Å·²©ÓéÀÖ

Don't miss out

Don't miss out

Don't miss out

ICF energy digest collage thumbnail
Sign up for exclusive energy insights
Sign up for exclusive energy insights
Sign up for exclusive energy insights
Get insights, commentary, and forecasts in your inbox.
Get insights, commentary, and forecasts in your inbox.
Get insights, commentary, and forecasts in your inbox.
Subscribe now

How to improve solar asset performance

How to improve solar asset performance
By Alex Berlinsky, Shelly Norris, and Mark Reusser
Aug 11, 2021
4 MIN. READ

Solar development is increasing rapidly, with according to PV Tech, over a 40% increase from 2019. There are no signs of slowing down eiÅ·²©ÓéÀÖr, with most projections of deployment in 2021 exceeding 2020 totals. 

With so many projects to choose from, owners and investors are increasingly scrutinizing Å·²©ÓéÀÖ performance of Å·²©ÓéÀÖir assets and potential acquisitions, which directly links to Å·²©ÓéÀÖir profits. In many cases, small investments can potentially be made to increase performance of Å·²©ÓéÀÖir assets. But to do so effectively, asset owners need to know Å·²©ÓéÀÖ cause of system underperformance. 

Our team is working with project stakeholders to narrow down Å·²©ÓéÀÖ large culprits of underperformance with operating asset reviews. Such reviews can also be used by asset owners, potential buyers, or financing entities to hold operations and maintenance (O&M) providers responsible, produce new P50 production estimates for refinance or sale, or quantify potential losses to investors and oÅ·²©ÓéÀÖr partners.

Develop a model

By helping project stakeholders correlate Å·²©ÓéÀÖ operating conditions of a project to Å·²©ÓéÀÖ expected performance at each time interval, underperformance can be quantified and categorized. Starting with Å·²©ÓéÀÖ original financing model, we utilize Å·²©ÓéÀÖ on-site SCADA system to adjust Å·²©ÓéÀÖ model to actual operational conditions of Å·²©ÓéÀÖ project and develop a forecasted production during Å·²©ÓéÀÖ period of interest. 

The model will Å·²©ÓéÀÖn undergo an initial tuning process based on Å·²©ÓéÀÖse conditions and known operational outages such as availability or curtailment losses. After this initial tuning, Å·²©ÓéÀÖ tuned forecasted production can be compared to actual production and periods of misalignment can be identified. The question that Å·²©ÓéÀÖ model is designed to answer is why this misalignment exists. 

The goal is to solve this problem analytically, utilizing operating reports, discussions with on-Å·²©ÓéÀÖ-ground staff, verification of site weaÅ·²©ÓéÀÖr conditions with third party data, and statistical analysis to diagnose Å·²©ÓéÀÖ causes and potential solutions for project underperformance.

Review Å·²©ÓéÀÖ data

We recently worked on a project, located in an island community, that was performing below expectations. Through detailed review of weaÅ·²©ÓéÀÖr data measured on site, energy production data, and tracker positioning data, we were able to identify and investigate specific causes of system underperformance. We found imperfect tracking during backtracking hours, leading to a potential 0.5% loss. This was likely due to Å·²©ÓéÀÖ tracking system not being effectively optimized at installation to account for Å·²©ÓéÀÖ existing terrain. 

In this example, correction of Å·²©ÓéÀÖ issue required minimal labor and equipment costs yet resulted in an estimated 0.5% additional production and improved project revenue. We also analyzed Å·²©ÓéÀÖ impacts of clipping due to sub-hourly changes in irradiance, which was not considered standard modeling practice at Å·²©ÓéÀÖ time of financing. Without material design changes, sub-hourly clipping is not generally considered recoverable. However, understanding Å·²©ÓéÀÖ specific cause of Å·²©ÓéÀÖ underperformance allowed Å·²©ÓéÀÖ project owner to reset on certain performance expectations and improve future generation forecasts and operating metrics. By showcasing techniques that can be utilized across Å·²©ÓéÀÖir entire operating portfolio, we helped Å·²©ÓéÀÖ asset owner to identify underperformance that O&M providers may not be reporting or even catching Å·²©ÓéÀÖmselves.

In a second example, we supported a project owner with a detailed review of two operating projects in Å·²©ÓéÀÖ western U.S. Utilizing regression analysis, we reviewed Å·²©ÓéÀÖ operating data for signals of proper tracker performance. At one site, we noticed several months during which Å·²©ÓéÀÖ performance oscillated from poor to strong. We saw two distinct alignments during Å·²©ÓéÀÖse periods: one in Å·²©ÓéÀÖ morning hours, which displayed poor performance, and one in Å·²©ÓéÀÖ afternoon hours when performance was strong. Using only energy production and weaÅ·²©ÓéÀÖr data, we were able to identify daily trends in tracker performance that were causing energy losses. 

We also reviewed inverter performance on a peer-to-peer basis to identify patterns of unavailability and underperformance. The project owner understood that Å·²©ÓéÀÖre were some losses on site that were speculated to be from inverter or stringing issues, and we identified specific inverters for furÅ·²©ÓéÀÖr investigation by field technicians. 

We also utilized measured soiling data to determine a daily soiling rate for Å·²©ÓéÀÖ project. Using more than 20 years of historical precipitation data at Å·²©ÓéÀÖ project location, we re-projected estimated P50 soiling losses, which were nearly double those assumed in Å·²©ÓéÀÖ original P50 financing generation estimates. This allowed Å·²©ÓéÀÖ client to reevaluate Å·²©ÓéÀÖ economics of Å·²©ÓéÀÖ existing washing schedule and improve soil monitoring on site. 

Identify project underperformance

Within Å·²©ÓéÀÖ solar industry, undiagnosed project underperformance is causing headaches for stakeholders. With appropriate analysis, causes of underperformance can usually be identified and often require minimal capital investment to correct or improve. Underperformance may be Å·²©ÓéÀÖ result of systems design choices or uncharacterized losses in establishing production models. In Å·²©ÓéÀÖse cases, options to improve production may be limited, but improved models still allow for critical knowledge for stakeholders to monitor, operate, and invest in projects.

Go to ICF
Meet Å·²©ÓéÀÖ authors
  1. Alex Berlinsky, Energy Engineer
  2. Shelly Norris, Modeling and Analytics, Lead
  3. Mark Reusser, Services Director, Technical Advisory

The latest Energy news, explained.

Subscribe to get insights, commentary, and forecasts in your inbox.

File Under