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2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE)最新文献

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SEGE 2021 Cover Page SEGE 2021封面
Pub Date : 2021-08-11 DOI: 10.1109/sege52446.2021.9535080
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引用次数: 0
Fast Frequency Response Using Model Predictive Control for A Hybrid Power System 基于模型预测控制的混合电力系统快速频率响应
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9534981
Abhishek Varshney, Renuka Loka, A. M. Parimi
Large-scale penetration of Renewable Energy Sources (RESs) in Hybrid Power Systems (HPSs) consists of predominantly asynchronously interfaced sources. Asynchronous interconnection of RESs is made possible by using Power Electronic Converters (PECs); however, it subsequently reduces the system inertia due to less rotational mass. The decrease in system inertia causes a high Rate of Change of Frequency (RoCoF). Consequently, frequency control becomes challenging with high RoCoF. To maintain the frequency at a nominal value, the power balance between the load and generation is necessary. The excess or deficit in power from RES is uncertain, and stochastic load disturbances should match generation and storage changes. Owing to high RoCoF, the response of the system to maintain power balance should be obtained within a minimum time. Therefore, Fast Frequency Response (FFR) using the available reserves is of prime significance. This paper addresses the FFR problem by proposing a modified Model Predictive Control (MPC) by introducing RoCoF in the objective function to achieve FFR using primarily Fuel Cell (FC) storage in a Hybrid Power System (HPS). The modified MPC controller's performance is compared with the conventional PID and MPC controllers by testing the dynamic model for both situations - i) constant step and ii) random load fluctuations and wind disturbances using MATLAB/Simulink. Simulation results under various cases show that the proposed MPC has improved the performance parameters (settling time, peak overshoot, and peak-peak magnitude) of the step response.
可再生能源(RESs)在混合电力系统(hps)中的大规模渗透主要由异步接口源组成。通过使用电力电子转换器(PECs)实现RESs的异步互连;然而,它随后减少了系统的惯性,由于较少的旋转质量。系统惯性的减小导致了较高的频率变化率(RoCoF)。因此,高RoCoF的频率控制变得具有挑战性。为了将频率维持在标称值,负载和发电之间的功率平衡是必要的。可再生能源的电力过剩或亏缺是不确定的,随机负荷扰动应与发电和储存的变化相匹配。由于高RoCoF,系统维持功率平衡的响应应在最短时间内获得。因此,利用可用储量进行快速频率响应(FFR)是至关重要的。本文提出了一种改进的模型预测控制(MPC),通过在目标函数中引入RoCoF来实现混合动力系统(HPS)中主要使用燃料电池(FC)存储的FFR问题。通过MATLAB/Simulink对恒步长和随机负载波动和风扰动两种情况下的动态模型进行测试,比较了改进后的MPC控制器与传统PID控制器和MPC控制器的性能。各种情况下的仿真结果表明,所提出的MPC改善了阶跃响应的性能参数(沉降时间、峰值超调量和峰值幅值)。
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引用次数: 1
Using Critical Slowing Down Features to Enhance Performance of Artificial Neural Networks for Time-Domain Power System Data 利用临界慢化特征增强人工神经网络处理时域电力系统数据的性能
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9535027
Austin Lassetter, E. Cotilla-Sánchez, Jinsub Kim
This paper explores deep learning approaches to event classification on real world time-domain power system data. We use a statistical method to measure a physical phenomenon known as critical slowing down (CSD) and use this as a feature engineering preprocessing framework to localize events from large intervals of data. Several previous works have discussed power system event detection, including statistical methods like correlation, Principal Component Analysis (PCA) reconstruction, and local outlier factor search. This work aims to improve upon the statistical methods that have been linked to high-sample rate time-domain event detection and then will be evaluated using artificial neural networks. To evaluate how well CSD localizes events from non-events in high sample rate time-series data, we used a Z-score function to predict the time of an event and extract a six second interval centered around the prediction. The performance of CSD-applied data against the raw data was then compared using two ANN architectures: the Fully Convolutional Network (FCN) and the Residual Neural Network (ResNet). The results of both architectures demonstrate that applying CSD to the data significantly improves event localization for larger data intervals, thus signifying an improvement in event detectability.
