An Optimal Power Scheduling Method for Demand Response in Home
Energy Management System
(IEEE TRANSACTIONS ON SMART GRID, VOL. 4, NO. 3, SEPTEMBER
2013)
An Optimal Power Scheduling Method for Demand Response in Home
Energy Management System
Zhuang Zhao, Won Cheol Lee, Member, IEEE, Yoan Shin, Senior
Member, IEEE, and Kyung-Bin Song
Prepared By:M. Asghar KhanElectrical Engineering Dept.COMSATS
Institute of IT Islamabad.11OUTLINEBACKGROUND
CONTRIBUTION
SYSTEM MODEL
PROPOSED APPROACH
SIMULATION RESULTS
CONCLUSION
2BACKGROUND (1/6)Smart Grid: A modernizedelectrical gridUses
information and communications technologyto gather and act on
information, in an automated fashion To improve the efficiency,
reliability, economics, and sustainability of the production and
distribution of electricity
HEMS (Home Energy Management System): Any device or system in
the home used to: 1. Control an energy-consuming device 2. Identify
or diagnose energy saving opportunities 3. Provide information to
occupants to influence how they consume energy 3BACKGROUND (2/6)HAN
(Home Area Network): A type oflocal area networkFacilitate
communication and interoperability among home appliances Appliances
capable of participating in this network are called smart
appliances
AMI (Advanced Metering Infrastructure): A system that measure,
collect, and analyze energy usage, andCommunicate with smart meters
either on request or on a schedule
SM (Smart Meter): Anelectronicdevice that records consumption
ofelectric energyin intervals andCommunicates that informationat
least daily back to theutilityfor monitoring and billing
purposes
4BACKGROUND (3/6)IBR (Inclining Block Rates):IBR divide the
electricity price into several steps or blocksThe first block of
electricity is at the lowest price
RTP (Real Time Pricing): RTP is generally an hourly rate which
is applied to usage on an hourly basis
Demand Response (DR): DR refers to, actions taken to change
residential electricity demand in response to prices over time
55BACKGROUND (4/6)HG (Home Gateway): A HGallows the connection
of ahome area network(HAN) to an internet, i.e.,wide area
network(WAN)
With the development of smart grid, users have the opportunity
to schedule their power usage in home by themselves
The purpose of load scheduling is to reduce the power
Peak-to-Average Ratio (PAR) and accordingly to reduce electricity
expense6BACKGROUND (5/6)7
BACKGROUND (6/6)8
CONTRIBUTION
A general architecture of Energy Management System (EMS) in Home
Area Network (HAN) is proposed based on Smart Meter (SM)
Also, an efficient scheduling method (i.e., Genetic Algorithm
(GA) based optimization) for home power usage is presented
An alternative billing mechanism (i.e., Real Time Pricing (RTP)
combined with Inclining Block Rates (IBR)) is developed
9SYSTEM MODEL (1/4)
Components of the proposed EMS (Energy Management System)
architecture are:
AMI (Advanced Metering Infrastructure)SMHG (Home Gateway)EMC
(Energy Management Controller)Home appliances IHD (In-Home
Display)10
SYSTEM MODEL (2/4)
11Objectives of Adopting the EMS are:
To minimize the expense of electricity
To reduce the PAR by scheduling the pattern of electricity
usage
Minimizing the Delay Time Rate (DTR) of home appliancesSYSTEM
MODEL (3/4)
12Classification of the home appliances:
Automatically Operated Appliances (AOAs) (e.g., washing machine,
dish washer, air conditioner)Interruptible (e.g., washing machine)
Non-interruptible (e.g., electric kettle)
Manually Operated Appliances (MOAs) (e.g., computer, television,
vacuum cleaner)
SYSTEM MODEL (1/4)
13The home appliances that could be scheduled are only AOAsThe
AOAs mentioned in this paper are smart home appliancesThe AOAs do
not interact with each other, they only interact with the HG
insteadAll the operations of AOAs would have been scheduled by the
EMC at the beginning of the day
PROPOSED APPROACH
14Usage Pattern of Home Electric Appliances:
HG receives DR information and the profile of RTEP (Real Time
Electricity Price) from the utility company
The EMC can make decision on power schedule for all AOAs in the
home
It is necessary for residents to set the time parameters for
each AOA, i.e., a. LOT (Length of Operation Time) from start to
endb. OTI (Operation Time Interval) during which the appliance is
valid to be scheduled, and c. Its power consumption per hour.
