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Contrasting distributed and centralized photovoltaic system performance using regionally distributed pyranometers
Solar Energy 160 (2018) 1–9
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Solar Energy journal homepage: www.elsevier.com/locate/solener
Contrasting distributed and centralized photovoltaic system performance using regionally distributed pyranometers
T
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Katherine Adye , Nathaniel Pearre, Lukas Swan Renewable Energy Storage Laboratory – Dalhousie University, 1360 Barrington St., PO Box 15000, Halifax, Nova Scotia B3H 4R2, Canada
A R T I C L E I N F O
A B S T R A C T
Keywords: Distributed Centralized Photovoltaic Ramp rate Electricity grid Integration
Using measured irradiance data from 215 homes across a municipal area we analyze the performance differences between distributed and centralized photovoltaic systems. Data on a one minute time step for a complete year was used, with pyranometers oriented perpendicular to nominal roof plane. The distributed and centralized groupings were defined and modeled with an aggregate 100 MW photovoltaic capacity. Analysis was performed in different spatial groupings with respect to power intensity and ramp-rate to determine the impact upon the regional electricity grid and temporal alignment with load. It was determined that the differences between distributed and centralized systems are relatively small at a 15-min or greater timescale. Within a 5-min timescale, centralized systems ramp more than twice as fast as distributed systems of identical capacity.
1. Introduction The province of Nova Scotia is considering policy alternatives to support uptake of solar photovoltaic (PV) installations for electricity generation. Such installations can be categorized as small-scale distributed systems or large-scale centralized systems. Distributed systems are typically sized 1–100 kW and are placed on buildings (residential, commercial, institutional) throughout communities using net-metered interconnections. Centralized systems are typically sized 1 + MW and are ground-mounted in a large defined area. (Rosenbloom and Meadowcroft, 2014) We compare the performance and output characteristics of distributed and centralized PV systems using a unique set of 215 pyranometers measured at high time step resolution over a one-year period. Special emphasis is placed on ramp-rates, which is the rise and fall of output power with respect to time. This metric is important because the Nova Scotia electricity utility must modulate other electricity generators to compensate for the fluctuations in solar PV output. We expect that the distributed nature of PV on buildings spread about communities will enhance consistency of production and reduce ramprates, when compared to specific centralized locations. To date several studies have been done on the effects of distribution on PV generation. These studies focus on smoothing of the PV output and look at variables such as density of the PV installations, distance between installations, installation size, and varying time steps. Some of these studies use completely modelled data, while others use measured data to produce a model. (Hoff and Perez, 2010) ⁎
modelled the effects of PV spacing by introducing a dispersion factor and calculating the output variability based on cloud speed, distance between PV panels, and size of the PV fleet. (Jewell and Ramakumar, 1987) modelled insolation and cloud patterns and looked at different sizes of PV fields with different cloud types to determine the amount of capacity lost over different time steps. (Kern and Russell, 1988) used data from several instrumented locations to model solar irradiance over a large area and then examined ramp-rates to determine that dispersed systems provide output smoothing. (Murata and Otani, 1997) also modelled irradiance using measured data but instead looked at smoothing through correlation and tie-line capacity between different service areas of a grid. In these studies the PV panels were modelled to be in the same orientation, or there was no mention of panel layout. Such oversimplification is unlikely to capture the variability of tilt and azimuth among highly distributed PV systems. Studies that use measured data give a better representation of real world expected performance. (Otani et al., 1997) used a grid of 9 pyranometers to measure irradiance values, examining fluctuation factor and power spectral density. Both of these metrics showed smoothing when comparing data combined from several sensors to data from a single sensor. (Lave et al., 2012) used a network of 6 sensors spread irregularly across a university campus. The irradiance data from these sensors was analysed with respect to ramp-rate and energy spectrum. It was shown that the 6 sites were independent at timescales shorter than 5-min and therefore produce a smoothing effect for shorter timescales. (Dyreson et al., 2014) used a grid of 9 and 25 sensors to represent different PV plant sizes and compared these to output from a
Corresponding author. E-mail addresses: [email protected] (K. Adye), [email protected] (N. Pearre), [email protected] (L. Swan).
https://doi.org/10.1016/j.solener.2017.11.042 Received 4 July 2017; Received in revised form 28 September 2017; Accepted 18 November 2017 0038-092X/ © 2017 Elsevier Ltd. All rights reserved.
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period an average of 145 homes are represented for each time step instead of the full 215. The nature of the HSC program means the resulting WEL data set is unique. This data represents homes from across the Halifax region with a variety of roof orientations and panel angles that represent true and practical installations of solar panels on rooftops. The effects of local weather trends on solar intensity can be seen for homes both close together and far apart. The nature of this data coming from real houses is a significant opportunity for this study. As opposed to “research” datasets with few points that require significant assumptions to scale up, these “consumer” datasets capture the variability of installations, such as rooftop panel orientations and shading from trees and other buildings. While this comes at the expense of the quality of instruments and consistency of reporting, we feel that such highly-distributed real-installation sensor networks give a very realistic dataset and this increases confidence in our findings.
single sensor. The variability index and ramp rate distributions were analysed and it was shown that the variability of a solar PV field is better represented by several sensors versus a single sensor. Aggregate power output data from centralized solar plants were used in several studies to examine the effects of distance between plants, number of plants in a combination, and size of the plants. (Jamil et al., 2014) uses 5 solar PV plants of equal size located between 50 and 275 km apart. The power and energy outputs were analysed and it was found that power fluctuations decrease significantly when the 5 plants are combined compared to a single plant. (Marcos et al., 2012) compared 7 PV plants of different rated capacities from 1 to 9.5 MW and separated between 6 and 320 km. Power fluctuations were analysed for different combinations of plants and it was found that both the magnitude and frequency of fluctuations are reduced when PV plants’ outputs are combined. (Klima and Apt, 2015) analysed the power spectral densities of combinations of up to 20 PV plants located up to 470 km apart. The results show that for the location studied connecting 4–5 plants provides about half of the smoothing compared to connecting all of the plants, and plants added after that show diminishing marginal returns. Other studies using measured data are those that use data from roof mounted PV systems, similar to our study. These studies however have fewer data sources, from 100 systems spread across Germany to 25 systems spread across the city of Utrecht, located in the Netherlands. (Wiemken et al., 2001) studied PV in Germany and looked at 1 panel versus all 100 panels with respect to power fluctuations and energy spectrum. They found that for the combined systems power fluctuations were reduced in magnitude and energy output was between 0 and 65% of the total installed capacity. (Elsinga and van Sark, 2015) studied PV in Utrecht and focussed on decorrelation distance with respect to time step and different weather conditions and seasons. Their results show that longer time steps result in larger decorrelation distances and that in winter the distance required for decorrelation is generally less than in summer. Our study uses measured data collected from 215 homes participating in the municipal Halifax Solar City program. This program installed sensors and data-acquisition systems on homes across the Halifax municipality, spanning approximately 82 km. This gives multiple years of solar intensity measurements on a 1-min time step from 215 rooftops at angles representative of real-world residential rooftop solar PV installations. This data set is unique in that the solar intensity measurements were not taken at ideal angles, and instead represent the broad range of roof angles and orientations that exist in the Halifax municipality. We examine these data and model distributed and centralized solar systems to determine the performance across spatial and temporal ranges.
