2021 IEEE Electrical Power and Energy Conference (EPEC)最新文献_第2页

Case Study of Designing a Locally Distributed Real-Time Simulation Infrastructure 局部分布式实时仿真基础设施设计案例研究

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621665

Moritz Weber, Michael Kyesswa, U. Kühnapfel, V. Hagenmeyer, H. Çakmak

{"title":"Case Study of Designing a Locally Distributed Real-Time Simulation Infrastructure","authors":"Moritz Weber, Michael Kyesswa, U. Kühnapfel, V. Hagenmeyer, H. Çakmak","doi":"10.1109/EPEC52095.2021.9621665","DOIUrl":"https://doi.org/10.1109/EPEC52095.2021.9621665","url":null,"abstract":"With an increasing number of power electronics connected renewable energy sources, electromagnetic transient (EMT) simulations become increasingly important to accurately simulate the behavior of electric power grids. EMT simulations are computationally expensive, and often specialized hardware, such as Real-Time Digital Simulators (RTDS), are used to run simulations in real-time. As individual simulators are limited in their computational capabilities, they can be interconnected in order to fully leverage their potential and enable more complex simulations. However, since these simulators are expensive and often located near power hardware for hardware-in-the-loop simulation, they are regularly distributed across multiple locations of different research institutions or organizations. The main contribution of this paper is the assessment of different approaches to connect multiple RTDS systems to form a locally distributed simulation infrastructure. For this, we combine a realistic and comprehensive model of a campus grid with a microgrid model to investigate the benefits and limitations of the different connection approaches.","PeriodicalId":102311,"journal":{"name":"2021 IEEE Electrical Power and Energy Conference (EPEC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126228760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Molecular Orbital Properties of C4-PFN and C5-PFK with DFT Analysis C4-PFN和C5-PFK分子轨道性质的DFT分析

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621714

H. Duzkaya, S. S. Tezcan, S. Dincer, M. Dincer

引用次数: 1

Application of Magnetic Sensors for Measurement of Current Phasors in Power Systems 磁传感器在电力系统电流相量测量中的应用

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621523

Prasad Shrawane, T. Sidhu

引用次数: 2

Virtual Synchronous Generator Controller for Solar Photovoltaic System 太阳能光伏发电系统虚拟同步发电机控制器

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621542

Mahmoud M. Badreldien, B. Johnson

{"title":"Virtual Synchronous Generator Controller for Solar Photovoltaic System","authors":"Mahmoud M. Badreldien, B. Johnson","doi":"10.1109/EPEC52095.2021.9621542","DOIUrl":"https://doi.org/10.1109/EPEC52095.2021.9621542","url":null,"abstract":"The concern about the green-house gases has led to government policies to encourage adoption of renewable resources. These policies combined with decreased and improved performance of photovoltaic and wind generation resources have fostered a significant increase in installations worldwide. Furthermore, the number of these renewable energy resources is expected to continue increasing over the next few decades. Thus, the number of inverter-based resources is expected to rise in electric power systems. This non-negligible share of electric power from the power converters with respect to conventional synchronous generators could have a dramatic effect on power system stability due to a reduction in the total system inertia. To overcome this problem, inverter-based resources are combined with energy storage to emulate the synchronous generator characteristics and act as virtual synchronous generators. This approach of control of power converters emulates the effects of the inertia of traditional synchronous generator. This paper provides an explanation for general features offered by a virtual synchronous generator control strategy, mathematical model, and its controller design and the system simulation has been verified using electromagnetic transient simulation.","PeriodicalId":102311,"journal":{"name":"2021 IEEE Electrical Power and Energy Conference (EPEC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116077569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Optimization of Placement and Sizing on Distributed Generation Using Technique of Smalling Area 基于小面积技术的分布式发电布局与规模优化

