In space, it’s never cloudy and in geosynchronous orbits, it’s never night. Space, the final frontier, offers untapped opportunities for governments and energy companies—here on Earth—to harvest solar power 24 hours a day, 365 days a year.
To date, nations such as the U.S., China, India, and Japan each have explored the potential for space-based solar harvesting projects. Future projects could involve the use of robots to assemble solar arrays that provide enormous amounts of renewable energy that would be transmitted wirelessly.
For instance, researchers at Lawrence Livermore National Laboratory have examined how the use of self-assembling satellites equipped with reflectors and a microwave or a laser-power transmitter could be launched into space. The reflectors would be spread out across a vast stretch of space, directing solar radiation onto solar panels. The panels would then convert solar power into either a microwave or laser and beam the power back to Earth.
Although space-based solar power offers tremendous potential for supplying massive amounts of renewable energy to our planet’s nations and cities, the efforts to deploy solar collection and transmission devices and other ancillary projects are fraught with challenges. Chief among these are the costs associated with the space launches needed for satellite deployments.
According to Lawrence Livermore, the estimated cost to launch, assemble, and operate a single microwave-equipped satellite with solar reflectors that can weigh up to 80,000 metric tons can cost tens of billions of dollars. Moreover, it would likely require up to 40 launches to send all of the necessary materials into space.
Data analysis and, specifically, predictive analytics can play an instrumental role in assessing and identifying methods for tackling some of the challenges posed. For instance, data analytics can be used to devise efficient approaches to reduce the number of launches required to dispatch solar equipment into space. This may include opportunities to design less bulky and expensive equipment to launch into space. Analytics may also enable scientists to identify low-orbit satellite trajectories that are less costly.
Meanwhile, analytics can help decision-makers to assess and act on other logistical issues such as automated maintenance requirements along with steps to mitigate potential risks, preventing equipment from colliding with space debris, for example.
The use of data and analytics can enable scientists and other decision-makers to identify and respond to a variety of risks associated with space debris (man-made and natural). Analytics can also alert engineers of the most effective approaches to design, launch, and maintain space-based solar power gear and how best to cost-effectively harness and distribute energy captured by this equipment.
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