Modelling RTP-based residential load scheduling for demand response in smart grids

High electricity demand peaks and uncertain supply from renewable energy sources have a significant impact on the electricity price and the network capacity. One mechanism proposed to tackle this issue is the use of real-time pricing (RTP) at the end customer level. Here electricity retail prices are set in real-time in response to varying supply-demand conditions in a way that reduces peak demand. This way customers have an incentive to switch their consumption to times with low demand. The RTP-based Residential Load Scheduling problem (RTP-RSP) deals with scheduling the customer consumption such that the overall network consumption is balanced, the electricity price is minimized, and customer satisfaction maximized. In this work, we introduce different formulations of the RTP-RSP. We first introduce a formulation where the electricity price is assumed to be known a priori. This model is embedded in a heuristic approach that iteratively adapts the electricity price, based on the actual consumption that is computed by the models. Second, we present a two-stage stochastic optimisation model, where the electricity prices are stochastic. We evaluate both formulations on data based on real-world figures and present some preliminary results.