Modelling RTP-based Residential Load Scheduling for Demand Response in Smart Grids ⋆ Shan He 12 , Ariel Liebman 1 , Andrea Rendl 12 , Mark Wallace 12 , Campbell Wilson 1 1 Faculty of IT, Monash University, Australia 2 National ICT Australia (NICTA) Victoria {shan.he,ariel.liebman,andrea.rendl, mark.wallace,campbell.wilson}@monash.edu Abstract. 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 prob- lem (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 formula- tions 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. 1 Introduction Interconnected networks, commonly known as grids, deliver electricity from suppliers, such as power plants, to consumers. Since electricity is not stored within the grid, the electricity supply and demand must balance at any time and at any point in the network. Over the past decades electricity demand has grown steadily, while in recent years in Australia and other countries with extreme hot weather conditions, peak demand has grown faster due to recent affordability of reverse-cycle air conditioners, leading to very high demand peaks during the day and low utilisation levels of networks. This puts considerable pressure on electricity power plants as well as on the networks them- selves; higher demand requires increased high-voltage transmission network capacity and low-voltage distribution network capacity. However, reinforcing the grid for peak demand, as well as building new power plants, is a costly way to meet increased demand and can lead to a substantial increase in the electricity price. Furthermore, the supply from renewable energy sources, such as solar panels or wind turbines, is a function of ⋆ NICTA is funded by the Australian Government as represented by the Department of Broad- band, Communications and the Digital Economy and the Australian Research Council.