Modeling Electric Usage in Residential Areas

Because electricity cannot be practically or economically stored in large quantities, the electricity generation and distribution system must match supply and demand on a minute-by-minute basis. Delivery of electricity for residential use has traditionally been done by matching the supply to the demand, with little or no control over the demand. This causes severe distortions in the system operation and economics when the demand hits unusually high peak values. When these peaks are particularly high, some effort to reduce demand is done via roadside signs, television appeals, etc. (the California heat spell of July, 2006, for example).

There is presently considerable effort in the electricity industry and regulation organizations to implement “demand responsive” mechanisms into the system. These would involve signals sent to the residence that would cause reductions in usage through pricing or other means. When such demand response (DR) signals are sent, the system, consisting of large numbers of residences, will respond with dynamic behavior based on the aggregate properties of all of the residences involved. The nature of the dynamic behavior will affect the effectiveness of the DR strategy, for example, peak power usage generated at the end of a DR period, the so-called rebound peak. In this project, we are constructing a model of how a large number of residences would respond to various types of DR signals so various strategies can be compared and effective strategies can be designed.