Clean energy in the age of AI – striking the right balance

By Roy Bedlow, Chief Executive and Founder of Low Carbon

This thought piece was first published as a Guest Post in ESG Today.

Much has been made about AI’s potential to accelerate, but also disrupt, the energy transition.

In a world of increasing investment into renewable energy, the constraint on these critical infrastructure projects is often grid connections, capacity planning and the challenge of overcoming red tape to get spades in the ground.

AI and big data are increasing energy demands, driving the need for us to solve these challenges. They might also hold part of the solution.

This matters because today, the renewable energy industry is at a critical juncture. Despite efforts to roll back the green agenda in the US and to a lesser extent in the UK and Europe, the economic argument for renewables remains unassailable. Clean energy is the most environmentally sustainable option and continues to be the most affordable source of energy.

With increasing corporate demand for renewables – evident from the likes of Amazon, which has been the largest corporate renewable energy buyer for five consecutive years – Power Purchase Agreements (PPAs) remain a vital instrument for scaling up renewable energy capacity and securing long-term supply. This continued demand underscores a crucial point: companies are no longer waiting for policy certainty. Rather, they are forging ahead with their own ambitions to secure the cheapest and most secure form of energy.

To make progress, one of the significant challenges we face is how we integrate increasing volumes of renewable energy into the grid. This task is deepened by the rapidly growing data centre sector, which already consists of more than 8,000 facilities globally and is poised to expand further as the demand for data soars, driven by AI. Ensuring that these energy-intensive facilities run on renewables is not just an opportunity – it is an absolute necessity if we are to reduce emissions.

The grid connection process is just one example of where AI is already making a tangible impact. Traditionally, securing a grid connection has been a slow and costly endeavour, delaying renewable energy projects. AI-driven analytics can accelerate this process by identifying optimal connection points based on transmission capacity, infrastructure constraints, and projected demand.

For an Independent Power Producer (IPP), the convergence of AI, data, and renewables is a game-changer. The question now is not whether the grid can handle this shift, but how quickly can renewables scale to meet it?

PPAs will play a crucial role in ensuring this happens by enabling energy intensive users to secure and develop reliable clean energy supplies that meet their needs without cutting capacity for other users. By leveraging AI to optimise energy forecasting and improve grid integration, IPPs can offer more reliable, cost-effective renewable energy solutions. This is particularly important as corporate offtakers look for ways to meet sustainability goals while managing costs.

The future of renewable energy is not just about building more solar, wind and battery projects — it is about deploying smarter, more efficient solutions that maximise their potential. AI is a critical enabler in this respect. By harnessing the power of data and automation, we can streamline the grid connection process – identifying optimal connection points and anticipating constraints – ensuring that renewables not only meet growing electricity demand but also strengthen energy security and climate resilience.

Global energy demand is only going to increase, largely driven by AI and digitalisation. For IPPs, this presents an unparalleled opportunity to lead the energy transition. Those who embrace AI and big data will be best positioned to scale renewable capacity efficiently, deliver cost-effective solutions to offtakers and play a pivotal role in building a sustainable future.