Accelerating the Green Transition - AI-Powered Green Energy

As we move towards a more sustainable future, the integration of renewable energy sources into our energy networks is becoming increasingly important. One way to maximise the use of renewables is by utilising artificial intelligence (AI*) technology to optimise the network. In this article, we will explore the potential benefits of using AI to develop a green energy network, the challenges faced when implementing this model into the existing infrastructure, and how AI can be used to maximise renewables with the least amount of investment.

*AI refers to the branch of computer science that develops machines and software with humanlike intelligence.

It is important to split out the challenges that may be faced designing a network from scratch vs upgrading a network already in existence to better accommodate green energy assets using AI. The development of a green energy network from scratch requires significant investment and a long-term vision, political buy in and policy changes that support the rapid deployment of renewables. It will also need to incentivise innovation and accept that the outcome may suggest significant change to the existing model.

It is quite obvious that the benefits of operating a green energy network has huge public support today. By integrating and accelerating the deployment of renewable energy sources such as solar and wind power, we can reduce our reliance on fossil fuels, lower greenhouse gas emissions, reduce the cost of energy supplied to our homes and businesses and improve our energy security faster than we ever imagined. AI technology can help to optimise and accelerate this mission making it more efficient and cost-effective, leading to lower energy costs for consumers.

However, designing the green energy network with AI technology into our existing grid infrastructure comes with several challenges. Integration, regulatory policy challenges, investment in research and development, cost and financing, and public acceptance are all issues that may need to be addressed.

So with these in mind, what would we expect to see as the key opportunities when thinking about the deployment of AI into the design and management of our grid?

1. Optimal site selection: AI can analyse vast amounts of data related to climate, geography, and energy demands to identify the most suitable locations for renewable energy infrastructure. This could increase the efficiency of energy production by minimising the cost of energy transmission and distribution. Thinking about the grid as a whole system taking into account the spatial and temporal constraints would be key. Identifying the optimal locations for new generation and storage by assessing grid availability, environmental constraints, local consumption, planning restrictions and project risks would most likely turn out a model that is vastly different to the developer model we see today.
2. Predictive maintenance: By using AI algorithms, sensors, and machine learning techniques, renewable energy infrastructure can be monitored to predict when maintenance is required. This can help to minimise downtime, reduce maintenance costs, and extend the lifespan of renewable energy assets. There is such a huge opportunity to increase the performance of renewables today with predictive maintenance without the use of AI that I think we could quickly identify patterns for better maintenance practices much faster than our best data scientists are finding today with AI.
3. Real-time energy demand management: With AI technology, renewable energy generation and consumption can be better managed in real-time. By predicting energy demand and supply, the green energy network can adjust the energy production to meet the needs of consumers. This could help reduce energy waste and increase overall energy efficiency. We are just seeing the dawn of consumer Demand programmes from National grid ESO in the UK, but with the proliferation of smart mobility, storage and heat over the next 10 years, AI would deliver a huge benefit to how these assets are managed in real time, shifting patterns of consumption without the consumer ever being aware of the change. Ultimately minimising grid management and upgrades in line with mass adoption of the technologies we need to power our lives.
4. Reduced carbon footprint: The development of a green energy network from scratch would allow for the implementation of the latest and most efficient renewable energy technologies. This could significantly reduce the carbon footprint of energy production and consumption, contributing to a more sustainable future for the planet. In addition, AI-based software solutions are able to help determine engineering design parameters when testing is not possible. This would result in significant savings in terms of human time and effort spent in experimentation that would improve quality of performance or longevity of the technology placed on the network.
5. Grid optimisation: AI could be used to optimise the grid to make it more efficient and reliable. For example, machine learning algorithms could be used to analyse data from the grid and identify areas where upgrades are needed to improve performance. This could include adding new transformers, upgrading substations, or installing new distribution lines, minimising grid upgrade and management costs by only replacing or upgrading infrastructure where it's essential or in some cases avoiding them all together.
6. Energy trading: By using algorithms to analyse data on energy supply and demand, the network could optimise energy trading to reduce costs and maximise renewable energy usage. This could involve using machine learning to predict energy prices and optimise trading strategies in real-time.
7. Weather forecasting: Weather patterns have a significant impact on renewable energy production, so accurate weather forecasting is essential for efficient operation of a green energy network. AI could be used to analyse data from a variety of sources, including satellite imagery, weather sensors, and historical data, to develop more accurate weather forecasts. This would enable the network to better anticipate changes in energy supply and demand and adjust energy production accordingly.
8. Smart metering: Smart meters are becoming increasingly common in many parts of the world, and they provide a wealth of data on energy consumption patterns. By using AI to analyse this data, the green energy network could identify areas where energy efficiency could be improved and develop targeted strategies to reduce energy waste. The other potential benefit could be to identify new energy consumer archetypes, enabling us to learn much more about the predictability of energy use across the system and the types of renewable energy solutions required in every home.
9. Energy storage: Energy storage is a critical component of any green energy network, as it allows energy to be harvested when it is abundant and used when it is needed. AI could be used to optimise energy storage by analysing data on energy production and consumption patterns, and developing algorithms to control the charging and discharging of energy storage systems. This would improve the efficiency of the network and reduce the cost of energy storage.

