Real-time electricity pricing has the potential to provide incentives for retail consumers to offer flexibility services by altering their consumption patterns. However, such incentive schemes have met with limited success in the real world due to problems such as low consumer interest and the creation of rebound peaks after periods of high pricing. In this paper, a model of an individual consumer’s response to real-time prices, which captures these effects, is presented. A contract between a retail service provider and a consumer is proposed, and a method for personalized real-time price generation based on smart meter data, using reinforcement learning, is implemented. Initial results suggest that the approach can be used to achieve grid-level objectives while rewarding consumer flexibility.