The Experts below are selected from a list of 450630 Experts worldwide ranked by ideXlab platform
Jan Kleissl - One of the best experts on this subject based on the ideXlab platform.
-
simulated pv power plant Variability impact of utility imposed ramp limitations in puerto rico
Photovoltaic Specialists Conference, 2013Co-Authors: Matthew Lave, Jan Kleissl, Abraham Ellis, Felipe A MejiaAbstract:The Variability of solar PV power plants has led to some utilities imposing ramp limitations. For example, the Puerto Rico Electric Power Authority (PREPA) includes a 10% of capacity per minute limit on ramp rates produced by PV power plants in its minimum technical requirements for photovoltaic generation projects. However, it is difficult to determine storage requirements to comply with ramp limitations for plants in the planning or construction phase since the Variability of the plant output is not known. In this paper, we use the wavelet Variability Model (WVM) to upscale irradiance measured in Mayaguez, PR to simulate various sizes of PV power plants. The results show that ramps will often exceed 10%, even for the largest plants (60MW) that benefit the most from in-plant spatial smoothing, meaning significant amounts of storage will be needed to meet the PREPA requirement. The results from Puerto Rico are compared to sites in San Diego and Oahu, Hawaii. Significant differences are seen in the ramp rate distributions of the three locations, demonstrating the importance of performing location-specific simulations.
-
cloud speed impact on solar Variability scaling application to the wavelet Variability Model
Solar Energy, 2013Co-Authors: Matthew Lave, Jan KleisslAbstract:Cloud Speed Impact on Solar Variability Scaling - Application to the Wavelet Variability Model Matthew Lave Jan Kleissl University of California, San Diego 9500 Gilman Dr. #0411 La Jolla, CA 92093 mlave@ucsd.edu jkleissl@ucsd.edu Abstract The wavelet Variability Model (WVM) for simulating solar photovoltaic (PV) powerplant output given a single irradiance sensor as input has been developed and validated previously. Central to the WVM method is a correlation scaling coefficient ( ) that calibrates the decay of correlation of the clear sky index as a function of distance and timescale, and which varies by day and geographic location. Previously, a local irradiance sensor network was required to derive . In this work, we determine from cloud speeds. Cloud simulator results indicated that the value is linearly proportional to the cloud speed ( ): . Cloud speeds from a numerical weather Model (NWM) were then used to create a database of daily values for North America. For validation, the WVM was run to simulate a 48MW PV plant with both NWM values and with ground values found from a sensor network. Both WVM methods closely matched the distribution of ramp rates (RRs) of measured power, and were a strong improvement over linearly scaling up a point sensor. The incremental error in using NWM values over ground values was small. The ability to use NWM-derived values means that the WVM can be used to simulate a PV plant anywhere a single high- frequency irradiance sensor exits. This can greatly assist in module siting, plant sizing, and storage decisions for prospective PV plants. 1. Introduction The variable nature of power produced by PV power plants can be of concern to electric operators. For example, the Puerto Rico Electric Power Authority (PREPA) requires that utility-scale PV plants in Puerto Rico limit ramps (both up and down) to 10% of capacity per minute (PREPA). At short timescales such as 1-minute, the Variability of solar PV power production is mostly caused by the movement of clouds across the PV plant. While a single PV module can produce highly variable output due to the instantaneous crossing of cloud edges, geographic diversity of modules within a PV plant will lead to smoothing of the total power output. Geographic diversity can be quantified through the correlation coefficients between the timeseries of power output of different PV modules within the plant. This correlation generally decreases with distance and increases with fluctuation timescale. Irradiance and power measurements have been used to quantify the relative reduction in aggregate Variability for a combination of sites. Sites a few to hundreds of kilometers apart were shown to lead to a smoothed aggregate output and the amount of smoothing varied based on the distances between sites and local meteorological conditions (Curtright and Apt, 2008, Lave and Kleissl, 2010, Otani, et al., 1997, Wiemken, et al., 2001). Other investigators (Mills and Wiser, 2010, Perez, et al., 2011, Perez, et al., 2012) calculated the correlation of irradiance fluctuations between sites and found decorrelation distances – the distances over which sites become independent of one another – to vary based on fluctuation timescale and distance between sites. Accounting for cloud speed further enhanced the accuracy of these correlation Models (Hoff and Perez, 2012). Correlation was also shown to depend on orientation relative to the direction of cloud motion (Hinkelman, et al., 2011).
