As an increasing amount of megawatts feeds into the grid from variable renewable energy sources, predicting capacity levels has become paramount to secure a stable and efficient grid. This is due to the fact that with renewables taking up a greater share of the grid, there is a loss of baseload generation from sources such as coal, which provide grid inertia via the presence of heavy rotating equipment such as steam and gas turbines. Without grid inertia, power networks will be unstable and susceptible to blackouts. Now, with the application of sensor technology, solar and wind generation can provide an enormous amount of real-time data, allowing AI to predict capacity levels.
Before harnessing AI, most forecasting techniques relied on individual weather models that offered a narrow view of the variables that affect the availability of renewable energy. Now, AI programs have been developed – such as IBM’s program for the US Department of Energy’s SunShot Initiative – which combine self-learning weather models, datasets of historical weather data, real-time measurement from local weather stations, sensor networks and cloud information derived from satellite imagery and sky cameras.
The result has been a 30% improvement in accuracy in solar forecasting, leading to gains on multiple fronts. “We found that improved solar forecasts decreased operational electricity generation costs, decreased start and shutdown costs of conventional generators, and reduced solar power curtailment,” says Hendrik Hamann, Distinguished Researcher and Chief Scientist for Geoinformatics at IBM.
Forecasts of the base variables – wind speed and global horizontal irradiance, as well as the resulting power output – allows for a view on a range of time horizons, from minutes and hours ahead (for maintaining grid stability and dispatching resources) to day-ahead (optimizing plant availability), to several days ahead (scheduling maintenance).
With increasingly larger data sets becoming available, predictions can now go far beyond the weather to train algorithms to predict more remarkable outcomes. For instance, how much additional power is used during a festive holiday, a large-scale international event, or how much altitude impacts a community’s energy use.
For generators and energy traders, more accurate forecasting of variable renewable energy at shorter timescales allows them to better forecast their output and to bid in the wholesale and balancing markets – and, importantly, to do so while avoiding penalties.
“The earlier and more accurately you can predict, the more efficient it is for energy traders to rebalance their position. I see AI providing a way of dealing with lots more sites and using more granular and diverse data than historic forecast methods,” says Alex Howard, Head of Strategy at Origami. “Ultimately, that means making a better financial return.”