Residential load scheduling has been introduced in recent years as a result of implementing demand response programmes in the residential sector. Up to now, studies have focussed on developing algorithms for scheduling the residential loads with the objective of reducing the electricity payment while satisfying the users' comfort level. To aim at this goal, the user should determine the desired scheduling window for each appliance to initiate the scheduling process. Although these algorithms designed to benefit the end-users, determining the scheduling window for each appliance at the beginning of the scheduling process can be challenging. In this study, an intelligent load scheduling model is proposed to perform the scheduling with the least user intervention. The proposed model predicts the preferred scheduling window for each appliance and initiates the scheduling process. Furthermore, the degradation cost of the battery pack in the vehicle-to-grid mode is considered in the cost function. The model is applied to a case study containing plug-in hybrid electric vehicle and flexible loads such as a washing machine and dishwasher under inclining block rates and hourly pricing tariffs. The results indicate that implementing the model in a residential building can decrease the electricity payment while performing the residential load scheduling with the least user intervention.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering