For decades, this worked well. Energy demand was predictable, and utilities burned enough coal and gas to meet it – a bit less on long summer days, a bit more on short winter ones, or when the national team reaches the cup final.
But things have changed, and this way of doing things is no longer sufficient. Reliable coal and gas furnaces are out, and intermittent solar and wind are in. Demand is changing rapidly and unevenly, thanks to the electrification of transport, heating, and industrial processes, as well as to extreme weather that makes hot days hotter and cold days colder.
All of this transforms the grid from means of carrying energy from power stations to consumers, into a complex, dynamic, marketplace for energy. Aging infrastructure – that was not designed for decentralized energy – doesn’t help matters.
It will need people who can combine high-quality data from multiple sources to build highly predictive models and generate actionable insights for the company and its customers. This is not just about modeling, but also making smart decisions about data, such as where to focus limited resources, what data sources to acquire and use, and whether to build AI tools in the cloud or at the edge.
It will need processes to gather data. That will mean changes to your own data sources – e.g., by deploying smart meters to gather data, and adding connected assets (smart new ones or retrofitting aging ones with practical sensors) to monitor performance and build a cohesive model of the grid. It will need new relationships to secure data from third party sources – from weather companies to EV sales analysts, to government electric heating installation programs.
And utilities companies will need to build the IT infrastructure backbone that securely collects data from these many sources and transports it into a shared cloud platform. It will need tools to aggregate disparate data into consistent formats that can be used to build new models and feed existing ones. And it will need to deliver those insights – via purpose-built digital interfaces – to the people who need to act on them, whether network planners, asset maintenance engineers, or energy users.
Conclusion
Better technology for data collection and model-building (both AI and classical), will be critical to transforming the grid into one that is fit for the future. Technology is often thought of as an enabler of change, but in this case, that is thinking too small. Technology is the driver of change. It is the only way to create a smart grid that will deliver the decentralized, decarbonized energy system we need
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