本文探讨了基于实时电力系统数据的深度学习事件分类方法。我们使用统计方法来测量称为临界减速(CSD)的物理现象,并将其用作特征工程预处理框架,从大数据间隔中定位事件。以前的一些工作讨论了电力系统事件检测,包括统计方法,如相关、主成分分析(PCA)重建和局部离群因子搜索。这项工作旨在改进与高采样率时域事件检测相关的统计方法,然后将使用人工神经网络进行评估。为了评估CSD如何在高样本率时间序列数据中定位事件和非事件,我们使用Z-score函数来预测事件的时间,并提取以预测为中心的6秒间隔。然后使用两种人工神经网络架构:全卷积网络(FCN)和残差神经网络(ResNet)来比较应用csd的数据与原始数据的性能。这两种体系结构的结果都表明,对数据应用CSD可以显著改善更大数据间隔的事件定位,从而提高事件可检测性。
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引用次数: 1
Utility Scale Battery as Capacity Source for Electric Grid Systems 作为电网系统容量源的公用事业规模电池
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9535072
Omid Pourkhalili, R. Sawhney, S. A. Biyouki, H. Parsian
United States Federal Energy Regulatory Commission passed order No. 841 in 2018 that requires energy market operators in their jurisdiction to allow storage resources to be utilized as capacity source. A literature review is performed on grid systems day-ahead order estimates and real-time demand scenarios from the supply chain perspective. We consider an electric grid system integrated with utility scale battery storage to maintain supply and demand balance during the the peak hours, when grid encounters with the most fluctuated demands. Having integrated lithium-ion batteries with grid systems as potential capacity source, meets the real-time demand with minimum real-time orders. Integration of battery storage responds to day-ahead order error through different services such as ancillary and transmission deferral. It consequently minimizes the use of fossil fuels and low efficient real-time power generation emission. We defined all involved resources during the real-time power supply and translated them to mathematical transitions. Then we used a Polynomial linear regression to find a model that describes nonlinear relationship between demand and time. The aforementioned model can be used and simulated for the grid systems aim to implement and integrate the utility scale batteries as capacity source to compensate part or all of real-time orders. The required capacity size is adjustable for different users and their system characteristics such as demand and power dispatch time periods.
美国联邦能源监管委员会于2018年通过了第841号命令,要求其管辖范围内的能源市场运营商允许将储能资源用作容量来源。从供应链的角度对电网系统日前订单估计和实时需求情景进行了文献综述。我们考虑一个电网系统与公用事业规模的电池存储集成,以保持供需平衡,在高峰时段,当电网遇到波动最大的需求。将锂离子电池与电网系统集成为潜在容量源,以最小的实时订单量满足实时需求。电池储能集成通过辅助和传输延迟等不同服务来应对日前订单误差。因此,它最大限度地减少了化石燃料的使用和低效率的实时发电排放。我们定义了实时供电过程中所涉及的所有资源,并将其转换为数学转换。然后,我们使用多项式线性回归找到描述需求与时间之间非线性关系的模型。上述模型可用于以实现和集成公用事业规模电池作为容量源来补偿部分或全部实时订单为目标的电网系统。可根据不同的用户及其需求、调度时间段等系统特点调整容量大小。
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引用次数: 2
Modular Bidirectional Converter with Multiple Power Sources for Fast Charging of Electric Vehicles 面向电动汽车快速充电的多电源模块化双向变换器
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9535008
Abdalrahman Elshora, Y. Elsayed, H. Gabbar
Canadian transportation sector has been reported recently as the second-largest source of GHG. Therefore, researchers have been interested in developing charging control systems for electrical vehicles. The main two challenges are the size of the energy storage and the charging time. Researches prove that hybrid energy storage can increase energy density and reliability besides reducing the total cost of energy. However, managing multiple sources of energy is a big challenge. This paper introduces a bidirectional DC-DC converter that can manage hybrid energy storage composed of multiple sources of energy. It enables the modular extension of input energy sources by adding few components. It enables power flow in all possible directions. The proposed converter has been simulated by using Matlab Simulink and validated the most operating scenarios of charging and discharging successfully.