2.1. Measurement device and data rate The solar intensity data was collected from each home using a photo-diode of size 1 mm by 1 mm. The photo-diode provides a linear electrical current output as a function of solar irradiance. This output is measured by an analog-to-digital card which converts from units of current (µA) to units of irradiance power (W/m2). These data are reported by the WEL data logger to an internet cloud database system. The data-acquisition system uses a propriety solar sensor and conversion package developed by Thermo-Dynamics Limited.2 To validate the proprietary system measurement accuracy, we took simultaneous measurements with a recently calibrated Apogee Instruments pyranometer model SP-215 and compared them to three nearby operating WEL sites on clear days. Note that the operating units were at private residences and so the Apogee pyranometer was placed such that all sites were within a 4 km radius. The WELs and Apogee orientation differences were limited to 7° azimuth and 10° slope. Validation data is shown in Fig. 1. Linear response produced R2 values of 0.96 or greater, and linear coefficients of 0.86–0.99. Because these are active sites with some spatial separation and orientation differences, these range of linear coefficients are expected. Data was collected on a 1-min time step. This high data-rate enables detailed ramp-rate analysis. While data acquisition systems typically record at a 15-min or 60-min rate, these are insufficient for investigating the short-term impacts on the electricity grid. Slower data sampling masks the true effects, giving an average response, and makes it difficult to confidently design for integration with fast reacting electricity systems. 2.2. Map
2. Data source Using the location data, a map of the homes in the HSC program was constructed, giving approximate locations of houses. As seen in Fig. 2 the homes span from the Western and Northern edges of the Halifax Regional Municipality to past Musquodoboit Harbour in the East, a distance of 82 km. There are many WEL units on the Halifax Peninsula and in Dartmouth corresponding to the higher population density of those areas. The homes have been divided into five groups based on their regional microclimate to help support spatial analysis.
The data used in this study was obtained through the Halifax Solar City (HSC) program. This program aided homeowners in the procurement and financing of solar thermal panels installed on their homes with the option of data monitoring using Web Energy Loggers (WEL).1 These WEL units recorded data on a 1-min basis to a cloud server. For this study we use only the solar intensity (W/m2) value obtained from a photo-diode mounted alongside and sensing perpendicular to the plane of the solar thermal panel. Thus, the data represents total incident radiation on the panels. The HSC program began collecting data on 1 May 2013, with additional homes fitted with reporting units over time. By 29 July 2015 there was data for 228 homes, of which 215 of these homes have location information. The analyses performed here use data from the period of July 2014 to June 2015. Due to gaps in the data during this 1
2.3. Panel layout The panel layout information, consisting of panel azimuth and tilt, was obtained for each of the 215 homes. Of these homes 4 were missing layout information. The azimuth values are spread evenly from 55°E to 61°W. There are 6 homes facing due south, 90 homes facing south-east, 2
http://www.welserver.com.
2
http://www.thermo-dynamics.com/.
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3.1. Data analysis The data was analysed from both temporal and spatial perspectives. The principal metrics for evaluation are power intensity (W/m2) and ramp-rate (W/m2/min.). These were analysed at different time scales (minutes to monthly) and distances (km). For temporal analysis the data was examined at both its original 1min resolution and at various other resolutions by averaging the 1-min data over a period. Other resolutions include 5-min, 15-min, and monthly. The data is presented in the form of statistical distributions, weekly plots, and yearly plots. To support spatial analysis interpretations, the houses were grouped into five geographic areas: Coast, West, North, Peninsula, and Dartmouth. The map presented in Fig. 2 shows the various groupings with different coloured markers. The groups each have a different number of homes, as shown in the legend of Fig. 2. The difference between the largest and smallest groups is ten homes, indicating good distribution. To compare centralized versus distributed PV systems, a group of homes was selected to represent a centralized PV field. This group contained 7 houses located within 1.5 km of each other, to represent the variability in solar resource across a large PV field. The group of houses had azimuth angles ranging from −18 to −21°, and tilt angles ranging from 30 to 45°. Out of the 365 day period studied there were only 210 days where all of the selected houses had data. These days are spread throughout the year and represent a variety of weather patterns
Fig. 1. Verification of solar pyranometer readings between three nearby WEL sites and a reference Apogee SP-215 pyranometer.
Fig. 2. Approximate locations of HSC homes with data monitoring, shown by regional group.
and seasons. It was preferred that these centralized homes be in the rural regions (North, West) due to the land area requirements of large centralized solar farms. However, only the Peninsula region offered an appropriately dense cluster of homes for this purpose. This is acceptable because the focus of this research is on ramp rates rather than energy production. To evaluate the degree to which the 7 houses reflect the solar resource variation across a similarly sized photovoltaic field, a sensitivity analysis was conducted. This consists of a comparison of ramp-rate
and 115 homes facing south-west. The tilt values range from 16° to 50° and there are 57 panels with an angle of 45°. The average tilt is 32.2°. A plot showing the distribution of panel orientations can be seen in Fig. 3.
3. Method This section discusses techniques and metrics for analyzing the data, and the measures used to validate data quality. 3
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Fig. 3. Plot showing the distribution of panel azimuth and tilt angles separated by geographic grouping.
Fig. 4. Plots showing the sensitivity analysis for one or more homes of the representative centralized group of seven homes.
sized PV capacity from each home to the total output of 100 MW. This would represent a 5 kW PV system on 20,000 homes, so each of these 215 homes represents about 93 homes in close proximity, such as those found in a community or neighbourhood. This assumption gives a combined PV system output that represents a widely-distributed solar policy that recognizes typical suburban residential layouts.
distributions for groups of houses ranging from 1 to 7 homes. The year of data discussed above, consisting of 210 days spread over the course of a year, was used to calculate the ramp rates. We found that beyond 4 homes, there was negligible change in the frequency of large-magnitude ramp-rates. This is shown through the plots in Fig. 4. These plots show the difference in yearly occurrences for ramp rate distributions between various numbers of homes and the group of all 7 homes. One home experiences many large solar intensity ramps compared to the group; but by 4 homes, differences greater than 150 W/m2 per 5-min period are very rare. This gives us confidence that 7 homes captures the bulk of this resource variability over the spatial length of 1.5 km, and that additional homes would not substantially change ramp-rate distributions. Our findings are consistent with those of (Lave et al., 2011), which show that the 99th percentile ramp-rates of 7 houses closely matches that of a dense cluster of 477 houses. In order to predict the effects of installed solar a 100 MW PV system was modelled for both centralized and distributed groupings. A value of 100 MW total installed capacity was used as this is an appropriate “order of magnitude” potential target for a solar electricity policy under investigation by the Nova Scotia government. The PV system model assumed 100 MW power output at 1000 W/m2 solar intensity, and proportions thereof. The model is not temperature compensated, but this has only a small impact on short-term variability of PV efficiency. The distributed system consists of all 215 homes and assumes equal
3.1.1. Ramp-rate When looking at solar data this output can be given as solar intensity (W/m2) or as modelled PV output electrical power (W or MW). Ramp-rate is the change in output over time. When speaking of solar PV scenario outputs, this report will give ramp-rate in units of MW per 5min period, or MW per 15-min period. We have confirmed with the Nova Scotia provincial electricity utility that these are good periods due to system adequacy and reserve requirements. By using 5- and 15-min periods we present a distribution for comparison across their control capabilities. Ramp-rate is of interest because of its effects on the control and stability of the electricity grid. Large uncontrolled ramp-rates from PV systems must be balanced and compensated for by fast-ramping (modulating) other sources of power, likely fossil fueled. This is undesirable, as such modulations place significant stress on conventional generators and reduce their energy efficiency. A possible source of large PV solar system ramp-rates are clouds moving over a service area. When a cloud covers a PV panel the output 4
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(86.85% remaining). 6. Manually remove any bad data identified visually after the above filters have been run. Visual inspection was completed for each reporting house. (86.75% remaining).
almost instantaneously ramps down, as there are no storage elements in PV modules.3 When the sun re-emerges the output of the panel experiences a ramp upward. If several panels are experiencing this at the same time (such as in a field of PV panels) then the effect is multiplied and the electricity grid sees very large ramp-rates.
