ANALYSIS - Digitalization necessitates new thinking, new business models

ANALYSIS - Digitalization necessitates new thinking, new business models Author: Rachel Johnson.

What if you own a wind turbine that could self-diagnose a gearbox vibration it detected and "call home" to a remote monitoring operations center to ask for an oil change? The industry is only a few years away from the digital industrial revolution having a major impact on the way in which the industry operates and generates revenue.

The push towards data creation and the ability to leverage it has resulted in some of the Tier 1 wind turbine OEMs pioneering new digital technologies and hiring or reassigning huge numbers of personnel to work in their new internal digital technology labs.

But many companies still need to consider what the adoption of digital technology means to their business, along with the new business models necessary to take full advantage of this push towards digitalization.

What is Digitalization?

The digital revolution is about creating a digital representation of a physical world, including the machines and people who operate them. While that might sound like something from The Matrix (1999), it's quickly becoming part of the real world. There are many different labels, including some proprietary ones that companies invented, which are associated with different aspects of digitalization such as the Internet of Things, Industrial Internet, Industry 4.0, etc.

In practical terms, what this means to the wind energy industry is the development of computer models representing the full value chain; from the electric grid, to a wind park, to a wind turbine, to a gearbox, to the gears, bearings, lubricants and other internal components right down to an atomic level of the materials which comprise them.

Data Science Meets Materials Science

Being able to evaluate components based on their material composition and predict their behavior when subjected to different operating conditions opens the door to new materials being developed or utilized for applications such as life extension or predictive maintenance scheduling.

On the macro level, a digital version of a wind park will be able to harness historical operating data (wind speed, power, yaw angles, gearbox temperature, etc.) and use the trend analysis for individual turbines or an entire fleet of turbines as input criteria to a predictive model.

What exactly does this model predict? How the individual wind turbines or the wind park as a whole can and should be operated in order to achieve a specific goal, such as maximum power output to take advantage of time-of-day price arbitrage when feeding wind power into an energy storage system or even a life extending curtailment mode of operation.

Digitalization = Speed

You can even consider the ability of service providers to be fed instructions from a central data processing server that has determined a sequence of turbines in a wind park can be taken offline today for service with the minimal impact to the availability guarantee.

The server is programmed to look at current and future weather data, historical (seasonal) park production, remaining useful life of specific components, and the availability of spare parts in inventory at the maintenance facility.

Instructions on what components to inspect, repair or replace, and what order to fix them in will be seamless. This information can be delivered to a smartphone, tablet, or a helmet that may have virtual reality or augmented reality technology to help facilitate the workflows.

Compare this new environment to 25 years ago when wind turbines may have not even been network- or internet-connected due to the cost. Knowing a wind turbine needed maintenance meant driving to a site and visually inspecting it.

Get Ready for the Renewable Energy ‘App Store’

So what's new and innovative about this? Digitalization represents the first time in history when a single operator can have a fully integrated digital model that can be used to accurately predict the future. This allows them to control and shape that future with maximum profitability in mind.

Companies are seeking to develop a platform on which a host of capabilities can be offered. Both turbine purchasers and also asset operators who leverage the platform will be able to offer a range of capabilities, which is likely to result in an increase of independent asset operators.

Think of this scenario like an app store on your smartphone. There are likely apps developed by the owner of the store for some basic functions, but there are plenty more which are contributed by the industry community at large.

In our world, one module or "app" would be for energy output optimization, while another "app" would be for ordering spare parts inventory as part of a predictive maintenance scheduling program, while yet another "app" would be for calculating the remaining useful life of components by analyzing the wear rate of components.

Whether the capability was provided by the OEM or a third party through the OEM platform, a content licensing and revenue sharing agreement is likely to be utilized. It will also become important to address data access rights by the platform holder for the content created and warehoused by the third party “app” supplier.

Specifically, would a company such as GE get access to the data cultivated by an asset owner who is using a third party developed “app” for price optimization of energy output just because the capability was being delivered by the GE platform?

These aspects of the business models in the digital world are yet to be fully resolved, but they will become of critical importance as the industry drives towards a digital future.

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About the author
Browse all articles from Philip Totaro

Philip Totaro is the Founder & CEO of IntelStor, a market research and strategic advisory company focused on renewable energy. He has over 11 years of experience in the power generation industry, having previously worked for General Electric as well as Clipper Windpower. His company has helped cultivate over 600 inventions and file over 350 patents. Their strategic market analysis has led to the funding justification of over USD 600 million in R&D investment, and they have advised on over USD 1.8 billion in M&A transactions.

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