“Operators have a tendency to operate in a very narrow region of the operational envelope, simply because of historical behaviors,” Kwan said. Because the plants are designed to operate at a particular level, this up and down cycling decreases efficiency and burns more fuel than necessary, thereby contributing to the plant’s overall carbon emissions. However, because of the intermittency of these renewable resources, they must be backed up by a more reliable form of generation, typically a combined-cycle gas-fired plant.Īs the levels of renewables flow up and down the operators of the combined-cycle plant have to continuously increase and decrease the electricity they produce. One way involves the use of cognitive computing, based on AI and signal processing technologies, and neural networks, computer systems patterned after the networks in a human brain.īecause power generated by wind and solar energy is usually cheaper than power from natural gas-fired plants - as well as having greater climate benefits - grid operators tend to rely on these forms of renewable energy as much as they can. “Using traditional physics-based modeling is inefficient or too slow.”ĪI is also helping grid operators reduce their overall carbon footprint in several ways. “This is a perfect application of artificial intelligence, because you can take into account many variables and be able to provide a recommendation in a very timely manner to support the changing needs of the consumer on a 15-minute basis,” he said. “Taking into account consumer behavior to ensure that supply matches demand as much as possible is a very large puzzle,” said Steve Kwan, director of power generation for Beyond Limits, an AI technology company. For example, power demand in a given area could spike as suburban commuters all return from the office in the evening and plug in their EVs to recharge for the night. In addition, the adoption of electric vehicles (EV), which is expected to ramp up dramatically in the coming years, will have a significant effect on electricity demand and the timing of that demand. This is going to become more important in the future as the demand for electricity is expected to rise, with consumers increasingly purchasing smart devices able to transmit and receive data via the Internet, commonly known as the Internet of Things. It changes from hour to hour, sometimes even every few minutes,” Johnson said.ĪI technology is playing an increasing vital role in managing the electric grid to ensure that there is power available when and where it’s needed. “For our customer class, it’s quite dynamic. The AI software helps the operators forecast what individual customers’ load patterns are going to be, when they’re going to be consuming power and what the cost of power will be during different times of day. “When you think about it, the battery doesn’t do anything by itself, so you need the intelligence to understand how best to use that battery,” Larsh Johnson, Stem chief technical officer, said. Stem, a Silicon Valley-based battery storage company, uses AI technology to determine the most optimal and economic times to recharge the batteries and to release the energy onto the grid. AI software is also being deployed to accommodate the addition of battery storage onto the grid as that technology becomes more mature and is commercialized.
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