Artificial Intelligence and Machine Learning Are Silently Saving Our Energy Grid

Given the recent buzz around ChatGPT and generative AI, including misgivings about its future impact, concern has emerged related to AI’s potential misuse. Less often discussed, however, is AI’s transformative impact on the acceleration of renewable energy adoption: in the clean energy sector, the application of AI is yielding remarkable outcomes that are beyond human capabilities.

Artificial Intelligence and Machine Learning are crucial tools for enabling our modern energy transition. Distributed energy management systems (DERMS), which include cloud-based software platforms such as Virtual Power Plants (VPPs), are increasingly employing AI and ML to optimize the integration of distributed energy resources (DERs) such as electric vehicles, solar panels, and smart loads. The DERMS must simultaneously monitor, forecast, and then orchestrate tens of thousands of heterogeneous distributed devices, which simply is not feasible with traditional tools. The application of AI can strengthen the resiliency of our grid and accelerate the transition from harmful, polluting energy sources to cleaner, more sustainable alternatives.

How Artificial Intelligence and Machine Learning Are Driving the Clean Energy Transition

AI and ML are complicated but complementary systems, and both are key to enabling Virtual Power Plants and Distributed Energy Resources to more effectively manage the global energy supply. Intermittent renewable energy resources need to be balanced and firmed along with conventional fossil fuel generation to ensure grid reliability.  Without AI tools, demand side resources could not fully participate in the balancing of the grid due to limitations in visibility, control, latency, and scalability. Now, AI-powered VPPs can aggregate large portfolios of DERs to help ensure the stability of clean energy, reducing the necessity of balancing clean energy with inefficient and polluting sources such as gas peaker plants.

AI and ML can be applied to enhance distributed energy systems in myriad ways. For example, algorithms using both ML and AI can monitor and manage thousands of distributed devices to optimize energy usage and anticipate potential issues, improving the grid’s overall efficiency. As for optimizing energy, AI and ML can consider a wide range of constraints and inputs in determining the ideal dispatch plan, including at the site level, the distribution grid, and the bulk electrical system.  Through analyzing large amounts of data, including grid data and weather forecasts, AI-powered software systems can inform decisions to lower costs and reduce carbon emissions, by appropriately adjusting and managing different aspects of the energy supply. This flexible aggregated capacity can provide greater resiliency and reliability during instances of potential disruption, including in the face of extreme weather and failing infrastructure.

AI and ML Deliver On the Full Potential of DERMs, DERs, and VPPs

To achieve energy transition goals for the grid, DER assets such as solar panels, battery storage systems, and electric vehicles must be optimally integrated into grid operations with the help of AI and ML algorithms. These algorithms, by accounting for each DER’s specific flexibility profile and constraints, can maximize overall utilization and value of each DER to improve grid efficiency and balance supply and demand in real-time. Through taking advantage of the capabilities offered by AI and ML, DERs can be transformed into valuable grid assets, and the full potential of DERs can be realized.

VPPs rely on both AI and ML to balance a diverse range of distributed energy resources, allowing them to optimize and take full advantage of DERs quickly and in real time. Not only can AI-powered VPPs handle a high level of complexity as it relates to aggregating and managing distributed energy, but they can also analyze price fluctuations as well as supply and demand to lower overall cost. Similarly, when applied to EVs, AI-powered software systems can more easily integrate and govern EV charging to bolster EV infrastructure while simultaneously decreasing overall costs for consumers.

Closing Thoughts 

While AI and ML have prompted some suspicion and discomfort as it relates to their capacities and potential misuse, one thing is clear: the world cannot get to 100% renewable energy without them. AI-powered software systems are the key to unlocking a whole host of benefits related to our transition to renewable energy. These technologies allow us to leverage more distributed resources, in more powerful use cases, to not only benefit our energy grid and global power supply, but also to aid in the clean energy transition as a whole.  AI and ML deserve deliberate and thoughtful consideration as they are deployed across our society, but for the energy grid, they are a critical enabling technology.