Renewable Energy Analytics Add Predictability

Forecasting can be a tricky business. That certainly can be said for gauging the growth of renewables. Those with a stake in the game continue to point to the ongoing under projections by agencies such as Energy Information Agency and International Energy Agency (IEA). Critics of the forecasts have a point, as policy makers rely on EIA and IEA data. EIA even acknowledged the agency's lack of accuracy when it comes to renewables in a report earlier this year.

However, if you follow most any source that tracks the money trail or gets direct feedback from utility executives, you’ll have a much better sense of where the market is headed. What it all comes down to is that renewable energy is becoming cemented within the utility model and, as such, tied integrally to growth. And that’s where renewable energy analytics comes into play, adding elements of predictability to these resources.

Normalizing renewable energy

As these renewables continue to grow in terms of strategic importance for utilities, they are becoming more central to the business model and operational processes. This “normalization” can be seen on many levels, including:

• General acceptance by utilities in mature markets that renewables can sufficiently meet new capacity demand


• In those same markets, operational processes designed to ensure maximum utilization rate of renewables to meet that capacity


• Continued drop in renewable costs, particularly for solar


• Construction of larger, more powerful turbines for offshore wind projects


• A breadth of renewable assets to manage across the warranty spectrum, from young fleets to mature assets


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Though these resources have some operating upside versus fossil generation, such as no ongoing fuel costs, they are dispersed geographically, often difficult to reach places. Some, such as offshore wind, require logistical planning and solution costs, such as maintenance vessels. Additionally, power purchase contract duration is being reconsidered or rewritten in many locations. Operating and maintenance processes are still being standardized.

Even with these challenges, utilities are more committed to them than ever to leverage renewable generation. That commitment requires operators of these utility-scale resources to maximize the output of the assets, delivering predictability of performance that utilities need to operate effectively. Renewable energy analytics can deliver this predictability.

Renewable energy analytics make performance more predictable

Making these resources more predictable−i.e. maximizing reliability and output capacity−means that operators need to apply predictive maintenance that eliminates asset failure. Operators can use predictive analytics to provide that capability, tapping vast amounts of historic and real-time data associated with these renewable resources.

Predictive renewable energy analytics can detect patterns leading to downtime, such as gearbox failure in wind turbines, well in advance. This insight can then be used to schedule work so that the downtime never occurs. This reorientation to proactive maintenance also has the added benefit of being less costly than reactive work.

Sourcing a predictive analytics solution can be a challenge in today’s confusing market, which is something I’ve discussed before. However, there are some positives when applying advanced analytics to utility-scale renewables:

• There is a track record of successful renewable energy analytics. In terms of non-metered use cases, it’s a pretty mature area when it comes to advanced analytics.


• The data that can be used−temperature, rotational speed, blade angles, nameplate, SCADA, etc.−is pretty straightforward, meaning that the analytics data models don’t need to be updated much.


• Existing business intelligence tools can be leveraged to simplify the visualization of the analytics, lessening the change management impact on personnel engaged in operations and maintenance.


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Given the importance to utilities of utility-scale variable renewable energy (VRE), operators need to consider advanced analytics a key tool for managing the performance of these assets. Simply put, renewable energy analytics ensure the predictability of performance needed for VRE.

To provide utilities with an inside view of how this can be successfully done, I will lead a renewable energy analytics session at our 21st Annual ARC Industry Forum in Orlando in February of 2017. To get involved or for information on the session, please contact your client manager, send us a note indicating your interest, or shoot me an email.