EE5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home
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Transcript of EE5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home
EE5900: Advanced Embedded System For Smart Infrastructure
Single User Smart Home
Smart Grid
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Classical Power System v.s. Smart Grid
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The Classical Power System
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Smart Grid: Making Every Component Intelligent
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Clean Reliable Secure
Energy EfficientMoney Efficient
IBM Smarter Planet
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Renewable Energy
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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
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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
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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
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Smart Home System Flow
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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
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Landry machineDish washer
PHEVAC
Start End
……
13:00 18:0009:00 18:00
08:0018:0017:00 N/A
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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
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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
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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?
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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
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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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Max Load Constraint
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Appliance Load Constraint
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Appliance Speed Limit and Execution Period Constraint
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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
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0 t1 t2 t3 t4
Task i
Price
Power
Time
Time
Cannot handle noninterruptible home appliances
Greedy based Deterministic Scheduling For Multiple Home Appliances
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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
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Dynamic Programming
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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
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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
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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
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(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
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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
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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
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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
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Generate appliances schedule by
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Derive current trip rate using Monte Carlo simulation
Current trip rate ≤ Target
Update β
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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
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Monte Carlo Simulation takes 5000 samples Latin Hypercube Sampling takes 200 samples
Current S
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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
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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
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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.
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Experimental Setup on Weekday Using DP
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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
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Fig2. Monetary cost comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programmingbased scheduling.
Experimental Setup on Weekend Using DP
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Fig3. Energy consumption distribution comparison of Test Case II. (a) Traditional scheduling(b) Dynamic Programming based scheduling.
Energy Consumption Distribution on Weekend
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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
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Traditional vs. LP vs. Discrete Greedy
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Cost
Household appliances
Only DP Can Handle Non Interruptible Task set
Cost
Household appliances
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Comparison of Worst Case, Best Case Design and Stochastic Design
Energy Cost (cents) Trip Rate (%)
10 seconds
Household appliances Household appliances
Cost Rate
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Online vs. Offline
Household appliances
Cos
t (ce
nts)
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Example of a Task Set
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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
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Thanks
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