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Analyzing the Travel and Charging Behavior of Electric Vehicles - A Data-driven Approach 电动汽车行驶与充电行为分析——数据驱动方法
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446240
Sina Baghali, Samiul Hasan, Zhaomiao Guo
{"title":"Analyzing the Travel and Charging Behavior of Electric Vehicles - A Data-driven Approach","authors":"Sina Baghali, Samiul Hasan, Zhaomiao Guo","doi":"10.1109/KPEC51835.2021.9446240","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446240","url":null,"abstract":"The increasing market penetration of electric vehicles (EVs) may pose significant electricity demand on power systems. This electricity demand is affected by the inherent uncertainties of EVs' travel behavior that makes forecasting the daily charging demand (CD) very challenging. In this project, we use the National House Hold Survey (NHTS) data to form sequences of trips, and develop machine learning models to predict the parameters of the next trip of the drivers, including trip start time, end time, and distance. These parameters are later used to model the temporal charging behavior of EVs. The simulation results show that the proposed modeling can effectively estimate the daily CD pattern based on travel behavior of EVs, and simple machine learning techniques can forecast the travel parameters with acceptable accuracy.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132675958","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
Open-Source PSCAD Grid-Following and Grid-Forming Inverters and A Benchmark for Zero-Inertia Power System Simulations 开源PSCAD电网跟踪和电网形成逆变器和零惯性电力系统仿真基准
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446243
R. Kenyon, A. Sajadi, Andy Hoke, B. Hodge
{"title":"Open-Source PSCAD Grid-Following and Grid-Forming Inverters and A Benchmark for Zero-Inertia Power System Simulations","authors":"R. Kenyon, A. Sajadi, Andy Hoke, B. Hodge","doi":"10.1109/KPEC51835.2021.9446243","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446243","url":null,"abstract":"This paper presents open-source, flexible, and easily-scalable models of grid following and grid forming inverters for the PSCAD software platform. The models are intended for system integration studies, particularly transient stability analyses of power systems with a high penetration of inverter-based generation. To verify the model functionality, they are implemented in a IEEE 9-bus system in a zero-inertia operational scenario of 100% inverter-based generation where the presence of grid-forming inverters are necessary. The models, including the 9 bus network, have been made available open source at the PyPSCAD NREL GitHub page (https://github.com/NREL/PyPSCAD).","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122098287","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: 35
Accurate Smart-Grid Stability Forecasting Based on Deep Learning: Point and Interval Estimation Method 基于深度学习的智能电网稳定性精确预测:点区间估计方法
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446196
M. Massaoudi, H. Abu-Rub, S. Refaat, I. Chihi, F. Oueslati
{"title":"Accurate Smart-Grid Stability Forecasting Based on Deep Learning: Point and Interval Estimation Method","authors":"M. Massaoudi, H. Abu-Rub, S. Refaat, I. Chihi, F. Oueslati","doi":"10.1109/KPEC51835.2021.9446196","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446196","url":null,"abstract":"The power grid stability is highly impacted by the fluctuating nature of renewable energy sources. This paper proposes a deep learning method-based bidirectional gated recurrent unit for smart grid stability prediction. For automatic tuning, this study employs Simulated Annealing algorithm to optimize the selected hyperparameters and enhance the model forecastability. The proposed forecasting model's performance is evaluated using electrical grid stability simulated data set. The proposed method provides an accurate point and interval grid stability prediction. Simulation results are conducted to prove the high performance of the proposed method. Furthermore, comparative analysis is performed to demonstrate the superiority of the proposed strategy over some state-of-the-art available solutions.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123901260","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: 10
Optimized Dispatch of Distributed Energy Resources for Resiliency and Power Quality Improvements at the Grid-Edge 面向电网边缘弹性和电能质量改善的分布式能源优化调度
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446241
Prithwiraj Roy Chowdhury, S. Essakiappan, M. Manjrekar, K. Schneider, Stuart Laval
