An analysis of peak demand reductions due to elasticity of domestic appliances

Abstract

Unlike prior work on demand management, which typically requires industrial loads to be turned off during peak times, this paper studies the potential to carry out demand response by modifying the elastic load components of common household appliances. Such a component can decrease its instantaneous power draw at the expense of increasing its duration of operation with no impact on the appliance's lifetime. We identify the elastic components of ten common household appliances. Assuming separate control of an appliance's elastic component, we quantify the relationship between the potential reduction in aggregate peak and the duration required to complete the operation of appliances in four geographic regions: Ontario, Quebec, France and India. We find that even with a small extension to the operation duration of appliances, peak demand can be significantly reduced in all four regions both during winter and summer. For example, during winter in Quebec, a nearly 125 MW reduction in peak demand can be obtained with just a 10% increase in appliance operation duration. We conclude that exploiting appliance elasticity to reduce peak power demand should be an important consideration for appliance manufacturers. From a policy perspective, our study gives regulators the ability to quantitatively assess the impact of requiring manufacturers to conform to "smart appliance" standards.

Incentivizing Advanced Load Scheduling in Smart Homes

“…Similarly, while slide loads significantly reduce costs if deferred multiple hours into the future (to low cost nighttime periods), such long delays impose a significant burden on users. Likewise, stretching loads only provides significant savings for unrealistically large stretch factors, e.g., > 3x, that are much greater than the 10% assumed in prior work [17]. While energy storage and renewable energy are capable of lowering electric bills, they both require a large capital investment that generally negates any savings on an electric bill.…”

“…For example, if electricity prices are much lower in the evening, consumers might choose to perform energy-intensive tasks, such as doing their laundry or running their dishwasher, at that time, rather than in the middle of the day. In parallel, the benefits of reducing the grid's peak usage have motivated researchers to develop a variety of advanced load scheduling algorithms for buildings and homes that programmatically control when electrical devices (or loads) operate to lower a building's electricity bill, e.g., [2, 3,7,9,11,12,17,18]. Instead of requiring consumers to manually alter their behavior, which many consumers may choose not to do regardless of electricity's price, these scheduling algorithms exploit a limited degree of scheduling freedom available in a subset of loads.…”

“…Unfortunately, as above, users are often unwilling to delay start times many hours into the future, e.g., from daytime to nighttime, which severely limits scheduling freedom. Stretching power refers to extending an elastic load's running time, while lowering its average power usage to keep its energy consumption constant for a particular task [17]. Typically, elastic loads utilize resistive heating elements or variable drive motors, which enable a scheduler to precisely adjust their temperature or speed, respectively, to dictate a specified running time.…”

“…For example, running dryers at high heat for a short duration works well for heavy fabrics, while low heat for a long duration is better for delicate fabrics. Thus, prior work [17] places a relatively low upper limit (⇠10%) on stretching a load's running time. Storing large amounts of power during low price periods and then using this stored power during high price periods is the most effective way to transparently schedule loads and lower a consumer's electricity bill under today's variable rate pricing plans.…”

Smart-Grid Electricity Allocation via Strip Packing with Slicing

“…For example, a water heater requires a certain amount of electricity to heat the water, but can equally well heat the water in one continuous interval or in multiple short intervals. 4 It is anticipated that future smart grids would obtain (at each substation) daily "demand schedules" for appliance use from the consumers in its local area, and then automatically re-schedule appliance use to minimize peak load [18].…”