4. Results 3.2. Data quality The analyses examine the solar resource in different spatial and temporal groupings, applying PV systems to the data and comparing their performance, and then comparing these PV systems to the net load on the electricity system.
As with all measured data, opportunities exist for data quality issues, and thus the data must be validated prior to analysis. This is especially important given the highly distributed consumer-grade pyranometers and cloud based data storage. Some issues are easily spotted visually when looking at several years of data, while other issues were found by focusing and checking the data one week at a time. Almost all the data issues affected only parts of the data set. This means that the data could have no issues, then an issue would begin suddenly, and then it would self-correct after a period of time. Some issues lasted for a couple of days to a week while others remained for several months. The quality issues found in the data include:
• • • • •
4.1. Solar resource analysis The first analysis was performed by looking at two consecutive days of data, a mixed weather day (sun and cloud) and a sunny day, on three different spatial scales. Plots of these days can be seen in Fig. 5.
• Single house: The top plot shows the solar intensity data for just one
Spikes in value – Solar intensity values should range from 0 or slightly negative (nighttime) to approximately 1200 W/m2 (sunny noontime with reflection from snow). Values outside of this range, including spikes over 25,000 W/m2, were seen. It is likely that electrical noise or a poor electrical contact caused these spikes. Constant data values – These showed up as long periods of values usually at zero (not overnight), but also appeared as other numeric values as well. Overnight offset – Some of the WELs showed a bias issue overnight by not reaching zero solar intensity. These overnight values could be consistently at one value, such as 100 W/m2, or the value could change each night. Intermittent data – This was seen as blocks of data missing from every day, such as all the overnight periods, and as data missing from random times throughout the day. Time shifted data – These are days when sunrise appears to occur just after midnight, the solar intensity peaks early in the morning, and the sun appears to set sometime before noon. These days had a several hour gap in the data at the end of the day.
• •
house for the two chosen days. This is to give the reader an understanding of the nominal curves and fluctuations at a site. Centralized system: The middle plot shows the solar intensity data for a key group of houses within a tight spatial area which represent the combined output of a large centralized facility. Distributed systems: The bottom plot gives five lines, each showing solar intensity data averaged across all the houses in each geographic group. This represents the combined output of highly distributed PV systems across many buildings within a given region.
The top plot illustrates nominal response of a single-house. The sunny day shows a clear half sinusoidal output corresponding with dawn, midday, and sunset. It is not a perfect half sinusoid of intensity given some morning intermittency (clouds) and an afternoon discontinuity that is likely caused by geometric shading and reflection. The intermittent cloudy day can be seen to also follow the half sinusoid shape, but has very significant peaks and valleys of intensity associated with clouds passing overhead. In just a matter of minutes, intensity drops from nearly full to nearly none, and rebounds shortly thereafter. The middle plot shows the centralized solar for each day which is similar in trend to the single-house, but with less variability due to the aggregation effects over the 1.5 km resource space. The bottom plot shows that there are only small differences between the averaged solar intensities of each group. This indicates that one region is not significantly better than any of the others in terms of power generation. This was somewhat unexpected since the coastal region is typically foggy in the morning which should result in lower solar intensity. This does not appear to be the case for the two days analysed. On the mixed weather day there are a few more differences, such as clouds over one area but not another. The next analysis compares the different geographic regions in terms of average monthly power intensity, as shown in Fig. 6. In addition to showing how the five regions compare month to month the plot also shows the overall trend for the year, which is typical of the northern hemisphere. In the year selected for observation (July 2014 to June 2015) there was a peak in May and as expected the winter values were lower and the summer values were higher. A peak in June is normally expected due to the longer days, however this does not take into account weather trends. There was not enough data to plot other years and see if this trend of a May peak was due to panel angle (where a May peak would occur every year) or weather trends (where a May peak only occurred due to good weather in the year chosen). Halifax is notably foggy in summertime due to its coastal area. On a month-to-month basis the average power intensity of each region is similar. One notable difference occurs in February and March where the North region had lower power intensity values compared to
The issues discussed above were removed using a set of filters that were run in the order listed below, complete with the quantity of data remaining after the operation. Only the affected sensors had data removed, instead of all the sensors for the affected time period. 1. Remove high and low values. Values removed are < −10 and > 1700 W/m2. (99.99% remaining). 2. Remove days that are missing more than 70 min of data. This was found to adequately identify days with large missing blocks of data, while accepting short term outages. (92.98% remaining). 3. Remove days that have large blocks (23 h) of low values. A low value is < 10 W/m2. The code determines the total number of cells that are low value or NaN and if the total is more than 23 h the whole day is removed. (88.20% remaining). 4. Remove days that have a large value 30 min before or after midnight. A large value is > 40 W/m2. Check previous day for a period of 30 min before midnight and current day for a period of 30 min after midnight. Only remove day if more than one data point is above limit. (87.43% remaining). 5. Remove days that have a standard deviation less than 5 W/m2. Snow covered sensors are realistic but result in a low standard deviation, so days from December to March were excluded from this rule.
3 Energy storage systems can be added alongside PV systems to mitigate these effects, but come at increased capital cost and maintenance.
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Fig. 5. Plots showing two days of solar data (May 13 = intermittent cloudy, May 14 = sunny) for different spatial groupings (top = one house; middle = centralized system; bottom = regional averages).
Fig. 6. Graph comparing average monthly power intensity for different geographic regions.
year the values were 20% ± 4%. If February and March are excluded this changes to 20% ± 2.1%. This shows that there are no significant differences between the regions in terms of average solar intensity.
the other regions. This was during a particularly snowy winter and is likely due to snow covering the panels. The power intensity values were compared to give a better idea of whether one region had a trend of higher production than another. If all five regions were equal, they would have a 20% share of each month. When looking at the whole 6
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Fig. 7. Example intermittent cloudy and sunny weather days for distributed and centralized PV systems.
changes. The difference between distributed and centralized here is less pronounced, with the range of the distributed ramps approaching that of centralized ramps. However, the centralized system still has more occurrences of ramps at larger magnitudes when compared to the distributed system. This averaging level (15 min) removes the effects of fast-moving intermittent singular clouds and focuses on larger cloud and weather transitions. Again, the distributions are symmetrical, with distributed PV systems having less aggregated ramp-rate. The magnitude of ramps are still within the ± 50 MW range, but are now spread over 15 min, which the electricity system operator will find easier to respond to.
