Energy Storage in Renewable Power

Artificial Intelligence (AI) is emerging as a powerful tool in the renewable energy sector, unlocking new possibilities for optimising modern energy systems. However, the adoption of AI in the green industry comes with notable challenges, including high initial costs for software and specialised personnel. Additionally, automating large volumes of sensitive energy data—such as customer, plant, and grid information—introduces significant security risks. Safeguards must meet GDPR standards and defend against cyber threats. As AI advances, its potential to balance supply and demand, especially for variable renewable sources, becomes a crucial focus for the industry’s future.

AI-Driven Solutions for Optimising Energy Storage 

With its advanced analytical potential, AI presents a genuine solution to optimising energy storage. We explore how AI algorithms can enhance the efficiency of battery storage systems, as well as other storage technologies: 

Battery storage systems  

By nature, renewable energy is a volatile method of generating energy, so we can experience higher and lower levels of energy generation based on environment, weather and climate change. To mitigate the periods of energy ‘drought’ and energy ‘flooding’ when supply dramatically increases or decreases, energy stores such as batteries can be introduced to store or release energy when required. AI studies and considers these periods based on the data captured during these times, as well as factors such as grid stability, pricing of energy and demand, and uses algorithms to make decisions on when energy should be released or retained. This is particularly important for grids supplying facilities like hospitals, who cannot do without a constant supply of energy.  

Other energy storage technologies 

Other storage technologies, such as batteries, can benefit from AI input. These include carbon capture processes, which remove and store carbon from the environment. AI can help human operators make decisions on the best conditions or methods for storing and using captured carbon at a later date.  

Load Balancing: The Challenge of Fluctuating Energy Demand  

Renewable energy’s volatility can cause energy supply to fluctuate, but so can consumer energy demand. These are two of the key challenges of load balancing in energy grids—but how can AI help us address these issues? 

A method used to stabilise the grid known as Demand Response Management (DRM) could hold the key in this scenario. Used to monitor and amend the amount of energy usage by businesses and organisations, it often balances demand and supply during high-demand periods. AI can assist by doing the same for renewable plant owners and consumers, identifying patterns and quickly monitoring actual energy usage acting in real-time to adjust energy supply in response to demand.  

Integrating Renewable Energy Sources: AI in Action  

AI integrating solar into existing power grids 

These algorithms can also help to successfully integrate solar power into an existing solar site in the grid, by using AI software to identify the current optimal operating conditions for a particular solar array by analysing data gleaned directly from the potential solar site, predicting how a new array will perform based on current data. This data can be gathered from solar networks using technology such as DC optimisers or micro-inverters to examine each solar panel, and flag if one module from the new installation is failing or not performing as expected.  

AI integrating wind into existing power grids 

AI can be used in a similar way to integrate wind power into existing power grids. Wind energy can be volatile, partly due to factors like wear and tear on equipment. This is because wind farms need wind to function, which is usually the result of exposed regions or areas prone to storms, which generate wind. As a result, wind arrays can get damaged due to extreme weather - AI can help to integrate new wind technology by monitoring the health of current wind arrays to identify if a new site will need more intense maintenance than usual and the types of damage and faults that occur. AI can also be used to predict potential faults that may lead to downtime and loss of income before these failures occur. 

Predictive Analytics and Energy Forecasting 

AI can integrate new renewable networks onto an existing grid and improve the accuracy of energy production and consumption.  

Due to wind and solar’s unpredictability, it’s important that existing data is analysed to predict optimal conditions for energy production. This is usually done via data gathered via the Internet of Things: an intricate network of sensors and analysis tools that monitor and record all information about a renewable plant and the energy generated, distributed, and consumed. Data collected tends to focus on historical production levels in relation to weather conditions as well as current conditions for comparison.  

AI can speed up the process of analysing this data and spotting trends, potentially predicting pressure points where these optimal conditions don’t occur - for example, during a wind drought in the case of a wind plant - and predicting potential failure, notifying the grid before these incidents occur, creating higher grid resilience. It can also highlight areas of extreme weather activity, which in the past could have led to the destruction of equipment and shut-down, again notifying the grid before a disaster occurs.  

During periods of high energy generation, AI can predict when is the best time for energy to be stored in energy storage technology. If a storm is forecast to hit a specific area, which could generate a larger amount of energy than usual - sometimes called ‘flooding’ the grid with energy - AI can predict that this event will occur, advising the diversion of energy into storage facilities to be used during an energy drought, for example during cloudy periods in a region mostly powered by solar energy. This, in turn, can lead to better planning and resource allocation for the grid as a whole.  

AI is set to play a vital role in the future of renewable energy, from enhancing grid stability to improving energy forecasting. As the technology evolves, its integration in renewables will be key to building a resilient, sustainable energy infrastructure.