Pub Date : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645330
F. Ding, K. Loparo
Network reconfiguration is a combinatorial optimization problem that can be used to reduce power losses in distribution systems while satisfying all operating constraints. Distribution systems are greatly impacted by the increasing integration of distributed generation (DG) resources and thus it is necessary to develop methods that can quantify the effects of DG on network reconfiguration. This paper first introduces an efficient methodology to reconfigure distribution systems to minimize power losses, and analyzes the effects of DG on the reconfiguration problems, then discusses how to take advantage of distributed generation to improve distribution system reconfiguration.
{"title":"Network reconfiguration analysis with the consideration of distributed generation","authors":"F. Ding, K. Loparo","doi":"10.1109/ENERGYTECH.2013.6645330","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645330","url":null,"abstract":"Network reconfiguration is a combinatorial optimization problem that can be used to reduce power losses in distribution systems while satisfying all operating constraints. Distribution systems are greatly impacted by the increasing integration of distributed generation (DG) resources and thus it is necessary to develop methods that can quantify the effects of DG on network reconfiguration. This paper first introduces an efficient methodology to reconfigure distribution systems to minimize power losses, and analyzes the effects of DG on the reconfiguration problems, then discusses how to take advantage of distributed generation to improve distribution system reconfiguration.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121370393","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645342
S. Leibholz
Planning for recovery from exogenous or endogenous failures or outages of complex networks, including communications networks, power stations and entire grids as well as physical-transport networks such as pipelines, trucking, airways and railroads-for the purpose of smartly executing recovery-present planning difficulties because of the combinations of events which can occur, whether random, weather-induced, cascading or the effects of well-planned terrorism. These are poorly modeled by simple “Monte Carlo” runs and probability calculations because multiple events are rarely statistically independent, especially when (1) smart terrorists are the protagonist, or (2) when cascade effects occur, as at Fukushima. We discuss a mathematical model based on this author's analogous communications network model, CAINS (Communications and Information Network Solver) for assisting in the forward and contemporaneous evaluation and planning of backups and responses to such events. Abjuring Monte-Carlo simulation for stated reasons, the model is analytical and statistical in nature, avoids the problems with Monte-Carlo run length and chaotic algorithms, and thus runs very rapidly when planning for large numbers of failure modes.
{"title":"Analytical methods for planning for, and recovery from, multiple network or system failures due to nature or sabotage","authors":"S. Leibholz","doi":"10.1109/ENERGYTECH.2013.6645342","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645342","url":null,"abstract":"Planning for recovery from exogenous or endogenous failures or outages of complex networks, including communications networks, power stations and entire grids as well as physical-transport networks such as pipelines, trucking, airways and railroads-for the purpose of smartly executing recovery-present planning difficulties because of the combinations of events which can occur, whether random, weather-induced, cascading or the effects of well-planned terrorism. These are poorly modeled by simple “Monte Carlo” runs and probability calculations because multiple events are rarely statistically independent, especially when (1) smart terrorists are the protagonist, or (2) when cascade effects occur, as at Fukushima. We discuss a mathematical model based on this author's analogous communications network model, CAINS (Communications and Information Network Solver) for assisting in the forward and contemporaneous evaluation and planning of backups and responses to such events. Abjuring Monte-Carlo simulation for stated reasons, the model is analytical and statistical in nature, avoids the problems with Monte-Carlo run length and chaotic algorithms, and thus runs very rapidly when planning for large numbers of failure modes.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"34 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131992144","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645362
J. Freeman, J. Varghese, Rekha P. Manoj
This paper proposes a wireless sensor network based heliostats control method for an industrial scale solar cooking system. Even though different designs are available they have suffered from different problems especially regarding effective tracking, high cost and convenience and other constraints such as scalability and simplicity. This paper, therefore, describes an efficient design that overcomes the earlier problems by using a highly precise astronomical calculation based approach along with wireless communication for efficient tracking of the sun which in turn reduces the overall system cost and simplicity. The proposed system uses a one tier wireless sensor network (WSN). The main feature of this work is the implementation of the highly accurate NREL algorithm, with precision timing provided by a GPS receiver within a low-cost wireless sensor network framework.
