EE5900: Advanced Embedded System For Smart
Infrastructure
Single User Smart Home
Smart Gird
 A smart grid puts information and communication technology into
generation, transmission, distribution and end user, making systems
cleaner, safer, and more reliable and efficient.
2
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.
http://www.yousharez.com/2010/11/20/house-of-dreams-a-smart-house-concept/
3
Smart Appliances
Smart Appliances Characterized by
• Compact OS installed
• Remotely controllable
• Multiple operating modes
4
Home Appliance Remote Control
5
ZigBee Certified Appliances and Home Area Network (HAN)
http://www.zigbee.org/
6
System
Power flow
Internet
Control flow
7
Dynamic Pricing from Utility Company
Price ($/kwh)
Illinois Power Company’s price data
Pricing for one-day ahead time period
8
Benefit of Smart Home
– Reduce monetary expense
– Reduce peak load
– Maximize renewable energy usage
9
Smart Home Control Flow
PHEV
10
Transition between the Renewable Energy and
Power Grid Energy
A 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.
11
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
12
VFD Impact
Powerr
Power
5 cents/kwh 3 cents / kwh
5 cents/kwh 3 cents / kwh
10 kwh
5 kwh
1
2
(a)
Time
1
2
(b)
3
Time
cost = 10 kwh * 5 cents/kwh = 50 cents cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents
13
Uncertainty of Appliance Execution Time
 In advanced laundry machine, time to do the laundry depends on the
load. How to model it?
14
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
Solutions for
continuous VFD
Solutions for
discrete VFD
1
2
3
4
15
The Procedure of the Our Proposed Scheme
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
16
The Proposed Scheme Outline
A deterministic scheduling with continuous frequency
A deterministic scheduling with discrete frequency
• Optimal Greedy based Deterministic Scheduling
• Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations
17
Linear Programming for Deterministic Scheduling
with Continuous Frequency
 t imeint erval
minimize:
( y  c  y  c )  b


u
T
subject to:
u
s
s
c
 Ic
 x  L ,   T
a
a A
a
T
T
a
cs unit price of t hesolar energy
, a  A

xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]
x

a A
a



 yb  y s  yu ,   T
ys  zs  es ,   T

 1
 1
 1
zb  zb  z s  yb ,  [2,...,T ]
bc   zs bu
 T
ys solar energy
cu unit price of t heenergy fromgrid
u
x  E


yu energyfromgrid
bc bat t erycost
i cost
I c inst allaton
xa energy consumedby an appliance
Lu energy limit
EaT t ot alenergy consumpt ion of an appliance
Pa max energy consumpt ion of an appliance
in t het imeint erval
yb energyfrom bat t ery
ys energyfromsolar panel
zs energyst ored t o bat t ery
es solar energyin t het imeint erval
zb remainingenergyin bat t ery
bu unit priceof t hebat t ery
18
Max Load Constraint
( y  c  y  c )  b


u
T
u
s
s
c
 Ic
 x  L ,  T
To avoid tripping out, in
every time window we have
load constraint
aA
a
u
x  E


T
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]

Lu
 x  y  y  y ,   T
a A
a
b
s
u
ys  zs  es ,   T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
 timeinterval
yu energy fromgrid

es solar energy
yb energyfrom bat tery
ys energyfromsolar panel
zs energyst oredto bat tery
cu unit price of theenergy fromgrid es solar energyin thetimeinterval
cs unit price of thesolar energy
zb remainingenergyin bat tery
yu energy from thegrid
bu unit priceof thebat tery
bc batterycost
I c installation cost
xa energy comsumedby an appliance
bc   zs bu
 T
Lu energy limit
EaT totalenergy comsumption of an appliance
Pa max energy comsumption of an appliance
in thetimeinterval
19
Appliance Load Constraint
( y  c  y  c )  b


u
T
Sum up in each time window
appliance power consumption is
equal to its input total power
u
s
s
c
 Ic
 x  L ,   T
a A
a
u
x  E