-
a wavelet based Variability Model wvm for solar pv power plants
IEEE Transactions on Sustainable Energy, 2013Co-Authors: Matthew Lave, Jan Kleissl, Joshua S SteinAbstract:A wavelet Variability Model (WVM) for simulating solar photovoltaic (PV) power plant output given a single irradiance point sensor timeseries using spatio-temporal correlations is presented. The Variability reduction (VR) that occurs in upscaling from the single point sensor to the entire PV plant at each timescale is simulated, then combined with the wavelet transform of the point sensor timeseries to produce a simulated power plant output. The WVM is validated against measurements at a 2-MW residential rooftop distributed PV power plant in Ota City, Japan and at a 48-MW utility-scale power plant in Copper Mountain, NV. The WVM simulation matches the actual power output well for all Variability timescales, and the WVM compares well against other simulation methods.
-
Testing a wavelet-based Variability Model (WVM) for solar PV power plants
2012 IEEE Power and Energy Society General Meeting, 2012Co-Authors: Matthew Lave, Jan KleisslAbstract:A wavelet Variability Model (WVM) for simulating photovoltaic (PV) power plant output given a single irradiance point sensor as input is tested at the 48MW Copper Mountain solar PV plant. 4 days with different amounts of Variability are chosen for validation of the Model. Comparisons of wavelet fluctuation power index (fpi) and power output ramp rates (RRs) between the input point sensor, WVM simulated power output, and actual power output are presenwavelet fluctuation power indexted for the 4 test days. At all timescales, the WVM simulated power output is found to match the Variability of the actual power output well, and to be a strong improvement over the input point sensor.
Matthew Lave - One of the best experts on this subject based on the ideXlab platform.
-
simulated pv power plant Variability impact of utility imposed ramp limitations in puerto rico
Photovoltaic Specialists Conference, 2013Co-Authors: Matthew Lave, Jan Kleissl, Abraham Ellis, Felipe A MejiaAbstract:The Variability of solar PV power plants has led to some utilities imposing ramp limitations. For example, the Puerto Rico Electric Power Authority (PREPA) includes a 10% of capacity per minute limit on ramp rates produced by PV power plants in its minimum technical requirements for photovoltaic generation projects. However, it is difficult to determine storage requirements to comply with ramp limitations for plants in the planning or construction phase since the Variability of the plant output is not known. In this paper, we use the wavelet Variability Model (WVM) to upscale irradiance measured in Mayaguez, PR to simulate various sizes of PV power plants. The results show that ramps will often exceed 10%, even for the largest plants (60MW) that benefit the most from in-plant spatial smoothing, meaning significant amounts of storage will be needed to meet the PREPA requirement. The results from Puerto Rico are compared to sites in San Diego and Oahu, Hawaii. Significant differences are seen in the ramp rate distributions of the three locations, demonstrating the importance of performing location-specific simulations.
