1. EE5900 Cyber-Physical Systems Smart Home CPS
Professor Shiyan Hu
EERC 518
Department of Electrical and Computer Engineering
Michigan Technological University 1 1

2. Classical Power System v.s. Smart Grid2

3. 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

4. The Integrated Power and Information System4

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

6. Smart Community 6

7. Smart Building 7

8. Smart Home To Minimize Expense and Maximize Renewable Energy Usage 8
To Minimize Expense and Maximize Renewable Energy Usage 8

9. Smart Appliances 9

10. 10 VFD Impact Power Powerr 5 cents/kwh 3 cents / kwh 5 cents/kwh2 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 10

11. Smart Home: Industrial Perspective11

12. 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. 12

13. Why we schedule? The Single User Smart Home 13 Power flow Internet
Control flow 13

14. 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.
Peak
Average
PAR
Source: Ontario Energy Board 14

15. Dynamic Electricity Pricing
Set high prices at peak energy hours to discourage energy usage there for energy load balancing
Hourly Price from Ameren Illinois 15

16. Renewable Energy 16

17. 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
The smart home scheduler computes scheduling solutions for future, so it needs the future pricing. How? 17

18. 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. 18

19. Dynamic Pricing + Game Theory = U.S. Solution
Multiple Users?
Dynamic Pricing + Game Theory = U.S. Solution
Customer 1
Customer 2
Customer n
Game theory is used to handle interactions among customers. 19

20. Case Study
5 communities in which each one contains 400 customers, and 2 utilities.
Simulation time horizon is 24 hours from the current time, which is divided into 15-minutes time slots. 20

21. Average Energy Consumption and Bill
Many issues beyond energy and bill
Impact to electricity market
Architecture
City level deployment
Centralized, decentralized, hierarchical
Reliability
Privacy
Cybersecurity 21

22. Uncertainty of Appliance Execution Time
In advanced laundry machine, time to do the laundry depends on the load. How to model it? 22

23. Uncertainty in Renewable Energy23

24. The Implementation Using FPGA24

25. Schematic of FPGA Implementation25

26. Smart Home Deployment in Urban Area26

27. Summary What is smart home scheduling?
What are dynamic electricity pricing and predictive guideline pricing? 27