变量进行比较，对可再生能源（RES）和电动汽车（EV）主导的电力系统的不确定性量化，严格验证了所提出方法（PM）的有效性。结果强调了PM的优越精度，在IEEE 33-， - 69, - 85， - 141-和不平衡三相37总线系统的状态估计中，L1 -相对误差分别低至0.22%,0.19%,0.16%，0.12%和0.43%。此外，PM在概率和分类分析方面表现出卓越的能力，在识别节点电压违规方面达到98.27%,98.72%，98.63%和98.95%的分类准确率，在检测IEEE - 33， - 69， - 85和141总线网络中的过载线路潮流方面分别达到91.06%,99.58%，92.94%和93.11%的准确率。此外，与低秩近似方法、高斯过程回归（GPR）和深度稀疏GPR的比较分析强调了PM在处理复杂概率计算和分类任务方面的鲁棒性。

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引用次数: 0

Price-based bi-level operation of hybrid electric vehicle swapping–charging stations in a high-renewable multi-energy community considering user bounded rational 考虑用户有界理性的高可再生多能源社区混合动力汽车交换充电站基于价格的双能级运行

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-23 DOI: 10.1016/j.segan.2025.102017

Mingcan Zhang , Da Xu , Ziyi Bai , Dongjie Shi

Hybrid electric vehicle (HEV) play critical roles in the transition to transportation electrification and further global net-zero emissions. However, existing works only focus on electric vehicle (EV) swapping or charging station. For the first time, this paper proposes a transactive operational framework of HEV swapping–charging stations in a high-renewable multi-energy community. In this framework, HEV swapping–charging station is capable of HEV electricity-hydrogen charging and battery swapping, while high-renewable community feature the synergistic interactions of coupled multi-energy carriers. The transactive operation of HEV swapping–charging stations is a demanding problem due to its inherent energy-interest couplings and heterogeneous HEV decision-makings. A bounded rational HEV user model is proposed to simulate heterogeneous charging/swapping strategies based on behavioral economics theory. A price-based bi-level approach is further developed via a transactive real-time pricing, where upper level is to optimize community operation cost and lower level is to maximize the HEV swapping–charging station profit. The proposed framework is benchmarked to show the effectiveness and superiority in technical and economical performances.

混合动力电动汽车（HEV）在向交通电气化过渡和进一步实现全球净零排放方面发挥着关键作用。然而，现有的工作只集中在电动汽车交换或充电站方面。本文首次提出了高可再生多能源社区中混合动力汽车交换充电站的交互运行框架。在该框架下，混合动力汽车换电站具有混合动力汽车电-氢充电和电池交换的能力，而高可再生社区具有耦合多能载体的协同作用。混合动力汽车交换充电站的交互运行由于其固有的能量-利益耦合和混合动力汽车决策的异构性而成为一个复杂的问题。基于行为经济学理论，提出了一种有界理性混合动力汽车用户模型来模拟异构充电/交换策略。通过交互式实时定价，进一步发展了基于价格的双层定价方法，其中上层是优化社区运营成本，下层是最大化混合动力汽车交换充电站的利润。对所提出的框架进行了基准测试，以显示其技术和经济性能的有效性和优越性。

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引用次数: 0

Consequence-driven optimization for detection and localization of stealth false data injection attacks against state estimation in power distribution systems 基于结果驱动的配电系统状态估计隐形假数据注入攻击检测与定位

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-22 DOI: 10.1016/j.segan.2025.102027

