EE5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home

1. EE5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home

2. Smart Gird • A smart grid puts information and communication technology into generation, transmission, distribution and end user, making systems cleaner, safer, and more reliable and efficient.  2

3. Smart Home • Smart home technologies are viewed as users end of the Smart Grid. • A smart home or building is equipped with special structured wiring to enable occupants to remotely control or program an array of automated home electronic devices.  • Smart home is combined with energy resources at either their lowest prices or highest availability, e.g. taking advantage of high solar panel output. http://www.yousharez.com/2010/11/20/house-of-dreams-a-smart-house-concept/ 3

4. Smart Appliances • Smart Appliances Characterized by • Compact OS installed • Remotely controllable • Multiple operating modes 4

5. Home Appliance Remote Control 5

6. ZigBee Certified Appliances and Home Area Network (HAN) http://www.zigbee.org/ 6

7. System Power flow Internet Control flow 7

8. Dynamic Pricing from Utility Company Illinois Power Company’s price data Price ($/kwh) Pricing for one-day ahead time period 8

9. Benefit of Smart Home • Reduce monetary expense • Reduce peak load • Maximize renewable energy usage 9

10. Smart Home Control Flow PHEV 10

11. Transition between the Renewable Energy and Power Grid Energy A transfer switch is an electrical switch that reconnects electric power source from its primary source to a standby source. Switches may be manually or automatically operated. 11

12. Smart Scheduling • Demand Side Management • when to launch a home appliance • at what frequency • The variable frequency drive (VFD) is to control the rotational speed of an alternating current (AC) electric motor through controlling the frequency of the electrical power supplied to the motor • for how long • use grid energy or renewable energy • use battery or not 12

13. 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 • 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 VFD with considering variations • Solutions for continuous VFD • Solutions for discrete VFD • 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 frequency • A deterministic scheduling with discrete frequency • Stochastic Programming for Appliance Variations • Online Schedule for Renewable Energy Variations 16

17. The Proposed Scheme Outline 17

18. Linear Programming for Deterministic Scheduling with Continuous Frequency 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 frequency 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. Deterministic Scheduling for Discrete Frequency Flow • Appliances • Determine Scheduling Appliances Order An appliance • Schedule Current Task Not all the appliance(s) processed • Update Upper Bound of Each Time Interval All appliance process • Schedule 26

27. The Proposed Scheme Outline 27

28. Greedy based Deterministic Scheduling for Task i Task i Power 0 Time t1 t2 t3 t4 Price Time Cannot handle noninterruptible home appliances 28

29. The Proposed Scheme Outline 29

30. Dynamic Programming based Deterministic Scheduling for Task i • For a solution in time window i, energy consumption e and cost c uniquely characterize its state. • For pruning: {e1, c1} will dominate solution {e2, c2}, if e1>= e2 and c1<= c2 . Dynamic Programming returns optimal solution (15, 20) (11, 22) Runtime : (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 • # of distinct power levels = k • # time slots = m 30

31. Handling Multiple Tasks • According an order of tasks • Perform the dynamic programming algorithm on each task 31

32. The Proposed Scheme Outline 32

33. Variation impacts the Scheme Worst case design Evaluate Best case can be improved Cost can be reduced Best Price Window t1 t2 t3 t4 33

34. Best Case Design t1 t2 t3 t4 34

35. Variation Aware Design An adaptation variableβ is introduced to utilize the load variation. t1 t2 t3 t4 35

36. Monte Carlo Simulation It takes 5000 different task sets, to evaluate a β value. Evaluate how many samples do not violate trip rate requirement. Trip rate = trip out event / total event Uncertainty Aware Algorithm 36

37. 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 37

38. 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 38

39. Exercise • How to generalize deterministic dynamic programming to an variation aware dynamic programming? 39

40. The Proposed Scheme Outline 40

41. 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 41

42. 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. 42

43. LP-based Approach vs. Traditional Approach Energy Cost (cents) Runtime (s) time Cost household appliance household appliance 43

44. Traditional vs. Continuous VFD vs. Greedy Cost Household appliance 44

45. Only D.P. Can Handle Non Interruptible Task set Cost Household appliance 45

46. Comparison of Worst Case, Best Case Design and Stochastic Design Energy Cost (cents) Trip Rate (%) Rate Cost 10 seconds Household appliance Household appliance 46

47. Online vs. Offline Cost (cents) Household appliance 47

48. Example of a Task Set 48

49. Summary • This project proposes a stochastic energy consumption scheduling algorithm based on the time-varying pricing information released by utility companies ahead of time. • Continuous speed and discrete speed are handled. • Simulation results show that the proposed energy consumption scheduling scheme achieves up to 53% monetary expenses reduction when compared to a nature greedy algorithm. • The results also demonstrate that when compared to a worst case design, the proposed design that considers the stochastic energy consumption patterns achieves up to 24% monetary expenses reduction without violating the target trip rate. • The proposed scheduling algorithm can always generate a monetary expense efficient operation schedule within 10 seconds. 49

50. Multiple Users • Pricing at 10:00am is cheap, so how about scheduling everything at that time? Will not be cheap anymore 8:00 50