Clean AI is becoming a core technology for energy consumption, renewable energy integration, and energy system optimization. The article describes how AI can help make a more sustainable and robust energy system.

The Economy is Being Decarbonised Through Clean AI.

Energy decarbonisation is one of the greatest sustainability issues of the world today. The energy industry alone already accounts for more than 70 per cent of all greenhouse gas emissions worldwide. The developing world will consume almost twice as much energy by 2050 because of an interplay of urbanisation, population and rising living standards.

The good news is that the International Energy Agency (IEA) says that with increased production of renewable energy and improved energy efficiency, the planet could save more than 40 per cent of CO2 emissions by 2040. In our Whitepaper “Sustainable AI for Enterprise Transformation, Innovation and Growth” we present how Building Sustainable AI platforms can decarbonize the whole economy. In this bog post we have an overview of some of the findings for energy production and transition to renewable energy with proper energy management and seamless deployment of renewables.

Transitioning to Renewable Energy

AI is making it possible to transition to renewable energy. The old system was extremely straightforward, just a few big “power on demand” generation plants close to the end consumer and then you could manage them and deliver power to meet demand very rapidly and easily. Solar generation is a lot more challenging, and needs much more sophisticated management, and much more problems to get up and running.

The solution to providing reliable clean renewable energy requires AI.

The Utility of AI in Energy Control?

37% of CO2 reduction will have to come from energy efficiency improvement, 8 per cent through complicated fuel switching (heptane, etc) or carbon sequestration (9% CCUS), according to the International Energy Agency (IEA). Renewables will only make up about 32% of total savings, in contrast.

The World Energy Outlook (2022) predicts technology-based electricity limits that hit all companies in 2030. Particularly technology companies have to prove themselves in more meaningful ways as efficient firms in this space.

All that energy savings will have to come from seeking out the small returns across operations, from micro power and production to transport efficiency. We can’t control this rising complexity without the Sustainable AI platforms.

The energy producers around the world are under increasing pressure to move more rapidly away from fossil fuels and toward cleaner, renewable energies like wind, solar, hydroelectric, geothermal and wave energy. But these generators will have to incorporate as many renewable (often weather-dependent) energies as possible, and not so much non-renewable, fossil-fuel based generation.

This is complicated even more by having to regulate smart grids to support small-scale local and micro power generation which in many cases also varies from the weather. At the same time, renewable energies are replacing fossil fuels in heating and cooking at home and greener transportation technologies such as electric vehicles (EVs) are changing the power demand and transmission capacity required to maintain supply.

Virtual Power Plants and Grid Administration.

AI-powered utility management has created the need for virtual power plants (VPPs), a federated system of decentralized distributed energy resources (DERs). Those are solar, wind, batteries, and flexible demand-side solutions like smart homes and electric cars.

All of these different components are then interconnected and controlled by powerful software and communication systems to work as one centralized generator. A key objective of a VPP is to make electricity production, storage and use efficient, which means a lot of improvement to grid stability, efficiency and reliability.

The UK’s National Grid Electricity System Operator (ESO) is an example of a grid operator that integrates the latest AI-based grid management to provide demand and supply, with renewable energy and the rise of EVs. Google’s DeepMind is collaborating with the UK National Grid to apply AI to forecasting energy demand and balancing supply and demand so that the grid is more stable.

The same goes for IBM’s Watson, which utilities are using to process large amounts of smart grid data, predict energy use and enable the integration of renewable sources.

Sustainable AI at the Heart of Energy Security.

Ai has become an indispensable coping mechanism against this increasing complexity. Forecasting demand, balancing generation, and managing smart grids all allow AI to provide you with reliable power in all weather conditions. Here, AI-enabled VPPs are more than a technology development – they are a strategic necessity for energy management in the future.

Demand-side: Businesses have more and more advanced ESG management systems that report emissions, as the law increasingly mandates, but also mitigate emissions by managing energy sources, optimising production and orchestrating supply chains. In both directions, the ability to link and better harmonise supply and demand with Sustainable AI platforms is expanding as AI operations become part of them.

Energy Efficiency: Reduce Consumption and Build in Renewables

The energy use is one of the biggest sustainability problems of cities. Cities have enormous energy needs for their residences, shops and factories – and they still need much of it in the form of fossil fuels. AI can transform energy networks by automating demand prediction, energy distribution, and the integration of renewable energy sources into the grid.

In Los Angeles, for example, AI-powered demand response monitoring analyses energy use in real time and rebalances the distribution dynamically. When there is a high demand, AI algorithms forecast consumption spikes and move the power around so that it does not overload the grid.

Such real-time reactivity also minimizes dependence on fossil fuels to satisfy energy peaking. In addition, thanks to machine learning, Los Angeles’s AI platforms can make more use of renewable sources of power, such as solar and wind, even out supply fluctuations and avoid dependence on non-renewable sources of energy.

Then there is Google’s artificial intelligence-driven energy management in data centers. Google has saved up to 40 per cent on cooling costs of its data centres through DeepMind’s AI, and has saved about 15 per cent on overall energy consumption, after calculating and predicting energy demand. And such measures, scaled up to an urban space, can make huge energy savings and greenhouse gas emissions reductions.

The Power of Green AI in Energy Technology.

As the planet faces climate change and energy shortages for yet another generation, AI in energy balancing will become all the more essential. As AI makes possible cleaner and greener energy infrastructures, it is ushering in a greener, cleaner future. But taking AI to the next level of energy management will take more research and development, collaboration between governments, enterprises and technology companies.

Embedding AI into energy systems is not always easy. Data privacy, cybersecurity and strong regulatory systems will all need to be addressed to allow AI technology to be safely and effectively deployed. Furthermore, the implementation of AI-powered energy solutions must be governed by sustainability and fairness, in such a way that the fruits of such technologies are made accessible to all.

Conclusion

sustainable supply-demand integration is at the heart of net-zero emissions and AI is an essential facilitator of this process. In cutting energy use, adopting renewables and increasing the efficiency of energy infrastructure, AI is shaping a more sustainable and resilient energy landscape. In the future, continuing the research and implementation of AI will play a major role in the world’s energy issues and a cleaner energy future.