-
cloud speed impact on solar Variability scaling application to the wavelet Variability Model
Solar Energy, 2013Co-Authors: Matthew Lave, Jan KleisslAbstract:Cloud Speed Impact on Solar Variability Scaling - Application to the Wavelet Variability Model Matthew Lave Jan Kleissl University of California, San Diego 9500 Gilman Dr. #0411 La Jolla, CA 92093 mlave@ucsd.edu jkleissl@ucsd.edu Abstract The wavelet Variability Model (WVM) for simulating solar photovoltaic (PV) powerplant output given a single irradiance sensor as input has been developed and validated previously. Central to the WVM method is a correlation scaling coefficient ( ) that calibrates the decay of correlation of the clear sky index as a function of distance and timescale, and which varies by day and geographic location. Previously, a local irradiance sensor network was required to derive . In this work, we determine from cloud speeds. Cloud simulator results indicated that the value is linearly proportional to the cloud speed ( ): . Cloud speeds from a numerical weather Model (NWM) were then used to create a database of daily values for North America. For validation, the WVM was run to simulate a 48MW PV plant with both NWM values and with ground values found from a sensor network. Both WVM methods closely matched the distribution of ramp rates (RRs) of measured power, and were a strong improvement over linearly scaling up a point sensor. The incremental error in using NWM values over ground values was small. The ability to use NWM-derived values means that the WVM can be used to simulate a PV plant anywhere a single high- frequency irradiance sensor exits. This can greatly assist in module siting, plant sizing, and storage decisions for prospective PV plants. 1. Introduction The variable nature of power produced by PV power plants can be of concern to electric operators. For example, the Puerto Rico Electric Power Authority (PREPA) requires that utility-scale PV plants in Puerto Rico limit ramps (both up and down) to 10% of capacity per minute (PREPA). At short timescales such as 1-minute, the Variability of solar PV power production is mostly caused by the movement of clouds across the PV plant. While a single PV module can produce highly variable output due to the instantaneous crossing of cloud edges, geographic diversity of modules within a PV plant will lead to smoothing of the total power output. Geographic diversity can be quantified through the correlation coefficients between the timeseries of power output of different PV modules within the plant. This correlation generally decreases with distance and increases with fluctuation timescale. Irradiance and power measurements have been used to quantify the relative reduction in aggregate Variability for a combination of sites. Sites a few to hundreds of kilometers apart were shown to lead to a smoothed aggregate output and the amount of smoothing varied based on the distances between sites and local meteorological conditions (Curtright and Apt, 2008, Lave and Kleissl, 2010, Otani, et al., 1997, Wiemken, et al., 2001). Other investigators (Mills and Wiser, 2010, Perez, et al., 2011, Perez, et al., 2012) calculated the correlation of irradiance fluctuations between sites and found decorrelation distances – the distances over which sites become independent of one another – to vary based on fluctuation timescale and distance between sites. Accounting for cloud speed further enhanced the accuracy of these correlation Models (Hoff and Perez, 2012). Correlation was also shown to depend on orientation relative to the direction of cloud motion (Hinkelman, et al., 2011).
-
a wavelet based Variability Model wvm for solar pv power plants
IEEE Transactions on Sustainable Energy, 2013Co-Authors: Matthew Lave, Jan Kleissl, Joshua S SteinAbstract:A wavelet Variability Model (WVM) for simulating solar photovoltaic (PV) power plant output given a single irradiance point sensor timeseries using spatio-temporal correlations is presented. The Variability reduction (VR) that occurs in upscaling from the single point sensor to the entire PV plant at each timescale is simulated, then combined with the wavelet transform of the point sensor timeseries to produce a simulated power plant output. The WVM is validated against measurements at a 2-MW residential rooftop distributed PV power plant in Ota City, Japan and at a 48-MW utility-scale power plant in Copper Mountain, NV. The WVM simulation matches the actual power output well for all Variability timescales, and the WVM compares well against other simulation methods.
-
Testing a wavelet-based Variability Model (WVM) for solar PV power plants
2012 IEEE Power and Energy Society General Meeting, 2012Co-Authors: Matthew Lave, Jan KleisslAbstract:A wavelet Variability Model (WVM) for simulating photovoltaic (PV) power plant output given a single irradiance point sensor as input is tested at the 48MW Copper Mountain solar PV plant. 4 days with different amounts of Variability are chosen for validation of the Model. Comparisons of wavelet fluctuation power index (fpi) and power output ramp rates (RRs) between the input point sensor, WVM simulated power output, and actual power output are presenwavelet fluctuation power indexted for the 4 test days. At all timescales, the WVM simulated power output is found to match the Variability of the actual power output well, and to be a strong improvement over the input point sensor.
Joshua S Stein - One of the best experts on this subject based on the ideXlab platform.
