Interest in renewable energy sources is at an all-time high in 2020. More and more countries are adopting renewable energy sources, such as wind and solar, as a way to shift away from fossil fuels and their heavy carbon emissions. Implementing these “clean” energy sources is crucial if countries wish to meet the targets set in the 2030 Climate and Energy Framework.
Renewable energy sources have gained popularity over the years due to their falling production costs. The costs of these energy sources have been declining for decades, but this drop in prices is starting to plateau. We may see the rate of adoption for these renewable energy sources slow down in the coming years due to this reason.
Some believe the next renewable energy revolution may be ushered in by AI technology. Artificial intelligence or AI is set to make renewable energy sources more cost effective and easier to integrate with current grid infrastructure. Here are some ways AI is set to be a game changer for renewable energy.
1. Smarter energy production
In the future AI could be used to monitor energy grids and automatically make parameter adjustments to reduce energy wastage. AI can provide grid operators with a more in-depth understanding of the load on the grid and how it could be allocated more effectively. This will ensure that energy demands are met with the appropriate energy supply that minimises waste.
AI is also used to automate processes that would otherwise be completed by company staff. This technology can perform tasks faster and more accurately than humans, so it may be able to make safe decisions in a more efficient manner and through methods that consider more real-time data points.
2. Optimizing production based on weather conditions
One of the main hurdles to get around when using renewable energy is the inconsistent nature of weather. Electricity grids that rely on renewable energy sources are at the mercy of weather changes.
AI can optimize energy production from these renewable sources by using algorithms to predict the accuracy of weather forecasts and allocate resources towards the grid to ensure optimal supply.
Modern technology and ever-evolving machine learning has made weather prediction models more accurate than ever. When integrated with energy distribution and management systems, each energy source could work together to contribute to the needed demand on a given day.
3. Aiding with maintenance
Renewable energy sources, like non renewables, rely on storage and transmission systems that are prone to wear and tear and require routine monitoring and maintenance.
Monitoring the state of each solar panel in a one thousand-panel solar farm can be a challenge for any grid operator. Similarly, checking wind turbine blades for faults or monitoring generator temperatures can be difficult at wind farms with dozens of turbines.
Through smart sensor systems, AI can keep track of their condition and alert grid operators in a predictive manner or immediately when a component fails or requires maintenance. This allows energy companies to save on labour costs while keeping production running smoothly on the day-to-day. Furthermore, last year, the world’s first fully autonomous offshore wind farm repair system project was launched to transfer the reliance on humans to autonomous vessels, aerial drones and crawling repair robots.
4. Improved energy management
AI’s role in renewable energy extends far beyond just electricity production. AI technology such as Verv are also being used to make energy consumption more efficient in homes, for communities and commercial buildings
Smart home assistants can use AI to monitor the energy consumption of each appliance in the home. Users can then view a detailed breakdown of their energy usage and make lifestyle changes to reduce energy wastage.
For example, a particular kitchen appliance may be using significant amounts of energy when it is in standby mode. This energy consumption can add up over time, resulting in large quantities of electricity from renewable resources being wasted. If Verv alerts homeowners about the high energy consumption from these appliances, they can unplug them to avoid excessive energy wastage.
There will be a larger role for artificial intelligence in energy and utilities management in the future if these technologies are adopted by housing associations and commercial building managers. Energy management consulting firms have touted the benefits of using AI for energy management for years, but many companies have been slow to adopt AI for this purpose.
5. Better energy storage
A major challenge for renewable energy production companies lies with energy storage. These production companies have no control over the weather, so they must carefully and sometimes selectively choose when to charge and discharge their energy storage systems if they wish to minimise energy losses.
Network operation centers (NOCs) already monitor energy storage systems around the clock, but their personnel cannot keep track of the vast quantities of data they are receiving every second. AI can be used to help energy companies monitor the state of their energy storage systems and keep track of ones that require maintenance.
Operators at these centers can work alongside AI to prioritise maintenance for different storage components and automatically alert staff during unit failures or emergencies.
These are just some of the ways in which AI is set to make renewable energy more efficient and cost-effective in the future. This technology is always undergoing further development, so new uses for AI in the renewables sectors may be discovered in the near future.
The 2020s are set to be the decade in which the interests of the power generation industry and the artificial intelligence industry converge. This shared interest in renewable energy can help us achieve a sustainable and greener future.
The importance of renewable energy is only going to continue growing with time. The threat of climate change is looming above us all. So power generation companies should spend more time looking into AI and learning how their operations could benefit from implementing it.