4.2. PV scenario assessment of 100 MW An analysis was performed by modeling a 100 MW PV system using the data and examining the impacts of energy production and the resultant ramp-rates. Two of these PV systems were compared, a distributed system and a centralized system. These systems are described in Section 3.1. Fig. 7 shows the 100 MW PV system output of the centralized and distributed systems. This plot uses the same intermittent cloudy day and sunny day as was presented for the first analysis above. Generally, there is only a minor difference between the power outputs of the two systems. The centralized system achieves higher power output on the sunny day because the homes selected for this system have more favourable panel orientations. There is also a time offset between the peaks of the distributed and centralized systems on the sunny day. This asymmetry can be attributed to a 25° difference in the average azimuth of the panels in each grouping. Close examination of the intermittent cloudy day shows that while the distributed system experiences similar swings in output as the centralized system, the peaks and valley are somewhat dampened. It can be seen that the distributed line has fewer and less major oscillations throughout the day than the centralized system. To gain greater perspective on these oscillations, the ramp-rate must be considered statistically. To determine the differences in fluctuations between the distributed and centralized system, a ramp-rate investigation was carried out. The 5min scale represents very short term fluctuations such as patchy clouds on a windy day, and the 15-min scale represents longer cloudy periods such as an oncoming storm front. The ramp-rate values for these time intervals were found by averaging the 1-min data into 5- and 15-min time steps, then finding the ramp-rates between these new points. These ramp-rate distributions are plotted in Fig. 8. Note that the y-axis on both plots uses a log scale to give detail at the high ramp-rate tails on the x-axis. Starting with the 5-min time step in the top of Fig. 8 we can see that the distributed system has more occurrences of small ramp-rates and fewer occurrences of large magnitude ramp-rates when compared to the centralized system. All the distributed ramp-rates are also within the range of less than ± 20 MW/5 min whereas the centralized system has rates more than twice that at almost ± 50 MW/5 min. The distributions are symmetrical, which indicates that intermittent clouds pass on as fast as they arrive (it also confirms the sun rises as fast as it sets). The chart gives results with a high degree of granularity, but it may be summarized that for the centralized system, approximately 1300 fiveminute periods will experience positive or negative ramp-rates in the range of 15–25 MW/5 min. This number, along with occasional ramprates of ± 40 MW/5 min is quite significant considering only 100 MW capacity of solar PV is modelled. Moving on to the 15-min time step in the bottom of Fig. 8 the trend
4.3. Integration of PV with net electricity load The 100 MW PV system models were compared to the load of the provincial electricity system. Analysis was conducted on weekly and annual timescales, and the PV systems’ effect on grid ramp-rate were evaluated. The provincial electricity utility, Nova Scotia Power, provided time series data of grid load in the province on a 5-min timescale for this research. This data is publicly available at a 1-h timescale.4 Fig. 9 below shows one week of PV and grid load data, starting on a Monday. The week was selected to show a variety of weather conditions, including sunny, cloudy, and mixed weather days. The solar PV output matches well in that there is no power output overnight and the grid load is at minimum overnight. However, it does not match in that the solar power is ramping down as the load is ramping up in the evenings. Note that the grid load data uses the scale on the right which is 10 times larger than the PV power scale on the left. For ramp-rates the net load was considered for 5- and 15-min increments. The net load is equal to the ramp-rate of the grid minus the ramp-rate of PV generation at that point in time. For example, if the grid is ramping up but solar is ramping down it will result in a larger overall ramp-rate. Fig. 10 shows the distribution of these ramp-rates for 5-min and 15-min intervals. The top plot for each time step shows the net load as described above, and the bottom plot shows the difference between the net load and the original grid load. This helps to emphasize the differences caused by the PV systems. Note that the abscissa scale is twice the magnitude for the 15-min period, even though it is three times as long as the 5-min period. In both figures, when PV is added to the grid the number of small magnitude ramp-rates are decreased while the number of large magnitude ramp-rates are increased. This change however is small since the PV system is 100 MW and contributes only a small part to the whole grid load which peaks at over 2000 MW. 4 http://oasis.nspower.ca/en/home/oasis/monthly-reports/hourly-total-net-novascotia-load.aspx.
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Fig. 8. Distribution of ramp-rates for 100 MW PV systems (distributed and centralized) given in MW per 5min and 15-min. On both plots the x-axis was capped at ± 50 MW/time interval. This resulted in 6 points outside this range to not be shown.
Fig. 9. One week of data comparing NS grid load and two 100 MW solar PV systems.
5. Conclusion
The results of the analyses point to several conclusions. From a spatial perspective it was shown that the 5 different geographic regions of the Halifax municipal area, spanning just over 80 km, receive similar annual solar resource. This was unexpected as the coastal regions anecdotally have more fog than inland regions. A distributed PV system is better in terms of reduced ramp-rates over short periods (5 min), but less so over longer periods (15 min). The range of ramp-rates was up to ± 50 MW/ 5 min for a 100 MW centralized system, whereas it peaks at ± 20 MW/ 5 min for a correspondingly sized distributed system. These centralized
Using a unique dataset of 215 regionally distributed pyranometers, we contrast performance of centralized and distributed PV systems and compare these against the electricity system loads. This work was made possible by the high-resolution (1-min) solar intensity data collected from real rooftop solar systems. Data analysis includes assessment of data quality issues which are paramount for highly distributed consumer based systems, along with performance metrics of power and ramp-rate. 8
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Fig. 10. Distribution of ramp-rates for grid load and net grid load given in MW per 5-min and 15-min periods.
ramp rates mean that the PV system can ramp up to half of its capacity in a 5-min period. These centralized PV system ramps are also more than twice the maximum ramp rates for the distributed PV system. For the 15-min ramps the centralized PV system can still ramp up to half of its capacity, however now it is over a longer timespan which makes it more manageable for an electricity grid operator. The numbers and multiples given above are regionally specific because of the source data. The analysis methods and contrasts between centralized and distributed PV system types are more broadly applicable. These findings are of value to those who develop solar policy/ projects and those who operate electricity utilities, as they give another point of contrast when weighing the many facets that influence a decision to support a specific PV system type.
Variability Model. Sol. Energy 110, 482–495. Kern, E.C., Russell, M.C., 1988. Spatial and temporal irradiance variations over large array fields. In: Conference Record of the Twentieth IEEE Photovoltaic Specialists Conference, vol. 2, pp. 1043–1050. Elsinga, B., van Sark, W., 2015. Spatial power fluctuation correlations in urban rooftop photovoltaic systems. Prog. Photovolt. Res. Appl. 23, 1390–1397. Hoff, T.E., Perez, R., 2010. Quantifying PV power output variability. Sol. Energy 84, 1782–1793. Klima, K., Apt, J., 2015. Geographic smoothing of solar PV: results from Gujarat. Environ. Res. Lett. 10. Lave, M., Stein, J.S., Ellis, A., Hansen, C.W., Nakashima, E., Miyamoto, Y., 2011. Ota City: Characterizing Output Variability from 553 Homes with Residential PV Systems on a Distribution Feeder. SAND2011-9011. Lave, M., Kleissl, J., Arias-Castro, E., 2012. High-frequency irradiance fluctuations and geographic smoothing. Sol. Energy 86, 2190–2199. Jamil, M., Anees, A.S., Subramaniam, M., Anwer, N., 2014. Reduction of power fluctuation in grid connected SPV plant through aggregate distributed generation. In: 2014 6th IEEE Power India International Conference (PIICON), pp. 1–6. Marcos, J., Marroyo, L., Lorenzo, E., García, M., 2012. Smoothing of PV power fluctuations by geographical dispersion. Prog. Photovolt. Res. Appl. 20, 226–237. Murata, A., Otani, K., 1997. An analysis of time-dependent spatial distribution of output power from very many PV power systems installed on a nation-wide scale in Japan. Sol. Energy Mater. Sol. Cells 47, 197–202. Otani, K., Minowa, J., Kurokawa, K., 1997. Study on areal solar irradiance for analyzing areally-totalized PV systems. Sol. Energy Mater. Sol. Cells 47, 281–288. Rosenbloom, D., Meadowcroft, J., 2014. Harnessing the Sun: reviewing the potential of solar photovoltaics in Canada. Renew. Sustain. Energy Rev. 40, 488–496. Jewell, W., Ramakumar, R., 1987. The Effects of Moving Clouds on Electric Utilities with Dispersed Photovoltaic Generation. IEEE Transactions on Energy Conversion EC-2 0, pp. 570–576. Wiemken, E., Beyer, H.G., Heydenreich, W., Kiefer, K., 2001. Power characteristics of PV ensembles: experiences from the combined power production of 100 grid connected PV systems distributed over the area of Germany. Sol. Energy 70, 513–518.