T
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]
 x  y  y  y ,   T
a
t
E
x1a
 timeinterval
yu energy fromgrid

es solar energy
xa2
a A
ys energyfromsolar panel
zs energyst oredto bat tery
cu unit price of theenergy fromgrid es solar energyin thetimeinterval
cs unit price of thesolar energy
zb remainingenergyin bat tery
yu energy from thegrid
bu unit priceof thebat tery
b
s
u
ys  zs  es ,   T
xa3
yb energyfrom bat tery
a
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
bc batterycost
I c installation cost
xa energy comsumedby an appliance
bc   zs bu
 T
Lu energy limit
EaT totalenergy comsumption of an appliance
Pa max energy comsumption of an appliance
in thetimeinterval
20
Appliance Speed Limit and Execution Period Constraint
The frequency is upper bounded
( y  c  y  c )  b


u
T
u
s
s
c
 Ic
 x  L ,   T
a A
a
u
x  E


T
Appliance cannot be executed
before its starting time or after its
deadline
a
a
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]
 x  y  y  y ,   T
a A
a
b
s
u
ys  zs  es ,   T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
 timeinterval
yb energyfrom bat tery
yu energy fromgrid
ys energyfromsolar panel
es solar energy
zs energyst oredto bat tery
cu unit price of theenergy fromgrid es solar energyin thetimeinterval
cs unit price of thesolar energy
zb remainingenergyin bat tery
yu energy from thegrid
bu unit priceof thebat tery
bc batterycost
I c installation cost
xa energy comsumedby an appliance
bc   zs bu
 T

Lu energy limit
EaT totalenergy comsumption of an appliance
Pa max energy comsumption of an appliance
in thetimeinterval
21
Power Resource
Power resource can be various
( y  c  y  c )  b


u
T
u
s
s
c
 Ic
 x  L ,   T
a A
a
u
x  E


T
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]
 x  y  y  y ,   T
a A
a
b
s
u
ys  zs  es ,   T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
 timeinterval
yu energy fromgrid

es solar energy
yb energyfrom bat tery
ys energyfromsolar panel
zs energyst oredto bat tery
cu unit price of theenergy fromgrid es solar energyin thetimeinterval
cs unit price of thesolar energy
zb remainingenergyin bat tery
yu energy from thegrid
bu unit priceof thebat tery
bc batterycost
I c installation cost
xa energy comsumedby an appliance
bc   zs bu
 T
Lu energy limit
EaT totalenergy comsumption of an appliance
Pa max energy comsumption of an appliance
in thetimeinterval
22
Solar Energy Distribution Constraint
Solar Energy can be directly used
by home appliances or stored in
the battery
( y  c  y  c )  b


u
T
u
s
s
c
 Ic
 x  L ,   T
a A
a
u
x  E


T
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]
 x  y  y  y ,   T
a A
a
b
s
u
ys  zs  es ,  T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
 timeinterval
yb energyfrom bat tery
yu energy fromgrid
ys energyfromsolar panel
es solar energy
zs energyst oredto bat tery
cu unit price of theenergy fromgrid es solar energyin thetimeinterval
cs unit price of thesolar energy
zb remainingenergyin bat tery
yu energy from thegrid
bu unit priceof thebat tery
bc batterycost
I c installation cost
bc   zs bu
 T
xa energy comsumedby an appliance
Lu energy limit
EaT totalenergy comsumption of an appliance
Pa max energy comsumption of an appliance
in thetimeinterval
23
Battery Energy Storage Constraint and Charging Cost
( y  c  y  c )  b


Solar Energy Storage
u
T
u
s
s
c
 Ic
 x  L ,   T
a A
a
u
x  E


T
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]
Battery Charging Cost
 x  y  y  y ,   T
a A
a
b
s
u
ys  zs  es ,   T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
 timeinterval
yu energy fromgrid