-
a wavelet based Variability Model wvm for solar pv power plants
IEEE Transactions on Sustainable Energy, 2013Co-Authors: Matthew Lave, Jan Kleissl, Joshua S SteinAbstract:A wavelet Variability Model (WVM) for simulating solar photovoltaic (PV) power plant output given a single irradiance point sensor timeseries using spatio-temporal correlations is presented. The Variability reduction (VR) that occurs in upscaling from the single point sensor to the entire PV plant at each timescale is simulated, then combined with the wavelet transform of the point sensor timeseries to produce a simulated power plant output. The WVM is validated against measurements at a 2-MW residential rooftop distributed PV power plant in Ota City, Japan and at a 48-MW utility-scale power plant in Copper Mountain, NV. The WVM simulation matches the actual power output well for all Variability timescales, and the WVM compares well against other simulation methods.
Siyuan Ji - One of the best experts on this subject based on the ideXlab platform.
-
A Product Line Systems Engineering Process for Variability Identification and Reduction
IEEE Systems Journal, 2019Co-Authors: Mole Li, Alan Grigg, Charles E. Dickerson, Lin Guan, Siyuan JiAbstract:Software product line engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through the reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system Models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a product line systems engineering process is proposed. Specifically, the process extends research in the system orthogonal Variability Model to support hierarchical Variability Modeling with formal definitions; utilizes systems engineering concepts and legacy system Models to build the hierarchy for the Variability Model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subjected to system Model availability, reduction of 14%–40% in the number of variation points is demonstrated in the case studies.
-
A Product Line Systems Engineering Process for Variability Identification and Reduction.
arXiv: Software Engineering, 2018Co-Authors: Mole Li, Alan Grigg, Charles E. Dickerson, Lin Guan, Siyuan JiAbstract:Software Product Line Engineering has attracted attention in the last two decades due to its promising capabilities to reduce costs and time to market through reuse of requirements and components. In practice, developing system level product lines in a large-scale company is not an easy task as there may be thousands of variants and multiple disciplines involved. The manual reuse of legacy system Models at domain engineering to build reusable system libraries and configurations of variants to derive target products can be infeasible. To tackle this challenge, a Product Line Systems Engineering process is proposed. Specifically, the process extends research in the System Orthogonal Variability Model to support hierarchical Variability Modeling with formal definitions; utilizes Systems Engineering concepts and legacy system Models to build the hierarchy for the Variability Model and to identify essential relations between variants; and finally, analyzes the identified relations to reduce the number of variation points. The process, which is automated by computational algorithms, is demonstrated through an illustrative example on generalized Rolls-Royce aircraft engine control systems. To evaluate the effectiveness of the process in the reduction of variation points, it is further applied to case studies in different engineering domains at different levels of complexity. Subject to system Model availability, reduction of 14% to 40% in the number of variation points are demonstrated in the case studies.
Ian Welch - One of the best experts on this subject based on the ideXlab platform.
-
Modelling impacts of utility-scale photovoltaic systems Variability using the wavelet Variability Model for smart grid operations
Sustainable Energy Technologies and Assessments, 2019Co-Authors: Michael Emmanuel, Ramesh Rayudu, Ian WelchAbstract:Abstract The increasing presence of large-scale photovoltaic (PV) systems in the distribution network requires a thorough interconnection study for effective planning and reliable grid operations. The proliferation of such systems now creates a critical need for their accurate Modelling to enable planners and operators understand and fully characterize centralized PV Variability with the ability to develop realistic projections of PV plant output Variability. This article Models impacts of Variability and the locational value of such utility-scale (centralized) PV plants deployed close to distribution feeder source, midpoint and end using the wavelet Variability Model (WVM). This Model is used to accurately simulate solar irradiance Variability and the PV plant output taken into account its entire footprint and density, time series irradiance data from a single point sensor, and location-dependent cloud-speed coefficient. Also, since the Variability observed from a single point irradiance sensor cannot provide the exact Variability across the entire PV plant, this study uses a high-frequency solar irradiance data and geographic smoothing for accurate Modelling of PV output Variability. Further, impacts on the tap changer operation, voltage profile, load demand offset and line loading reduction on the IEEE-34 distribution test feeder are investigated.
