Throw a dart at a current weather map of the U.S., and it won’t be hard to hit a city feeling the effects of triple-digit temperatures. As of late July, 28 states were under heat advisories, and southwestern states like Texas and Oklahoma had regions where the mercury went well past 100.
This extreme heat puts a massive strain on power grids and energy producers trying to meet the demand for electricity to keep homes, businesses, and public facilities cool. Well before the onset of high temps across the country by mid-June, Texas had broken a record set three years earlier for total power demand of more than 75,000 megawatts, according to data from the Electric Reliability Council of Texas (ERCOT). In that same period, usage records in North Carolina and South Carolina also fell due to extreme temperatures.
This year’s summer forecast from the North American Electric Reliability Council (NERC) specified that across the midwest, a return to normal post-pandemic energy demand was expected to increase 1.7% over summer 2021, while generation capacity was expected to drop by 2.3%. That was causing a probable need to look toward “load modifying resources or non-firm imports, to meet reserve requirements under normal peak summer conditions. More extreme temperatures, higher generation outages, or low wind conditions expose the (Midcontinent Independent System Operator) North and Central areas to higher risk of temporary operator-initiated load shedding to maintain system reliability.”
And in Texas, ERCOT noted that recent additions in total solar and wind capacity were the difference makers in meeting demand caused by ongoing drought conditions throughout the state that tends to coincide with heat events that spike the demand for power.
AI steps in to meet demand
The constant, high demand for energy from producers means it is more important than ever that operations happen as efficiently and reliably as possible. Forecasting production potential and preventing (or anticipating) future maintenance issues with monitoring and analytics provided by artificial intelligence tools like the SparkCognition™ Renewable Suite can ensure renewable energy projects stay online when they are most needed and potentially profitable.
Renewable Suite is an AI-based asset management platform that helps generate more accurate production forecasts, improve plant reliability with predictive analytics, and leverage physics-informed AI tools to maximize wind, solar, and energy storage asset performance.
Peak demand due to extreme weather makes communication between producers and balancing authorities crucial for keeping up with demand by integrating renewable energy production with traditional power sources. By using AI to forecast wind or solar generation patterns in the near future, producers at those sites can plan for when wind or sun will be plentiful or alert utilities to prepare for times when production is likely to fall short.
Having the power of predictive maintenance is a huge plus for all large mechanical assets, but data provided from AI tools constantly monitoring overall health is even more valuable in times of peak demand. In extreme conditions like these, predictive analytics empowers utility providers by providing insight on whether certain maintenance or repairs are absolutely needed or if they can be skipped temporarily to avoid unscheduled downtime during peak demand.
With advanced physics-based tools, there is a history of demonstrated prevention of critical failures that preserved operation or reduced downtime and maintenance costs, helping to increase annual energy production by up to 2%, decrease O&M costs by 10%, and improve operational efficiency.
SparkCognition’s Renewable Suite has:
- Detected a yaw misalignment of five degrees or more with 96% accuracy with only two months’ worth of data
- Predicted pitch bearing failures with more than 90% accuracy up to six months in advance, preventing emergency crane calls that cost $150,000
- Improved management of soiling cleanup programs on solar projects, reducing costs and curtailing the 3-4% drop in annual production caused by soiling issues
With the weather out of our control, energy demand for the rest of the summer in most parts of the country could very well push production capacities to near their limits.
And while there are a lot of factors such as grid limitations that fall outside the control of utility providers when they’re dealing with peak demand in extreme conditions, implementing an artificial intelligence-supported asset management system will inevitably help alleviate some of that pressure.