es solar energy
yb energyfrom bat tery
ys energyfromsolar panel
zs energyst oredto bat tery
cu unit price of theenergy fromgrid es solar energyin thetimeinterval
cs unit price of thesolar energy
zb remainingenergyin bat tery
yu energy from thegrid
bu unit priceof thebat tery
bc batterycost
I c installation cost
xa energy comsumedby an appliance
bc   zs bu
 T
Lu energy limit
EaT totalenergy comsumption of an appliance
Pa max energy comsumption of an appliance
in thetimeinterval
24
The Proposed Scheme Outline
A deterministic scheduling with continuous frequency
A deterministic scheduling with discrete frequency
• Optimal Greedy based Deterministic Scheduling
• Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations
25
Deterministic Scheduling for Discrete Frequency
Flow
Appliances
Determine Scheduling
Appliances Order
An appliance
Schedule Current Task
Not all the appliance(s)
processed
Update Upper Bound of
Each Time Interval
All appliance process
Schedule
26
The Proposed Scheme Outline
A deterministic scheduling with continuous frequency
A deterministic scheduling with discrete frequency
• Optimal Greedy based Deterministic Scheduling
• Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations
27
Greedy based Deterministic Scheduling for Task i
Task i
Power
0
t1
t2
t3
t4
Time
Price
Time
Cannot handle noninterruptible home appliances
28
The Proposed Scheme Outline
A deterministic scheduling with continuous frequency
A deterministic scheduling with discrete frequency
• Optimal Greedy based Deterministic Scheduling
• Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations
29
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 .
(15, 20)
Price
0
(3,6)
(3,3)
(2,4)
(2,2)
(1,2)
(1,1)
(0,0)
t1
(0,0)
(11, 22)
Dynamic Programming returns
optimal solution
(6, 9) (5, 7) (4, 5) (3, 3)
(5, 8) (4, 6) (3, 4) (2, 2)
(4, 7) (3, 5) (2, 3) (1, 1)
Time
t2
– # of distinct power levels = k
Runtime :
O(m2k )
– # time slots = m
30
Handling Multiple Tasks
 According an order of tasks
 Perform the dynamic programming algorithm on each task
31
The Proposed Scheme Outline
A deterministic scheduling with continuous frequency
A deterministic scheduling with discrete frequency
• Optimal Greedy based Deterministic Scheduling
• Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations
32
Variation impacts the Scheme
( y  c  y  c )  b


u
T
u
s
s
c
 Ic
 x  L ,   T
Worst case design
a A
u
x  E


Evaluate Best case can be improved
T
a
T
a
, a  A
xa  Pa , a  A,  T
Cost can
be reduced
Best Price
Window
a
xa  0, a  A,  [ a ,  a ]




x

y

y

y
 a b s u ,   T
a A
t1
t2
t3
t4
ys  zs  es ,   T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
bc   zs bu
 T
33
Best Case Design
t1
t2
t3
t4
34
Variation Aware Design
An adaptation variable β is introduced
to utilize the load variation.




(
y

c

y

c
 u u s s )  bc  I c
 T
 x  L ,   T
a A
t1
t2
t3
t4
a
u
x  E


T
a
T
a
, a  A
xa  Pa , a  A,  T
xa  0, a  A,  [ a ,  a ]




x

y

y

y
 a b s u ,   T
a A
E  (1   )  
T
a
min
a
  
max
a
ys  zs  es ,   T
zb  zb 1  zs 1  yb 1 ,  [2,...,T ]
bc   zs bu
 T
35
Uncertainty Aware Algorithm
 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
36
Algorithmic Flow
Input: Task set with tasks which
can be scheduled
β from 0 to 0.25
Core 1
β from 0.25 to 0.5
Core 2
up date task
load based on
β
Update
β
solving the LP
β
up date task
load based on
β
Generate
appliances
schedule by
solving the LP
Generate
appliances
schedule by
solving the LP
Update
β
β
Yes
up date task
load based on β load based on
Generate
Update
Update
β
Current
trip rate
≤ Target
Core 4
Generate
appliancesappliances schedule by
schedule bysolving the LP
Derive current
trip rate using
Monte Carlo
simulation
No
β from 0.75 to 1
Core 3
up date task
load
upbased
dateon
task
β
Generate
appliances
schedule by
solving the LP
β from 0.5 to 0.75
Derive current
current trip rate using
tripDerive
rate using
MonteMonte
Carlo Carlo simulation
simulation
No
Current
Current trip rate
trip rate
≤ Target ≤ Target
No
Yes
Update
β
Derive current
trip rate using
Monte Carlo
simulation
Current
trip rate
≤ Target
Yes
Derive current
trip rate using
Monte Carlo
simulation
No
Current
trip rate
≤ Target
Yes
Output: Schedule
37
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
38
Exercise
 How to generalize deterministic dynamic programming to an variation
aware dynamic programming?
39
The Proposed Scheme Outline
A deterministic scheduling with continuous frequency
A deterministic scheduling with discrete frequency
• Optimal Greedy based Deterministic Scheduling
• Optimal DP based Deterministic Scheduling
Stochastic Programming for Appliance Variations
Online Schedule for Renewable Energy Variations
40
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
41
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
Cost
Energy Cost (cents)
household appliance
time
Runtime (s)
household appliance
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Traditional vs. Continuous VFD vs. Greedy
Cost
Household appliance
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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
Cost
Energy Cost (cents)
Household appliance
Rate
Trip Rate (%)
Household appliance
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Cost (cents)
Online vs. Offline
Household appliance
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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?
Will not be cheap anymore
8:00
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Game Theory Based Scheduling
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Thanks
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