Circuit boards are the lifeblood of most electronics, but they are particularly empowering to renewable energy infrastructure as it undergoes buildout. They convert and transmit power, and years of innovation have transformed them into the most efficient versions of this crucial component. Artificial intelligence (AI) promises to be a welcome disruption, making it smarter and more productive. How does this help Europe’s clean energy plans?
Optimizing Grid Operations
Intermittency plagues renewable energy discourse, preventing stakeholders from feeling convinced about its viability. Solar and wind power, especially in places like northern Scandinavia, feel like questionable investments. However, these nations were able to foster a bustling solar economy, despite the latitudes.
AI algorithms remove these uncertainties. They can predict energy demand and generation potential simultaneously, configuring grids optimally for their specific population and environmental circumstances.
As AI learns more about usage patterns, it will become increasingly accurate at handling supply fluctuations and load distribution. Variability becomes a concern of the past, as AI converts and directs electricity during emergencies and peak times.
Enhancing Energy Efficiency
Older electronics contain outdated circuit boards, are less optimized and emit energy waste as a result. Retrofitting these systems with AI can make renewable power assets more adept at managing the needs of the largest and most complex infrastructure. MIT researchers discovered that algorithms were capable of lowering the power usage of some machinery by 12%-15% by using energy caps, cutting carbon footprints.
AI-powered circuit boards are particularly helpful for industrial operations, which must focus on consumption awareness before implementing renewables in the long term. These mechanisms help companies reach goals by discovering wastage and excess consumption to allow for proactive decision-making.
Improving Renewable Energy Integration
Circuit boards are part of off- and on-site generation tools, as well as energy storage solutions. Therefore, they are the backbone of production potential and distribution efficacy, even during blackouts.
A commercial-scale solar farm produces at least 200 megawatts of electricity for its constituents, while residential setups generate around 5 megawatts. Circuit boards need to manage consumption regardless of capacity, and AI simplifies complex integration, especially for on-site renewables.
Properties could get more out of their technology with smarter circuit boards. If there is surplus generation, they can optimize storage and even automate excess to support demand-response and net-metering programs. The financial boons encourage further integration with minimal maintenance headaches.
Enabling Predictive Maintenance
Many people repair energy equipment reactively as it fails or starts to perform below expectations. Because the circuit board is connected to so many of the device’s functions, it can learn what causes downtime or unsatisfactory output. Additionally, unexpected failures put financial and labor pressures on organizations, especially when repairing commercial equipment.
AI-powered predictive maintenance informs technicians of the best times to schedule planned repairs, while using anomaly detection to spot concerns as they arise. It minimizes the number of invasive fixes that will need to be done over the product’s life cycle, extending its lifespan by preserving its components.
Using AI-Driven Energy Trading Platforms
Distributed power resources, individual households and small businesses may have difficulty navigating complex energy markets, which are undergoing their fifth reform across nations. Energy markets vary across borders, which deters many from participating in demand response and other contributions to the grid. AI-driven trading platforms could collect data from all over Europe, leading to greater transparency and understanding about the landscape.
The trading platforms consider historical data alongside new renewable energy resources being installed. The circuit boards contribute to this dataset, allowing customers to maximize revenue streams and see market forecasts. It boosts energy literacy across Europe by empowering customers to learn the best ways to participate in the market that best suits their needs.
Developing Virtual Power Plants
While decentralized power resources are becoming more common throughout the continent, they are still not as widespread as they could be. AI-powered circuit boards could contribute to their development by adding to the public’s understanding of virtual power plants. These assets can use aggregated data from the circuit boards in decentralized networks to visualize a community’s renewable energy portfolio.
The insights from AI-enhanced circuit boards will enable decentralized markets to become as robust as utility-scale providers. AI could leverage reinforcement learning to use energy more strategically, eventually learning how to self-heal after equipment failures or adapt to an unexpected increase in cloud cover. The comprehensive image of seemingly disparate assets allows communities to refine asset management and see how their individual impacts contribute to the greater good.
The Connection Between Circuit Boards and Renewable Energy Rollout
Adoption of renewable energy across Europe is more likely to occur if its foundational mechanism — the circuit board — can mitigate opposing arguments against implementation. As the continent makes moves toward more aggressive climate objectives, enhancing components like the circuit board with AI will be pivotal in solving optimization and efficiency issues on private, corporate and national grids.
About the author: Jane works as an environmental and energy writer. She is also the founder and editor-in-chief of Environment.co
Energy in Demand - Sustainable Energy - Rod Janssen 
One of the strangest articles I have read. I can see how in multi-layer PCBs some design assistance can help – particularly with things such as capacitance between layers etc. In the case of optimising grid operations: which part? The distribution network or the transmission network? In the case of the Disnet – autonomous systems with a bit of machine learning (with respect to weather forecasts and load) is quite sufficient to handle renewables. A.I.? hardly. As for useage patterns – quite easy to predict right now. TSOs were doing it quite well in the 1970s. Some TSOs are mucking around with A.I. it WILL end in tears. The section starting “older electronics” gives a link to an article deeply critical of A.I. yet the paragraph implies it will have a positive impact. I will stop here – the whole article came across as an A.I. puff piece. A.I. has a place in renewables, I use it from time to time, but I found the article deeply strange, almost unhinged.