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Private Sector | Not Classified - india | PID: 203418
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The AES-owned solar farm, which is under contract with Amazon, is utilizing a roboto called Maximo to help build the farm, aiming to reduce solar installation timelines with the support of new Amazon Web Services (AWS) technology.
The AES Corporation introduced “Maximo”, an AI-powered robot that enhances solar installation speed, efficiency, and safety. Maximo is a robotic solution that works alongside construction teams to help meet rapidly growing renewable energy demand.
Invented and owned by the energy company and developer AES, Maximo can cut solar installation timelines and costs by up to 50%. AES reports that this technology is accelerating project timelines, creating new workforce opportunities, and making the process less strenuous for construction crews, particularly in hot weather conditions. It exemplifies how Amazon and renewable energy developers are leveraging AI solutions to address climate change on a large scale.Maximo recently made its utility-scale debut at the Oak Ridge Solar Farm in Louisiana—a project enabled by Amazon—where it efficiently installed solar panels despite high temperatures. The robot is now moving to support construction at another Amazon-backed renewable energy project in Kern County, California—Bellefield, the large solar-plus-storage project in the US..
“AI is a critical tool that is already helping us develop sustainability solutions and address climate change at scale,” said Kara Hurst, Amazon’s Chief Sustainability Officer. “As society’s energy needs grow, the demand for new solar and wind projects is also increasing. We’re excited to collaborate with renewable energy developers like AES, who are bringing new renewables to the grid and prioritizing innovative technologies to accelerate these efforts.”
“The demand for clean energy continues to grow, and at AES, we believe that innovation can help us meet those needs for our customers,” said Deise Yumi Asami, founder of Maximo at AES. “Maximo is supercharging the workforce, enabling the installation of solar panels in half the time and at half the cost. It utilizes advanced technology to ensure the fastest and most precise installation, even in the most challenging outdoor environments. With Maximo, we will transform the pace of our solar project delivery and help meet the world’s growing energy demand.”
To perform its tasks, Maximo employs AI features such as computer vision and data analysis to ensure precise panel placement and real-time construction intelligence. The robot’s design process at AES included various AWS tools, including AWS RoboMaker, a cloud-based simulation service that supports robotics developers in running, scaling, and automating simulations.
In addition to automating heavy lifting, Maximo can operate in nearly any weather or lighting condition. This capability is particularly beneficial for the Bellefield project, located in a sandy desert area known for extreme heat. Once Maximo arrives later this year, it will work alongside crews to lift hundreds of heavy solar panels into place.
According to AES, using Maximo reduces time spent in the desert heat for work crews and accelerates renewable energy delivery. It also allows a broader group of people to work alongside it and is expected to help address workforce shortages in the long term. The demand for U.S. solar workers is projected to double in less than 10 years.
Once complete, Bellefield will feature a 1 GW solar farm paired with up to 1 GW of battery storage. This battery will store excess solar energy generated during the day and release it at night or during peak grid demand, providing a steady supply of carbon-free energy for extended periods. Bellefield will be capable of powering 467,000 California homes annually.
Maximo is just one example of how Amazon and AES are advancing renewable energy projects and AI solutions to combat climate change. At Baldy Mesa, another solar-plus-storage farm enabled by Amazon and developed, owned, and operated by AES, machine learning models powered by AWS are used to predict when and how the project’s battery units should charge and discharge energy back to the grid.
| Updated on: 19 - Sep - 2024
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