## 1 Introduction
The proposed scheme is able to reduce the electricity cost by up to 53% and peak load by up to 35%. In addition to that, some researchers have focused on the load-scheduling based upon the priorities of the appliance operation [30; 31]. The authors in Reference [32] design a price-based HEM framework where priorities of operating different appliances are interpreted as the value of loss load (VOLL). The reliability cost which is a function of VOLL is incorporated in the objective function. The results demonstrated 7.5% and 12% reduction in electricity cost with TOU and IBR pricing schemes, respectively. The present work focuses on real-time appliance scheduling, i.e., to meet the immediate load demand without a priory knowledge of future load profile. In other words, instead of modeling the future load demand and optimizing, the optimization problem is solved at each time step using MPC (_receding horizon control_). MPC is an advanced method of control that emerges from application in process industry in late 70s and early 80s [33]. MPC represents a class of advanced control methods in which the model of the process is considered explicitly to predict the future evolution of the process to optimize the control input while respecting certain constraints. So, in this case, the optimization problem is solved at each time step with updated values of the real-time electricity prices and other relevant information like external weather conditions. In literature, many studies have investigated the use of MPC strategy for demand response [34; 35; 36] to achieve peak load reduction. In these studies, the focus is on the control of HVAC system in the building [37; 38; 39]. Another popular application of MPC is in residential microgrid management [40; 41; 42; 43]. In these studies, the residential house is considered to be equipped with various distributed energy resource and MPC is used to optimally control them. However, the scope of the present work is different in the manner that it investigates use of predictive control with receding horizon strategy to schedule thermal and non-thermal appliances both. For the thermal appliances, at each time step, the future external disturbance and constraints are updated; whereas, for non-thermal appliances the future deadline of the appliance is updated at each time step. The user has the flexibility to change the deadline as desired while appliance is still in operation, which consequently change the optimization problem. In literature, there are some studies which investigate the problem of scheduling of thermal and deferrable appliances using MPC. The authors in Reference [44] investigated the problem of load-scheduling of thermal and non-thermal appliances using MPC under different price schemesand achieves the total electricity cost saving of up to 20%. But the previous study do not focus on the peak-load reduction by employing constraints on the total maximum power consumption. In Reference [45], the author addressed the similar problem of deferrable appliance-scheduling considering distributed generation (DG) using a multi-time stochastic MPC to consider the uncertainty emerged due to the intermittent DG power. In the best case scenario, the total electricity cost reduction of 53% is achieved in this study. The limitation of the previous study is that some deferrable appliances are operated during a fixed optimal time interval only which is obtained using genetic algorithm. This takes away the flexibility for the customer to intervene for using the appliances at desired time intervals.