Trade the Clean Energy that You Produce from Your Home
Imagine a grid system that connect to your home and any other homes in your city. Let us say your solar panel or wind propellers produce energy more than your need at the particular time and batteries are full already.
Imagine a grid system that connect to your home and any other homes in your city. Let us say your solar panel or wind propellers produce energy more than your need at the particular time and batteries are full already. That is where the system starts working; it sends the electricity that you don’t use to the other houses that need energy. It also does the same thing for your home, when you need more power, the grid system sends some electricity to your home.
READ ALSO: World’s Long-Term Solar Energy Plan Opens a Door for Cleaner Earth
In Germany, technicians from Sonnenbatterie came up with this system that will allow the wind or solar power to be stored and traded. This trading system that is planning to launch in early 2016 will be available to anyone on the German grid. The company’s primary purpose is to establish a potential alternative to the utility grid. The CEO Boris von Bormann calls it ” Airbnb of energy.”
There are also other energy sharing programs doing something similar that Germans do. The Dutch platform Vandebron has 38,000 subscribers. Energy consumers pay a monthly fee to contract directly with the clean energy producer. Also, the user can choose their particular energy supplier and producers to get to name their price. In the UK, there is a program just work for businesses between energy providers. But the system that German company came up is let consumer share the energy with each other.
The idea is very efficient and will save millions of dollars worth energy that wasted every year. Hoping that this idea will start to use by other countries as well. The company also begin to install their battery system in The U.S. and here is their website.
photo credit: renewables.seenews.com/
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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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