As artificial intelligence (AI) continues to evolve at an unprecedented pace, questions are beginning to emerge about how the technology can be used to solve grand societal challenges. Nowhere is this more pronounced than energy optimisation and climate modelling. Case in point, this summer the UK government announced it would be investing in ‘green AI innovations’ – the latest gambit in a mission to accelerate industrial decarbonisation across the country.
The race to net zero is on, and there’s little doubt AI will have a part to play. From predicting solar panel output to optimising the generation of energy from wind farms, this technology is already revolutionising the green tech landscape by driving efficiency, reliability, and innovation.
Changing the narrative around renewable-powered energy grids
Historically, the energy market has relied on control room expertise developed over decades of managing predominantly static energy networks. The interconnected nature of these networks, coupled with the possibility of small decisions within one element causing ripple effects, often raises questions about efficiency. Consequently, this has led to the perception that networks dependent on renewable energy are somehow unreliable or difficult to manage. That narrative is about to change.
The use of digital twin technology, powered by AI and the Internet of Things (IoT), is helping to address the challenges of integrating low-carbon technology. These real-time digital replicas of physical networks enable control engineers to monitor, analyse, and predict network behaviour, ensuring optimised energy distribution.
AI-driven predictive algorithms also assist asset engineers to plan maintenance schedules, reduce unplanned outages, and enhance network performance. They can even introduce greater flexibility into the network, automating Battery Energy Storage Systems (BESS) and other storage and demand-side management options. Combined, these solutions lay to rest the narrative that intermittency with renewable options means they cannot be made to work at scale.
Furthermore, AI algorithms can optimise grid operations by dynamically adjusting power plants, energy storage, and EV charging stations, ensuring that excess renewable energy is stored when abundant and released when required, so none goes to waste, and fossil fuel usage is kept at a minimum. And with AI managing renewable energy assets with such precision, fewer physical repairs are needed, which ultimately extends the lifetimes of the systems.
Refining climate models with AI
As climate change continues to grow in relevance, we are now benefitting from a greater quantity of data to produce accurate and adaptable climate models. Technology such as AI, with its ability to scrutinise and find patterns in these massive data sets, can provide the real-time adaptability needed to detect localised weather changes.
This adaptability will ensure that energy systems maintain peak efficiency under varying conditions. What’s more, AI’s ability to analyse extensive patterns in energy consumption allows it to anticipate demand, leading to more strategic energy distribution and management on a local, regional, or even international scale.
However, AI isn’t just for predicting responses – it’s for shaping them too. By evaluating energy consumption trends, AI can be used to formulate ‘demand response’ scenarios, backed by innovative pricing mechanisms, to adjust energy distribution fairly and efficiently.