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

EE5900: Advanced Embedded System For Smart Infrastructure

Single User Smart Home

Smart Grid

2

Classical Power System v.s. Smart Grid

3

The Classical Power System

4

Smart Grid: Making Every Component Intelligent

5

Clean Reliable Secure

Energy EfficientMoney Efficient

IBM Smarter Planet

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Renewable Energy

7

The Integrated Power and Communication System

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Smart Power Transmission and Distribution

More devices integrated such as IED, PMU, FRTU, FDR Improved monitoring and control Improved cybersecurity Energy efficiency Expense efficiency

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Smart Community

http://www.meti.go.jp

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Smart Home

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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/

Smart Home System

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Smart Appliances

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

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Home Appliance Remote Control

14

ZigBee Home Area Network (HAN)

http://www.zigbee.org/

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ZigBee Local Area Network (LAN)

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Smart Home Deployment in Urban Area

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Relationship With Smart Building

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Property 1: Dynamic Pricing from Utility Company

Illinois Power Company’s price data

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Pricing for one-day ahead time period

Pric

e ($

/kw

h)

Property 2: Renewable Energy Resource

20

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

Benefit of Smart Home– Reduce monetary expense

– Reduce peak load

– Maximize renewable energy usage

21

Smart Home System Flow

22

Power flowInternet Control flow

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

Landry machineDish washer

PHEVAC

Start End

……

13:00 18:0009:00 18:00

08:0018:0017:00 N/A

24

Electric Vehicles (EV)

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Powered by one or more Electric Motors

Plug-in Hybrid Electric Vehicles (PHEV)

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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

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

27

Charging of PHEV at Home

Using mobile connector29 miles of range per hour charge

The fastest way to charge at home58 miles of range per hour charge

5 cents/kwh 3 cents / kwh

5 kwh

10 kwh

Power Powerr

Time Time1 2 1 2 3

(a) (b)

VFD Impact

5 cents/kwh 3 cents / kwh

cost = 10 kwh * 5 cents/kwh = 50 cents cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents

28

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

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

30

Solutions for continuous VFD

Solutions for discrete VFD

1 2

3 4

The Procedure of the Our Proposed Scheme

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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

The Proposed Scheme Outline

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Linear Programming for Deterministic Scheduling with Continuous Power Level

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Power Resource

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Solar Energy Distribution Constraint

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Battery Energy Storage Constraint and Charging Cost

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The Proposed Scheme Outline

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Greedy based Deterministic Scheduling for Task i

41

0 t1 t2 t3 t4

Task i

Price

Power

Time

Time

Cannot handle noninterruptible home appliances

Greedy based Deterministic Scheduling For Multiple Home Appliances

42

Determine Scheduling Appliances Order

Schedule Current Home Appliance by Greedy

Algorithm

Update Upper Bound of Each Time Interval

An appliance

Schedule

Appliances

Not all the appliance(s) processed

All appliances are processed

The Proposed Scheme Outline

43

Dynamic Programming

44

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

Characterizing

45

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)

Pruning

46

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)

Dynamic Programming based Appliance Optimization

47

(1,2)

(2,4)

(3,6)

(1,1)

(2,2)

(3,3)

0 t1 t2

(6, 9) (5, 8)(4, 7)

(5, 7) (4, 6)(3, 5)

(4, 5) (3, 4)(2, 3)

(0,0) (0,0)

(3, 3) (2, 2)(1, 1)

Price

Time

Dynamic Programming returns optimal solution

Power level: {1, 2, 3}

Handling Multiple Tasks

According an order of tasks Perform the dynamic programming algorithm on each task

48

49

Determine Scheduling Appliances Order

Schedule Current Home Appliance by DP

Update Upper Bound of Each Time Interval

An appliance

Schedule

Appliances

Not all the appliance(s) processed

All appliances are processed

DP based Deterministic Scheduling For Multiple Home Appliances

The Proposed Scheme Outline

50

Variation impacts the Scheme

t2 t3 t4

Worst case design

It can be improved

t1

Best PriceWindow

Cost can be reduced

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Best Case Design

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Variation Aware Design

An adaptation variable β is introduced to utilize the load variation.

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Uncertainty Aware Algorithm

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Trip rate = trip out event / total event

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The Design Flow Uncertainty Aware Algorithm

Algorithmic Flow

Output: Schedule

Input: Task set with tasks which can be scheduled

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

Core 1up 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

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

up date task load based on

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Generate appliances

schedule by solving the LP

Derive current trip rate using Monte Carlo simulation

Current trip rate ≤ Target

Update β

No

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

YesYesYes

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

56

Monte Carlo Simulation takes 5000 samples Latin Hypercube Sampling takes 200 samples

Current S

57

Latin Hypercube Sampling is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution

Algorithm Improvement

The Proposed Scheme Outline

58

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

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.

60

Experimental Setup on Weekday Using DP

61

Energy Consumption Distribution on Weekday

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Fig1. Energy consumption distribution comparison of Test Case I. (a) Traditional scheduling(b) Dynamic Programming based scheduling.

Monetary Cost Distribution on Weekday

63

Fig2. Monetary cost comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programmingbased scheduling.

Experimental Setup on Weekend Using DP

64

65

Fig3. Energy consumption distribution comparison of Test Case II. (a) Traditional scheduling(b) Dynamic Programming based scheduling.

Energy Consumption Distribution on Weekend

66

Fig4. Monetary cost comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programmingbased scheduling.

Monetary Cost Distribution on Weekend

Experimental Results Using LP

Energy Cost (cents) Runtime (s)

household appliances household appliances

Cost time

67

Traditional vs. LP vs. Discrete Greedy

68

Cost

Household appliances

Only DP Can Handle Non Interruptible Task set

Cost

Household appliances

69

Comparison of Worst Case, Best Case Design and Stochastic Design

Energy Cost (cents) Trip Rate (%)

10 seconds

Household appliances Household appliances

Cost Rate

70

Online vs. Offline

Household appliances

Cos

t (ce

nts)

71

Example of a Task Set

72

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.

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Multiple Users

Pricing at 10:00am is cheap, so how about scheduling everything at that time?

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Will not be cheap anymore

8:00

Game Theory Based Scheduling

75

Thanks

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