What is The News About?
The solar panels at the Baldy Mesa farm are neatly arrayed across a western section of the Mojave Desert in Southern California. They are converting the abundant sunlight into carbon-free energy and supplying it to the grid. All solar power plants eventually shut down when the sun goes down, just like any other power plant. That doesn’t imply the energy stops flowing at this solar-plus-storage farm. In May, a BESS the size of a football field will begin sending the power generated by the solar panels back to the grid, guaranteeing that clean energy is accessible at all hours of the day and night.
One of the quickest methods to help decarbonize power networks is to switch to renewable energy sources like solar and wind power, however the quantity of this power might fluctuate when the sun isn’t shining. One way to address this is by combining solar power with battery storage, which can extend the amount of time that carbon-free energy is available. Baldy Mesa is a solar farm that AES built, owns, and operates. AWS-powered machine learning (ML) models are assisting with the project’s battery unit’s scheduling of charging and discharging to the grid.
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Why Is It Important?
Up to this point, ten solar power projects that incorporate battery energy storage systems have been made possible by Amazon, totaling almost 1.5 GW of battery energy storage capacity. Among these endeavors are the solar-plus-storage megaprojects Baldy Mesa and Bellefield, the biggest of its kind in the United States, and the San Bernadino Air Hub, where Amazon placed its first rooftop solar array and battery storage unit. A total of ten projects spread out across Arizona and California are contributing to the green power mix that Amazon uses to power its data centers, office buildings, and fulfilment centers.
Owners and operators of carbon-free energy sources are increasingly turning to machine learning to bolster carbon-free energy output and assist in grid stabilization; one such trend is the use of artificial intelligence (AI) to maximize the performance of the batteries at Baldy Mesa.
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Data like weather reports in real-time and power grid records from the past have become available due to digitization and the cloud. For instance, the IEA estimates that the global wind turbine fleet generates over 400 billion data points annually, which can be used by AI and ML models to enhance the operational efficiency of carbon-free energy projects.
According to Fluence, the solutions provider, software at Baldy Mesa that uses machine learning and was built using AWS service Amazon SageMaker is anticipated to analyze 33 billion data points annually. The software’s goal is to maximize the use of Baldy Mesa’s battery unit by adjusting the optimal times to purchase, store, and sell energy in response to changes in the grid. Last year, an ML solution was implemented at a comparable location in California. It helped predict the state-wide heatwave and stabilized the grid by sending stored solar energy at critical moments.
Climate change is making heat waves hotter and more frequent, which is putting a strain on electricity infrastructure. As a result, this AI invention is gaining importance. As the temperature rises, many homes and businesses increase the air conditioning to stay cool. This puts a strain on the grid operators who are trying to meet the increased demand, and traditional thermal power plants may limit their output, which could lead to outages. The necessity to quicken the world’s shift to clean energy has been highlighted by these severe weather disasters.A 2.5 MW battery energy storage unit and a 5.8 MW rooftop solar array are housed in the building of Amazon’s San Bernardino Air Hub, which is located one hour away from Baldy Mesa. The Air Hub runs on energy from solar panels when the weather is nice. However, the facility can easily switch to using batteries for part of its electricity when clouds move in or the sun goes down. By deploying solar panels during the night, Amazon’s Air Hub may reduce its dependence on the grid during peak demand times and ensure a steady supply of carbon-free electricity.
Teams at Amazon are hard at work on an artificial intelligence model that will use ML capabilities and performance data from Amazon’s rooftop solar arrays to assist reduce energy use in Amazon buildings like the Air Hub.
The teams are laying the groundwork for improved performance tracking and analysis by first collecting data from the solar panels on the Air Hub rooftop and other Amazon locations. They will then combine this data with local weather and building information at a central location in AWS Data Lake. Upon its release, the forthcoming AI model is anticipated to provide forecasts regarding the efficiency and output of the site. Due to the time and effort required for human examination and monitoring of each Amazon system and building, this work is presently not feasible.
Wrapping
In order to democratize our efforts across the broader energy sector with AWS customers and partners, and to tackle some of the most critical sustainability concerns, Amazon is utilizing AI in novel ways. Two examples of these techniques are the San Bernardino and Baldy Mesa projects. The need for renewable energy is growing, and AWS gives businesses the tools they need to improve their operations and innovate via the use of artificial intelligence. As an example, Greenko, a prominent renewable energy company in India, has connected all of its wind turbines to AWS, allowing for AI-powered near-real-time monitoring and analytics. Also, using cutting-edge data analysis and ML models, cities like Barcelona may cut their energy use by as much as 15% with Engie’s “Common Data Hub” that is built on AWS.
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