Solar Resource Reconciliation - 2014 SPI
1
What’s Going on at My Rooftops? System Issue or Just a Cloudy Day? Satellite Data for Solar Performance Reconciliation Francesca Davidson, Technical Communications Manager Gwendalyn Bender, Product Manager for Energy Assessment Vaisala INTRODUCTION It’s no secret that PV installations are on the rise in the U.S. Last year set a record year with 4750 MW installed and nearly 2000MW of that capacity was distributed generation from residential and commercial rooftops. This year is proving to be another strong growth year particularly for residential solar. All growth is, of course, very encouraging, but it is also forcing the energy system and the solar industry itself to adapt very quickly to manage the energy influx. To maximize the value of large fleets of rooftop solar, companies must use a range of monitoring software and devices. These technologies track huge amounts of real time information at the panel and inverter level, particularly with respect to power production. However, there is a key piece of information missing from the data stream – information about local irradiance conditions, or the actual source of the power, which is a critical parameter for the success of the PV system, sector, and the business models adopted. SOLAR IRRADIANCE VARIABILITY While less variable then wind, solar irradiance is not immune to climatic variability. It is impacted by a range of factors, from unpredictable events like volcanic eruptions and wild fires to cyclical events like monsoons and hurricanes. A stronger than normal North American Monsoon and the Yosemite National Park’s Rim Fire made the summer of 2013 no exception. The maps below illustrate these anomalies by showing how solar irradiance varied from average across the U.S. Figure 2. at right shows solar irradiance variance from normal on a +/-20% scale for each month (June-August) for both Global Horizontal Irradiance (or GHI, the key variable for PV projects) and Direct Normal Irradiance (or DNI, the key variable for solar thermal or tracking PV projects). Figure 1. at left shows the variance from normal on a +/-10% scale over the entire period (June-August) for GHI as well the locations of solar projects 1MW or larger. Based on the results of this study, the conclusion we draw is that massive projects now online in the Southwest and the concentration of numerous commercial installations in the Northeast saw reduced production – not due to a failure of equipment, but to solar resource variability. THE SOLUTION To bridge the information gap, satellite derived irradiance datasets are a cost- effective solution that easily integrate with existing monitoring systems through application programing interfaces (APIs). Available globally from Vaisala’s 3TIER Services and from a handful of other providers for the United States, this information helps evaluate what happened yesterday, last week, and last month to reconcile recent power performance. Frequent access to recent conditions allows analysts to do timely maintenance review by running hourly irradiance through a power curve to compare “theoretical power,” or the amount of power the project should have produced, with actual output. By plugging irradiance data into existing monitoring systems, remote operators can make informed decisions about power management, system improvements, repairs, and maintenance. THE PROBLEM Due to the rapid growth of distributed solar over the past few years, owner-operators managing large portfolios of rooftop generation now face the issue of performance reconciliation on a daily basis. The new challenge of determining whether underperformance was caused by weather or by equipment now affects both large and mid-sized companies with geographically dispersed fleets. Since these rooftop sites are operated remotely, owners have limited context on local conditions. When power suddenly dips, they cannot simply step outside, as a homeowner might, to see if the weather changed or if there is a system problem. Utility-scale projects typically have a full staff managing operations and high-quality ground measurements to reconcile recent performance. The scale of rooftop installations makes deploying a ground crew or measurement equipment at every location prohibitively expensive. However, companies like SolarCity and SunEdison still need to maximize the value of their generation portfolio while minimizing maintenance costs the same way a utility-scale project does. When the power goes down at a specific location they need to understand why. Was it just a cloudy day or is there a system issue such as soiling, shading, or equipment failure? Figures 4-7 show how these issues might impact performance. Due to the expense and scarcity of high-quality ground station data, satellite based methodologies developed by the global scientific community have become a broadly accepted alternative for estimating surface irradiance. In fact, they have proven to be the most accurate estimate