Imagine a future where a 100% renewables-powered electricity grid harnesses the abundance of energy beamed down on us daily, to give 100% reliability of supply. Enabling energy prices to be a fraction of what they are today, or perhaps even universally free. Let’s consider two paths of thought, one into the far future, the other more nearer-term for India, and rather imperative.
Looking to history to inform our future-gazing, the indications are that we’ll find ever newer ways to consume energy. So we may take indeterminate time to reach ‘surplus’ in the strictest sense, though that does not preclude universal free access. Even if we developed the ability to absorb all the energy emitted by a star - perhaps through the hypothesised Dyson Sphere - and become a ‘Type 1 civilisation’. Tech advancement will likely hog whatever we produce. More likely, we’ll develop ships that are electric instead of solid-fuel run, and hyperloops to replace trains. Energy is like money, we will always find ways to consume it - our way of life will just change to match.
Today however, India already has an energy surplus, with approximately 300GW of installed capacity, and only 150GW of peak demand. But this is a technical rather than practical reality. There is still load shedding in most parts of the country after all, and our per capita consumption remains low because most people still have only limited access, in term of both connectivity and availability of power. Further, India is a tropical country blessed with ample sunshine and wind, even in the monsoon months. Yet at present, India still imports much of its energy from abroad, notably in the form of oil and coal. The government is convincingly pushing for renewables, and wants 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 energy possible.
Once India reaches national energy independence, another equally fundamental development and national health challenge that can be addressed, is the stark water distress. Cheap energy will allow unlimited pumping of water across immense distances, enabling this demon to finally be dispelled. Nascent water desalination technologies - which already exist, but are hamstrung by huge scaling costs - will then take off along the coast. This will enable pumping of clean water to the northern inlands, and onwards to other countries in similar need.
Technologies such as distributed energy generation and storage will take some pressure off the future central electricity grid - for example by reducing the load on transmission infrastructure - there can be no such ‘distributed desalination’ equivalent for water. The oceans are fixed, so plants must remain coastal, or possibly at some suitable inland locations, such as Sambhar lake in Rajasthan. This will mean building significant new pumping infrastructure, with massive energy demands to fully service the country,
Thus, a prerequisite for clean water is vast amounts of cheap energy, and this can only be possible from solar and wind, which are inherently variable, so will increase grid-volatility. Today, even with just a small penetration of renewable generation, the intermittency has caused a significant rise in balancing costs, and in the future this could be huge.
Given this, we must identify solutions with the highest benefit-to-cost ratio across the energy-ecosystem, and software will always be cheaper and less cumbersome than physical buildout. This is where artificial intelligence and machine learning (AI & ML) come into frame and excel - crunching the exponentially growing volumes of data required to balance gird-volatility, forecast future trends, optimise existing assets, and plan future infrastructure. We are already seeing AI & ML starting to handle these issues in the real-world, from forecasting of generation and demand, to scheduling of supply. This bodes well for the future water-energy nexus.
So, there are a multitude of ways we can imagine using an envisaged future energy surplus, but solving clean water access for all, with the help of AI & ML, seems to be the one that will most benefit the country and its people.
Credit to Niladri Roy for co-writing this article.
Looking to history to inform our future-gazing, the indications are that we’ll find ever newer ways to consume energy. So we may take indeterminate time to reach ‘surplus’ in the strictest sense, though that does not preclude universal free access. Even if we developed the ability to absorb all the energy emitted by a star - perhaps through the hypothesised Dyson Sphere - and become a ‘Type 1 civilisation’. Tech advancement will likely hog whatever we produce. More likely, we’ll develop ships that are electric instead of solid-fuel run, and hyperloops to replace trains. Energy is like money, we will always find ways to consume it - our way of life will just change to match.
Today however, India already has an energy surplus, with approximately 300GW of installed capacity, and only 150GW of peak demand. But this is a technical rather than practical reality. There is still load shedding in most parts of the country after all, and our per capita consumption remains low because most people still have only limited access, in term of both connectivity and availability of power. Further, India is a tropical country blessed with ample sunshine and wind, even in the monsoon months. Yet at present, India still imports much of its energy from abroad, notably in the form of oil and coal. The government is convincingly pushing for renewables, and wants 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 energy possible.
Once India reaches national energy independence, another equally fundamental development and national health challenge that can be addressed, is the stark water distress. Cheap energy will allow unlimited pumping of water across immense distances, enabling this demon to finally be dispelled. Nascent water desalination technologies - which already exist, but are hamstrung by huge scaling costs - will then take off along the coast. This will enable pumping of clean water to the northern inlands, and onwards to other countries in similar need.
Technologies such as distributed energy generation and storage will take some pressure off the future central electricity grid - for example by reducing the load on transmission infrastructure - there can be no such ‘distributed desalination’ equivalent for water. The oceans are fixed, so plants must remain coastal, or possibly at some suitable inland locations, such as Sambhar lake in Rajasthan. This will mean building significant new pumping infrastructure, with massive energy demands to fully service the country,
Thus, a prerequisite for clean water is vast amounts of cheap energy, and this can only be possible from solar and wind, which are inherently variable, so will increase grid-volatility. Today, even with just a small penetration of renewable generation, the intermittency has caused a significant rise in balancing costs, and in the future this could be huge.
Given this, we must identify solutions with the highest benefit-to-cost ratio across the energy-ecosystem, and software will always be cheaper and less cumbersome than physical buildout. This is where artificial intelligence and machine learning (AI & ML) come into frame and excel - crunching the exponentially growing volumes of data required to balance gird-volatility, forecast future trends, optimise existing assets, and plan future infrastructure. We are already seeing AI & ML starting to handle these issues in the real-world, from forecasting of generation and demand, to scheduling of supply. This bodes well for the future water-energy nexus.
So, there are a multitude of ways we can imagine using an envisaged future energy surplus, but solving clean water access for all, with the help of AI & ML, seems to be the one that will most benefit the country and its people.
Credit to Niladri Roy for co-writing this article.
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