Acknowledgements This project was funded by the Nova Scotia Department of Energy. We appreciate the input and advisement on project structure by Peter Craig. We appreciate access to the solar data from Thermo-Dynamics Ltd., which was gathered by Tomi Allen. We appreciate access to electricity data from Nova Scotia Power Inc., which was provided by Dragan Pecurica. References Dyreson, A.R., Morgan, E.R., Monger, S.H., Acker, T.L., 2014. Modeling solar irradiance smoothing for large PV power plants using a 45-sensor network and the Wavelet
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Solar Energy journal homepage: www.elsevier.com/locate/solener
Contrasting distributed and centralized photovoltaic system performance using regionally distributed pyranometers
T
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Katherine Adye , Nathaniel Pearre, Lukas Swan Renewable Energy Storage Laboratory – Dalhousie University, 1360 Barrington St., PO Box 15000, Halifax, Nova Scotia B3H 4R2, Canada
A R T I C L E I N F O
A B S T R A C T
Keywords: Distributed Centralized Photovoltaic Ramp rate Electricity grid Integration
Using measured irradiance data from 215 homes across a municipal area we analyze the performance differences between distributed and centralized photovoltaic systems. Data on a one minute time step for a complete year was used, with pyranometers oriented perpendicular to nominal roof plane. The distributed and centralized groupings were defined and modeled with an aggregate 100 MW photovoltaic capacity. Analysis was performed in different spatial groupings with respect to power intensity and ramp-rate to determine the impact upon the regional electricity grid and temporal alignment with load. It was determined that the differences between distributed and centralized systems are relatively small at a 15-min or greater timescale. Within a 5-min timescale, centralized systems ramp more than twice as fast as distributed systems of identical capacity.
1. Introduction The province of Nova Scotia is considering policy alternatives to support uptake of solar photovoltaic (PV) installations for electricity generation. Such installations can be categorized as small-scale distributed systems or large-scale centralized systems. Distributed systems are typically sized 1–100 kW and are placed on buildings (residential, commercial, institutional) throughout communities using net-metered interconnections. Centralized systems are typically sized 1 + MW and are ground-mounted in a large defined area. (Rosenbloom and Meadowcroft, 2014) We compare the performance and output characteristics of distributed and centralized PV systems using a unique set of 215 pyranometers measured at high time step resolution over a one-year period. Special emphasis is placed on ramp-rates, which is the rise and fall of output power with respect to time. This metric is important because the Nova Scotia electricity utility must modulate other electricity generators to compensate for the fluctuations in solar PV output. We expect that the distributed nature of PV on buildings spread about communities will enhance consistency of production and reduce ramprates, when compared to specific centralized locations. To date several studies have been done on the effects of distribution on PV generation. These studies focus on smoothing of the PV output and look at variables such as density of the PV installations, distance between installations, installation size, and varying time steps. Some of these studies use completely modelled data, while others use measured data to produce a model. (Hoff and Perez, 2010) ⁎
modelled the effects of PV spacing by introducing a dispersion factor and calculating the output variability based on cloud speed, distance between PV panels, and size of the PV fleet. (Jewell and Ramakumar, 1987) modelled insolation and cloud patterns and looked at different sizes of PV fields with different cloud types to determine the amount of capacity lost over different time steps. (Kern and Russell, 1988) used data from several instrumented locations to model solar irradiance over a large area and then examined ramp-rates to determine that dispersed systems provide output smoothing. (Murata and Otani, 1997) also modelled irradiance using measured data but instead looked at smoothing through correlation and tie-line capacity between different service areas of a grid. In these studies the PV panels were modelled to be in the same orientation, or there was no mention of panel layout. Such oversimplification is unlikely to capture the variability of tilt and azimuth among highly distributed PV systems. Studies that use measured data give a better representation of real world expected performance. (Otani et al., 1997) used a grid of 9 pyranometers to measure irradiance values, examining fluctuation factor and power spectral density. Both of these metrics showed smoothing when comparing data combined from several sensors to data from a single sensor. (Lave et al., 2012) used a network of 6 sensors spread irregularly across a university campus. The irradiance data from these sensors was analysed with respect to ramp-rate and energy spectrum. It was shown that the 6 sites were independent at timescales shorter than 5-min and therefore produce a smoothing effect for shorter timescales. (Dyreson et al., 2014) used a grid of 9 and 25 sensors to represent different PV plant sizes and compared these to output from a
Corresponding author. E-mail addresses: [email protected] (K. Adye), [email protected] (N. Pearre), [email protected] (L. Swan).
https://doi.org/10.1016/j.solener.2017.11.042 Received 4 July 2017; Received in revised form 28 September 2017; Accepted 18 November 2017 0038-092X/ © 2017 Elsevier Ltd. All rights reserved.
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period an average of 145 homes are represented for each time step instead of the full 215. The nature of the HSC program means the resulting WEL data set is unique. This data represents homes from across the Halifax region with a variety of roof orientations and panel angles that represent true and practical installations of solar panels on rooftops. The effects of local weather trends on solar intensity can be seen for homes both close together and far apart. The nature of this data coming from real houses is a significant opportunity for this study. As opposed to “research” datasets with few points that require significant assumptions to scale up, these “consumer” datasets capture the variability of installations, such as rooftop panel orientations and shading from trees and other buildings. While this comes at the expense of the quality of instruments and consistency of reporting, we feel that such highly-distributed real-installation sensor networks give a very realistic dataset and this increases confidence in our findings.
single sensor. The variability index and ramp rate distributions were analysed and it was shown that the variability of a solar PV field is better represented by several sensors versus a single sensor. Aggregate power output data from centralized solar plants were used in several studies to examine the effects of distance between plants, number of plants in a combination, and size of the plants. (Jamil et al., 2014) uses 5 solar PV plants of equal size located between 50 and 275 km apart. The power and energy outputs were analysed and it was found that power fluctuations decrease significantly when the 5 plants are combined compared to a single plant. (Marcos et al., 2012) compared 7 PV plants of different rated capacities from 1 to 9.5 MW and separated between 6 and 320 km. Power fluctuations were analysed for different combinations of plants and it was found that both the magnitude and frequency of fluctuations are reduced when PV plants’ outputs are combined. (Klima and Apt, 2015) analysed the power spectral densities of combinations of up to 20 PV plants located up to 470 km apart. The results show that for the location studied connecting 4–5 plants provides about half of the smoothing compared to connecting all of the plants, and plants added after that show diminishing marginal returns. Other studies using measured data are those that use data from roof mounted PV systems, similar to our study. These studies however have fewer data sources, from 100 systems spread across Germany to 25 systems spread across the city of Utrecht, located in the Netherlands. (Wiemken et al., 2001) studied PV in Germany and looked at 1 panel versus all 100 panels with respect to power fluctuations and energy spectrum. They found that for the combined systems power fluctuations were reduced in magnitude and energy output was between 0 and 65% of the total installed capacity. (Elsinga and van Sark, 2015) studied PV in Utrecht and focussed on decorrelation distance with respect to time step and different weather conditions and seasons. Their results show that longer time steps result in larger decorrelation distances and that in winter the distance required for decorrelation is generally less than in summer. Our study uses measured data collected from 215 homes participating in the municipal Halifax Solar City program. This program installed sensors and data-acquisition systems on homes across the Halifax municipality, spanning approximately 82 km. This gives multiple years of solar intensity measurements on a 1-min time step from 215 rooftops at angles representative of real-world residential rooftop solar PV installations. This data set is unique in that the solar intensity measurements were not taken at ideal angles, and instead represent the broad range of roof angles and orientations that exist in the Halifax municipality. We examine these data and model distributed and centralized solar systems to determine the performance across spatial and temporal ranges.