Next comes the challenge of integrating this optimal renewable energy system designed by AI technology into the existing network. The key focus areas that would need attention to ensure sound investment of public money:

1. Integration with the existing infrastructure: The existing network was designed for traditional energy sources, and significant upgrades would be required to integrate renewable energy sources and the associated AI technology into this system. This would require careful planning and execution to ensure a seamless integration without disrupting the existing network and a whole system approach to ensure that the costs are minimised.
2. Regulatory and policy challenges: The development of a green energy network would require new policies and regulations to be established. This would include issues such as interconnection standards, power quality requirements, and safety regulations. To ensure sound investment of public money, policymakers would need to focus on creating a favorable regulatory and policy environment that incentivises the new approach that AI may suggest for the deployment of renewable energy generation across the grid. It may even uncover an optimised approach that contradicts public opinion or may suggest development in areas that current policy does not allow.
3. Investment in research and development: To implement a green energy network, significant investment in research and development would be needed. This would include the development of new technologies, such as energy storage systems, smart grids, and renewable energy sources, as well as the development of new AI algorithms to optimise the network. To ensure sound investment of public money, a focus on research and development would be essential to develop the best technologies and strategies for the network.
4. Cost and financing: A system like this would require significant upfront costs and financing for such projects can be challenging despite the fact that there is abundant money in this sector today. To ensure sound investment of public money, policymakers would need to focus on creating financial incentives to encourage private investment in the network. Additionally, a focus on cost optimisation, including minimising the cost of new infrastructure and optimising energy trading, would be important to ensure the long-term financial viability of the network. With any policy changes, there would need to be firm long term commitments to ensure that this private money had the confidence to back the upgrade of the system.
5. Public acceptance: The final challenge in this shift will be public acceptance and support for AI in their interaction with the new system. Policymakers would need to focus on educating the public about the benefits of the combinations of renewable energy and AI technology, and engaging with stakeholders to ensure that their concerns are addressed. Additionally, the network would need to be designed with the end-users in mind, to ensure that it is user-friendly and meets the needs of consumers. We already have issues with the perception that smart meters are an invasion of privacy. Imagine the perception that a human like software platform has access to data about you and your home. I can imagine that this will become worrying for many people, especially where AI is being used to support customer service.

In conclusion, AI technology has the potential to transform the energy market by making it more efficient and cost-effective. By integrating AI into the design and management of our grid, we can reduce our reliance on fossil fuels, lower greenhouse gas emissions, and improve our energy security faster and more effectively than we thought possible

That being said, we need to make these changes regardless of AI and we should embrace the idea that regular human intelligence is good enough and start thinking about how we can adopt whole system thinking in partnership to solve the challenges that we face today.

"If you do not change direction, you might end up where you are heading" - Lao Tzu