Demand response programs are becoming an integral part of the power system, helping create a closer alignment between the electrical service providers and customers. The research described in this dissertation uses the residential demand response (DR) program during a peak demand event to determine the demand reduction capacity as a virtual storage (VS). The amount of demand that is reduced due to the demand response program is analogous to the amount of energy discharged by storage to reduce the demand. Since there is no hard storage involved, demand reduction is taken as VS.
The aggregator is a third party who communicates between the client (electrical service provider) and customers to utilize the virtual storage capacity. The aggregator provides incentive to customers to take control over their thermostat and receive a reward from the client for load reduction. A mathematical model was developed based on reward and incentive to maximize the aggregator profit. Incentives must benefit both clients and customers in order for programs to succeed.
This dissertation is based on two concrete areas: predictive analytics to estimate the level of residential participation, and mathematical modeling of the load reduction capacity of a demand response program as a virtual storage system and its optimization.