2.1. Measurement device and data rate The solar intensity data was collected from each home using a photo-diode of size 1 mm by 1 mm. The photo-diode provides a linear electrical current output as a function of solar irradiance. This output is measured by an analog-to-digital card which converts from units of current (µA) to units of irradiance power (W/m2). These data are reported by the WEL data logger to an internet cloud database system. The data-acquisition system uses a propriety solar sensor and conversion package developed by Thermo-Dynamics Limited.2 To validate the proprietary system measurement accuracy, we took simultaneous measurements with a recently calibrated Apogee Instruments pyranometer model SP-215 and compared them to three nearby operating WEL sites on clear days. Note that the operating units were at private residences and so the Apogee pyranometer was placed such that all sites were within a 4 km radius. The WELs and Apogee orientation differences were limited to 7° azimuth and 10° slope. Validation data is shown in Fig. 1. Linear response produced R2 values of 0.96 or greater, and linear coefficients of 0.86–0.99. Because these are active sites with some spatial separation and orientation differences, these range of linear coefficients are expected. Data was collected on a 1-min time step. This high data-rate enables detailed ramp-rate analysis. While data acquisition systems typically record at a 15-min or 60-min rate, these are insufficient for investigating the short-term impacts on the electricity grid. Slower data sampling masks the true effects, giving an average response, and makes it difficult to confidently design for integration with fast reacting electricity systems. 2.2. Map
2. Data source Using the location data, a map of the homes in the HSC program was constructed, giving approximate locations of houses. As seen in Fig. 2 the homes span from the Western and Northern edges of the Halifax Regional Municipality to past Musquodoboit Harbour in the East, a distance of 82 km. There are many WEL units on the Halifax Peninsula and in Dartmouth corresponding to the higher population density of those areas. The homes have been divided into five groups based on their regional microclimate to help support spatial analysis.
The data used in this study was obtained through the Halifax Solar City (HSC) program. This program aided homeowners in the procurement and financing of solar thermal panels installed on their homes with the option of data monitoring using Web Energy Loggers (WEL).1 These WEL units recorded data on a 1-min basis to a cloud server. For this study we use only the solar intensity (W/m2) value obtained from a photo-diode mounted alongside and sensing perpendicular to the plane of the solar thermal panel. Thus, the data represents total incident radiation on the panels. The HSC program began collecting data on 1 May 2013, with additional homes fitted with reporting units over time. By 29 July 2015 there was data for 228 homes, of which 215 of these homes have location information. The analyses performed here use data from the period of July 2014 to June 2015. Due to gaps in the data during this 1
2.3. Panel layout The panel layout information, consisting of panel azimuth and tilt, was obtained for each of the 215 homes. Of these homes 4 were missing layout information. The azimuth values are spread evenly from 55°E to 61°W. There are 6 homes facing due south, 90 homes facing south-east, 2
http://www.welserver.com.
2
http://www.thermo-dynamics.com/.
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3.1. Data analysis The data was analysed from both temporal and spatial perspectives. The principal metrics for evaluation are power intensity (W/m2) and ramp-rate (W/m2/min.). These were analysed at different time scales (minutes to monthly) and distances (km). For temporal analysis the data was examined at both its original 1min resolution and at various other resolutions by averaging the 1-min data over a period. Other resolutions include 5-min, 15-min, and monthly. The data is presented in the form of statistical distributions, weekly plots, and yearly plots. To support spatial analysis interpretations, the houses were grouped into five geographic areas: Coast, West, North, Peninsula, and Dartmouth. The map presented in Fig. 2 shows the various groupings with different coloured markers. The groups each have a different number of homes, as shown in the legend of Fig. 2. The difference between the largest and smallest groups is ten homes, indicating good distribution. To compare centralized versus distributed PV systems, a group of homes was selected to represent a centralized PV field. This group contained 7 houses located within 1.5 km of each other, to represent the variability in solar resource across a large PV field. The group of houses had azimuth angles ranging from −18 to −21°, and tilt angles ranging from 30 to 45°. Out of the 365 day period studied there were only 210 days where all of the selected houses had data. These days are spread throughout the year and represent a variety of weather patterns
Fig. 1. Verification of solar pyranometer readings between three nearby WEL sites and a reference Apogee SP-215 pyranometer.
Fig. 2. Approximate locations of HSC homes with data monitoring, shown by regional group.
and seasons. It was preferred that these centralized homes be in the rural regions (North, West) due to the land area requirements of large centralized solar farms. However, only the Peninsula region offered an appropriately dense cluster of homes for this purpose. This is acceptable because the focus of this research is on ramp rates rather than energy production. To evaluate the degree to which the 7 houses reflect the solar resource variation across a similarly sized photovoltaic field, a sensitivity analysis was conducted. This consists of a comparison of ramp-rate
and 115 homes facing south-west. The tilt values range from 16° to 50° and there are 57 panels with an angle of 45°. The average tilt is 32.2°. A plot showing the distribution of panel orientations can be seen in Fig. 3.
3. Method This section discusses techniques and metrics for analyzing the data, and the measures used to validate data quality. 3
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Fig. 3. Plot showing the distribution of panel azimuth and tilt angles separated by geographic grouping.
Fig. 4. Plots showing the sensitivity analysis for one or more homes of the representative centralized group of seven homes.
sized PV capacity from each home to the total output of 100 MW. This would represent a 5 kW PV system on 20,000 homes, so each of these 215 homes represents about 93 homes in close proximity, such as those found in a community or neighbourhood. This assumption gives a combined PV system output that represents a widely-distributed solar policy that recognizes typical suburban residential layouts.