据报道,加拿大的交通运输部门是温室气体的第二大来源。因此,研究人员一直对开发电动汽车的充电控制系统感兴趣。主要的两个挑战是储能的大小和充电时间。研究证明,混合储能在降低能源总成本的同时,还能提高能量密度和可靠性。然而,管理多种能源是一个巨大的挑战。本文介绍了一种双向DC-DC变换器,它可以管理由多种能源组成的混合储能。它可以通过添加少量组件来实现输入能源的模块化扩展。它使能量向所有可能的方向流动。利用Matlab Simulink对所设计的变换器进行了仿真,成功验证了大多数充放电工况。
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引用次数: 0
Case Study on Effect of Transformer Rating on Impulse Voltage Distribution in Windings 变压器额定电压对绕组冲击电压分布影响的实例研究
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9535007
Harmanpreet Singh Sekhon, Pawan Rathore, Vaman Dommeti
The design of Power transformers are basically governed by certain vital parameters: Transformer rating in kilo Volt Amperes (kVA), frequency, Voltage ratings and ratio, tapping range, Impedance values, Losses, Temperature rises, Insulation levels, Sound levels etc. The domain of this paper is specifically focused on difference in winding design of same voltage class based on different kVA ratings of transformer. The major change is related to design and type of high and intermediate voltage windings with respect to impulse voltage distribution characteristics across windings. The impulse voltage distribution which is initially based on series and ground capacitance of windings is relatively more non-linear for Low kVA transformers as compared to high kVA transformers for same voltage classes. The results of impulse distribution validating relatively poor safety margins for intermediate voltage winding in low kVA transformer have been discussed. The challenges and results are based on example of 10,000 kVA 225/132/33kV Power Transformer taking reference of 132kV winding and comparison has been established with 60,000 kVA Power Transformer of same voltage class.
电力变压器的设计基本上是由一些重要参数决定的:变压器的额定电压(kVA)、频率、额定电压和比、分接范围、阻抗值、损耗、温升、绝缘等级、声级等。本文重点研究了基于不同千伏安额定值的变压器在同一电压等级下绕组设计的差异。主要的变化与高压和中压绕组的设计和类型有关,与绕组之间的脉冲电压分布特性有关。与相同电压等级的高千伏安变压器相比,低千伏安变压器的冲击电压分布(最初基于绕组的串联和接地电容)相对更非线性。讨论了验证低千伏安变压器中压绕组相对较差安全裕度的脉冲分布结果。以1万kVA 225/132/33kV电力变压器为例,参照132kV绕组,建立了与同电压等级6万kVA电力变压器的对比。
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引用次数: 0
Title Page 标题页
Pub Date : 2021-08-11 DOI: 10.1109/sege52446.2021.9534944
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引用次数: 0
Design of a Smart Controller Agent for Demand-Side Management with Low Payback Effect 低回报需求侧管理智能控制器代理的设计
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9535058
Pegah Yazdkhasti, C. Diduch
With high penetration of renewable resources such as wind and solar into conventional electric grid, new challenges are introduced due to the rapid fluctuation on the generation side. Direct load control of thermostatically controlled loads can play a significant role in demand side management (DSM) to cope with the uncertainties and variabilities of the generation. For this purpose, the system operator (SO) requires a reliable forecast of the demand and how much it can be shifted; in order to produce attainable desirable set points to reshape the demand to follow generation side. The focus of this paper is on designing a smart agent that uses a hybrid system of a model-based and a model-free structure to forecast the controllable load and its capacity to be reshaped, and follow the dispatch instructions of the SO, while minimizing the payback effect of the control actions and maintaining customers’ comfort. The main advantages of the proposed system are: 1) real-time model creation; thus, no need for historical data for training, 2) model free controller can automatically adapt to the changes in the system, 3) it can be used as a plug & play component in a DSM program. To evaluate the performance of the proposed controller, a numerical simulator was developed, and the controller was applied over the simulation engine to follow arbitrary desired power profiles. It was observed that the system can follow the dispatch command in less than 5 minutes with a negligible steady state error (less than 5%).
