Artificial intelligence (AI) and machine learning (ML) are playing a silent but crucial role in revolutionizing our energy grid and accelerating the adoption of renewable energy sources. While there has been concern about the potential misuse of AI, its transformative impact on the clean energy sector is often overlooked.
In the modern energy transition, AI and ML are essential tools for enabling distributed energy management systems, such as Virtual Power Plants (VPPs). These systems employ cloud-based software platforms to optimize the integration of various distributed energy resources (DERs) like electric vehicles, solar panels, and smart loads. Managing and coordinating tens of thousands of heterogeneous devices simultaneously is impossible without AI. By applying AI, we can strengthen the resiliency of our grid and expedite the shift away from polluting energy sources.
AI and ML work hand in hand to enable Virtual Power Plants and Distributed Energy Resources to manage the global energy supply more effectively. Balancing intermittent renewable energy resources with conventional fossil fuel generation is crucial for grid reliability. AI tools allow demand side resources to actively participate in grid balancing by overcoming limitations in visibility, control, latency, and scalability. AI-powered VPPs can aggregate diverse portfolios of DERs, ensuring stability for clean energy and reducing the need for inefficient and polluting sources like gas peaker plants.
The application of AI and ML brings numerous benefits to distributed energy systems. By utilizing algorithms that incorporate both ML and AI, thousands of distributed devices can be monitored and managed to optimize energy usage and anticipate potential issues, thereby improving grid efficiency. These technologies can consider various factors when determining the ideal dispatch plan, whether at the site level, distribution grid, or bulk electrical system. By analyzing vast amounts of data, including grid data and weather forecasts, AI-powered software systems can make informed decisions to reduce costs and carbon emissions by managing different aspects of the energy supply. This aggregated capacity offers greater resiliency and reliability, even in the face of extreme weather events and failing infrastructure.
To achieve the energy transition goals for the grid, DER assets such as solar panels, battery storage systems, and electric vehicles must be optimally integrated with the help of AI and ML algorithms. These algorithms, tailored to each DER’s flexibility and constraints, maximize overall utilization and value, improving grid efficiency and real-time supply-demand balance. AI and ML unlock the full potential of DERs, transforming them into valuable grid assets.
AI and ML are essential for the successful operation of VPPs, allowing them to balance a diverse range of distributed energy resources quickly and in real time. These AI-powered VPPs handle the complexity of aggregating and managing distributed energy while analyzing price fluctuations, supply, and demand to decrease overall costs. When applied to electric vehicles, AI-powered software systems facilitate integration and governance of EV charging, bolstering EV infrastructure and reducing costs for consumers.
Despite concerns about the capabilities and potential misuse of AI and ML, one thing is certain: we cannot achieve 100% renewable energy without them. AI-powered software systems unlock numerous benefits for the transition to renewable energy, enabling the leverage of distributed resources in powerful use cases. AI and ML deserve thoughtful consideration as they are deployed across society, particularly for the energy grid where they serve as critical enabling technologies.
