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How to combat electrical grid challenges with artificial intelligence

Oct. 8, 2021

Proper planning and accurate demand forecasts help keep the grid healthy. 

Beyond Limits' Stephen Kwan, Ph.D.

In the aftermath of natural disasters, thousands of American citizens are often left without power—an issue that can have life-threatening and costly consequences. The aging electrical grid is being put to the test as it stretches to support a much higher volume of users than it was designed for. And with climate-change experts indicating that global warming will cause an uptick in natural disasters in the coming years, there’s a clear need for a more resilient electrical grid.

Currently, US grids have no widespread way to store large quantities of electricity as backup for outages, even though some industrial batteries are being deployed in small numbers. The energy sector’s lack of resiliency and solutions for storing reserve power mean that the industry needs an update...now.

The combination of fragile electrical grids and a lack of backup power elevates the seriousness of natural disasters as people affected by disasters struggle to put their lives back together in the aftermath of these emergencies. Cities can have all the power in the world, but with no way to distribute it the energy is useless. Similarly, if there’s not enough power to distribute, even if the grid is healthy, that’s also useless. Electrical grids supply the essential power we rely on every day without much thought—but when these systems fail, it can lead to the wide-ranging, costly, difficult-to-remedy consequences that have made headlines in recent years.

However, hope is on the horizon as new AI technologies are forging the way for a data-focused energy industry, one able to more effectively predict and prevent electrical grid issues in the future. The planning capabilities that AI offers the energy sector will combat some of the industry’s most pressing challenges.

Specifically, AI technologies can help plant and city managers predict energy-consumption levels days in advance, enabling energy producers to plan their operations for the anticipated demands to more precisely match and plan energy generation. This allows energy producers to prep for anticipated production shortfall and reconcile any kind of a reserve power in case of unanticipated events—such as a sudden weather change or other issues within the power grid. This process of planning to support demand forecasts is a constant exercise for all the independent-system operators that are responsible for the safe and efficient management of the US power grid.    

The accuracy of matching demand forecast and energy-production planning leads to efficiency operations, adequate reserves and coverage for shortfalls. This not only saves energy, but also relieves stress put on the grid because energy flow on the power grid has restrictions and limitations, including line capacity and congestion. Similarly, sudden changes to levels of power generation due to unanticipated demand is stressful on the physical equipment and requires ramp-up time, as such changes are not instantaneous.

Proper planning and accurate demand forecasts help keep the grid healthy. AI can sort through mountains of historical data and take into consideration impending changes such as the proliferation of electric-car charging or impending severe weather events to provide more accurate demand forecasts. This will change the energy industry into a system that can better prepare for emergencies by precisely planning the power that will need to be sent to the grid and allowing for a more comprehensive understanding of available energy.

AI solutions can also play a key role in the integration of renewable-energy sources, like solar and wind, into the power grid. By anticipating weather conditions using historical data and impending weather patterns, these solutions can predict the availability of naturally-generated energy, providing managers with the ability to forecast the mix of renewable energy and traditional fossil fuel energy that is required to satisfy the anticipated demand while maximizing the use of renewable energy.

With an increasing amount of renewable energy coming online, AI solutions are required to handle the complexity of the entire system. This also makes renewable energy sources more appealing in the energy sector, as a previously unpredictable source of power will become increasingly reliable. In helping the industry transition to renewable-energy sources, AI solutions will light the way to a safer, greener future.

The ability to predict power needs will not only make the energy sector more structured and cost-effective, but will serve the communities hit hardest by climate change in the coming years. 

AI technologies provide a valuable tool for plant and city managers to overcome electrical grid challenges by predicting energy consumption and generation levels, oftentimes days in advance. Understanding energy needs ahead of time enables energy producers to schedule their production needs without an excess amount of spinning reserve to supply the electric grid without generating excess power that is either wasted or needed to be sent elsewhere for storage. And, as more cities look to switch to renewable sources of power like solar and wind, AI solutions can also factor in anticipated weather conditions to predict available energy from those resources, which fluctuate more than standard fossil fuel sources. 

New AI capabilities will make all the difference in an energy industry that has historically been functioning suboptimally with excess reserves and less agile with inaccurate forecasts. This will enable managers and the general public to better prepare for natural disasters and other unanticipated events.

Stephen Kwan, Ph.D., is the director of product management at Beyond Limits