{"title":"Optimized Dispatch of Distributed Energy Resources for Resiliency and Power Quality Improvements at the Grid-Edge","authors":"Prithwiraj Roy Chowdhury, S. Essakiappan, M. Manjrekar, K. Schneider, Stuart Laval","doi":"10.1109/KPEC51835.2021.9446241","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446241","url":null,"abstract":"Distributed Energy Resources (DERs) installed on low-voltage distribution systems, both utility-owned and nonutility owned may be employed to make improvements to system resiliency and power quality, in addition to simply serving load demand. By selectively dispatching active and reactive power from these existing DERs, improvements in voltage profiles and service availability during adverse events may be achieved. Further benefits can be gained by optimally choosing which DERs to dispatch power from, and the distribution of power from those DERs. For a given operating condition, using Linear Programming methods, optimal power dispatch values for individual DERs can be determined, while considering variables such as voltage gains, losses in the lines and the DERs, and DER reserve capacities. One such optimization technique is developed in this paper, and results from a real-time simulation study on a modified IEEE 13-bus distribution system with three DERs, are being presented. The operation of the optimized DER selection and dispatch algorithm is shown during normal operation of the transmission system when the DERs operate in grid-connected mode. Similar analyses can drive decision making during transmission system failure when the DERs operate in grid-forming or islanded mode.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442389","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
Forecasting of Mid- and Long-Term Wind Power Using Machine Learning and Regression Models 基于机器学习和回归模型的中长期风电预测
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446250
Sina Ibne Ahmed, P. Ranganathan, H. Salehfar
{"title":"Forecasting of Mid- and Long-Term Wind Power Using Machine Learning and Regression Models","authors":"Sina Ibne Ahmed, P. Ranganathan, H. Salehfar","doi":"10.1109/KPEC51835.2021.9446250","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446250","url":null,"abstract":"Environmental concerns over the past decade have driven the need to harness renewable energy resources, such as wind power generation. Forecasting wind power is beneficial to power utilities; however, predicting wind power generation has proven challenging due to wind speed variability. This paper has used two machine learning algorithms, Gradient Boosting Machine (GBM) and Support Vector Machine (SVM), along with the regression model Multivariate Adaptive Regression Splines (MARS), to predict wind-based power production over medium and long-term time frames. A comparative analysis of each forecasting method is presented with the predictions that account for all features. The critical feature among the independent variables is also determined and used for comparative analysis in this study. The preliminary case study results indicate that the SVM model performs better over other models to a greater extent for substantial uncertainty in dataset but suffers from larger computational run time.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124340422","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: 5
Optimization of a Pumped Thermal Energy Storage System Operating for Revenue Maximization 以收益最大化为目标的抽水蓄能系统优化
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/kpec51835.2021.9446212
M. Perez, R. Fan
{"title":"Optimization of a Pumped Thermal Energy Storage System Operating for Revenue Maximization","authors":"M. Perez, R. Fan","doi":"10.1109/kpec51835.2021.9446212","DOIUrl":"https://doi.org/10.1109/kpec51835.2021.9446212","url":null,"abstract":"A Pumped Thermal Energy Storage (PTES) system is one of many possible energy storage solutions that could help integrate variable renewable energy generators into the power grid. This paper analyzes the possible use of a PTES system to generate revenue in power systems through energy arbitrage, regulation service, and a combination of the two revenue generating techniques. Modeling a PTES system as a battery, linear optimization is used to optimize the PTES system operation and find which revenue technique will generate the most profit. Furthermore, the factors of power output and energy storage capacity are analyzed to demonstrate the effects they have on the maximum revenue generation capability of the PTES system. Extensive studies have shown that it is feasible to use PTES for revenue generation, and the maximum revenue could be achieved by using PTES to provide both arbitrage and regulation services.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123044152","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
Power System Network Reduction for Power Hardware-in-the-Loop Simulation 电力在环硬件仿真中的电力系统网络缩减
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446201
Bin Wang, Andy Hoke, Jin Tan
{"title":"Power System Network Reduction for Power Hardware-in-the-Loop Simulation","authors":"Bin Wang, Andy Hoke, Jin Tan","doi":"10.1109/KPEC51835.2021.9446201","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446201","url":null,"abstract":"This paper proposes single-port equivalent and two-port equivalent network reduction methods to respectively reduce single-port and two-port areas in a large power network. Parameters of the reduced systems are rigorously derived, which guarantees that the electrical quantities at the port(s) remain unchanged over the reduction, including voltage magnitude and phase and active and reactive power injections into the area to be reduced. The proposed techniques are applied to reduce a practical Maui grid, where the total numbers of buses, lines and transformers are respectively reduced from 212, 106 and 108 to 45, 30 and 13. Dynamic behaviors between the full model and the reduced model are compared in detail to illustrate the efficacy and accuracy of the proposed network reduction.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882006","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
