The use of renewables assets’ operational data is a very big opportunity for an industry that can leverage on many years’ worth of information and on centuries of engineering knowledge.
There is a wide consensus in considering the solution to optimum operations to be hidden in the information produced by the assets.
Data can be assimilated to become the “fuel” of digitalization, enabling co-creation between multiple stakeholders and, in line with mainstream researchers’ thoughts, the following should be considered when exploring the full potential of data: density, liquefaction and integration.
Density is the capability to mobilize the maximum amount of information to solve a specific problem; data liquefaction is the attribute linked to data accessibility: more accessible data usually implies enhanced solutions. Integration is the attribute linked to the capability of aggregating multiple sources of data.
But 2 questions arise:
- How easy are data density, liquefaction, and integration to achieve in a competitive environment like the renewable energy industry?
- Are there any ways to reduce barriers to exploit the full potential available in data at an industry wide level?
Data platforms and digital twins benefit from resource density and liquefaction
Data platforms are enablers of resource density and liquefaction, especially when they are designed with a classification body mindset like DNV’s Veracity: an independent platform created to enable data ingestion, data use and interactivity between multiple parties. The key to the success of digital platforms is trust in the independence of the platform provider, as the various contributors need to have comfort in sharing data and models without fearing risks of intellectual property leaks. At the same time, ease of access and the assurance that the multiple parties contributing get recognized for the value added, are other key enablers.
Digital twins are beneficiaries: amongst many things they are capable of detecting wind turbines’ underperformance. This specific function and application are an example of resource density in the form of knowledge acquired on thousands of hours of performance assessments, crystallized in digital algorithms that automatize the process. The effect of data fluidity plays a key role in increasing the power of a digital twin based on vast arrays of data from wind farms around the world.
Data aggregation controversy
One of the most contentious aspects is data aggregation. The competitive environment we currently operate in, is not facilitating its exploitation.
#1 – A couple of obvious examples are data or information sharing amongst players that carry out different activities in the renewables industry value chain, and data sharing between direct competitors in the same value chain.
#2 – We need to acknowledge the great value of the insights offered by the various players in the value chain.
In wind assets operations, for instance, there is the possibility to train machine learning models with insights coming from accurate ticketing systems.
To improve their effectiveness, the systems need input from operators on the field who accurately identify and report the source of underperformance or reliability loss (failures).
The challenge is that technicians and O&M providers might not have any incentives in sharing valuable insights on the source of troubles as it’s time consuming and it might slow operations. Profit margins are slim, and there is no spare time for extra work.
Accurate and detailed insights have great value to a provider of advanced analytics tools and to the asset owner who can more accurately predict if components are going to fail and therefore plan in advance how to reduce downtime and maximize revenue. Some of that value needs to be passed down the line to those who generate the data that the process relies on.
#3 – Data sharing opportunities between direct competitors to benchmark the performance of their assets is also being undervalued
This is a great chance to gauge the performance of operating assets and to identify subtle (or less subtle) underperformance trends that could become costly in the long run. Given a large enough data set, for instance the solar PV installations being monitored by GreenPowerMonitor, aggregated and anonymized insights could be easily filtered by technology, climate region, asset size and other parameters to give industry standard KPIs directly relevant to a particular plant.
It should be noted that data ownership, confidentiality and shareability is deliberately not covered here but is considered as a high priority to preserve DNV’s role of trusted advisor.
#4 – It’s evident that data can be a source of competitive advantage and data integration is an opportunity and a threat. The industry faces a clear dilemma.
In DNV we are working to solve this dilemma, the 2 main areas of focus are the business model behind data sharing and studies around data trading at various levels.
The data sharing dilemma
In DNV we are thinking about ways to overcome the typical barriers of data sharing and the questions we are asking ourselves are multiple, including why would organizations share data with other entities and potentially with competitors?
We also consider the lack of incentives for players lower down the value chain to share data or information that are likely to be the source of their competitive advantage with other players who have longer levers in the competition game.
It’s quite obvious that the benefits of sharing data need to overcome the risk of releasing information, becoming so valuable that the parties would be willing to engage. This could take the form of access to information that the parties will never get access to if they did not share their data.
These dilemmas could be solved with a data trading model where consenting contributors get proportionately rewarded for their aggregation effort.
Work is still in progress, but we have made a promising start.
Contact our expert
For more information, you can contact Giuseppe Ferraro, Director of Digitalization and Renewables Optimization, firstname.lastname@example.org.