An Optimized Approach for Home Appliances Scheduling in Smart Grid Anzar Mahmood ∗ Department of Electrical Power Engineering Mirpur University of Science and Technology Mirpur AJK, Pakistan ∗ Corresponding Author, anzarmahmood@gmail.com anzar.pe@must.edu.pk Nadeem Javaid CS, COMSATS Institute of Information Technology Islamabad, Pakistan Email: nadeemjavaidqau@gmail.com Naveed Ahmed Khan EE, COMSATS Institute of Information Technology Islamabad, Pakistan Email: naveedahmed512@yahoo.com Sohail Razzaq EE, COMSATS Institute of Information Technology Abbottabad, Pakistan Email: sohail.313@googlemail.com Abstract—In this paper, we present a novel home energy management model with enhanced load categorization and multiple scheduling options. Concepts of partial baseline and reserved interrupting loads are used to enhance the traditional load categorization. The proposed model uses multiple pricing schemes namely Time of Use (ToU) and Real Time Pricing (RTP) day ahead case. The energy cost minimization problem has been solved by multiple optimization techniques namely Knapsack and Particle Swarm Optimization (PSO). Comparative results of multiple pricing schemes have been discussed which prove the effectiveness of the proposed model. Index Terms—Smart Grid; Appliances Scheduling; Partial Baseline; Reserved Interrupting Load; DSM I. I NTRODUCITION AND RELATED WORK Appliances scheduling based home energy management in smart grid environment is a hot research topic. Appliances scheduling is used as an important tool for peak load shaving, energy cost minimization and carbon footprint reduction [1]. Various methods have been used for appliances scheduling to achieve the above mentioned goals. These methods are based on various load categories and optimization problems. Modern automated Home Energy Management Systems (HEMS) are built on the basis of appliances scheduling [2]. HEMS are considered significant players for peak load management and over all Demand Side Management (DSM) in future smart grids [3] - [4]. Authors in [5] have presented a home energy management model in which Real Time Pricing (RTP) and Inclining Block Rate (IBR) are combined to get optimized energy cost. The proposed model consists of three steps: load prediction, ac- tivity scheduling by user and cost optimization problem. A heuristic optimization technique, Genetic Algorithm (GA), is used for the solution of this problem. Authors have included RTP and IBR in their model, however our proposed scheme includes Time of Use (ToU), RTP and Critical Peak Pricing (CPP). Motivation of this work has been taken from [6] in which authors have divided the load into three categories and solved the appliances scheduling problem using binary integer pro- gramming and modified Spring algorithm. Only two types of loads have been included in cost optimization problem. Our proposed model divides the load into four categories with flexi- ble pricing and optimization options. Authors in [6] formulated an energy management problem by dividing the load into three categories: base line load, regular load and burst load. Base line load is kept out of scheduling optimization problem because it must be available at any time. Regular loads are operated according to thermostat or temperature limits e. g. refrigerator, room air conditioner etc. Burst loads may come online at any time and need certain time slots for operation completion. There are two types of burst loads: preemptive and non-preemptive. This approach has given a useful solution for house appliances scheduling and cost minimization, however in order to create some flexibility and better cost minimization, we have divided the load into four categories in which base line load is partially included in the energy cost optimization problem. In addition, a new category of Reserved Interrupting (RI) loads has been introduced which brings the flexibility in the users’ schedule. This category has been introduced for those users who need a certain load in specific hours. These hours may vary user to user based on their requirements. The mechanism can be implemented by specifying suitable time slots using the proposed scheduling methodology. Load shaping through quota from grid and inclusion of storage capability has been presented in [7] keeping in view the effects of dynamic pricing. Beyond the quota limit, electricity pricing is raised for the consumers. However, with the help of storage system, the consumer can get benefit of selling during peak hours. Authors in [8] used Particle Swarm Optimization (PSO) with ToU pricing scheme for home appliances scheduling and cost 978-1-5090-4300-2/16/$31.00 c ⃝ 2016 IEEE