Editor's Note: This is the first in a four-part series examining the growing role of machine learning and artificial intelligence in the power sector. Tomorrow, we look at how regional grid operators are using AI to optimize operations. The future of the electric grid is undoubtedly cleaner and more efficient and distributed, with hefty doses of technology and machine learning helping to operate it all. But if you're expecting a system dramatically transformed, experts say you'll be left waiting. Artificial intelligence and machine learning are already helping utilities run their networks more efficiently, extending the life of equipment and helping to dispatch energy into markets more efficiently.
Internet of Things (IoT), is a mammoth network of things connected with one another as well as individuals. This relationship could be between people to people, people to things and things to things. Basically, IoT is the concept to connect any device with an on and off switch with Internet or with each other. It is also applicable to any machine component e.g. There is a chance that everything is a part of IoT if it has an on and off switch.
At Microsoft, building a future that we can all thrive in is at the center of everything we do. On January 16, as part of the announcement that Microsoft will be carbon negative by 2030, we discussed how advances in human prosperity, as measured by GDP growth, are inextricably tied to the use of energy. Microsoft has committed to deploy $1 billion into a new climate innovation fund to accelerate the development of carbon reduction and removal technologies that will help us and the world become carbon negative. The Azure IoT team continues to invest in the platforms and tools that enable solution builders to deliver new energy solutions, customers to empower their workforce, optimize digital operations and build smart, connected, cities, vehicles, and buildings. Earlier, Microsoft committed $50 Million through Microsoft AI for Earth that provides technology, resources, and expertise into the hands of those working to solve our most complex global environmental challenges.
Growing up in a middle-class family in Kolkata and Midnapore in West Bengal, India, Dr Sayonsom Chanda was no stranger to strong winds and relentless rain knocking down electricity for hours, days, and even weeks. He and his family lived in East Midnapur through the horror of the 1999 Odisha cyclone and Sidr cyclone in 2007. It was one of the core reasons for him to start Sync Energy in 2017. The startup builds artificial intelligence (AI) tools that simplify emergency and disaster response planning for electric power distribution companies. The platform helps electric utilities reduce customer downtimes and be better informed about the impact of a disaster before it actually strikes. This, in-turn, will help electric power companies to decrease costs associated with emergency-related power outages.
If somebody hacked communications to grid-connected devices and interrupted a demand response (DR) event, peak demand might not be cut, capacity prices could spike and that somebody could make a lot of money. Because of the fast-rising number of grid-connected devices in DR programs like smart thermostats and water heaters and the even faster-rising number of smart phones and other Internet technologies through which customers communicate with DR programs, market manipulations like that are possible, cybersecurity experts from the Electric Power Research Institute (EPRI) told the Demand Response World Forum October 17. It is one of many potential intrusions of communications between utilities and customers with grid connected devices and distributed energy resources (DER), they said. To counter these threats, data analytics experts are using the laws of physics and unprecedented masses of data to find cybersecurity breaches. And their work is leading to machine learning (ML) and artificial intelligence (AI) algorithms which, though only just beginning to find actual deployment, are expected to soon advance the ability to identify patterns to the intrusions and raise the level of protection for critical power systems.