Mohammad Reza Dehbozorgi , Mohammadreza F.M. Arani , Mohammad Rastegar

Monitoring modern power distribution systems through state estimation (SE) is crucial for optimizing grid operation and ensuring reliability. However, SE is vulnerable to stealth false data injection attacks (FDIAs). Stealth FDIAs can evade conventional bad data detection algorithms, leading to operator misjudgments and erroneous decisions. FDIA misidentifications, i.e., false alarms and undetected FDIAs, have distinct monetary and operational consequences. Even within each type of misidentification, these consequences can vary based on factors like meter location, customer type, and sustained energy not supplied (ENS). This paper, therefore, proposes consequence-driven cost functions to quantify the monetary impact of FDIA misidentifications in the SE. The proposed method explicitly accounts for system topology, customer type, and ENS. The proposed approach is model-agnostic and can operate with any anomaly detection method. We use an autoencoder (AE) as a sample anomaly detection method to illustrate the proposed consequence-driven framework. The AE is trained on FDIA-free data to reconstruct normal meter behavior. Deviations are then passed to the largest normalized residual (LNR) test for detection and localization, enabling a detailed evaluation of FDIA misidentification costs. Additionally, an optimization formulation is introduced to adjust the LNR thresholds for each meter, minimizing the total misidentification cost. Simulations use IEEE 13-bus and 123-bus test feeders. Results show that optimal thresholds can reduce FDIA misidentification costs by up to 66 %. This offers a consequence-driven alternative to the accuracy-based metrics commonly used in the literature. It also provides a better fit for the complex, cyber-physical nature of power systems.

通过状态估计对现代配电系统进行监测是优化电网运行和保证电网可靠性的重要手段。但是，SE容易受到隐形虚假数据注入攻击（FDIAs）的攻击。隐形fdi可以逃避传统的不良数据检测算法，导致操作人员误判和错误决策。FDIA错误识别，即虚假警报和未被发现的FDIA，会产生明显的货币和操作后果。即使在每种类型的错误识别中，这些后果也会根据仪表位置、客户类型和持续未供应能源（ENS）等因素而有所不同。因此，本文提出了结果驱动的成本函数，以量化东南地区FDIA错误识别的货币影响。该方法明确地考虑了系统拓扑、客户类型和ens。该方法与模型无关，可以与任何异常检测方法一起工作。我们使用自编码器（AE）作为样本异常检测方法来说明所提出的结果驱动框架。在无fdia数据上训练声发射以重建正常的仪表行为。然后将偏差传递给最大归一化残差（LNR）测试以进行检测和定位，从而能够详细评估FDIA错误识别成本。此外，还引入了优化公式来调整每个仪表的LNR阈值，从而使总误识别成本最小化。仿真使用IEEE 13总线和123总线测试馈线。结果表明，最优阈值可使FDIA误识别成本降低66% %。这为文献中常用的基于准确性的度量标准提供了一个结果驱动的替代方案。它还提供了一个更好的适合复杂的，网络物理性质的电力系统。

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引用次数: 0

Real-time management of electric and hydrogen vehicle infrastructure using mobile and integrated charging stations 使用移动和集成充电站实时管理电动和氢燃料汽车基础设施

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102008

Muhammed Ali Beyazıt , Mohammad Reza Salehizadeh , Emre Demirel , Akın Taşcıkaraoǧlu , Jay Liu

The rapid growth of Battery Electric Vehicles (BEVs) and Fuel Cell Electric Vehicles (FCEVs) is accelerating the demand for reliable charging and refueling infrastructure, yet current systems face persistent challenges. Two critical issues are the overstaying phenomenon—where EV drivers continue to occupy chargers after completing charging, causing congestion and reducing station efficiency—and the limited availability of hydrogen at refueling stations. These shortcomings threaten the scalability and user acceptance of electromobility. To address these gaps, this study proposes a novel, holistic framework of three Integrated Charging Stations (ICSs) that combine Fixed Charging Stations (FCSs) and Hydrogen Refueling Stations (HRSs), enhanced by photovoltaic (PV) generation, electrolyzers, hydrogen storage, and Mobile Charging Stations (MCSs). Beyond introducing this system-level architecture, we also develop an operational strategy whereby the MCS mitigates overstaying and, when idle, supports hydrogen production through powering electrolyzers. As an extension of our earlier research, we formulate a new mathematical optimization model to coordinate the real-time operation of these coupled facilities, incorporating dynamic MCS routing between ICSs to alleviate congestion and strengthen hydrogen supply. The effectiveness of the proposed approach is demonstrated through three case studies, with results confirming its potential to improve infrastructure utilization, enhance user satisfaction, and support the sustainable expansion of BEVs and FCEVs.

