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An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

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Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.
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energies
Article
An Optimized Home Energy Management System
with Integrated Renewable Energy and
Storage Resources
Adnan Ahmad 1, Asif Khan 1, Nadeem Javaid 1,*, Hafiz Majid Hussain 2, Wadood Abdul 3,
Ahmad Almogren 3, Atif Alamri 3and Iftikhar Azim Niaz 1
1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan;
adnan.a.yousafzai@gmail.com (A.A.); akbarech@gmail.com (A.K.); ianiaz@comsats.edu.pk (I.A.N.)
2Center for Advanced Studies in Engineering (CASE), Islamabad 44000, Pakistan;
majid_hussain47@yahoo.com
3Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences,
King Saud University, Riyadh 11633, Saudi Arabia; aabdulwaheed@ksu.edu.sa (W.A.);
ahalmogren@ksu.edu.sa (A.A.); atif@ksu.edu.sa (A.A)
*Correspondence: nadeemjavaidqau@gmail.com; Tel.: +92-300-579-2728
Academic Editor: K. T. Chau
Received: 14 February 2017; Accepted: 11 April 2017; Published: 17 April 2017
Abstract:
Traditional power grid and its demand-side management (DSM) techniques are centralized
and mainly focus on industrial consumers. The ignorance of residential and commercial sectors
in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept
of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over
the traditional grid. In this context, this paper proposes an optimized home energy management
system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy
storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed
OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response
to the dynamic pricing of electricity market. First, the constrained optimization problem is
mathematically formulated by using multiple knapsack problems, and then solved by using the
heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven
optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms.
The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB
simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and
peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm
based home energy management system outperforms the other heuristic algorithms, and further
reduces the bill by 25.12% and PAR by 24.88%.
Keywords:
smart grid; demand side management; home energy management system; renewable
energy source; energy storage system; real time pricing; heuristic algorithms
1. Introduction
In recent decades, energy demand around the globe has shown the increasing trend. In past,
most of the power generation was being done from fossil fuels. However, to fulfil the increasing
electricity demand with minimal emissions of green house gases scientists have worked on
the new means of electricity generation: renewable and sustainable energy resources (RSERs).
But, the penetration of renewable energy sources (RESs) significantly increased power system
complexity and dynamics [
1
], and the existing power system is not capable of maintaining its stability
if the integration of RESs and distributed generation (DG) is done at a large scale. In this context,
Energies 2017,10, 549; doi:10.3390/en10040549 www.mdpi.com/journal/energies
Energies 2017,10, 549 2 of 35
one of the present solutions is the transformation of the existing power grid into the smart grid (SG)
with cutting edge information and communication technologies (ICTs) [
2
]. These advanced ICTs not
only enable SG to incorporate the DG and RESs but also enhance the stability and reliability of power
system. European technology platform (European Commission, 2006) defines the SG as, “a smart
grid is an electricity network that can intelligently integrate the actions of all users connected to
it-generators, consumers and those that do both in order to efficiently deliver sustainable, economic
and secure electricity supplies”. SG has different kinds of operational and energy measures like smart
meters (SMs), smart appliances, renewable energy and electric energy storage resources. The vital
aspect of SG is the control of power production, transmission and distribution through advanced
ICTs. These ICTs enable SG to send control commands within the time limits defined by numerous
international standards e.g., IEEE standards 1547 (i.e., the standards defined for the control and
management of distributed energy resources) [
3
]. Moreover, SG makes possible the access of the power
system operator and end-users at the same time intelligently and efficiently.
The key factors that make SG superior over traditional grids are: two-way communication,
advanced metering infrastructure (AMI) and information management units (IMUs). They introduce
intelligence, automation and realtime control to power system. The two-way communication in SG not
only keeps the end-users well informed about the varying electricity prices, maintenance schedules of
the distribution network and events/failures that come either due to equipment failures or natural
disasters but also enables the operator to monitor and analyze the realtime data of energy consumption
and makes realtime decision about the operation activities and standby generators. A comprehensive
comparison of traditional grid and SG features is shown in Table 1.
The SG makes the integration of RESs and DGs practicable and involves the residential and
commercial users into demand-side management (DSM) and demand response (DR) activities [
4
].
DSM is the modification of consumer demand for energy through various methods such as financial
incentives and behavioral change through education. Usually, the goal of DSM is to encourage the
consumers to use less energy during peak hours, or to move the time of energy use to off-peak hours [
5
].
DSM techniques are used to optimize energy consumption pattern, to efficiently utilize the limited
energy resources and to enhance the overall efficiency of the power system. The term DR is used for
the programs designed to encourage end-users to make short-term reductions in energy demand in
response to a price signal from the electricity hourly market, or a trigger initiated by the electricity
grid operator. It is a change in the power consumption of an electric utility consumer to better match
the demand for power with the supply. DR seeks to adjust the demand for power instead of adjusting
the supply. However, it is totally impractical to ask the consumers to schedule their energy usage
by compromising their comfort level. Therefore, an automatic home energy management system
(HEMS) is required, however, a little awareness of consumers is required to know the benefits of
various scheduling schemes. In this context, we present an OHEMS which not only integrates RES and
ESS into residential sector but also incorporate residential consumers into DSM activities.
