Currently, the installed renewable energy base is roughly 700 GW globally. This is expected to grow to more than 5,000 GW over the next 20 years; this means a 7x growth rate. This growth of solar will come in different shapes (utility scale, commercial and industrial, as well as residential systems), and will severely challenge the existing grid and distribution systems. As an intermittent form of energy (like wind), it will likely need to be supported by storage technologies and automation. This growth will need to be more sophisticated, distributed and intelligent. Asset managers for these renewable energy assets are already gearing up and planning for this growth by using data to make it more reliable, available and predictive. We need to go beyond fault codes to make planning more advanced.
Looking at different technologies to assist in the growth of the solar industry, this article goes deeper into its unique challenges and explores how digital twins may be the technology solution to address these issues.
Disproportional O&M costs
Fast dropping power purchase agreement (PPA) prices along with rising solar panel costs (prices fell quickly initially but are now going up owing to inflation) are forcing O&M teams and asset managers at solar plants to figure out ways to improve both operational efficiency and yield at the same time. They need to do this while also dealing with several operational variables, which include soiling, shading, weather conditions, measurement accuracy of equipment, installations that are not built as per design and finally degradation of equipment out in the field. This makes their job quite difficult.
Growing scale of problems and shrinking scale of solutions
To put this into perspective, a 1 MW plant would have approximately 70 strings (of 50 panels each) and be spread over 5-6 acres. Often, there would also be so many issues – inadequate instrumentation or calibration quality, along with unclear expectations of weather, micro-weather, and uneven degradation scenarios across the plant. That list is not finished. There are also issues of shading, soiling and so many short-term, fixable issues as well. When multiplied by 1,000s for plant and portfolio size, the sheer scale of issues indicates that plant managers are ultimately able to do only bare minimum fixes on time. These are limited to contractual preventive maintenance by the assigned O&M company and corrective maintenance, which happens when there are alarms during outages or when variances become too high.
Missing tools
While supervisory control and data acquisition (SCADA) systems support the O&M decision-making process, they cannot facilitate the kind of advanced analysis required to significantly improve plant performance on the asset manager’s watch. Standard industry benchmarks like PVSyst are very good for planning to understand the future financial performance. However, they are not completely accurate, granular, or actionable for really improving plant performance.
Challenges with traditional contracted (or expected) energy measures result from inaccuracies and noise and blind reliance on pyranometer data. Pyranometers may be very unreliable due to frequent calibration required to keep the equipment accurate, and the number of devices needed to cover large plant areas are not available. If we add no consideration of efficiency and behaviour of individual string combiner boxes (SCBs) at the different stages of ageing – this means that the benchmark may work at an aggregate level (for an entire plant for a longer period of time), but it is ineffective to make actionable, measurable improvements at the SCB level or provide weekly improvement plans for the plant.
Forced errors
Unrealistic yield expectations driven by IPPs and tacitly agreed by investors put O&M teams on a downward spiral as they are unable to make effective investments in technology. Second-guessing O&M teams has become routine for IPP management. This often means adding even more costs for preventive maintenance in drone and thermal imaging and I-V testing every 6-12 months.
Digital twins: Enable condition-based maintenance
One of the cutting-edge industry-specific solutions that addresses challenges posed by unrealistic yield expectations and razor-thin O&M margins across the industry is digital twin technology using artificial intelligence (AI). A digital twin is the digital copy of any physical asset, modelled to replicate the asset’s behaviour in real time under any field condition. A unique digital twin is built for each instance. This technology is easily used to create highly accurate and granular performance benchmarks, personalised for any plant and each underlying device in the plant, using historical and real-time data. These benchmarks are for acceptable DC yield, inverter conversion efficiency and AC transmission efficiency. Any deviations from these benchmarks can then be highlighted and acted upon depending on the severity of the issue. The issues can often also be quantified in terms of impacted revenue loss or production loss. All of this is done very quickly using machine learning (ML) and AI. Human beings are very good at seeing patterns in small amounts of information, but ML can see patterns in very large amounts of data. Today’s solar plants provide millions and millions of points of data and using those effectively is the key to improved solar plant operations.
Condition-based maintenance: Benefits
This simple digital twin-based benchmark leads to the practice of condition-based maintenance. Replacing preventive maintenance with prescriptive maintenance and corrective maintenance with predictive maintenance allows the improvement of solar plants by 2-4 per cent with lower costs. This is because O&M resources are utilised much more effectively. Instead of just remedying issues based solely on alarms, remedies are based on proactive, data-driven decisioning, reflecting the true condition of individual plant components. This combined transformation of intelligent prescriptive guidance of the current schedule and predictive planning of remediations results in reduced outages, increased yield, reduced despatches, lower O&M manpower requirement, and better inventory planning and management.
Condition-based maintenance: How?
Broadly, there are four steps for condition-based monitoring and asset management:
- Historical and real-time data acquisition from various data acquisition systems (DAS) like SCADA, dataloggers and third-party application programming interface.
- Data cleaning, harmonisation and standardisation to yield the best possible data quality, which can be used for actual data analysis.
- Creation of digital twins or replicas of each component of the asset, using both historical and real-time data. These begin from the smallest instrumented components from the ground up to the whole plant. Next, these yield expectations for each string in real time is added up to create the most accurate estimate (the digital twin benchmark) of the plant’s generation capacity at any time, and under any field condition, with unequalled granularity and precision.
- Lastly, it involves comparing the real-time output at any or all parts of the plants against their corresponding digital twins to drive condition-based decisions. The deviations from the benchmark should be tracked, characterised, and quantified for weather, structural degradations, shading, soiling, new-fixable issues, and ultimately converted into actionable, revenue-prioritised work orders.
Digital twins: Lots more there
Note that digital twins, as a benchmark driving condition-based maintenance, are the simplest and easiest benefit. One can zoom out to go back in time (time travel) for available data and create digital twins going all the way back to plant commissioning to answer questions for investor due diligence and raise longer-term degradation claims against the manufacturer or the EPC. Similarly, travelling forward, using TMY3 as inputs, the yield forecast using digital twins is far more accurate than traditional static-model based approaches. One can zoom in to find and address classification and quantification of shading, soiling, insulation and anomalies. Finally, as asset owners deploy varying original equipment manufacturers’ (OEMs) equipment in varying configurations, digital twins are perfect for uniquely mirroring each asset accurately, and an ideal tool for overall portfolio management. As multiple battery storage chemistries are being tried by OEMs, digital twins are becoming instrumental in both estimating the current and predicting the state of health as well as corresponding outage prevention.
Conclusion
In conclusion, a continuous, automated, granular and actionable condition-based maintenance allows asset managers to proactively replace their preventive maintenance schedules to improve yield (and eliminate outages). This also drives O&M efficiency improvement and reduces the frequency and cost of despatch with clustering for owners of many commercial and industrial installations. In addition, the accuracy of digital twins can give its users strong confidence in their OEM claims and due diligence processes. The state-of-the-art part of digital twins is that they begin to help immediately like an advanced monitoring system, they are used to optimise asset performance continually, and they keep getting even better with time. This becomes true condition-based maintenance guidance. This is because digital twins learn and adapt to the plants’ inherent characteristics over time, and thus keep getting better and better.
Today, in 2022, we are in the very early days of renewable energy growth. Most of our plants were built in the last three to four years, and we all understand that the real growth will come in the next 10-20 years. Planning for the management of this growth is what is needed today. If you are an IPP or owner or an O&M team, you will need to scale your assets under management 10x over the next few years. To do this effectively, you will need smart (multiple and specialised) digital twins so that your plants can learn to self-manage themselves.