电池电动汽车（bev）和燃料电池电动汽车（fcev）的快速发展加速了对可靠充电和加油基础设施的需求，但目前的系统面临着持续的挑战。两个关键的问题是过度停留现象——电动汽车司机在充电完成后继续占用充电器，造成拥堵并降低加油站的效率——以及加油站的氢气供应有限。这些缺点威胁到电动汽车的可扩展性和用户接受度。为了解决这些差距，本研究提出了一个由三个集成充电站（ics）组成的全新整体框架，该框架结合了固定充电站（FCSs）和加氢站（HRSs），并通过光伏（PV）发电、电解槽、储氢和移动充电站（MCSs）进行增强。除了引入这种系统级架构外，我们还制定了一项运营策略，使MCS能够减少超时停留，并在闲置时通过为电解槽供电来支持制氢。在此基础上，我们建立了一个新的数学优化模型来协调这些耦合设施的实时运行，并在ICSs之间引入动态MCS路由，以缓解拥堵并加强氢供应。通过三个案例研究证明了所提出方法的有效性，结果证实了其在提高基础设施利用率、提高用户满意度以及支持纯电动汽车和氢燃料电池汽车可持续发展方面的潜力。

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引用次数: 0

Enhanced grid integration through machine-learning optimized bidirectional EV chargers 通过机器学习优化双向电动汽车充电器增强电网整合

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102007

Pulkit Kumar , Harpreet Kaur Channi , Sita Rani , Aman Kataria , Punam Rattan

Large-scale electric vehicles (EVs) implementation depends on reliable and stable bidirectional charging that can be effectively implemented, efficient, and practical. This paper proposes a Bidirectional Learning-based Electric Vehicle Charger (BLEVC) with Battery Energy Storage (BES) that boosts grid stability during the Grid-to-Vehicle (G2V) and the Vehicle-to-Grid (V2G) operation modes. The innovation is the methodical comparison of 3 machine learning (ML) controllers: Dynamic Time Reversal (DTR), Recurrent Neural Network (RNN), and Support Vector Machine (SVM) with the traditional Proportional-Integral (PI) controller under the same testing parameters. Findings indicate the definite merits of ML strategies. RNN lowered G2V charging time (PI 35 mins to 8 mins) and voltage ripple at 48 G2V, and DTR showed a stable steady state response, although its computational requirement was high. On the other hand, SVM had infinite settling time and large ripple time; poor evidence of using it on dynamic duty-cycle regulation. In V2G mode, RNN and DTR have quicker and more constant energy dispatch than PI. Integration of the BES enhanced peak shaving 22 % and could smooth the state of charge to within 5 %, confirming its usefulness in grid support and demand reshaping. This contributed work offers a validated architecture of BLEVC and a comparative framework, which is a gap in the literature. Future work directions will be typhoon hardware in-loop (HIL)-hybrid ML-PI controllers, hyperparameter optimization, and pilot-scale tests with utilities to enable secure, scalable EV-grid integration.

大规模电动汽车的实施有赖于可靠、稳定的双向充电，才能有效实施、高效、实用。本文提出了一种具有电池储能（BES）的双向学习式电动汽车充电器（BLEVC），该充电器可提高电网对车（G2V）和车对网（V2G）运行模式下电网的稳定性。创新之处在于，在相同的测试参数下，将3种机器学习（ML）控制器：动态时间反转（DTR）、循环神经网络（RNN）和支持向量机（SVM）与传统的比例积分（PI）控制器进行了系统的比较。研究结果表明了机器学习策略的明确优点。RNN降低了G2V充电时间（PI 35 min至8 min）和48g2v时的电压纹波，DTR表现出稳定的稳态响应，但计算量较高。另一方面，支持向量机具有无限的沉降时间和较大的纹波时间；在动态占空比调节中使用它的证据不足。在V2G模式下，RNN和DTR比PI具有更快、更稳定的能量调度。集成的BES可提高调峰22% %，并可使充电状态平滑到5% %以内，证实了其在电网支持和需求重塑方面的有效性。这项贡献的工作提供了一个有效的BLEVC架构和一个比较框架，这是文献中的一个空白。未来的工作方向将是台风硬件在环(HIL)-混合ML-PI控制器，超参数优化，以及与公用事业公司进行中试规模测试，以实现安全，可扩展的电动汽车电网集成。

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引用次数: 0

Classification of load waveform distortion signature based on novelty detection for electric railway systems 基于新颖性检测的电气化铁路系统负载波形失真特征分类

