Can Machine Learning Enable an Energy Independent India?
These are high-times for Artificial Intelligence (AI), specifically its Machine Learning (ML) form. Both are increasingly coming into the public consciousness, as they are applied to an array of sectors. Applications in the power sector, though less visible than say physical robotics, are the most fundamental, especially in an Indian context, as its evolution will also transform many other sectors.
As a tropical country we are endowed with abundant sunshine and wind, but still import much of our energy in the form of oil and coal. The current Government is pushing to transition us to a ‘Solar Nation’, in no small part to reduce the national import bill. Achieving this could accelerate the Make in India campaign, because national energy independence will mean the cheapest power possible. The vision is for a 100 per cent renewable-powered grid, giving 100 per cent reliability of supply, enabling prices to be a fraction of what they are today.
Technically India already has ample power, with approximately 300GW of installed capacity, and only 150GW of peak demand. However, this is not a practical reality. Rather, it reflects the challenges of managing supply and demand. Our per capita consumption remains low because most people still have only a limited access, in terms of connectivity and availability. Further, though abundant, Solar and Wind are inherently variable, and will exacerbate grid-volatility as integration increases. Even with the small penetration of renewable generation today, the intermittency has caused a significant rise in balancing costs, which in the future could be huge.
Added into the mix, the future will see increasing Distributed Energy - both localised green generation, and storage. Though in some instances this will take pressure off the central grid, for example by reducing the load on transmission infrastructure, overall it will increase volatility. Today generation and consumption locations are essentially fixed, we at least know where they occur, even if not quite what volumes and when. But we have little idea of the future profile, especially with Electric-Mobility coming into the frame.
It is already difficult for humans to manage this complexity, but these challenges are not dire portents for the sector. What we need is a modern approach to address key deficiencies, by analysing disparate real-time data for optimal decision-making. This must be cost-effective, and of all available avenues software will always be much cheaper and more efficient than physical buildout.
This is where AI & ML excel, with by far the greatest benefit-to-cost ratio. Crunching the exponentially growing volumes of systems data required to balance gird-volatility, forecast future trends, optimise existing assets and plan future infrastructure. They are already handling these issues in the real-world, from forecasting of solar generation to prices on the power markets, and even detection of power-theft.
In a renewable and distributed energy future, AI & ML can manage key aspects of the energy system, with human-oversight replacing human-intervention for the highest decision levels. It thus has the capability to help us realise the vision of an energy-independent India, with affordable power for all.
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