of solar resources beyond 25 km of a well-maintained ground station. This technology uses visible satellite imagery to determine cloudiness, which is then combined with additional data sources such as elevation, snow cover, and atmospheric turbidity from water vapor and aerosols (see Figure 3. for more detail). The final result is a long-term (15 to 16 year) modeled record of surface irradiance at any location worldwide. While not perfectly accurate, (average error for uncorrected data from the 3TIER global dataset is 5%) even costly ground measurements can experience substantial errors, which makes satellite data a practical and feasible option. Additionally, satellite derived irradiance datasets provide the benefit of a long, historical record, which gives operators monthly context on how the recent month compared to the long-term. These datasets are better suited for reconciliation than TMY (Typical Meteorological Year) data, since TMY datasets only represent average conditions, not actual conditions over a specific period of time. Since pre-construction energy estimates for rooftop installations are often calculated using TMY, owner-operators often enter the power production phase without a realistic sense of the variability in output they are likely to see. To maximize the profitability of distributed generation, reconciling performance at rooftop sites and understanding power variability is becoming increasingly critical. As seen in the maps at left, solar irradiance can vary significantly – even in sunny, desert regions during peak generation months. The low production experienced in the Southwest the summer of 2013 is a prime example. Figure 1. GHI Variance from Normal for 2013 Summer Months Figure 2. Monthly GHI and DNI Variance from Normal Figure 4. Process for Calculating Surface Irradiance from Satellite Imagery Figure 4. Normal Performance on a Clear Day 0 200 400 600 800 1000 1200 Clear Sky GHI Actual GHI Modeled Power Actual Power Figure 5. Performance Suggesting Soiling 0 200 400 600 800 1000 1200 Clear Sky GHI Actual GHI Modeled Power Actual Power Figure 6. Normal Performance on a Cloudy Day Figure 7. Performance Suggesting Shading 0 200 400 600 800 1000 1200 Clear Sky GHI Actual GHI Modeled Power Actual Power 0 200 400 600 800 1000 1200 Clear Sky GHI Actual GHI Modeled Power Actual Power Figure 3. Process for Calculating Surface Conditions with Satellite Imagery
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Transcript of Solar Resource Reconciliation - 2014 SPI
What’s Going on at My Rooftops? System Issue or Just a Cloudy Day? Satellite Data for Solar Performance Reconciliation
Francesca Davidson, Technical Communications Manager
Gwendalyn Bender, Product Manager for Energy Assessment Vaisala
INTRODUCTION It’s no secret that PV installations are on the rise in the U.S. Last year set a record year with 4750 MW installed and nearly 2000MW of that capacity was distributed generation from residential and commercial rooftops. This year is proving to be another strong growth year particularly for residential solar. All growth is, of course, very encouraging, but it is also forcing the energy system and the solar industry itself to adapt very quickly to manage the energy influx. To maximize the value of large fleets of rooftop solar, companies must use a range of monitoring software and devices. These technologies track huge amounts of real time information at the panel and inverter level, particularly with respect to power production. However, there is a key piece of information missing from the data stream – information about local irradiance conditions, or the actual source of the power, which is a critical parameter for the success of the PV system, sector, and the business models adopted.
SOLAR IRRADIANCE VARIABILITY
While less variable then wind, solar irradiance is not immune to climatic variability. It is impacted by a range of factors, from unpredictable events like volcanic eruptions and wild fires to cyclical events like monsoons and hurricanes. A stronger than normal North American Monsoon and the Yosemite National Park’s Rim Fire made the summer of 2013 no exception. The maps below illustrate these anomalies by showing how solar irradiance varied from average across the U.S. Figure 2. at right shows solar irradiance variance from normal on a +/-20% scale for each month (June-August) for both Global Horizontal Irradiance (or GHI, the key variable for PV projects) and Direct Normal Irradiance (or DNI, the key variable for solar thermal or tracking PV projects). Figure 1. at left shows the variance from normal on a +/-10% scale over the entire period (June-August) for GHI as well the locations of solar projects 1MW or larger. Based on the results of this study, the conclusion we draw is that massive projects now online in the Southwest and the concentration of numerous commercial installations in the Northeast saw reduced production – not due to a failure of equipment, but to solar resource variability.