PROPOSED APPROACH
15These parameters can be set on the IHD device and then
transmitted to the EMC via HG
Since each MOA is operated manually and nobody can tell in
advance when and for how long they will use an MOA
Therefore, only the impact of AOAs on electricity cost and PAR
are considered PROPOSED APPROACH
16B. Final Goal of the Proposed Approach: 1 hour is divided into
5 time slots (i.e., 1 time slot=12 minutes) and so 1 day= 24 x
5=120 slots, denoted by The shortest operation time of any
appliance is set to be 12 minutes
Hence, the LOT of the air conditioner can be set to integer
multiples of the 12-minute intervalThe unit of LOT is time
slots
PROPOSED APPROACH
17A denotes the set of AOAsFor each appliance , we assume that
is the power consumption scheduling vector:
where denotes the power consumption value for appliance during
the time slot, and their unit is kWhwe assume that the power
consumption values per hour for all appliances are all fixed
PROPOSED APPROACH
18When the power consumption value per hour of appliance is
denoted by , during the time slot, the corresponding power
consumption is
The objective is to optimize the power consumption scheduling
vector
PROPOSED APPROACH
19RTP Combined With IBR:
As the problem with RTP is load synchronization and
IBR suffers due to fixed prices for most of the times
So in this new pricing scheme the EP (Electricity Price) will be
different within the same time slot based on the total power
consumption
PROPOSED APPROACH
20Two electricity price levels are set in IBR, and the EP
changes every hourThe EP function is,
where denotes the total power consumption in the home during the
hour, is the RTEP during the hour in a day, is the second
electricity price level that should be greater than and is the
threshold of power consumption at hour of IBR.The unit of EP is in
cents/kWh
PROPOSED APPROACH
21As one hour has been divided into five time slots, a
modification in the basic EP function is neededDividing and by 5,
we obtain the total power consumption value and the IBR threshold
in every 12-minute, respectively Then, the EP function can be
altered as,
The number of variables becomes 120 instead of 24 due to the
time division
PROPOSED APPROACH
22To avoid the problem of load synchronization, in this paper
the author assumed that,
According to the British Columbia Hydro, the value of is
determined to be 1.4423It seems unrealistic to utilize this price
function because it is impossible to obtain the entire EP function
ahead of the dayHowever, several electricity price prediction
methods have been proposed
PROPOSED APPROACH
23Problem Formulation:For residents it is necessary to set some
parameters for each AOAThe OTI (Operation Time Interval) will be as
the indexes of the start and the end time slots, respectivelyLet
indicates the LOT, (i.e., the number of time slots for the
operation of appliance )In addition, must be greater than or equal
toThe greater the value of is, the more possible solutions there
would be
23PROPOSED APPROACH
24The Operation Start time (OST) of appliance isThe power
consumption scheduling vector of appliance would be determined once
we have Since and are all known parametersThe figure (next slide)
shows the relationship of these parametersFour different kinds of
AOA are included.