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621610

J. Raharjo, K. B. Adam, W. Priharti, Hermagasantos Zein, Jaspar Hasudungan, E. Suhartono

引用次数: 2

Wind Speed Forecasting Using ARMA and Neural Network Models 基于ARMA和神经网络模型的风速预报

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621650

Uzair Zaman, Hamid Teimourzadeh, Elias Hassani Sangani, Xiaodong Liang, C. Chung

引用次数: 3

Towards a Co-simulation of Electric Vehicles with Power Grid 电动汽车与电网联合仿真研究

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621584

Arsham Bakhtiari, Francesco Ciari, A. Moeini, A. Hajebrahimi

{"title":"Towards a Co-simulation of Electric Vehicles with Power Grid","authors":"Arsham Bakhtiari, Francesco Ciari, A. Moeini, A. Hajebrahimi","doi":"10.1109/EPEC52095.2021.9621584","DOIUrl":"https://doi.org/10.1109/EPEC52095.2021.9621584","url":null,"abstract":"Electric vehicles have gained much more attention in the last decades, as they are considered a promising substitution for internal combustion engine vehicles, in terms of lower CO2 emissions and dependence on fossil fuels. Many developed and developing countries have announced incentive policies to support the electric vehicle industry. As a result of these advantages and incentives, it is anticipated that the number of electric vehicles overtakes the number of fossil-fueled vehicles in near future. This high penetration of electric vehicles might cause some problems to the power grid, as the current power grid infrastructures are not designed for mobile loads. In this regard, the interaction between the electricity sector and the electrified transportation sector as a potential future load or a flexible resource for power system should be considered in a developed scheme. This paper proposes a co-simulation scheme between electric vehicles and the power grid to provide policymakers and authorities with a powerful and scalable simulation tool for managing and mitigating the potential impacts of electric vehicles on the power grid.","PeriodicalId":102311,"journal":{"name":"2021 IEEE Electrical Power and Energy Conference (EPEC)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117298738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Locational Marginal Price Forecasting Based on Deep Neural Networks and Prophet Techniques 基于深度神经网络和先知技术的区位边际价格预测

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621733

Abdussalam Mohamed, H. Aly, T. Little

{"title":"Locational Marginal Price Forecasting Based on Deep Neural Networks and Prophet Techniques","authors":"Abdussalam Mohamed, H. Aly, T. Little","doi":"10.1109/EPEC52095.2021.9621733","DOIUrl":"https://doi.org/10.1109/EPEC52095.2021.9621733","url":null,"abstract":"In many of the electricity markets in North America, the electricity prices are in terms of the locational marginal price (LMP) which reflects the cost of supplying the next MWh of electricity at a bus, considering transmission constraints. Electricity price forecasting provides vital information on system conditions to the independent system operator (ISO). It indicates important signals pertaining to the need of investing in the new generation, upgrading transmission, or reducing electricity consumption. Power suppliers and consumers use the forecasted price to optimize the profit in the day-ahead market and bilateral contracts. Facility owners rely on the forecasted price to make investment decisions. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are applied to improve short-term (time series) LMP forecasting accuracy. Dataset from the ISO-NE power market is utilized in modeling and analysis in which the proposed techniques are applied using Matlab and Python software. Various methods are evaluated and compared, and the conclusions achieved show that LSTM has lower error rates and higher accuracy than the Prophet forecasting model in 24 h-ahead LMP forecasting.","PeriodicalId":102311,"journal":{"name":"2021 IEEE Electrical Power and Energy Conference (EPEC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121280446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study of Hourly Wind Speed and Power Forecasting Using Deep Learning Networks, Weka Time Series, and ARIMA Algorithms for Smart Grid Integration 基于深度学习网络、Weka时间序列和ARIMA算法的每小时风速和功率预测的比较研究

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621652

Abdussalam Mohamed, H. Aly, T. Little

{"title":"A Comparative Study of Hourly Wind Speed and Power Forecasting Using Deep Learning Networks, Weka Time Series, and ARIMA Algorithms for Smart Grid Integration","authors":"Abdussalam Mohamed, H. Aly, T. Little","doi":"10.1109/EPEC52095.2021.9621652","DOIUrl":"https://doi.org/10.1109/EPEC52095.2021.9621652","url":null,"abstract":"In modern development, renewable energy is playing a crucial role for smart grid integration and in electricity demand growth as it is green and clean. Including all sources of renewable energy, wind power is particularly prevalent as it is pollution-free, cheap, and highly efficient. The main challenge that restrains the expansion of wind power utilization within the power grid is wind speed variation and uncertainties. Thus, precise wind speed forecasting is a difficult modeling approach, that greatly influences the wind power and optimal operation of the power grid. Prediction of wind speed is vital for wind power calculation and forecasting. Renewable energy forecasting is important for minimizing the power cost, scheduling energy resources, and planning maintenance. The advanced wind power forecast models help advances efficient operation and maintenance for wind turbines. This paper analytically studies the state-of-the-art approaches of wind speed forecasting regarding statistical methods (ARIMA, Weka time-series, and Deep Learning Networks), including data preprocessing, features engineering, and factors that touch prediction accuracy and modeling time. Likewise, this study provides a comparison to find the most accurate time series forecasting method based on performance evaluation.","PeriodicalId":102311,"journal":{"name":"2021 IEEE Electrical Power and Energy Conference (EPEC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130238555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating a Microgrid Control System Using Controller Hardware in the Loop Simulations 用控制器硬件在环仿真评估微电网控制系统

2021 IEEE Electrical Power and Energy Conference (EPEC) Pub Date : 2021-10-22 DOI: 10.1109/EPEC52095.2021.9621626

Mo’ath Farraj, Roshani Kaluthanthrige, A. Rajapakse

引用次数: 1