The value of accurate forecasts and a probabilistic method for robust scheduling of residential distributed energy resources

Abstract

We describe a decision-support tool that optimizes the energy services of residential end-users by scheduling the operation of available distributed energy resources. We discuss the application of the tool to a `smart' home case study and the solution to the resulting highly-dimensional scheduling problem. We then use the optimal schedules formulated by the tool to determine the value of the forecasted information used when the schedules are created. This is achieved by computing the additional costs avoided by the end-users due to the accuracy of the forecasts. We also demonstrate how to use the tool to derive robust schedules when the end-users are not certain on the magnitude of solar insolation, magnitude of energy service demands, availability of a plug-in hybrid vehicle as storage, and status of Critical Peak Pricing. The robust schedule is derived by maximizing the expected net benefit when the schedule is applied to all likely scenario outcomes.