{"title":"WSN based heliostat control for a solar thermal cooking system","authors":"J. Freeman, J. Varghese, Rekha P. Manoj","doi":"10.1109/ENERGYTECH.2013.6645362","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645362","url":null,"abstract":"This paper proposes a wireless sensor network based heliostats control method for an industrial scale solar cooking system. Even though different designs are available they have suffered from different problems especially regarding effective tracking, high cost and convenience and other constraints such as scalability and simplicity. This paper, therefore, describes an efficient design that overcomes the earlier problems by using a highly precise astronomical calculation based approach along with wireless communication for efficient tracking of the sun which in turn reduces the overall system cost and simplicity. The proposed system uses a one tier wireless sensor network (WSN). The main feature of this work is the implementation of the highly accurate NREL algorithm, with precision timing provided by a GPS receiver within a low-cost wireless sensor network framework.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131028410","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645345
F. Cheng, W. Greenwood, B. Arellano, J. Hawkins, O. Lavrova, A. Mammoli, S. Willard
A Smart Grid demonstration project is currently underway at Public Service Company of New Mexico (PNM). This project is designed to combine a battery storage system with a photovataic (PV) system to smooth out the cloud induced intermittency of PV output. The whole system is already in use and the smoothing algorithm successfully smoothes the PV output as designed. This paper introduces the structure of the system, and discusses how to design the smoothing algorithm in five aspects, including choosing the smoothing reference as well as choosing the window size of smoothing algorithm. Specifically this paper puts forward a new method to recover the state of charge (SoC) of the battery while performing intermittency mitigation.
{"title":"Real-time control of utility-scale storage on a distribution feeder","authors":"F. Cheng, W. Greenwood, B. Arellano, J. Hawkins, O. Lavrova, A. Mammoli, S. Willard","doi":"10.1109/ENERGYTECH.2013.6645345","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645345","url":null,"abstract":"A Smart Grid demonstration project is currently underway at Public Service Company of New Mexico (PNM). This project is designed to combine a battery storage system with a photovataic (PV) system to smooth out the cloud induced intermittency of PV output. The whole system is already in use and the smoothing algorithm successfully smoothes the PV output as designed. This paper introduces the structure of the system, and discusses how to design the smoothing algorithm in five aspects, including choosing the smoothing reference as well as choosing the window size of smoothing algorithm. Specifically this paper puts forward a new method to recover the state of charge (SoC) of the battery while performing intermittency mitigation.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128614287","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645360
Ding Li, S. Jayaweera
In this study, we propose two types of approaches to model the uncertainty in customer load demand. The first approach is based on a first order non-stationary Markov chain. A maximum likelihood estimator (MLE) is derived to estimate the time variant transition matrix of the Markov chain. The second approach is based on time series analysis techniques. We present linear prediction models such as standard autoregressive (AR) process and time varying autoregressive (TVAR) process, according to different assumptions on the stationarity of customer load profile: piecewise stationarity, local stationarity and cyclo-stationarity. Two important issues in AR/TVAR models are addressed: determining the order of AR/TVAR models and calculating the AR/TVAR coefficients. The partial autocorrelation function (PACF) is analyzed to determine the model order and the minimum mean squared error (MMSE) estimator is adopted to derive the AR/TVAR coefficients, which leads to the Yule-Walker type of equations. For the AR model, the customer load profile is divided into small segments which can be considered to be stationary. For the TVAR model, by doing basis function expansion based coefficient parametrization, we replace the scalar process with a vector one and turn the original non-stationary problem into a time-invariant problem. All the proposed models are tested against the same set of real measured customer load demand data. Prediction performances of different models are analyzed and compared, advantages and disadvantages are discussed.
{"title":"Uncertainty modeling and prediction for customer load demand in smart grid","authors":"Ding Li, S. Jayaweera","doi":"10.1109/ENERGYTECH.2013.6645360","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645360","url":null,"abstract":"In this study, we propose two types of approaches to model the uncertainty in customer load demand. The first approach is based on a first order non-stationary Markov chain. A maximum likelihood estimator (MLE) is derived to estimate the time variant transition matrix of the Markov chain. The second approach is based on time series analysis techniques. We present linear prediction models such as standard autoregressive (AR) process and time varying autoregressive (TVAR) process, according to different assumptions on the stationarity of customer load profile: piecewise stationarity, local stationarity and cyclo-stationarity. Two important issues in AR/TVAR models are addressed: determining the order of AR/TVAR models and calculating the AR/TVAR coefficients. The partial autocorrelation function (PACF) is analyzed to determine the model order and the minimum mean squared error (MMSE) estimator is adopted to derive the AR/TVAR coefficients, which leads to the Yule-Walker type of equations. For the AR model, the customer load profile is divided into small segments which can be considered to be stationary. For the TVAR model, by doing basis function expansion based coefficient parametrization, we replace the scalar process with a vector one and turn the original non-stationary problem into a time-invariant problem. All the proposed models are tested against the same set of real measured customer load demand data. Prediction performances of different models are analyzed and compared, advantages and disadvantages are discussed.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131209669","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645303
J. Stevens, Daniel J. Rogers
The offshore node will be a key component of a future European Supergrid. The offshore AC hub (or SuperNode) is presented as one possible topology of an offshore node. Tranche A is an area within Dogger Bank, which is one of the UK's largest offshore wind development zones. It is a likely location of an offshore AC hub and therefore is used as a case study. Two strategies are presented for control of voltage, current and power within the offshore AC hub. The performance of each control strategy is compared under both planned and unplanned changes in operating conditions. Simulations are carried out using the SimPower toolbox of MATLAB Simulink. It is found that both control strategies are able to maintain satisfactory control of voltage, current and power. The communication requirements of each strategy are also briefly discussed.
