# Residential Demand Response Scheduling with Consideration of Consumer Preferences
## 1 Introduction  
In recent years, the power grids have become more stressed due to growing consumer demand and less secure with the penetration of distributed generation resources. Moreover, the increasing share of fossil fuel usage during peak hours raises environmental concerns. Since more than one-third of the electrical energy is consumed at residential premises, one effective way to address the aforementioned challenges is the employment of smart home energy management systems. Such systems integrate communication, control and sensing technologies to efficiently shape the electricity consumption [1]. Therefore, this paper proposes a new energy scheduling framework for a group of households having different numbers of occupants, family decompositions and employment statuses equipped with a variety of home appliances [2]. The model aims to exploit the diversity in the customer preferences and shows that the cost of electricity consumption can be reduced by scheduling the jobs within a time window that is acceptable by the consumers. There has been an increasing body of literature on demand response management programs [3,4]. Some examples include the lowering of peak customer demand [5] by shifting the consumption towards the periods of high renewable energy production [6]. However, this approach requires the jobs to be very flexible, because the time of the generation and the demand may not perfectly align. Overall, the vast majority of the optimization problems presented in the literature are closely related to different variations of classical optimization, such as job scheduling and load balancing [7, 8, 9]. The scheduling is done based on the requests from the consumers, which are given in advance for individual appliances with flexible timeslots. For example, a consumer can request the use of a dishwasher during the time period between 12 and 2 p.m. or 5 and 7 p.m. Similarly, if the user has a plug-in electric vehicle, he or she would like the vehicle battery to be fully charged by 7 a.m. Based on the collected requests, the grid controller will schedule all such request with a specific duration in a way to optimize some performance parameters, such as minimizing the peak demand or the total cost of electricity production. It is noteworthy that this type of problem could also be considered in an online optimization in which consumer requests are given in real time [10]. From this basic concept, many demand side models have been explored in varying levels of detail and system properties for electric vehicles [11, 12, 13] and household appliances [14, 15, 16, 17, 18, 19]. The objective function in such problems is, in most cases, based on the minimization of total electricity cost. The optimization frameworks also focus on cost-reductions during the peak hours, as the cost function typically follows a quadratic convex behavior, meaning that the increase in electric consumption over some level has disproportional increased the expenses needed to provide the service [10,14,18]. In the paper by [PERSON] _et al._[20], the cost minimization problem is presented as a multi-objective one, which also takes the utilization levels into account. The computational complexity of the previously-developed models is proportional to the constraints used on the behavior of appliances, e.g., power levels and job deadlines. It has been shown that the offline scheduling problem with jobs (consumer request for individual appliances) having an arbitrary duration or an arbitrary power requirement is NP-complete [10]. In the studies conducted by [PERSON] _et al._[14], a simplified model is given, in which all of the home appliances have a unit power requirement, unit duration and arbitrary time slots for which the jobs can be served. For this problem, a polynomial time algorithm has been developed for finding optimal solutions. An extensive analysis of different appliances that can be a part of this problem is given in [19].