Pub Date : 2015-08-01DOI: 10.1109/SEGE.2015.7324591
Ali Shiri, M. Afshar, A. Rahimi-Kian, B. Maham
Accurate electricity price prediction is one of the most important parts of decision making for electricity market participants to make reasonable competing strategies. Support Vector Machine (SVM) is a novel algorithm based on a predictive modeling method and a powerful classification method in machine learning and data mining. Most of SVM-based and non-SVM-based models ignore other important factors in the electricity price dynamics and electricity price models are built regard to just historical electricity prices; However, electricity price has a strong correlation with other variables like oil and natural gas price. In this paper, single SVM model is used to combine diverse influential variables as 1-Historical Electricity Price of Germany 2-GASPOOL price as first natural gas reference price 3-Net-Connect-Germany (NCG) price as second natural gas reference price 4- West Texas Intermediate (WTI) daily price as US oil benchmark. The simulation results show that using oil and natural gas prices can improve SVM model prediction ability compared to the SVM models built on mere historical electricity price.
{"title":"Electricity price forecasting using Support Vector Machines by considering oil and natural gas price impacts","authors":"Ali Shiri, M. Afshar, A. Rahimi-Kian, B. Maham","doi":"10.1109/SEGE.2015.7324591","DOIUrl":"https://doi.org/10.1109/SEGE.2015.7324591","url":null,"abstract":"Accurate electricity price prediction is one of the most important parts of decision making for electricity market participants to make reasonable competing strategies. Support Vector Machine (SVM) is a novel algorithm based on a predictive modeling method and a powerful classification method in machine learning and data mining. Most of SVM-based and non-SVM-based models ignore other important factors in the electricity price dynamics and electricity price models are built regard to just historical electricity prices; However, electricity price has a strong correlation with other variables like oil and natural gas price. In this paper, single SVM model is used to combine diverse influential variables as 1-Historical Electricity Price of Germany 2-GASPOOL price as first natural gas reference price 3-Net-Connect-Germany (NCG) price as second natural gas reference price 4- West Texas Intermediate (WTI) daily price as US oil benchmark. The simulation results show that using oil and natural gas prices can improve SVM model prediction ability compared to the SVM models built on mere historical electricity price.","PeriodicalId":409488,"journal":{"name":"2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318532","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}
Pub Date : 2015-08-01DOI: 10.1109/SEGE.2015.7324603
Do-Young Kim, Young-Chon Kim
Over the years, renewable energy has experienced great growth and attention. The renewable power plants are distributed over large geographical areas and the number of renewable power plants is exponentially increasing. These distributed energy sources may make power system complex and unstable. Further, monitoring and control by existing system such as supervisory control and data acquisition (SCADA) system is facing the limitation to capture power system dynamics. In order to combat these problems, phasor measurement units (PMUs) are installed at power plants and substations to implement wide area measurement system (WAMS). To get accurate, high sampling frequency and synchronized data from PMUs at distributed locations, the WAMS needs high speed and reliable communication network. The communication network plays an important role in WAMS because real-time synchrophasor data and control messages are transmitted through communication network. In this paper, we present a hierarchical communication network architecture for WAMS. The proposed communication network consists of three network levels: generation, substation, and control center. To evaluate the performance of the proposed network, communication network for WAMS is modeled and simulated through OPNET. The simulation results are validated by comparing with the results of numerical analysis. The network performance is evaluated in terms of network delay under various link bandwidth and background traffic.
{"title":"Design and performance evaluation of hierarchical communication network for wide area measurement system","authors":"Do-Young Kim, Young-Chon Kim","doi":"10.1109/SEGE.2015.7324603","DOIUrl":"https://doi.org/10.1109/SEGE.2015.7324603","url":null,"abstract":"Over the years, renewable energy has experienced great growth and attention. The renewable power plants are distributed over large geographical areas and the number of renewable power plants is exponentially increasing. These distributed energy sources may make power system complex and unstable. Further, monitoring and control by existing system such as supervisory control and data acquisition (SCADA) system is facing the limitation to capture power system dynamics. In order to combat these problems, phasor measurement units (PMUs) are installed at power plants and substations to implement wide area measurement system (WAMS). To get accurate, high sampling frequency and synchronized data from PMUs at distributed locations, the WAMS needs high speed and reliable communication network. The communication network plays an important role in WAMS because real-time synchrophasor data and control messages are transmitted through communication network. In this paper, we present a hierarchical communication network architecture for WAMS. The proposed communication network consists of three network levels: generation, substation, and control center. To evaluate the performance of the proposed network, communication network for WAMS is modeled and simulated through OPNET. The simulation results are validated by comparing with the results of numerical analysis. The network performance is evaluated in terms of network delay under various link bandwidth and background traffic.","PeriodicalId":409488,"journal":{"name":"2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133292764","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}
Pub Date : 2015-08-01DOI: 10.1109/SEGE.2015.7324619
A. Helmy, A. Shaltout, N. Abdel-Rahim
This paper investigates improving the efficiency and the performance of Wind Energy Conversion Systems (WECS) equipped with Doubly Fed Induction Generator (DFIG). The main objective is to maximize the output power while minimizing the total copper loss simultaneously. This can be achieved using an analytical approach to determine the proper rotor current commands which give maximum mechanical power and minimum loss based on the measured generator speed. A hardware setup was constructed for validation of simulation results of maximum power point tracking and for further investigation of the change in the wind speed on the ability of the control system to respond to these changes with the proper control commands to obtain the maximum output power of the DFIG.
{"title":"Improving the efficiency of a Doubly-Fed Induction Generator in variable speed wind turbines under different modes of operation considering core loss","authors":"A. Helmy, A. Shaltout, N. Abdel-Rahim","doi":"10.1109/SEGE.2015.7324619","DOIUrl":"https://doi.org/10.1109/SEGE.2015.7324619","url":null,"abstract":"This paper investigates improving the efficiency and the performance of Wind Energy Conversion Systems (WECS) equipped with Doubly Fed Induction Generator (DFIG). The main objective is to maximize the output power while minimizing the total copper loss simultaneously. This can be achieved using an analytical approach to determine the proper rotor current commands which give maximum mechanical power and minimum loss based on the measured generator speed. A hardware setup was constructed for validation of simulation results of maximum power point tracking and for further investigation of the change in the wind speed on the ability of the control system to respond to these changes with the proper control commands to obtain the maximum output power of the DFIG.","PeriodicalId":409488,"journal":{"name":"2015 IEEE International Conference on Smart Energy Grid Engineering (SEGE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116637388","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：