{"title":"Control of multiple VSC-HVDC converters within an offshore AC-hub","authors":"J. Stevens, Daniel J. Rogers","doi":"10.1109/ENERGYTECH.2013.6645303","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645303","url":null,"abstract":"The offshore node will be a key component of a future European Supergrid. The offshore AC hub (or SuperNode) is presented as one possible topology of an offshore node. Tranche A is an area within Dogger Bank, which is one of the UK's largest offshore wind development zones. It is a likely location of an offshore AC hub and therefore is used as a case study. Two strategies are presented for control of voltage, current and power within the offshore AC hub. The performance of each control strategy is compared under both planned and unplanned changes in operating conditions. Simulations are carried out using the SimPower toolbox of MATLAB Simulink. It is found that both control strategies are able to maintain satisfactory control of voltage, current and power. The communication requirements of each strategy are also briefly discussed.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124795205","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645293
D. H. Moore, J. M. Murray, F. Maturana, T. Wendel, K. Loparo
A lab-scale hardware DC MicroGrid system has been designed and built as part of a suite of electrical grid simulation environments. It is intended for use as an educational tool and in research as a testbed for control strategy development. The testbed has the capability to rapidly transition between hardware and software simulation of the DC MicroGrid system and consists of three components: Software in the Loop Simulation (SILS), Hardware in the Loop Simulation (HILS), and Hardware. SILS uses a PLC emulator and a Simulink model to simulate the plant and control system. In HILS, the emulator is replaced with an actual hardware controller. In the transition from HILS to hardware, a software switch shifts the PLC I/O from the model to the PLC I/O modules connected to the DC MicroGrid. In tandem with the PLC ladder code, Rockwell Automation's agent-based distributed control software enables the investigation of complex interactions between autonomous decision makers on the grid.
{"title":"Agent-based control of a DC MicroGrid","authors":"D. H. Moore, J. M. Murray, F. Maturana, T. Wendel, K. Loparo","doi":"10.1109/ENERGYTECH.2013.6645293","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645293","url":null,"abstract":"A lab-scale hardware DC MicroGrid system has been designed and built as part of a suite of electrical grid simulation environments. It is intended for use as an educational tool and in research as a testbed for control strategy development. The testbed has the capability to rapidly transition between hardware and software simulation of the DC MicroGrid system and consists of three components: Software in the Loop Simulation (SILS), Hardware in the Loop Simulation (HILS), and Hardware. SILS uses a PLC emulator and a Simulink model to simulate the plant and control system. In HILS, the emulator is replaced with an actual hardware controller. In the transition from HILS to hardware, a software switch shifts the PLC I/O from the model to the PLC I/O modules connected to the DC MicroGrid. In tandem with the PLC ladder code, Rockwell Automation's agent-based distributed control software enables the investigation of complex interactions between autonomous decision makers on the grid.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128960899","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645335
C. Kraemer, K. Goldermann, C. Breuer, P. Awater, A. Moser
Renewable energy sources (RES) will play a major role in future energy systems. Political RES goals are only defined in terms of targeted capacity or energy. This paper presents an approach to calculate the corresponding optimal location of RES generation. Potential areas for the expansion of the considered generation technologies (wind onshore and offshore, photovoltaics and concentrated solar power) are calculated in a land use analysis. The corresponding renewable energy supply is determined via a meterological analysis. Eventually optimal RES investment decisions are determined. Data reduction and handling is a major focus in the model implementation which can be applied for whole Europe. Exemplary results for wind turbines show that the developed approach can be used to fundamentally plan future RES locations. Wind energy should mainly be expanded on coastlines but higher capacities need also efficient interior locations.
