Smart Home Energy CPS Scheduling

1. Smart Home Energy CPS Scheduling Professor Shiyan Hu Department of Electrical and Computer Engineering Michigan Technological University 1

2. Smart Home: Academic Perspective 5% energy efficiency improvement in residential home energy systems leads to carbon emission reduction equivalent to removing 53 million cars in U.S. 2

3. Smart Home http://www.yousharez.com/2010/11/20/house-of-dreams-a-smart-house-concept/ To Minimize Expense, Balance Energy Usage and Maximize Renewable Energy Usage 3

4. The Single User Smart Home Why we schedule? Power flow Internet Control flow 4

5. Varying Energy Consumption Typical summer energy load profile in State of Ontario, Canada. One can see the peak load around 7:00pm which usually involves a lot of human activities. Average Peak PAR Source: Ontario Energy Board 5

6. Dynamic Electricity Pricing Set high prices at peak energy hours to discourage energy usage there for energy load balancing Hourly Price from Ameren Illinois 6

7. Renewable Energy 7

8. Energy Scheduling for a Single Smart Home Given the electricity pricing, to decide when to launch a home appliance at what power level for how long utilize renewable energy subject to scheduling constraints Targets Reduce user bill Reduce PAR (peak to average ratio) of grid energy usage Maximize renewable energy usage The smart home scheduler computes scheduling solutions for future, so it needs the future pricing. How? 8

9. Two Pricing Models: Guideline and Realtime Pricing Guideline price: utility publishes it one day ahead to guide customers to schedule their appliances, through providing the predicted pricing in the next 24 hours. Real time price: utility uses it to bill customers, e.g., it obtains the total energy consumption in the past hour, computes the total bill as a quadratic function of the total energy, and then distributes the bill to each customer proportionally. 9

10. Electric Vehicles (EV) Powered by one or more Electric Motors 10

11. Multiple Mode Charging of EV • 2014 Honda Accord PHEV 120-volt: less than 3 hours 240-volt: one hour • 2013 Toyota Prius PHEV 120-volt: less than 3 hours 240-volt: 1.5 hours • 2014 Chevrolet Volt PHEV 120-volt: 10 – 16 hours 240-volt: 4 hours Using mobile connector 29 miles of range per hour charge The fastest way to charge at home 58 miles of range per hour charge 11

12. End Start Dish washer 13:00 18:00 Landry machine 09:00 18:00 EV 18:00 08:00 AC 17:00 N/A …… 12

13. Multiple Power Level (VFD) Impact Powerr Power 3 cents / kwh 5 cents/kwh 3 cents / kwh 5 cents/kwh 10 kwh 5 kwh 1 2 3 1 2 Time Time (b) (a) cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents cost = 10 kwh * 5 cents/kwh = 50 cents 13

14. Uncertainty of Appliance Execution Time and Energy Consumption • In advanced laundry machine, time to do the laundry depends on the load. How to model it? 14

15. Problem Formulation • Given n home appliances, to schedule them for monetary expense minimization considering multiple power level considering variations • Solutions for continuous VFD/power level • Solutions for discrete VFD/power level • Solutions for continuous VFD • Solutions for discrete VFD 1 2 3 4 15

16. The Procedure of the Our Proposed Scheme • Offline Schedule • A deterministic scheduling with continuous power level • A deterministic scheduling with discrete power level • Stochastic Programming for Appliance Variations • Online Schedule for Renewable Energy Variations 16

17. The Outline 17

18. Linear Programming for Deterministic Scheduling with Continuous Power Level minimize: subject to: 18

19. Max Load Constraint To avoid tripping out, in every time window we have load constraint 19

20. Appliance Load Constraint Sum up in each time window appliance power consumption is equal to its input total power 20

21. Appliance Speed Limit and Execution Period Constraint The power is upper bounded Appliance cannot be executed before its starting time or after its deadline 21

22. Power Resource Power resource can be various 22

23. Solar Energy Distribution Constraint Solar Energy can be directly used by home appliances or stored in the battery 23

24. Battery Energy Storage Constraint and Charging Cost Solar Energy Storage Battery Charging Cost 24

25. The Proposed Scheme Outline 25

26. Greedy based Deterministic Scheduling for Task i Task i Power 0 Time t1 t2 t3 t4 Price Time Cannot handle noninterruptible home appliances 26

27. The Proposed Scheme Outline 27

28. Dynamic Programming • Given a home appliance, one processes time interval one by one for all possibilities until the last time interval and choose the best solution 0 0 0 Choose the solution with total energy equal to E and minimal monetary cost 28

29. Characterizing • For a solution in time interval i, energy consumption e and cost c uniquely characterize its state 29