distributions for groups of houses ranging from 1 to 7 homes. The year of data discussed above, consisting of 210 days spread over the course of a year, was used to calculate the ramp rates. We found that beyond 4 homes, there was negligible change in the frequency of large-magnitude ramp-rates. This is shown through the plots in Fig. 4. These plots show the difference in yearly occurrences for ramp rate distributions between various numbers of homes and the group of all 7 homes. One home experiences many large solar intensity ramps compared to the group; but by 4 homes, differences greater than 150 W/m2 per 5-min period are very rare. This gives us confidence that 7 homes captures the bulk of this resource variability over the spatial length of 1.5 km, and that additional homes would not substantially change ramp-rate distributions. Our findings are consistent with those of (Lave et al., 2011), which show that the 99th percentile ramp-rates of 7 houses closely matches that of a dense cluster of 477 houses. In order to predict the effects of installed solar a 100 MW PV system was modelled for both centralized and distributed groupings. A value of 100 MW total installed capacity was used as this is an appropriate “order of magnitude” potential target for a solar electricity policy under investigation by the Nova Scotia government. The PV system model assumed 100 MW power output at 1000 W/m2 solar intensity, and proportions thereof. The model is not temperature compensated, but this has only a small impact on short-term variability of PV efficiency. The distributed system consists of all 215 homes and assumes equal
3.1.1. Ramp-rate When looking at solar data this output can be given as solar intensity (W/m2) or as modelled PV output electrical power (W or MW). Ramp-rate is the change in output over time. When speaking of solar PV scenario outputs, this report will give ramp-rate in units of MW per 5min period, or MW per 15-min period. We have confirmed with the Nova Scotia provincial electricity utility that these are good periods due to system adequacy and reserve requirements. By using 5- and 15-min periods we present a distribution for comparison across their control capabilities. Ramp-rate is of interest because of its effects on the control and stability of the electricity grid. Large uncontrolled ramp-rates from PV systems must be balanced and compensated for by fast-ramping (modulating) other sources of power, likely fossil fueled. This is undesirable, as such modulations place significant stress on conventional generators and reduce their energy efficiency. A possible source of large PV solar system ramp-rates are clouds moving over a service area. When a cloud covers a PV panel the output 4
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(86.85% remaining). 6. Manually remove any bad data identified visually after the above filters have been run. Visual inspection was completed for each reporting house. (86.75% remaining).
almost instantaneously ramps down, as there are no storage elements in PV modules.3 When the sun re-emerges the output of the panel experiences a ramp upward. If several panels are experiencing this at the same time (such as in a field of PV panels) then the effect is multiplied and the electricity grid sees very large ramp-rates.
4. Results 3.2. Data quality The analyses examine the solar resource in different spatial and temporal groupings, applying PV systems to the data and comparing their performance, and then comparing these PV systems to the net load on the electricity system.
As with all measured data, opportunities exist for data quality issues, and thus the data must be validated prior to analysis. This is especially important given the highly distributed consumer-grade pyranometers and cloud based data storage. Some issues are easily spotted visually when looking at several years of data, while other issues were found by focusing and checking the data one week at a time. Almost all the data issues affected only parts of the data set. This means that the data could have no issues, then an issue would begin suddenly, and then it would self-correct after a period of time. Some issues lasted for a couple of days to a week while others remained for several months. The quality issues found in the data include:
• • • • •
4.1. Solar resource analysis The first analysis was performed by looking at two consecutive days of data, a mixed weather day (sun and cloud) and a sunny day, on three different spatial scales. Plots of these days can be seen in Fig. 5.
• Single house: The top plot shows the solar intensity data for just one
Spikes in value – Solar intensity values should range from 0 or slightly negative (nighttime) to approximately 1200 W/m2 (sunny noontime with reflection from snow). Values outside of this range, including spikes over 25,000 W/m2, were seen. It is likely that electrical noise or a poor electrical contact caused these spikes. Constant data values – These showed up as long periods of values usually at zero (not overnight), but also appeared as other numeric values as well. Overnight offset – Some of the WELs showed a bias issue overnight by not reaching zero solar intensity. These overnight values could be consistently at one value, such as 100 W/m2, or the value could change each night. Intermittent data – This was seen as blocks of data missing from every day, such as all the overnight periods, and as data missing from random times throughout the day. Time shifted data – These are days when sunrise appears to occur just after midnight, the solar intensity peaks early in the morning, and the sun appears to set sometime before noon. These days had a several hour gap in the data at the end of the day.
• •
house for the two chosen days. This is to give the reader an understanding of the nominal curves and fluctuations at a site. Centralized system: The middle plot shows the solar intensity data for a key group of houses within a tight spatial area which represent the combined output of a large centralized facility. Distributed systems: The bottom plot gives five lines, each showing solar intensity data averaged across all the houses in each geographic group. This represents the combined output of highly distributed PV systems across many buildings within a given region.
The top plot illustrates nominal response of a single-house. The sunny day shows a clear half sinusoidal output corresponding with dawn, midday, and sunset. It is not a perfect half sinusoid of intensity given some morning intermittency (clouds) and an afternoon discontinuity that is likely caused by geometric shading and reflection. The intermittent cloudy day can be seen to also follow the half sinusoid shape, but has very significant peaks and valleys of intensity associated with clouds passing overhead. In just a matter of minutes, intensity drops from nearly full to nearly none, and rebounds shortly thereafter. The middle plot shows the centralized solar for each day which is similar in trend to the single-house, but with less variability due to the aggregation effects over the 1.5 km resource space. The bottom plot shows that there are only small differences between the averaged solar intensities of each group. This indicates that one region is not significantly better than any of the others in terms of power generation. This was somewhat unexpected since the coastal region is typically foggy in the morning which should result in lower solar intensity. This does not appear to be the case for the two days analysed. On the mixed weather day there are a few more differences, such as clouds over one area but not another. The next analysis compares the different geographic regions in terms of average monthly power intensity, as shown in Fig. 6. In addition to showing how the five regions compare month to month the plot also shows the overall trend for the year, which is typical of the northern hemisphere. In the year selected for observation (July 2014 to June 2015) there was a peak in May and as expected the winter values were lower and the summer values were higher. A peak in June is normally expected due to the longer days, however this does not take into account weather trends. There was not enough data to plot other years and see if this trend of a May peak was due to panel angle (where a May peak would occur every year) or weather trends (where a May peak only occurred due to good weather in the year chosen). Halifax is notably foggy in summertime due to its coastal area. On a month-to-month basis the average power intensity of each region is similar. One notable difference occurs in February and March where the North region had lower power intensity values compared to
The issues discussed above were removed using a set of filters that were run in the order listed below, complete with the quantity of data remaining after the operation. Only the affected sensors had data removed, instead of all the sensors for the affected time period. 1. Remove high and low values. Values removed are < −10 and > 1700 W/m2. (99.99% remaining). 2. Remove days that are missing more than 70 min of data. This was found to adequately identify days with large missing blocks of data, while accepting short term outages. (92.98% remaining). 3. Remove days that have large blocks (23 h) of low values. A low value is < 10 W/m2. The code determines the total number of cells that are low value or NaN and if the total is more than 23 h the whole day is removed. (88.20% remaining). 4. Remove days that have a large value 30 min before or after midnight. A large value is > 40 W/m2. Check previous day for a period of 30 min before midnight and current day for a period of 30 min after midnight. Only remove day if more than one data point is above limit. (87.43% remaining). 5. Remove days that have a standard deviation less than 5 W/m2. Snow covered sensors are realistic but result in a low standard deviation, so days from December to March were excluded from this rule.
3 Energy storage systems can be added alongside PV systems to mitigate these effects, but come at increased capital cost and maintenance.
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Fig. 5. Plots showing two days of solar data (May 13 = intermittent cloudy, May 14 = sunny) for different spatial groupings (top = one house; middle = centralized system; bottom = regional averages).
