1. EE5900: Cyber-Physical Systems Single User Smart Home System

2. Smart Grid

3. Classical Power System v.s. Smart Grid3

4. The Classical Power System4

5. Smart Grid: Making Every Component Intelligent
Distributed Generation and Alternate Energy Sources
Real-time Simulation and Contingency Analysis
Demand Response and Dynamic Pricing
Smart Home
Asset Management and On-Line Equipment Monitoring
Self-Healing Wide-Area Protection and Islanding
Clean
Reliable
Secure
Energy Efficient
Money Efficient

6. IBM Smarter Planet 6

7. Renewable Energy

8. The Integrated Power and Communication System8

9. Smart Power Transmission and Distribution
More devices integrated such as IED, PMU, FRTU, FDR
Improved monitoring and control
Improved cybersecurity
Energy efficiency
Expense efficiency

10. Smart Community 10

11. 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. 11

12. Smart Home System 12

13. Smart Appliances Characterized by Compact OS installed
Remotely controllable
Multiple operating modes 13

14. Home Appliance Remote Control14

15. ZigBee Home Area Network (HAN) 15

16. ZigBee Local Area Network (LAN)16

17. Smart Home Deployment in Urban Area17

18. Relationship With Smart Building18

19. Property 1: Dynamic Pricing from Utility Company
Illinois Power Company’s price data
Price ($/kwh)
Pricing for one-day ahead time period 19

20. Property 2: Renewable Energy Resource
Marcelo Gradella Villalva, Jonas Rafael Gazoli, and Ernesto Ruppert Filho. Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. IEEE Transactions on Power Electronics, Vol. 24, No. 5, May 2009 20

21. Benefit of Smart Home Reduce monetary expense Reduce peak load
Maximize renewable energy usage 21

22. Smart Home System Flow
Power flow
Internet
Control flow 22

23. Smart Home Scheduling Smart Home Scheduling
when to launch a home appliance
at what frequency or power level
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
Closely related to Demand Side Management
Demand Side Management is a top down approach
Smart Home Scheduling is a bottom up approach 23

24. 24 Landry machine Dish washer PHEV AC Start End …… 13:00 18:00 09:00
08:00
17:00
N/A 24

25. Electric Vehicles (EV)
Powered by one or more Electric Motors 25

26. Plug-in Hybrid Electric Vehicles (PHEV)
Powered by an Electric Motor and Engine
Internal combustion engine uses alternative or conventional fuel
Battery charged by outside electric power source, engine, or regenerative breaking
During urban driving, most power comes from stored electricity. Long trips require the engine 26

27. Charging of PHEV at Home
2014 Honda Accord PHEV
120-volt: less than 3 hours
240-volt: one hour
2013 Toyota Prius PHEV
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 27

28. 28 VFD Impact Power Powerr 5 cents/kwh 3 cents / kwh 5 cents/kwh1 2 1 2 3
Time
Time
(b)
(a)
cost = 10 kwh * 5 cents/kwh = 50 cents
cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents 28

29. 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? 29

30. 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 30

31. 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 31

32. The Proposed Scheme Outline
A deterministic scheduling with continuous power level
A deterministic scheduling with discrete power level
Optimal Greedy based Deterministic Scheduling
Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations 32

33. Linear Programming for Deterministic Scheduling with Continuous Power Level
minimize:
subject to: 33

34. Max Load Constraint
To avoid tripping out, in every time window we have load constraint 34

35. Appliance Load Constraint
Sum up in each time window appliance power consumption is equal to its input total power 35

36. Appliance Speed Limit and Execution Period Constraint
The power is upper bounded
Appliance cannot be executed before its starting time or after its deadline 36

37. Power Resource
Power resource can be various 37

38. Solar Energy Distribution Constraint
Solar Energy can be directly used by home appliances or stored in the battery 38

39. Battery Energy Storage Constraint and Charging Cost
Solar Energy Storage
Battery Charging Cost 39

40. The Proposed Scheme Outline
A deterministic scheduling with continuous power level
A deterministic scheduling with discrete power level
Optimal Greedy based Deterministic Scheduling
Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations 40

41. Greedy based Deterministic Scheduling for Task i
Power t1 t2 t3 t4
Time
Price
Time
Cannot handle noninterruptible home appliances 41

42. Greedy based Deterministic Scheduling For Multiple Home Appliances
Determine Scheduling Appliances Order
An appliance
Schedule Current Home Appliance by Greedy Algorithm
Not all the appliance(s)
processed
Update Upper Bound of Each Time Interval
All appliances are processed
Schedule 42