{"title":"Optimal positioning of renewable energy units","authors":"C. Kraemer, K. Goldermann, C. Breuer, P. Awater, A. Moser","doi":"10.1109/ENERGYTECH.2013.6645335","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645335","url":null,"abstract":"Renewable energy sources (RES) will play a major role in future energy systems. Political RES goals are only defined in terms of targeted capacity or energy. This paper presents an approach to calculate the corresponding optimal location of RES generation. Potential areas for the expansion of the considered generation technologies (wind onshore and offshore, photovoltaics and concentrated solar power) are calculated in a land use analysis. The corresponding renewable energy supply is determined via a meterological analysis. Eventually optimal RES investment decisions are determined. Data reduction and handling is a major focus in the model implementation which can be applied for whole Europe. Exemplary results for wind turbines show that the developed approach can be used to fundamentally plan future RES locations. Wind energy should mainly be expanded on coastlines but higher capacities need also efficient interior locations.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123758510","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645348
Venkatesh Yadav Singarao, Vittal S. Rao, Mark A. Harral
With the increasing grid penetration levels of wind power, there is an increasing need to study both the power control systems and the regulatory aspects associated with frequency control capabilities of wind power plants at various levels (turbine, wind farm and power system levels). This is especially very important in the United States with a goal of achieving 20% nation's electricity from wind energy by 2030. This paper provides observations and challenges identified with wind energy integration with respect to active power control strategies used in implementing frequency regulation at various levels. This paper also discusses the current and future aspects of market regulation of frequency responsive services offered by converter based variable generation technologies and the need for an appropriate utility market structure in order to provide settlements for these capabilities.
{"title":"Review on engineering and regulatory aspects associated with frequency control capabilities of wind power plants","authors":"Venkatesh Yadav Singarao, Vittal S. Rao, Mark A. Harral","doi":"10.1109/ENERGYTECH.2013.6645348","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645348","url":null,"abstract":"With the increasing grid penetration levels of wind power, there is an increasing need to study both the power control systems and the regulatory aspects associated with frequency control capabilities of wind power plants at various levels (turbine, wind farm and power system levels). This is especially very important in the United States with a goal of achieving 20% nation's electricity from wind energy by 2030. This paper provides observations and challenges identified with wind energy integration with respect to active power control strategies used in implementing frequency regulation at various levels. This paper also discusses the current and future aspects of market regulation of frequency responsive services offered by converter based variable generation technologies and the need for an appropriate utility market structure in order to provide settlements for these capabilities.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"1997 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126958377","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 : 2013-05-21DOI: 10.1109/ENERGYTECH.2013.6645302
G. Reed, Hashim A. Al Hassan, M. Korytowski, P. Lewis, B. Grainger
The potential to harness offshore wind power and transmit that energy to shore is of widespread interest in the electric power and energy industry sector, as well as within U.S. government agencies such as the Department of Energy. Studies are currently being conducted to find optimal placement of offshore wind turbines along the perimeter of the United States coast lines, and evaluations of current technology solutions for transmitting the electrical energy onshore are being investigated. Transmitting high capacities of energy from sea to shore presents a significant challenge due to the need for a very efficient, robust, and reliable technical solution that must be cost effective. High Voltage AC (HVAC) and two forms of High Voltage DC (HVDC), Voltage Source Converter (VSC) based and Line Commutated-Converter (LCC) based, are possible transmission solutions to these challenges. Therefore, we present herein the applicability of these topologies for offshore wind power transmission and present the necessary procedures to select the most cost effective solution for a given application.
{"title":"Comparison of HVAC and HVDC solutions for offshore wind farms with a procedure for system economic evaluation","authors":"G. Reed, Hashim A. Al Hassan, M. Korytowski, P. Lewis, B. Grainger","doi":"10.1109/ENERGYTECH.2013.6645302","DOIUrl":"https://doi.org/10.1109/ENERGYTECH.2013.6645302","url":null,"abstract":"The potential to harness offshore wind power and transmit that energy to shore is of widespread interest in the electric power and energy industry sector, as well as within U.S. government agencies such as the Department of Energy. Studies are currently being conducted to find optimal placement of offshore wind turbines along the perimeter of the United States coast lines, and evaluations of current technology solutions for transmitting the electrical energy onshore are being investigated. Transmitting high capacities of energy from sea to shore presents a significant challenge due to the need for a very efficient, robust, and reliable technical solution that must be cost effective. High Voltage AC (HVAC) and two forms of High Voltage DC (HVDC), Voltage Source Converter (VSC) based and Line Commutated-Converter (LCC) based, are possible transmission solutions to these challenges. Therefore, we present herein the applicability of these topologies for offshore wind power transmission and present the necessary procedures to select the most cost effective solution for a given application.","PeriodicalId":154402,"journal":{"name":"2013 IEEE Energytech","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125685979","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}
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