PROPOSED APPROACH
25PROPOSED APPROACH
26Now for each appliance there exists a group of parameters
comprising the OTI LOT and power consumption value per hour In
addition, OST is set as a variableThe range of OST of is
The range of is shown in the figure given in next slide
PROPOSED APPROACH
27
PROPOSED APPROACH
28A variable vector which is composed of the OSTs of all
AOAsTherefore, a power consumption scheduling matrix for all AOAs
can be defined as,
Where denotes a matrix in which each row stands for the power
schedule of a certain appliance is the index of columnThe
expression indicates that belongs to excluding the range
PROPOSED APPROACH
By summing up all the values of each column vector in the power
consumption scheduling matrix A total power consumption scheduling
vector would be determined as
stands for the column in the power consumption scheduling
matrix
29
PROPOSED APPROACH
30Delay Time Rate (DTR) Minimization:Residents usually hope that
home appliances can finish their work as soon as possibleTherefore,
minimizing the DTR of home appliances is considered
Where denotes the DTR of appliance The smallest and largest
values of are set to 0 and 1The later the appliance operates, the
larger the becomes
PROPOSED APPROACH
31A delay parameter is introduced and the relevant formula can
be expressed by
Since the delay parameter geometrically increases as continues
to increase So, for residents, the value of this formula is
expected to be as smaller as possibleThe concept is explained in
the next slide
Explaining the concept32
PROPOSED APPROACHThe power consumption scheduling problem is
presented as an optimization problem, is given below:
where
and are the weights representing the importance of the
individual objectives shown in (12) and (13) Where and In (12),
function denotes the EP at the time slot
33
PROPOSED APPROACH
After normalization, the final optimization formula is
constructed as follows:For each appliance since the maximum value
of isThe value of is equal to Where indicates the number of
AOAs
34
PROPOSED APPROACH
F. Genetic Algorithm (GA) Module:
The GA is adopted to optimize the OSTs of all AOAs
The GA randomly creates a solution population consisting of a
certain number of individuals
Each individual contains a solution set of all kinds of
variables represented as a chromosome
After calculating fitness values, selecting individuals,
crossover, and mutation
The new solutions is obtained that include both the old and the
new individuals35PROPOSED APPROACH
After analyzing that whether the expected generation number has
been satisfied or not, the new generation or the best fitness
solution is obtainedThe GA optimization and the specific
calculation process scheme is shown in figure
36
PROPOSED APPROACH
Since the OST is the only variable in the proposed scheme and
also the constraint parameters are set in the beginning
So the total fitness function is shown in Equation (14)
A roulette selection method in which the individual with a
better fitness value has a higher probability to be selected for
further processing is used referring to the selection process
37Simulation SetupNine kinds of AOAs in the home are selected
for simulation purposeSome AOAs may be used more than once by users
in a day, the number of AOAs operations in one day is up to 16Table
1 shows the information of all AOAs 38
Simulation SetupThe values of different parameters assumed
are;The power thresholdThe delay parameterFor GA optimization, the
population size of each generation is, 200The probability of
crossover between two strings is, 90%All strings have a probability
of 2% to mutate, which is denoted by When the generation number
reaches 1000, the evolution process will finish39
Simulation SetupAll known parameters used for the simulation of
the proposed approach are listed in Table 240
Simulation Results41Relationship Between Electricity Cost and
DTR:Within the OTI, the operation times of AOAs are not fixed due
to the RTEP and the operations of other AOAsThe average DTR can be
defined for all AOAs as,
Generally speaking, the relationship between the electricity
cost and is a trade-off
Simulation Results42The figure shows the simulation result of
the relationship between the electricity cost and the average
DTRFrom the figure , at the position it implies that the major
consideration is minimizing the delay timeSo in this case
andWhereas, when the minimum electricity cost is reached, and
Simulation Results43Impact of Inclining Block Rates:The RTEP
data is adopted from the Ameren Illinois Power Company, and the
date ranges from August 1st, 2012 to October 31st, 2012 (92 days)In
this case only minimizing the electricity cost; therefore, and The
average daily electricity cost in cents for three months is reduced
about 26.06%
Simulation Results44The figure shows that PAR reduces from 5.22
to 3.37 (about 35.44%), to conclude that the proposed approach is
