Determining Optimal PV System Monitoring Granularity
Inside this Article
Stiff competition in PV development is driving the need to simultaneously reduce costs and increase performance of solar assets. EPC contractors and solar integrators must be able to design, procure and construct projects at lower total costs to earn business. Financers, seeking lower capital costs from investors, apply pressure to reduce production risk on their downstream EPC, integrator or operations partners. Once an afterthought, the selection of monitoring systems is getting more attention at earlier stages of project development because monitoring is an essential tool for maintaining, managing and optimizing long-term system performance and revenue.
Small improvements in performance can have an amplified effect on net profit, so visibility is crucial. While most PV sites are instrumented with a revenue-grade meter, this monitoring approach alone rarely provides sufficient performance data. With pressure to balance cost, performance and risk, can developers afford to install additional dc-side data acquisition systems to safeguard against unforeseen losses? Can they afford not to? Although dozens of variables and risk factors come into play, an elegantly simple model can help guide these decisions in an economic and rational way.
A Call to Action
Monitoring provides visibility, both locally and remotely, to energy production and its variations, and assists in qualifying the need for field repairs. At minimum, monitoring provides the hourly, daily, weekly, monthly and quarterly assurances of expected performance. When a deviation or underperformance occurs, monitoring provides the first critical step in a longer process of detection, analysis and action.
The best monitoring solutions in the market create a call to action. The sequence of detection-analysis-action involves many factors, such as measurement accuracy, appropriate analysis, effective reporting, weather, identification of a root cause or causes, prescribing repair tickets, procuring parts, and scheduling technicians and on-site procedures. More often than not, diagnosis and repair require a site visit by a service technician. This “truck roll” is costly. Owners and operators must carefully consider the appropriate level of information needed from each site to guide this decision. How important is it to avoid performance and revenue losses? What variability in performance is expected? What level of site instrumentation is ideal?
A Revenue-Grade Meter Is Not Enough
Commercial-grade monitoring solutions typically start with a site-level revenue-grade meter to measure the collective ac power generation at the site. These highly accurate meters can measure power output with ±0.2% accuracy. So what are the potential benefits of inverter-, zone-, string- and module-level monitoring? (See Figure 1.)
Think of the power flow from dc to inversion to ac as one connected path that is measured at multiple points. A typical monitoring setup has an ac revenue meter at ±0.2% accuracy and an irradiance meter at ±5% accuracy. Operators often compare readings from the two meters to judge system performance. However, the accuracy of such comparisons are at the mercy of the weakest link in the chain. Statistics on error propagation imply an error of greater than 5% when taking the difference of these two measurements to judge performance issues. You need better granularity to achieve higher accuracy.
We interviewed asset managers who stated they often do not react to site underperformance unless it is greater than 5%, or in some cases 10%, of the expected monthly or quarterly target. Why? The combination of irradiance variation, instrumentation noise, normal system variation and lack of granularity add up to inaction.
For example, Figures 2 and 3 illustrate data from a 350 kWac PV site in North America. The two plotted graphs include data from the same system on two consecutive days. Figure 2 shows these two plots set side by side. They look similar, and at a casual glance they probably would not cause concern. However, if overlaid as in Figure 3, these two seemingly normal production days reveal differences in production of approximately 5%.
In this example, a seemingly indiscernible difference represents 5% in production losses. For a 350 kWac system producing electricity at a value of $0.15/kWh over a month, this 5% loss is worth several hundred dollars, and over a year it is worth approximately $4,000. Would you accept such losses if you could easily prevent them at a fraction of the cost of the lost revenue?