THE SOLUTION To bridge the information gap, satellite derived irradiance datasets are a cost-effective solution that easily integrate with existing monitoring systems through application programing interfaces (APIs). Available globally from Vaisala’s 3TIER Services and from a handful of other providers for the United States, this information helps evaluate what happened yesterday, last week, and last month to reconcile recent power performance. Frequent access to recent conditions allows analysts to do timely maintenance review by running hourly irradiance through a power curve to compare “theoretical power,” or the amount of power the project should have produced, with actual output. By plugging irradiance data into existing monitoring systems, remote operators can make informed decisions about power management, system improvements, repairs, and maintenance.
THE PROBLEM Due to the rapid growth of distributed solar over the past few years, owner-operators managing large portfolios of rooftop generation now face the issue of performance reconciliation on a daily basis. The new challenge of determining whether underperformance was caused by weather or by equipment now affects both large and mid-sized companies with geographically dispersed fleets.
Since these rooftop sites are operated remotely, owners have limited context on local conditions. When power suddenly dips, they cannot simply step outside, as a homeowner might, to see if the weather changed or if there is a system problem. Utility-scale projects typically have a full staff managing operations and high-quality ground measurements to reconcile recent performance. The scale of rooftop installations makes deploying a ground crew or measurement equipment at every location prohibitively expensive. However, companies like SolarCity and SunEdison still need to maximize the value of their generation portfolio while minimizing maintenance costs the same way a utility-scale project does. When the power goes down at a specific location they need to understand why. Was it just a cloudy day or is there a system issue such as soiling, shading, or equipment failure? Figures 4-7 show how these issues might impact performance.
Due to the expense and scarcity of high-quality ground station data, satellite based methodologies developed by the global scientific community have become a broadly accepted alternative for estimating surface irradiance. In fact, they have proven to be the most accurate estimate of solar resources beyond 25 km of a well-maintained ground station. This technology uses visible satellite imagery to determine cloudiness, which is then combined with additional data sources such as elevation, snow cover, and atmospheric turbidity from water vapor and aerosols (see Figure 3. for more detail). The final result is a long-term (15 to 16 year) modeled record of surface irradiance at any location worldwide.
While not perfectly accurate, (average error for uncorrected data from the 3TIER global dataset is 5%) even costly ground measurements can experience substantial errors, which makes satellite data a practical and feasible option. Additionally, satellite derived irradiance datasets provide the benefit of a long, historical record, which gives operators monthly context on how the recent month compared to the long-term. These datasets are better suited for reconciliation than TMY (Typical Meteorological Year) data, since TMY datasets only represent average conditions, not actual conditions over a specific period of time.
Since pre-construction energy estimates for rooftop installations are often calculated using TMY, owner-operators often enter the power production phase without a realistic sense of the variability in output they are likely to see. To maximize the profitability of distributed generation, reconciling performance at rooftop sites and understanding power variability is becoming increasingly critical. As seen in the maps at left, solar irradiance can vary significantly – even in sunny, desert regions during peak generation months. The low production experienced in the Southwest the summer of 2013 is a prime example.
Figure 1. GHI Variance from Normal for 2013 Summer Months Figure 2. Monthly GHI and DNI Variance from Normal
Figure 4. Process for Calculating Surface Irradiance from Satellite Imagery
Figure 4. Normal Performance on a Clear Day
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Clear Sky GHI
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Actual Power
Figure 5. Performance Suggesting Soiling
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Actual GHI
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Figure 6. Normal Performance on a Cloudy Day Figure 7. Performance Suggesting Shading
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Figure 3. Process for Calculating Surface Conditions with Satellite Imagery
