Assessing solar PV performance is crucial to ensure optimal plant operation and financial feasibility. This is relevant for any scale - on the wide spectrum from residential size to solar fields. A whole field of applied mathematics has developed to address this issue, with multiple researchers engaging in model optimizations to find the best possible tools and models to assess PV performance. There is a variety of tools for the task - such as onsite weather sensors, satellites, regional weather stations and novel solutions - all essentially utilized for calculating PV performance at various levels of accuracy.
The basic level of performance assessment for solar PV can be performed with common tools - manual review of inverter or utility energy meter and comparing the readings with historical monthly averages of the same system. Alternatively, a better independent benchmark for monthly performance assessment can be made using a performance model (with modeling tools such as PVSyst, PVSol, PVWatts and other). Those assessment techniques typically offer 15-20% accuracy on monthly resolution and about 10% accuracy on annual resolution - which is considered sufficient for financial reports. Another unsophisticated technique is a simple peak system power measurement (simplified way to measure Power Performance Index or PPI) - this can provide a fair estimate of system performance during the month at about 15% accuracy, but can even be utilized for daily resolution in many cases though with an even lower accuracy. Those methods are a sort of industry standard for residential and small commercial segment due to low cost and simplicity and are enough for basic financial reports, but not for professional Operation and Maintenance (O&M) requirements.
The advanced level of PV performance assessment can only be achieved with independent tools as a benchmark. Those can be onsite weather sensors, satellites or regional weather sensors. All can be utilized to calculate performance indicators including Performance Ratio (PR), Power Performance Index (PPI) and Energy Performance Index (EPI), though with varying levels of accuracy. While high-tier onsite weather sensors can enable accuracy of 5% on daily resolution (with Temperature correction), the lower tier weather sensors and satellites enable to reach an accuracy of just about 10%. Regional weather stations might be even less accurate, but note that those numbers are daily resolution enabling professional O&M.
The expert level of PV performance assessment is achieved via state-of-the-art hardware for weather sensing combined with professional software tools, utilizing Proprietary Algorithmics and often also Artificial Intelligence. The hardware is properly calibrated physically and also corrected with software in real-time, to reach an accuracy level of 2-5% on daily resolution. Such advanced tools are highly sensitive and enable to discover even minor anomalies in solar facilities in real-time. The only problem of this high-tier solution is the cost, requiring both expensive hardware and professional software.
Soltell is excited to take part in this global effort to optimize solar PV energy finance, operations and integration with Sensorless technology for measuring PV performance and with the proprietary developed "Intactness" index to measure site-specific performance at high accuracy. The Sensorless technology allows to apply quantitative performance assessment on any PV plant without dependence on external weather sensors, including obtaining Energy Pefrormance Index (EPI), Power Performance Index (PPI), Soiling Ratio (SR) and Availability. The Intactness index is in fact a superior quantitative approach to assess PV performance, combining both technical assessment of system components status and quantitative performance indicators with Proprietary Algorithmics. Way more accurate than monthly performance models based on seasonal averages (which have a monthly deviation of ±20% due to weather variation and model inaccuracies) and even superior to other performance indexes utilized across the decentralized energy resources (DERs) industry.
Soltell's Sensorless technology enables to apply the cost-effective Propritary Algorithm approach to residential and commercial PV plants, reaching an uncertainty level of 2-5% on daily resolution and hence performance assessment accuracy level of 95-95%, enabling superb industrial automation with priority-enabling ratings and predictive maintenance on par with the high-tier tools.
Interested in Sensorless technology for measuring PV performance and integrating it into an energy management software or a service application? Fill-in the demo request to get more details.