Fig. 6. Graph comparing average monthly power intensity for different geographic regions.
year the values were 20% ± 4%. If February and March are excluded this changes to 20% ± 2.1%. This shows that there are no significant differences between the regions in terms of average solar intensity.
the other regions. This was during a particularly snowy winter and is likely due to snow covering the panels. The power intensity values were compared to give a better idea of whether one region had a trend of higher production than another. If all five regions were equal, they would have a 20% share of each month. When looking at the whole 6
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Fig. 7. Example intermittent cloudy and sunny weather days for distributed and centralized PV systems.
changes. The difference between distributed and centralized here is less pronounced, with the range of the distributed ramps approaching that of centralized ramps. However, the centralized system still has more occurrences of ramps at larger magnitudes when compared to the distributed system. This averaging level (15 min) removes the effects of fast-moving intermittent singular clouds and focuses on larger cloud and weather transitions. Again, the distributions are symmetrical, with distributed PV systems having less aggregated ramp-rate. The magnitude of ramps are still within the ± 50 MW range, but are now spread over 15 min, which the electricity system operator will find easier to respond to.
4.2. PV scenario assessment of 100 MW An analysis was performed by modeling a 100 MW PV system using the data and examining the impacts of energy production and the resultant ramp-rates. Two of these PV systems were compared, a distributed system and a centralized system. These systems are described in Section 3.1. Fig. 7 shows the 100 MW PV system output of the centralized and distributed systems. This plot uses the same intermittent cloudy day and sunny day as was presented for the first analysis above. Generally, there is only a minor difference between the power outputs of the two systems. The centralized system achieves higher power output on the sunny day because the homes selected for this system have more favourable panel orientations. There is also a time offset between the peaks of the distributed and centralized systems on the sunny day. This asymmetry can be attributed to a 25° difference in the average azimuth of the panels in each grouping. Close examination of the intermittent cloudy day shows that while the distributed system experiences similar swings in output as the centralized system, the peaks and valley are somewhat dampened. It can be seen that the distributed line has fewer and less major oscillations throughout the day than the centralized system. To gain greater perspective on these oscillations, the ramp-rate must be considered statistically. To determine the differences in fluctuations between the distributed and centralized system, a ramp-rate investigation was carried out. The 5min scale represents very short term fluctuations such as patchy clouds on a windy day, and the 15-min scale represents longer cloudy periods such as an oncoming storm front. The ramp-rate values for these time intervals were found by averaging the 1-min data into 5- and 15-min time steps, then finding the ramp-rates between these new points. These ramp-rate distributions are plotted in Fig. 8. Note that the y-axis on both plots uses a log scale to give detail at the high ramp-rate tails on the x-axis. Starting with the 5-min time step in the top of Fig. 8 we can see that the distributed system has more occurrences of small ramp-rates and fewer occurrences of large magnitude ramp-rates when compared to the centralized system. All the distributed ramp-rates are also within the range of less than ± 20 MW/5 min whereas the centralized system has rates more than twice that at almost ± 50 MW/5 min. The distributions are symmetrical, which indicates that intermittent clouds pass on as fast as they arrive (it also confirms the sun rises as fast as it sets). The chart gives results with a high degree of granularity, but it may be summarized that for the centralized system, approximately 1300 fiveminute periods will experience positive or negative ramp-rates in the range of 15–25 MW/5 min. This number, along with occasional ramprates of ± 40 MW/5 min is quite significant considering only 100 MW capacity of solar PV is modelled. Moving on to the 15-min time step in the bottom of Fig. 8 the trend
4.3. Integration of PV with net electricity load The 100 MW PV system models were compared to the load of the provincial electricity system. Analysis was conducted on weekly and annual timescales, and the PV systems’ effect on grid ramp-rate were evaluated. The provincial electricity utility, Nova Scotia Power, provided time series data of grid load in the province on a 5-min timescale for this research. This data is publicly available at a 1-h timescale.4 Fig. 9 below shows one week of PV and grid load data, starting on a Monday. The week was selected to show a variety of weather conditions, including sunny, cloudy, and mixed weather days. The solar PV output matches well in that there is no power output overnight and the grid load is at minimum overnight. However, it does not match in that the solar power is ramping down as the load is ramping up in the evenings. Note that the grid load data uses the scale on the right which is 10 times larger than the PV power scale on the left. For ramp-rates the net load was considered for 5- and 15-min increments. The net load is equal to the ramp-rate of the grid minus the ramp-rate of PV generation at that point in time. For example, if the grid is ramping up but solar is ramping down it will result in a larger overall ramp-rate. Fig. 10 shows the distribution of these ramp-rates for 5-min and 15-min intervals. The top plot for each time step shows the net load as described above, and the bottom plot shows the difference between the net load and the original grid load. This helps to emphasize the differences caused by the PV systems. Note that the abscissa scale is twice the magnitude for the 15-min period, even though it is three times as long as the 5-min period. In both figures, when PV is added to the grid the number of small magnitude ramp-rates are decreased while the number of large magnitude ramp-rates are increased. This change however is small since the PV system is 100 MW and contributes only a small part to the whole grid load which peaks at over 2000 MW. 4 http://oasis.nspower.ca/en/home/oasis/monthly-reports/hourly-total-net-novascotia-load.aspx.
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Fig. 8. Distribution of ramp-rates for 100 MW PV systems (distributed and centralized) given in MW per 5min and 15-min. On both plots the x-axis was capped at ± 50 MW/time interval. This resulted in 6 points outside this range to not be shown.
Fig. 9. One week of data comparing NS grid load and two 100 MW solar PV systems.
5. Conclusion
The results of the analyses point to several conclusions. From a spatial perspective it was shown that the 5 different geographic regions of the Halifax municipal area, spanning just over 80 km, receive similar annual solar resource. This was unexpected as the coastal regions anecdotally have more fog than inland regions. A distributed PV system is better in terms of reduced ramp-rates over short periods (5 min), but less so over longer periods (15 min). The range of ramp-rates was up to ± 50 MW/ 5 min for a 100 MW centralized system, whereas it peaks at ± 20 MW/ 5 min for a correspondingly sized distributed system. These centralized
Using a unique dataset of 215 regionally distributed pyranometers, we contrast performance of centralized and distributed PV systems and compare these against the electricity system loads. This work was made possible by the high-resolution (1-min) solar intensity data collected from real rooftop solar systems. Data analysis includes assessment of data quality issues which are paramount for highly distributed consumer based systems, along with performance metrics of power and ramp-rate. 8
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Fig. 10. Distribution of ramp-rates for grid load and net grid load given in MW per 5-min and 15-min periods.
ramp rates mean that the PV system can ramp up to half of its capacity in a 5-min period. These centralized PV system ramps are also more than twice the maximum ramp rates for the distributed PV system. For the 15-min ramps the centralized PV system can still ramp up to half of its capacity, however now it is over a longer timespan which makes it more manageable for an electricity grid operator. The numbers and multiples given above are regionally specific because of the source data. The analysis methods and contrasts between centralized and distributed PV system types are more broadly applicable. These findings are of value to those who develop solar policy/ projects and those who operate electricity utilities, as they give another point of contrast when weighing the many facets that influence a decision to support a specific PV system type.
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Acknowledgements This project was funded by the Nova Scotia Department of Energy. We appreciate the input and advisement on project structure by Peter Craig. We appreciate access to the solar data from Thermo-Dynamics Ltd., which was gathered by Tomi Allen. We appreciate access to electricity data from Nova Scotia Power Inc., which was provided by Dragan Pecurica. References Dyreson, A.R., Morgan, E.R., Monger, S.H., Acker, T.L., 2014. Modeling solar irradiance smoothing for large PV power plants using a 45-sensor network and the Wavelet
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