28 Nov 2019

Solar’s Hidden Secret: The Data

28 Nov 2019  by Michael Kanellos and Steve Hanaw   
Solar enjoys tremendous advantages over conventional power.

It’s cleaner. It’s cheaper than traditional power in many parts of the world. And it’s popular with consumers. But there is also an often unrecognized aspect of solar that will play a leading role in further lowering costs and driving adoption.

Solar talks a lot.

Solar arrays generate a tremendous amount of digital information. Take a look at the chart below. A 1GW fossil or coal-based power plant on average will generate approximately 10,000 data streams. A similarly sized wind farm might produce 51,000, or 5 times as many. The same size solar farm? 439,000 data streams.

The discrepancy derives largely from the distributed nature of solar. Large fossil plants and wind farms are typically built around a relatively small number of very large assets while solar farms are built around a large number of very small assets. To take a random example, AEP Renewables Mehoopany wind farm features 88 turbines with an aggregate capacity of 141 MW. By contrast, its Boulder Solar II project generates 62MW, or less than half as much, but features 144,000 solar panels. And not only will each panel generate information about power production, temperature and other parameters, inverters, trackers, junction boxes will likewise produce continual streams about their current state or possible problem areas.

At first blush, this might sound like a bad thing. More data means have to install more computing capacity or incur extra cloud fees. Data, however, can and will play the leading role in reducing one of the costs that has been seemingly impervious to technology advances. Namely, labor costs associated with operations and maintenance.

Labor still comes to around 1% to 7% of total revenue for solar operations and labor costs and remain one of the largest expenses not linked to the original construction and financing. Again, it’s a function of the distributed nature of solar.

Efficient asset performance management can reduce the LCOE of a large power plant by 1% to 3.5%, a deceptively small number that becomes a looming force at scale. For a 50 megawatt plant running at 30% capacity factor generating power for 15 cents per kilowatt hour that can mean an additional $500,000 per year in revenue, with most of that incremental cash turning into profit. (50,000 kilowatt hours x 0.03 capacity factor x 8760 hours a year x 0.15 kWh x 0.03 LCOE reduction = $591,3000.) Over a 30 year period, the total comes to over $17 million. All of it flows directly to the bottom line. For an equity participant, the debt leverage grows by the same factor.

Here’s another way to look at it. The third chart (below) shows the operations and maintenance charts for a 10 MW ground-based system. Those first two columns which take up 80% of the total are preventative/planned maintenance and corrective maintenance. Almost all of the costs derive from labor. In fact, nearly every factor on the chart except spare parts is labor related.

Some are already benefitting. Arizona Public Service manages over 1700 megawatts of solar for its 1.2 million customers in a service territory that covers 36,000 square miles. The solar assets range from utility-scale solar arrays, urban commercial systems and utility-owned rooftop installations. It manages maintenance, however, will a small group—think around ten or less—of technicians who can prioritize repairs.

The decline in solar costs is one of the more remarkable technological stories of our time. Solar dropped 85% between 2010 and 2019, according to Bloomberg New Energy Finance, and is anticipated to drop another 63% by 2050. But it hasn’t happened through gravity. Early solar pioneers like SunPower focused on efficiency. Then came new cutting techniques that enabled producers to get more wafers out of an ingot. Microinverters, modular racking, trackers, better project management have similarly helped lower LCOE. Data has been comparatively underutilized. Expect that to change.


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