Although the solar industry sees itself as young, its assets are aging. Owners still struggle with the complexity of making the best use of big data analysis to improve plant efficiency and profitability. Ragna Schmidt-Haupt, of Everoze, examines why this has not changed, and what can or should be improved. Artificial intelligence, advanced data analytics, automated assessments and smart monitoring software – holistic solar asset management starts here.
From pv magazine, January edition.
Many solar assets are reporting high availability, but are actually not performing as well as they could. Focus is given to tightening operations and maintenance (O&M) contract terms and putting cost reduction pressure onto contractors, leaving little room for quality analysis.
Exactly three years ago, as a guest author for pv magazine, I flagged the untapped value to be made from transforming data into intelligence. Although some silver linings can be seen on the horizon, there are still many gaps. Vast quantities of data generated by solar assets are becoming available to owners and operators. Much of them remain untouched instead of being transformed into intelligence. Acting on acquired intelligence would lead to smarter operations – maximizing energy production, minimizing downtime and reducing life cycle O&M costs.
So why is the transition to smarter operations happening so slowly, despite potential profitability gains of 3% or beyond? How come the digital revolution is still mostly in people’s heads, instead of grounded in the fields? We should start perhaps by remembering the bigger picture: solar technology has matured to the point where there are significantly lower failure rates than wind or other comparable energy assets. O&M contractors have also improved efficiency with growing fleets under their management, even if this economy of scale may be reaching its natural limit in some cases. Their duties are often narrowly defined in O&M contracts delivered for ever lower fees. Many issues only get detected when non-standard assessments or out-of-the-box approaches are tried at different steps in the asset management value chain.
But despite the obvious gains, we cannot escape the reality that asset management teams manage much smaller budgets than their counterparts in investment teams. Owners tend to focus more on new investments and less on improving existing assets. An overwhelming emphasis is placed on cutting costs, leaving little room for quality analysis in operations. On top of this, monitoring and communications systems appear fragmented and lacking when it comes to the all-important question of data acquisition.
Most O&M contractors offer a single software platform for monitoring, optimization and control. But, as always, the devil is in the detail. Valuable opportunities to make use of digital data are often missed. In spite of the hope new components with built-in data analytics functions will revolutionize our understanding of the long-term condition of assets, re-powering opportunities still face the challenge of significant unbudgeted investment.
I take comfort though, that at least one of my predictions from three years ago has come true. Back then I said data analysis from drones would be part of the future toolkit of O&M operators and technical advisors. My team was one of the first to equip an unmanned aerial vehicle with a lightweight thermographic camera to accelerate site inspection of solar power plants and since then the practice has become commonplace in the sector.
Understanding the data
The key performance indicator trap and contract flaws: In the previous article, I described the issues of falling into the PR trap, only relying on one metric and not considering its evolution over time and under different climatic conditions, or not breaking down data to block and array-level performance. In addition, there is the availability trap. Advanced data analysis nowadays can easily be employed for several hundreds of inverters installed in one project, on the basis of one-minute supervisory control and data acquisition (SCADA) data broken down to string level.
By comparing different datasets against one another, as well as to the metered production data, and calculating potential production estimates based on satellite derived irradiance data, the detection of string and inverter technical unavailability even during times of SCADA data loss is possible. These actual availabilities, including during times of SCADA data loss, can deviate significantly from the availability stated in O&M reports, and as defined in the O&M contract. The tricky thing is that even after calculating a more accurate availability, not every performance and availability issue is compensated for by warranties, due to poor definitions in the O&M contract, especially dated ones.
Monitoring software failure: This leads to another benefit of SCADA data analysis. It is able to pinpoint failures in the monitoring software. In cases of significant SCADA data loss, unavailability can be reported wrongly because it only reports for SCADA data coverage periods and ignores unavailability during periods of data loss. This data loss may in some cases also not be stated in O&M reports.
Significant data issues affecting key signals can indirectly undermine the value of the asset, as they can increase the uncertainty of the future P50 forecasts necessary for asset valuation. Therefore, complete data coupled with advanced data analytics form an important part of preserving the value of an asset over its lifetime, unlocking value during operations and when it comes to refinancing.
Non-updated methodology: Automating the data assessments can help to reduce the time spent on data analysis. But since fleets are growing and diversifying in terms of technology, location and national regulations, sticking with the old approach is still sometimes the go-to choice, even if it isn’t the most efficient one.
The root cause from issues picked up during big data analysis can also lead to revealing preliminary hidden design or technology flaws, or to poorly performing contractors. It shows that beyond the scope of daily O&M activities, a comprehensive and holistic approach to solar asset management is key.
While the easiest step might be simply to repair or replace a component on a like-for-like basis – be it monitoring software, module or inverter – asset owners and operators often think twice before upgrading a component. But why wait another five years or so, if investing today will improve the asset value?
There is also the minefield of renegotiating the contractual terms of the O&M agreement or insurance. In times of price battles over new acquisitions, conducting a renegotiation of key KPIs, warranties, response times, liquidated damages or even changing the contractor might be a faster route to boosting overall profits.
My fancy pick of today for predicting what will be standard in three years’ time is about the rise of artificial intelligence (AI) for use in data analysis and failure prediction systems. Even more exciting is the emergence of self-learning algorithms that will enable real-time analysis of price levels.
In a future scenario of merchant markets, corporate PPAs and co-location with storage devices, algorithms that can make immediate decisions based on the asset health of a solar storage plant to either store energy or sell it, seem to me an ultimate fit. Why not hold me accountable on this prediction in three years’ time?
Ragna Schmidt-Haupt is a partner of Everoze, a technical and commercial energy consultancy specializing in renewables, storage and flexibility. With 15 years’ experience, Ragna has a strong background in finance and strategy consulting across renewables technologies, with an emphasis on PV. She has worked across a wide range of international markets and had a significant stint in Singapore, leading a team of technical and strategy consultants focused on mobilizing investment in Asian markets.
Source: pv magazine