1. BY: A. Mahmood, M. N. Ullah, S. Razzaq, N. Javaid, A. Basit, U. Mustafa, M. Naeem COMSATS Institute of Information Technology, Islamabad, Pakistan

2. Introduction (1/3) Increasing population and energy demand have worn out the traditional grid. It has been serving the humanity since decades. Excessive Generation is not a solution because of the economic and environmental constraints.

3. Introduction (2/3) Smart grid is one of the solutions proposed for smart use of energy resources. Smart Grid Applications: Transmission and distribution automation Optimized utilization Commodity trading of electricity in competitive markets etc.

4. Introduction (3/3) DSM consists of the activities designed for influencing the behaviour of customers regarding their electricity consumption. Early DSM schemes were introduced in late 1970s. A major portion of the global power consumption is reported in buildings which is approximately 40%

5. Related Work (1/2) Authors in [1] discussed a scheduling model in which a layered structure consisting of three modules was proposed Admission controller (Modified spring algorithm) Load Balancer (Mix integer programming) Demand response and Load forecaster (smart grid interfacing) [1] Costanzo, Giuseppe Tommaso, et al. "A system architecture for autonomous demand side load management in smart buildings." Smart Grid, IEEE Transactions on 3.4 (2012): 2157-2165.

6. Related Work (2/2) In [2], backtracking based scheme is used to schedule the home appliances for local and global peak load reduction. This scheduling model consists of actuation time, operation length, dead line and consumption profile. Scheduler copies the profile entry of different appliances one by one according to task profile in allocation table [ 2] Samadi, P., Mohsenian-Rad, H., Wong, V.W., Schober, R.. Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. Smart Grid, IEEE Transactions on 2013;4(2):1007– 1016.

7. Proposed Scheme (1/7) This work is based on the work of [3]. This model considers a set of consumers, in the assumed smart grid scenario, who obtain electricity from a single power supplying company as elaborated in Fig. 1. [3] Mohsenian-Rad, A.H., Wong, V.W., Jatskevich, J., Schober, R.. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Innovative Smart Grid Technologies (ISGT), 2010. IEEE; 2010, p. 1–6.

8. Proposed Scheme (2/7) Smart Grid architecture considered

9. Proposed Scheme (3/7) Energy consumption scheduling vector Where, Z n,a is Energy consumption of appliance “a” scheduled for 1 hour from user n. We also define

10. Proposed Scheme (4/7) Each user “n” specify his total daily energy consumption of appliance “a”, denoted by En;a, defined by user according to consumption profile. An objective function for daily predetermined energy consumption of user “n”is also defined.

11. Proposed Scheme (5/7) Where, E n,a : Predetermined daily energy consumption of appliance “a”. β n,a : Interval starting time that appliance consumption can be scheduled. γ n,a : Interval end time that appliance can be scheduled.

12. Proposed Scheme (6/7) To minimize the peak load in peak hours, optimal scheduling of appliances can be achieved by solving the following optimization problem S. t.

13. Proposed Scheme (7/7) Proposed Algorithm

14. Simulation Results (1/7) We consider the scenario of smart grid system where N=10 users; all users are equipped with ECC units. Each user has 10 major appliances with shiftable and non-shiftable operation. We include 4 shiftable appliances and 6 non-shiftable appliances.

15. Simulation Results (2/7) In our scheme, we schedule the appliances from 12:00 AM to next day 12:00 AM. We consider peak hours between 6:00 PM to 10:00 PM. We apply Time of Use (ToU) pricing scheme in our model. Per unit electricity price is different in peak hours and off-peak hours.

16. Simulation Results (3/7) Time of Use pricing Peak Load Reduction Monetary Cost Reduction

17. Simulation Results (4/7) When we schedule the appliances according to our proposed scheme, load evenly distribute over the entire day. Energy consumption reduces to 24% Peak load reduction

18. Simulation Results (5/7) When users equipped with ECC units in smart meters and all subscribers utilize the energy consumption in efficient way; consequently energy cost reduces by 21%. By scheduling of energy consumption, monthly bill of each user also reduces.

19. Simulation Results (6/7) Users Percentage load in peak hours

20. Simulation Results (7/7) Monetary cost reduction