Proactive Scheduling of Hydrogen Systems for Resilience Enhancement of Distribution Networks 提高配电网弹性的氢系统主动调度
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446230
Hamed Haggi, Wei Sun, J. Fenton, P. Brooker
{"title":"Proactive Scheduling of Hydrogen Systems for Resilience Enhancement of Distribution Networks","authors":"Hamed Haggi, Wei Sun, J. Fenton, P. Brooker","doi":"10.1109/KPEC51835.2021.9446230","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446230","url":null,"abstract":"Recent advances in smart grid technologies bring opportunities to better control the modern and complex power grids with renewable integration. The operation of power systems, especially distribution network (DN), is facing with preeminent challenges from cyber-physical-human (CPH) threats and natural disasters. In order to provide better response against threats and improve the resilience of power grid, proactive plans and operational schemes are required by system operators to minimize the damages caused by CPH threats. To that end, this paper proposes a proactive plan for DN operation by using hydrogen (H2) systems to enhance the resilience through cost-effective long-term energy storage. Unlike batteries, H2 energy can be stored in the storage tanks days before the extreme event, and transformed into power by stationary fuel cell units to reduce load curtailment caused by CPH threats. The proposed framework is validated by testing on 33-node test feeder. Simulation results demonstrate that H2 systems can improve the resilience of DN during $N-m$ outages with more than 10 hours.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114179042","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
Layout Planning of Medium-voltage Switching Station Based on Demand-side Grid Division 基于需求侧电网划分的中压开关站布局规划
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/kpec51835.2021.9446219
Yanru Liu, Guangyuan Shi, Peiren Du, Xinyi Lai, F. Wen, Linyao Zhang
{"title":"Layout Planning of Medium-voltage Switching Station Based on Demand-side Grid Division","authors":"Yanru Liu, Guangyuan Shi, Peiren Du, Xinyi Lai, F. Wen, Linyao Zhang","doi":"10.1109/kpec51835.2021.9446219","DOIUrl":"https://doi.org/10.1109/kpec51835.2021.9446219","url":null,"abstract":"Grid division can transform a complex physical model into several simple entities, and is an effective method for power distribution system planning and the whole process management. It also provides a new perspective for the optimal layout planning of switching stations as well as the development of computer-aided planning software packages. This paper first defines the demand grid for low-voltage electricity access demand. Then, an optimization model of switching station layout planning based on grid division is proposed, with the objectives of maximizing the average equipment utilization rate and minimizing the total number of switching stations, and with five constraints including the block boundary, crossing, power supply radius, planned capacity of the site, and the maximum load rate of the equipment. Besides, this paper further describes the algorithm flowchart developed based on this method and the application in the layout planning of actual medium-voltage switchyards.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124856674","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
On Self-Security of Grid-Interactive Smart Inverters 电网交互智能逆变器的自安全性研究
2021 IEEE Kansas Power and Energy Conference (KPEC) Pub Date : 2021-04-19 DOI: 10.1109/KPEC51835.2021.9446213
Mehmetcan Gursoy, B. Mirafzal
{"title":"On Self-Security of Grid-Interactive Smart Inverters","authors":"Mehmetcan Gursoy, B. Mirafzal","doi":"10.1109/KPEC51835.2021.9446213","DOIUrl":"https://doi.org/10.1109/KPEC51835.2021.9446213","url":null,"abstract":"The capability to exchange information with utility operators, aggregators, and nearby smart devices can make a grid-interactive inverter an intelligent cyber-physical device. However, the capability of exchanging information can also put the inverters at the risk of insecure operation. In this paper, possible software manipulations into the inverters are studied to understand their vulnerability to cyber-attacks. Moreover, the state-of-the-art system-level and device-level cyber-defense measures are discussed, and advantages and drawbacks of each technique are provided. Studies show that a reference model can be implemented in device-level security to effectively examine incoming setpoints for detecting and preventing malicious or harmful actions. This paper particularly underlines the significance of device-level self-security and its advantages for grid-interactive inverters. Finally, recommendations for future studies are provided.","PeriodicalId":392538,"journal":{"name":"2021 IEEE Kansas Power and Energy Conference (KPEC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688598","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: 12
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