Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets

Abstract

In this paper, a new framework is proposed for optimal energy resource management in a smart home comprising of controllable and non-controllable appliances and local renewable resources. Here, the smart home can procure the required energy through both the spot and contractual markets under Real-Time Pricing (RTP) mechanism. Uncertainties in the production of renewable resources, spot market price and non-controllable appliances, modeled by sets of scenarios, are considered. Controllable appliances are classified into two groups of continuous and interruptible, while the non-controllable appliances are classified into elastic and inelastic groups. Both RTP and Inclining Block Rate (IBR) tariffs are used to prevent the consumption in certain hours in addition to a reflection of spot market fluctuations. First-order Markov chain and Multi-Layer Perceptron (MLP) neural networks are used to predict the production of renewable resources. The optimization problem is designed to minimize the consumer’s expected net cost in the form of a two-stage stochastic problem and is formulated as a MILP problem. The model is applied to smart home to illustrate the impact of the proposed model. Simulation results show the positive impact of the proposed scheduling method on reducing consumer costs and network Peak to Average Ratio (PAR).