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 Gird
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A smart grid puts information and communication technology into generation, transmission, distribution and end user, making systems cleaner, safer, and more reliable and efficient.
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 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 Certified Appliances and Home Area Network (HAN)
http://www.zigbee.org/
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System
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Power flow
Internet Control flow
Dynamic Pricing from Utility Company
Illinois Power Company’s price data
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Pricing for one-day ahead time period
Pri
ce
($/k
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Benefit of Smart Home
– Reduce monetary expense
– Reduce peak load
– Maximize renewable energy usage
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Smart Home Control Flow
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PHEV
Transition between the Renewable Energy and Power Grid EnergyA 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.
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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
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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
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 VFD with considering variations
– Solutions for continuous VFD
– Solutions for discrete VFD
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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 frequency
A deterministic scheduling with discrete frequency
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 Frequency
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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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Deterministic Scheduling for Discrete Frequency Flow
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Determine Scheduling Appliances Order
Schedule Current Task
Update Upper Bound of Each Time Interval
An appliance
Schedule
Appliances
Not all the appliance(s) processed
All appliance process
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
The Proposed Scheme Outline
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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 .
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(15, 20) (11, 22)
(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)
– # of distinct power levels = k
– # time slots = m)( 2kmORuntime :
Price
Time
Dynamic Programming returns optimal solution
Handling Multiple Tasks
According an order of tasks Perform the dynamic programming algorithm on each task
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The Proposed Scheme Outline
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Variation impacts the Scheme
t2 t3 t4
Worst case design
Evaluate Best case can be improved
t1
Best PriceWindow
Cost can be reduced
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Best Case Design
t1 t2 t3 t4
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Variation Aware Design
An adaptation variable β is introduced to utilize the load variation.
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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
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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
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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
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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
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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
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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
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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
Exercise How to generalize deterministic dynamic programming to an variation
aware dynamic programming?
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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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LP-based Approach vs. Traditional Approach
Energy Cost (cents) Runtime (s)
household appliance household appliance
Cost time
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Traditional vs. Continuous VFD vs. Greedy
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Cost
Household appliance
Only D.P. Can Handle Non Interruptible Task set
Cost
Household appliance
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Comparison of Worst Case, Best Case Design and Stochastic Design
Energy Cost (cents) Trip Rate (%)
10 seconds
Household appliance Household appliance
Cost Rate
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Online vs. Offline
Household appliance
Cos
t (c
ents
)
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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 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.
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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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