Still a Long Way to Go for Solar Even as $1 per Watt Emerges
No one can argue that solar power isn’t a very good idea. But, even with all the marketing and investment around it today, solar power still only accounts for about 1 percent of US electricity, with coal and natural gas over 65 percent, and that is not likely to change anytime soon.
No one can argue that solar power isn’t a very good idea. But, even with all the marketing and investment around it today, solar power still only accounts for about 1 percent of US electricity, with coal and natural gas over 65 percent, and that is not likely to change anytime soon.
More people are signing up for solar power across the nation, but there is still along way to go before it is one of the most used sources, and growth may even slow in the forthcoming years. But, the target set by the US Department of Energy‘s SunShot Initiative for the cost of building solar farms was to drop to less than $1 per watt by 2020, and it looks as though we are on par to achieve this.
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However, if you look more closely into it, $1 is not really the true value of per watt solar power as this does not take into consideration the building of transmission lines in which to deliver the power, the storage systems, or software or personnel required to run the solar power farm. When all of this is taken into consideration, it is quite similar costs to that of producing electricity from a natural gas plant, but only when the sun is shining, so is it really that good a bargain?
However, the costs to for the consumer will still be seen to be competitive with other forms of energy, so for that reason alone we will see an increase as to the amount of solar power being used, albeit a slow development. As long as governments and organizations can regulate the solar power industry, and not produce excess amounts they are unable to store, then everyone should benefit from solar power and will be a more healthy option for the environment also.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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