随着风能、太阳能等可再生能源在常规电网中的高度渗透,发电侧的快速波动带来了新的挑战。恒温控制负荷的直接负荷控制在需求侧管理(DSM)中发挥着重要作用,以应对发电的不确定性和可变性。为此,系统运营商(SO)需要一个可靠的需求预测,以及它可以转移多少;为了产生可达到的理想设定值来重塑需求跟随发电方。本文的重点是设计一个智能代理,该智能代理使用基于模型和无模型结构的混合系统来预测可控负荷及其需要重塑的容量,并遵循SO的调度指令,同时最小化控制动作的回报效应并保持客户的舒适性。该系统的主要优点是:1)实时模型创建;因此,不需要历史数据进行训练,2)无模型控制器可以自动适应系统的变化,3)它可以作为一个即插即用组件在DSM程序中使用。为了评估所提出的控制器的性能,开发了一个数值模拟器,并将控制器应用于仿真引擎上以遵循任意期望的功率分布。结果表明,该系统可以在不到5分钟的时间内完成调度指令,稳态误差小于5%,可以忽略不计。
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引用次数: 0
Stability Analysis of a Remote DC Subgrid/Microgrid Connected to a Very Weak AC Grid 与极弱交流电网连接的远程直流子电网/微电网稳定性分析
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9534971
S. Rezaee, A. Radwan, M. Moallem, Jiacheng Wang
Instability issues can arise due to the high penetration of remote voltage source converter (VSC)-interfaced DC microgrid (MG) to the ac weak grid (WG), in which the grid impedance is large. This is due to the dynamic interaction between the VSCs and the WG impedance. In this work, a small-signal analysis is conducted to derive the full-order linearized model of the VSC-WG interconnection. Furthermore, a participation factor analysis is presented to identify the effect of varying the grid impedance on the VSC-WG dominant modes in the inversion and rectification modes of operation. It is found that although the system is initially stable in both modes, it tends to move toward the unstable region when the grid impedance increases. In this study, the initial locations of the corresponding dominant eigenvalues are fairly similar for both modes. However, unlike previous works, it is shown that the dominant modes in the rectification mode are much more sensitive to the grid impedance variation than the inversion mode. Time-domain simulations are conducted on a 7.25 MW dc MG which is interfaced to the ac grid via a VSC system to verify the validity of small-signal analysis in both modes.
由于远端电压源变换器(VSC)接口的直流微电网(MG)对电网阻抗较大的交流弱网(WG)的高渗透,会产生不稳定问题。这是由于vsc和WG阻抗之间的动态相互作用。本文通过小信号分析,推导了VSC-WG互连的全阶线性化模型。此外,通过参与因子分析,确定了在逆变和整流运行模式中,电网阻抗变化对VSC-WG主导模式的影响。研究发现,虽然系统在两种模式下都是初始稳定的,但随着栅极阻抗的增大,系统逐渐向不稳定区域移动。在本研究中,两种模态对应的优势特征值的初始位置相当相似。然而,与以往的工作不同的是,整流模式中的主导模式对栅极阻抗变化的敏感程度远高于逆变模式。对一个通过VSC系统与交流电网连接的7.25 MW直流MG进行了时域仿真,验证了两种模式下小信号分析的有效性。
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引用次数: 0
Environmental Assessment of Digital Infrastructure in Decentralized Smart Grids 分布式智能电网中数字基础设施的环境评价
Pub Date : 2021-08-11 DOI: 10.1109/SEGE52446.2021.9535061
Daniela Wohlschlager, Anika Neitz-Regett, Bastian Lanzinger
This paper examines the life cycle-based direct environmental impact of information and communication technology (ICT) in German smart grids. Specifically, it explores the global warming potential associated with smart metering infrastructure and the use case of decentralized flexibility markets. Results show an annual footprint of 513,679 t CO2-eq. for the intelligent metering infrastructure expected in low-voltage levels by 2030. Digitalization measures required for a household to provide flexibility from decentralized assets cause approx. 27 to 43 kg CO2-eq. per household and year. Given the marginal data volume associated with the use case, the operation and production phases of hardware cause the greatest impact. Accordingly, considerable reduction potentials lie in decarbonizing the electricity mix and ensuring high energy efficiency and longevity of components. As more data-intensive use cases emerge, the method provided in this paper enables further environmental assessments of direct effects and the derivation of recommendations for a sustainable technical design. First qualitative estimations of indirect environmental effects indicate the need for subsequent research in the context of smart grids, including behavioral research and energy system modeling approaches.
本文研究了德国智能电网中基于生命周期的信息和通信技术(ICT)对环境的直接影响。具体来说,它探讨了与智能计量基础设施和分散灵活市场用例相关的全球变暖潜力。结果表明,年足迹为513,679 t co2当量。预计到2030年,低压水平的智能计量基础设施。家庭为分散资产提供灵活性所需的数字化措施引起了广泛的关注。27至43千克二氧化碳当量按家庭和年计算。考虑到与用例相关的边缘数据量,硬件的操作和生产阶段会产生最大的影响。因此,相当大的减排潜力在于使电力结构脱碳,并确保组件的高能效和寿命。随着更多数据密集型用例的出现,本文提供的方法可以对直接影响进行进一步的环境评估,并为可持续技术设计提供建议。首先,对间接环境影响的定性估计表明,需要在智能电网的背景下进行后续研究,包括行为研究和能源系统建模方法。
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引用次数: 2
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2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE)
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