43. The Proposed Scheme Outline
A deterministic scheduling with continuous power level
A deterministic scheduling with discrete power level
Optimal Greedy based Deterministic Scheduling
Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations 43

44. 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
Choose the solution with total energy equal to E and minimal monetary cost 44

45. Characterizing
For a solution in time interval i, energy consumption e and cost c uniquely characterize its state
Time interval i
Time interval i+1
(ei, ci)
(ei+1, ci+1) 45

46. Pruning
For one time interval, (e1, c1) will dominate solution (e2, c2), if e1>= e2 and c1<= c2
Time interval i
(15, 20)
(15, 25)
(11, 22) 46

47. Dynamic Programming based Appliance Optimization
Power level: {1, 2, 3}
Dynamic Programming returns
optimal solution
(6, 9)
(5, 8)
(4, 7)
(5, 7)
(4, 6)
(3, 5)
(4, 5)
(3, 4)
(2, 3)
(3, 3)
(2, 2)
(1, 1)
(3,6)
(3,3)
Price
(2,4)
(2,2)
(1,2)
(1,1)
Time
(0,0) t1
(0,0) t2 47

48. Handling Multiple Tasks
According an order of tasks
Perform the dynamic programming algorithm on each task 48

49. DP based Deterministic Scheduling For Multiple Home 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 49

50. The Proposed Scheme Outline
A deterministic scheduling with continuous power level
A deterministic scheduling with discrete power level
Optimal Greedy based Deterministic Scheduling
Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations 50

51. Variation impacts the Scheme
Worst case design
It can be improved
Cost can
be reduced
Best Price
Window t1 t2 t3 t4 51

52. Best Case Design t1 t2 t3 t4 52

53. Variation Aware Design
An adaptation variable β is introduced to utilize the load variation. t1 t2 t3 t4 53

54. Uncertainty Aware Algorithm
Trip rate = trip out event / total event 54

55. The Design Flow Uncertainty Aware Algorithm55

56. Algorithmic Flow Input: Task set with tasks which can be scheduled
Core 1
up date task load based on β
Generate appliances schedule by solving the LP
Derive current trip rate using Monte Carlo simulation
Current trip rate ≤ Target
Update β No
Yes
Core 2
Core 3
Core 4
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
Yes
up date task load based on β
Generate appliances schedule by solving the LP
Derive current trip rate using Monte Carlo simulation
Current trip rate ≤ Target
Update β No
Output: Schedule 56

57. Algorithm Improvement
Monte Carlo Simulation takes 5000 samples
Latin Hypercube Sampling takes 200 samples
Latin Hypercube Sampling is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution
Current S 57

58. The Proposed Scheme Outline
A deterministic scheduling with continuous power level
A deterministic scheduling with discrete power level
Optimal Greedy based Deterministic Scheduling
Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations 58

59. 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 59

60. 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 KD 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 KD P series PV modules, available at
[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 , 2005.
[4] Lifespan and Reliability of Solar Panel,available at
[5] Real-Time Price, available at 60

61. Experimental Setup on Weekday Using DP61

62. Energy Consumption Distribution on Weekday
Fig1. Energy consumption distribution comparison of Test Case I. (a) Traditional scheduling
(b) Dynamic Programming based scheduling. 62

63. Monetary Cost Distribution on Weekday
Fig2. Monetary cost comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming
based scheduling. 63

64. Experimental Setup on Weekend Using DP64

65. Energy Consumption Distribution on Weekend
Fig3. Energy consumption distribution comparison of Test Case II. (a) Traditional scheduling
(b) Dynamic Programming based scheduling. 65

66. Monetary Cost Distribution on Weekend
Fig4. Monetary cost comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming
based scheduling. 66

67. Experimental Results Using LP
Energy Cost (cents)
Runtime (s)
Cost
time
household appliances
household appliances 67

68. Traditional vs. LP vs. Discrete Greedy
Cost
Household appliances 68

69. Only DP Can Handle Non Interruptible Task set
Cost
Household appliances 69

70. Comparison of Worst Case, Best Case Design and Stochastic Design
Energy Cost (cents)
Trip Rate (%)
Cost
Rate
10 seconds
Household appliances
Household appliances 70

71. Online vs. Offline
Cost (cents)
Household appliances 71

72. Example of a Task Set 72

73. 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 power level and discrete power level 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. 73

74. Multiple Users
Pricing at 10:00am is cheap, so how about scheduling everything at that time?
Will not be cheap anymore
8:00 74

75. Game Theory Based Scheduling75

76. Thanks 76