effective in reducing both the electricity cost and PAR
Simulation Results45The comparison between IBR combined with RTP
and RTP scheme alone is shown in the figureThe figure shows that if
only RTP is used, the PAR value would be high, whereas the Proposed
RTP combined with IBR is a better way to reduce PAR
Simulation Results46The impact of IBR on peak power usage in
home as shownThe figure shows the RTEP profile in the United States
on August 7th, 2012The EP within the 16th20th time slots on that
day is 1.646 cents/kWh, which is the lowest during the day
Simulation Results47The figure shows that with the power
scheduling using RTP scheme aloneEven if the electricity cost would
reduceA large amount of powerdemand would be shifted to the
16th-20th time slots due to the low EP at that time
Simulation ResultsHowever, with the proposed power scheduling
approach based on the RTP combined with IBR pricing schemeThe power
demand will be dispersive due to the constraint of the power
threshold48
Simulation ResultsC. Impact of MOAs:Seven kinds of MOAs are
involved4-8 MOAs operations are considered in one day that are
detailed in Table 3 given below
These MOAs operate in a relatively random time slot during a
probable OTI
49
Simulation ResultsBecause MOAs are involved, so the electricity
cost is much more greater than that of without MOAsWith the
proposed power scheduling scheme, the average electricity cost
during three months is reduced about 15.51% in cents50
Simulation ResultsDue to the increase of the average power
level, the PAR value is relatively smaller than that of without
MOAsThe PAR is reduced from 4.26 to 3.42, which is about 19.71%
reduction51
Simulation ResultsImpact of Multiple Users:10 residential users
are considered for the simulation purposeThe figure shows that even
if the number of residents increases, the average PAR of 10
residential users aggregated power demand is still reduced from
4.34 to 2.84 (about 34.56%)
52
Simulation ResultsThis figure shows that all the residents can
reduce their monthly electricity cost effectively by adopting the
proposed scheme
53
CONCLUSION54For residents, the beneficial features obtained by
applying the proposed scheme areReduction of the electricity cost
and alsoThe delay time rate (DTR) of home appliances operations
simultaneouslyIn addition, the benefit rewarded to utility
companies is the reduction of the PAR which would increase the
stability of the entire electricity systemSurely, the proposed
approach can be a reliable solution for future EMS in HAN of smart
gridGenetic Algorithm (GA)Genetic algorithms are a particular class
of evolutionary algorithms that use techniques inspired by
evolutionary biology such as inheritance, mutation, selection, and
crossover (also called recombination)The flow chart represents
different steps of GARepresent a solution to the problem as agenome
(orchromosome) The genetic algorithm then creates a population of
solutions and applies genetic operators such as mutation and
crossover to evolve the solutions in order to find the best one
55
1.Billing/Pricing MechanismExisting billing techniques
Inclining Block Rates (IBR)Time Of Use Pricing (TOU)Critical
Peak Pricing (CPP)Real Time Pricing (RTP) 56a.IBR Example: IESCO
General Supply Tariff- Residential57
b. TOU Example: IESO Ontario (Canadian Province)58
Smart meters track the energy use in your home on an hourly
basis and send this information automatically to your local
distribution company (LDC). By automating the meter-reading
function, smart meters deliver a number of benefits:They support
the implementation of time-of-use prices. By time-stamping your
consumption data, local distribution companies will be able to
determine how much electricity was used during off-peak times and
how much was consumed during on-peak periods. This capability
allows homeowners to find electricity savings by shifting their
electricity use.They help LDCs to identify power theft and respond
to meter failures and outages more quickly.They provide greater
operational efficiencies in local distribution system
management.statutory/staCHtr/AdjectiveRequired, permitted, or
enacted by statute: "the courts did award statutory damages to each
of the plaintiffs".(of a criminal offense) Carrying a penalty
prescribed by statute: "statutory theft".Synonymslegal
58
c.CPP (Critical Peak Pricing)
Very high critical peak prices are assessed for certain hours on
event days (often limited to 10-15 per year) Prices can be 3-10
times as much during these few hours Typically combined with a TOU
rate, but not always Typical goal of CPP is to more dramatically
reduce load during the relatively few, very expensive hours
Typically, a CPP is added to a TOU rate
59
d.RTP (Real Time Pricing) Real-time pricing (RTP) is generally
an hourly rate which is applied to usage on an hourly basis60
The1 centeuro coin(0.01) has a value of one-hundredth of
aeuroand is composed ofcopper-coveredsteel.60
