Andrew K . Wright N - Dimension Solutions Nonintrusive Load - Shed Verification

D emand response, a cornerstone of smart-grid technology, lets consumers participate directly in energy markets by limiting their energy use during periods of emergency or peak demand. In a direct-control demand-response approach, an electricity service provider (ESP) offers consumers discounts or other incentives if they agree to let the ESP send load-shed instructions (LSIs) to specified appliances. For instance, an ESP might adjust the set points on an air conditioner’s thermostat. Direct control can save consumers money and provide ESPs with valuable tools for controlling energy generation costs and grid stability. But these benefits depend on the LSIs producing the expected response from appliances. Load-shed verif ication (LSV) can improve reliability and eliminate freeloaders who accept incentives without implementing direct controls. However, this generates many trust challenges because the consumer owns and operates the appliance and because effective demand response depends on the integrity of the appliances’ responses to LSIs. To address these challenges, we’ve implemented an algorithm based on a nonintrusive load monitoring (NILM) learning phase that runs during an initialization period at the ESP. The result is a distributed NILM algorithm— a nonintrusive load-shed verification (NILSV) algorithm deployed on the residential meter. We built a prototype and conducted experiments in a residence to illustrate NILSV’s promise along with some of its challenges.