Optimal household appliance scheduling considering consumer's electricity bill target

This paper presents an optimization algorithm that schedules the operations of household appliances to minimize the monthly electricity bill and maintain comfort for electricity consumers through the interaction between a home energy management system (HEMS) and consumers. In comparison with existing HEMS optimization algorithms over a single day time horizon without considering the monthly electricity bill, the novelty of the proposed method is that the monthly bill target preferred by the consumer is achieved through the optimal operation of appliances over a multi-day time horizon. A key part of the proposed approach is to relax the multi-day optimization problem by adding a penalty on the range of consumer preferred indoor temperature related to operation of the air conditioner, consequently leading to calculation of the desired monthly electricity bill. Simulation studies are illustrated with a single household and four households equipped with smart home appliances in time of use (TOU) and inclining block rate (IBR) pricing tariffs. The results demonstrate that the proposed method enables the total electricity cost and cumulative electricity consumption to successfully converge to each consumer's target. Furthermore, the impacts of the penalty variable for the relaxed consumer thermal condition and the energy trade between households on the performance of the proposed algorithm are investigated.