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102010

Rafael S. Salles , Sarah K. Rönnberg , Andrea Mariscotti

Electric railway systems (ERS) are characterized by several particularities regarding the return current circuits, moving loads, multiple sources of waveform distortion, and extensive deployment of static converters from various manufacturers, topologies, and solutions. This work presents a methodology for application of load monitoring to classify rolling stock (RS) waveform distortion signatures. The proposed methodology combines the benefits and advantages of unsupervised deep learning and reconstruction error performance classification for performing non-intrusive load monitoring (NILM) in ERS. It consists of adapting autoencoder-based novelty detection for load classification problems. The method is applied to pantograph measurements from four rolling stock items using two types of data input (harmonic spectra up to kHz and VI diagram images), which are compared in binary classifications of the same kind of railway electrification. The methodology shows suitable classification performance with high accuracy, scoring an average of 98.81 % for spectrum input and 97.77 % for VI diagram input. It has also been validated with a NILM dataset (LIT) for multi-class applications showing 99.13 % for spectrum input and 94.28 % for VI diagram input. The proposed method has suitable computational times and scalability, allowing application to a wide range of NILM and classification problems using distortion signatures.

电气铁路系统（ERS）具有以下几个特点：回流电路、移动负载、多种波形失真源，以及各种制造商、拓扑结构和解决方案广泛部署的静态转换器。本文提出了一种应用负荷监测对机车车辆（RS）波形失真特征进行分类的方法。该方法结合了无监督深度学习和重构误差性能分类在ERS中执行非侵入式负载监测（NILM）的优点和优点。它包括自适应的基于自编码器的新颖性检测来解决负载分类问题。将该方法应用于四种机车车辆的受电弓测量，使用两种类型的数据输入（最高kHz谐波谱和VI图图像），并将其在同一类型铁路电气化的二分类中进行比较。该方法具有良好的分类性能和较高的分类准确率，光谱输入的平均准确率为98.81%，VI图输入的平均准确率为97.77%。它还通过一个多类别应用的NILM数据集（LIT）进行了验证，显示光谱输入为99.13%，VI图输入为94.28%。该方法具有合适的计算时间和可扩展性，允许应用于广泛的NILM和使用失真签名的分类问题。

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引用次数: 0

Impact of limited information on estimating aggregated electric vehicle loading: A distribution system operator perspective 有限信息对估计电动汽车总负荷的影响：一个配电系统运营商的观点

IF 5.6 2区工程技术 Q2 ENERGY & FUELS

Sustainable Energy Grids & Networks

Pub Date : 2025-10-17 DOI: 10.1016/j.segan.2025.102012

Damianos Cheilas, Henrik W. Bindner, Tilman Weckesser

The major deployment of electric vehicles (EVs) brings challenges to the planning and operation of distribution grids, necessitating effective models and estimation approaches for EV charging in distribution grid management. However, distribution system operators (DSOs) may have limited or no access to relevant charging data. This paper investigates how diverse levels of charging information affect the estimation accuracy of aggregated price-sensitive EV power profiles, focusing on distribution system management. Three approaches utilizing varying levels of information are implemented to model the EV charging profiles: a full-information formulation and two reduced-information formulations that rely on aggregate energy measurements combined with either charger occupancy or plug-in statistics. The approaches are evaluated against the individually price-optimized EV schedules over a year, and the resulting cost-based optimal power profiles and associated errors are analyzed. The results indicate that while more detailed information increases the general accuracy of the estimated profiles, reduced-information models can still provide robust estimates for peak load assessment to support congestion management decisions, considering the data constraints and the risk-averse attitude of DSOs.

电动汽车的大规模部署给配电网的规划和运行带来了挑战，配电网管理中需要有效的电动汽车充电模型和估算方法。然而，配电系统运营商（dso）对相关收费数据的访问可能有限或无法访问。本文以配电系统管理为重点，研究了不同水平的充电信息对价格敏感型电动汽车总功率分布估计精度的影响。三种方法利用不同级别的信息来模拟电动汽车充电曲线：一种全信息公式和两种减少信息公式，这些公式依赖于总能量测量，结合充电器占用率或插电统计数据。在一年内，对这些方法进行了单独的价格优化电动汽车计划评估，并分析了基于成本的最优功率分布图和相关误差。结果表明，虽然更详细的信息增加了估计概况的总体准确性，但考虑到数据约束和dso的风险规避态度，减少信息模型仍然可以为峰值负载评估提供稳健的估计，以支持拥塞管理决策。

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