AI is helping Renewable Energy Flourish - Here’s how

As Europe is dealing with a difficult-to-contain energy crisis (with energy costs increasing to ~10x in some cases!), the entire world has started to feel its impact. According to the world bank, global growth is expected to decrease from 5.7 percent in COVID-affected 2021 to 2.9 percent in 2022. With climate change leading to devastating floods in Pakistan and Europe’s hottest recorded summer in 2022, we require renewable sources of energy more than ever to reduce carbon emissions.

Over the years, we have been focusing on using renewable energy sources like the wind, water and sun to promote a sustainable future, but it still forms a very little percentage of total energy generation in the world. The two main reasons for this are cost and unpredictability. Artificial intelligence is one of the newer fields, which when integrated into the renewable energy sector, could help solve these problems and more. Though the integration of AI in the energy sector is pretty new, we may be able to see a lot of growth and development because of it. Here are some ways how:

Grid Management

Grid management is one of the most important aspects of the energy industry. The grid takes care of different aspects of energy utilization, including its production, transmission and delivery to its respective customers. If a company wants to focus on reliable electricity supply and overall development, then a stable and efficient grid is one of the most important infrastructure requirements. This aspect can be made simpler and easier using artificial intelligence and machine learning.?

AI can be used to predict future weather conditions and make the grid ready to handle such conditions. There are AI algorithms, such as the one developed by IBM for the US Department of Energy’s SunShot Initiative, that use self-learning weather models, datasets of historical weather data, real-time measurements from local weather stations, sensor networks, and cloud information derived from satellite imagery and sky cameras for more accurate predictions than what were possible earlier.

Summers can often damage power lines and affect transmission systems. Autonomous AI algorithms can initiate grid resilience protocols and take necessary steps to avoid power outages. Another aspect where AI offers great help would be predicting cloud cover and daily weather forecasts. Solar energy is one of the most important renewable energy sources, and cloudy skies and rainy weather can often affect it. To reduce possible chances of energy outrage, AI algorithms can help draw energy from local distributed solar batteries, or take other necessary steps.

AI-generated image via DALL.E 2

Companies train their AI models with data from previous years, as well as data regarding the growing population to make an estimate regarding the possible consumption in the current as well as coming years. Moreover, the increasing popularity of electric vehicles, electric heating systems along with the proliferation of distributed energy resources (DERs) need companies and energy providers to balance their assets properly. A great difference in demand and supply might lead to grids collapsing, or even worse! Forecasts suggest that approximately 36 million assets such as solar panels, electric vehicles and energy storage will be added to the grid in Europe in 2025, and 89 million by 2030. This could increase the unpredictability and fluctuations in energy requirements and create instability in grids. AI could help in seamless delivery of energy by forecasting the demand.

The 2021 Texas power crisis shows how vulnerable our current electric grids can be! AI can help direct excess power to wherever it is needed, without splurging or skimping on production. Data analytics using AI can help companies predict usage as well as minor changes in the usage pattern to reduce wastage and manage resources economically.?

Maintenance

Irrespective of how efficiently the grid system is managed, regular maintenance work has to be done to keep all the components of the grid in optimal running condition. With the application of artificial intelligence, computers can predict specific grid parts that need maintenance work to prevent unexpected downtime. After detection, it can inform customers and the concerned authorities regarding the maintenance work.?

For example, drone footage is combined with Computer Vision to auto-detect solar panels that require cleaning in large solar farms, improving the output of these panels before scheduled maintenance takes place.

Increasing the forecasting accuracy helps companies schedule their grid maintenance tasks and optimize plant availability. Brian Case, Chief Digital Officer at GE Renewable Energy, says, “Unexpected disruptions across the industry can cost 3%–8% of capacity and US$10b annual lost-production cost.” AI comes in handy to interpret industrial data for predicting machine health and recommending actions to improve efficiency for assets like wind farms. Using AI to predict and forecast system malfunctions can prevent chain reactions.

Battery Storage System

Batteries are a source of instant as well as backup power for diesel generators, coal-fired power plants and many others. Their quick activation speed and ease of use make them a favorite for satisfying excessive peaks when needed. A trained AI algorithm can forecast demand, renewable energy generation, prices as well as network congestion, among other variables to make decisions regarding efficient energy management from the batteries.

US-based SaaS provider, AMS uses an AI in battery storage systems by tracking fluctuations in energy rate to purchase electricity from the grid when prices are low, and then sell it back to the market when prices are high to maximize profits. Even Australia’s 150 MW Hornsdale battery uses an auto bidder AI algorithm that has been developed by Tesla to make as much as five times more profit as compared to any average energy trader.?

Battery storage systems operations involve a lot of constant monitoring, from the battery status, solar and wind outputs through to weather conditions and seasonality. These systems may often be required to make decisions on when to charge and discharge the systems in real-time, and that can be challenging for human operators. However, the use of a machine learning algorithm makes it easier to achieve this in real-time, around-the-clock for greater efficiency in energy storage operations. Typically, it includes four operations - data acquisition, prediction, simulation, and optimization to be able to handle, analyse and exploit data.

Power Plant Optimization

While we are looking at increasing the number of renewable energy power plants, we also need to optimize the existing ones. With tons of data being collected everyday, AI can sufficiently help us here. A recent example would be Vestas Wind Systems, the world’s largest wind turbine manufacturer, using AI to increase the efficiency of their wind turbines. A wind turbine always produces wake that slows down the turbines located downstream. To counter this effect, the rotors of the upstream turbine are misaligned so as to deflect the wake away from the downstream turbines. This process is called wake steering. In a wind farm, with multiple turbines and constantly changing wind speed and direction, it becomes a fairly complex problem to dynamically misalign the rotors of different turbines.?

Vestas used Reinforcement Learning, an interactive ML method that uses feedback, to solve this problem. The idea here is to reward favorable outcomes and punish unfavorable ones. Vestas used it to train controllers to react to changing wind conditions and rotor misalignment to produce the best results.?

AI-generated image via DALL.E 2

Conclusion

It must now be evident to you how artificial intelligence can serve as a backbone to the renewable energy industry. If one looks at the use of AI in renewable energy subjectively, they will easily be able to notice how it provides insight into human motivations tied to renewable energy adoption. Not only that, it also helps clarify how consumer behaviors could possibly be changed to optimize the energy system. AI is still a developing field, and lots of research is still pending in the field. In fact, in 2020, the US Department of Energy had allocated a fund of US$37 million for as R&D funding for AI. We can only wait and see how AI can help the renewable energy market in the future.?

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