Price-Induced Load-Balancing at Consumer Households for Smart Devices

Abstract

The rising share of renewable energies in today's power grids poseschallenges to electricity providers and distributors. Renewableenergies, like, e. g., solar power and wind, are not as reliable asconventional energy sources. The literature introduces several conceptsof how renewable energy sources can be load-balanced on the producerside. However, the consumer side also offers load-balancing potential.Smart devices are able to react to changing price signals. A rationalbehavior for a smart device is to run when electricity rates are low.Possible devices include washing machines, dryers, refrigerators, warmwater boilers, and heat pumps. Prototypes of these devices are juststarting to appear. For a field experiment with 500 households wesimulate adequate device behavior. The simulation leads to a mappingfrom price signal to load change. We then train a neural network tooutput an appropriate price signal for a desired load change. Ourresults show that even with strong consumer-friendly constraints onacceptable price changes the resulting load change is significant. Wecurrently implement the results with a leading energy services provider.