30. Pruning • For one time interval, (e1, c1) will dominate solution (e2, c2), if e1>= e2 and c1<= c2 30

31. Dynamic Programming based Appliance Optimization Power level: {1, 2, 3} Dynamic Programming returns optimal solution (6, 9) (5, 8) (4, 7) (4, 5) (3, 4) (2, 3) (3, 3) (2, 2) (1, 1) (5, 7) (4, 6) (3, 5) (3,6) (3,3) Price (2,4) (2,2) (1,2) (1,1) Time t2 (0,0) t1 (0,0) 0 31

32. DP based Deterministic Scheduling For Multiple Home Appliances • Appliances • Determine Scheduling Appliances Order An appliance • Schedule Current Home Appliance by DP Not all the appliance(s) processed • Update Upper Bound of Each Time Interval All appliances are processed • Schedule 32

33. The Proposed Scheme Outline 33

34. Variation impacts the Scheme Worst case design It can be improved Cost can be reduced Best Price Window t1 t2 t3 t4 34

35. Best Case Design t1 t2 t3 t4 35

36. Variation Aware Design An adaptation variableβ is introduced to utilize the load variation. t1 t2 t3 t4 36

37. Trip rate = trip out event / total event Uncertainty Aware Algorithm 37

38. The Design Flow Uncertainty Aware Algorithm 38

39. Algorithmic Flow Input: Task set with tasks which can be scheduled β from 0 to 0.25 β from 0.25 to 0.5 β from 0.5 to 0.75 β from 0.75 to 1 Core 1 Core 2 Core 3 Core 4 up date task load based on β up date task load based on β up date task load based on β up date task load based on β up date task load based on β Generate appliances schedule by solving the LP Generate appliances schedule by solving the LP Generate appliances schedule by solving the LP Generate appliances schedule by solving the LP Generate appliances schedule by solving the LP Update β Update β Update β Update β Update β Derive current trip rate using Monte Carlo simulation Derive current trip rate using Monte Carlo simulation Derive current trip rate using Monte Carlo simulation Derive current trip rate using Monte Carlo simulation Derive current trip rate using Monte Carlo simulation No No No No No Current trip rate ≤ Target Current trip rate ≤ Target Current trip rate ≤ Target Current trip rate ≤ Target Current trip rate ≤ Target Yes Yes Yes Yes Yes Output: Schedule 39

40. Monte Carlo Simulation takes 5000 samples Latin Hypercube Sampling takes 200 samples Algorithm Improvement Latin Hypercube Sampling is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution Current S 40

41. The Proposed Scheme Outline 41

42. Online Tuning • Actual renewable energy < Expected • Utilize energy from the power grid • Actual renewable demand > Expected • Save the renewable energy as much as possible • Actual renewable demand = Expected • Follow the offline schedule 42

43. Experimental Setup • The proposed scheme was implemented in C++ and tested on a Pentium Dual Core machine with 2.3 GHz T4500 CPU and 3GB main memory. • 500 different task sets are used in the simulation. The number of appliances in each set ranges from 5 to 30, which is the typical number of household appliances [1]. • Two sets of the KD200-54 P series PV modules from Inc [2] are taken to construct a solar station for a residential unit which are cost $502. • The battery cost is set to $75 [3] with 845 kW throughput is taken as energy storage. • The lifetime of the PV system is assumed to be 20 years [4]. • Electricity pricing data released by Ameren Illinois Power Corporation [5] [1] M. Pedrasa, T. Spooner, and I.MacGill, “Coordinated scheduling of residential distributed energy resources to optimize smart home energy services,” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 134–144,2010. [2] Data Sheet of KD200-54 P series PV modules, available at http://www.kyocerasolar.com/assets/001/5124.pdf. [3] T. Givler and P. Lilienthal, “Using HOMER software, NRELs micropower optimization module, to explore the role of gen-sets in small solar power systems case study: Sri lanka,” Technical Report NREL/TP-710-36774, 2005. [4] Lifespan and Reliability of Solar Panel,available at http://www.solarpanelinfo.com/solarpanels/solar-panel-cost.php. [5] Real-Time Price, available at https://www2.ameren.com. 43

44. Experimental Setup on Weekday Using DP 44

45. Energy Consumption Distribution on Weekday Fig1. Energy consumption distribution comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 45

46. Monetary Cost Distribution on Weekday Fig2. Monetary cost comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 46

47. Experimental Setup on Weekend Using DP 47

48. Energy Consumption Distribution on Weekend Fig3. Energy consumption distribution comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 48

49. Monetary Cost Distribution on Weekend Fig4. Monetary cost comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 49

50. Experimental Results Using LP Energy Cost (cents) Runtime (s) time Cost household appliances household appliances 50