The common objectives of different DSM and DR strategies in SG are the reduction of the electricity
cost and minimization of energy consumption in peak hours. To achieve these objectives numerous
algorithms for an efficient HEMS have been proposed, such as integer linear programming (ILP) [
6
],
mixed integer linear programming (MILP) [
7
], multi-parametric programming [
8
], etc. However,
these techniques cannot tackle a large range of various household appliances having unpredictable,
non-linear and complicated energy usage patterns.
Energies 2017,10, 549 3 of 35
Table 1. A brief comparison of traditional grid and smart grid (SG).
Infrastructure Traditional Grid SG
Power system
Centralized generation.
Uni-directional flow of energy
from utility to the consumers.
Decentralized generation. Two-way
flow of energy between the utility
and the prosumers.
Power Losses
High power losses due to
centralized structure and inadequate
storage facilities.
Significantly reduces the power
losses due to DG at distribution level
(i.e., the DG eliminates the losses of
transmission network).
Information system Aged metering and
monitoring system
Advanced metering and monitoring
system: AMI and supervisory
control and data acquisition system
(SCADA)
Communication system Wired technology Both wired and wireless
technologies
ESSs Main storage facility is pump-hydro
power plants.
Facilitate the distributed ESSs
integration
RSERs Mainly includes dispatchable RESs
(Hydro-power plants)
Provides decentralized control for
RSERs (solar, wind, tidal,
geo-thermal and biomass
energies etc.)
Self-healing
Reacts to stop further damage.
Emphasis is on protection of assets
following system faults.
Automatically senses and reacts to
actual and emerging contingencies.
Focus is on prevention.
Optimization of assets
Negligible incorporation of limited
operational data with assets
management processes and
technologies. Time based
maintenance.
Greatly expanded sensing and
measurement of grid conditions,
grid technologies deeply integrated
with assets management processes to
effectively manage assets and costs.
Condition based maintenance.
Consumer engagement
No proper involvement of the
consumers in DSM and DR activities
(i.e., no mechanism to send the
varying electricity prices to the
consumers in realtime and forced
load shedding is carried out to
maintain the balance between
demand and supply)
Provides dynamic pricing,
net metering and other incentive
based schemes.
Power Quality Focus only on the reduction of
failures and interruptions.
Ensure the quality of electricity for
the smooth operations of sensitive
electronics devices/equipments.
To overcome the deficiencies of the previously proposed HEMSs, this paper presents an optimized
home energy management system (OHEMS). The proposed scheme not only facilitates the integration
of RES and ESS into the residential sector but also reduces the prosumer’s electricity bill as well as the
PAR. In addition, the performance of the heuristic algorithms: genetic algorithm (GA), binary particle
swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO)
and hybrid GA-PSO (HGPO) are also evaluated in terms of energy consumption pattern, and electricity
bill as well as PAR minimization. First, an energy management system (EMS) is designed using
exogenous grid signals; day-ahead pricing (DAP) signal, ambient temperature, and solar irradiance.
Then, the heuristic algorithms are applied to get an optimum solution for the formulated objective
function. The adequacy of proposed OHEMS is validated via simulations. The simulations are
conducted in two stages, in the first case, the benefits of RES and ESS integration are highlighted,
Energies 2017,10, 549 4 of 35
while in the second case the performance of GA, BPSO, WDO, BFO and HGPO algorithms based
HEMSs is compared in terms of electricity bill and PAR reduction, and uniform distribution of energy
consumption patterns.
The rest of the paper is organized as follows. Sections 2and 3briefly describe the related work
and problem statement respectively. Section 4deals with problem formulation. Section 5and Section
6present the proposed system architecture and algorithms respectively. Results and discussions are
presented in Section 7, and the paper is concluded in Section 8.
2. Related Work
Recently, numerous DSM strategies have been proposed. Their common objectives are the
minimization of electricity cost, reduction of PAR, mitigation of carbon emissions, and enhancement
of power system efficiency. The appliances scheduling problem has been solved as an optimization
problem by numerous classical and heuristics algorithms. In this context, some of the recent studies
are given below, and a brief review of some state of the art trends is presented in Table 2.
In [
9
], the authors present a review of current trends in HEMS and DR in the residential
sector. The importance of HEMS for relocating and curtailment of the load is also discussed.
They give insights on existing optimization techniques: mathematical optimization, model predictive
control and heuristics algorithms. The impact of forecasting uncertainty, devices heterogeneity,
computational limitation and timing consideration in the design of optimization algorithms are
also discussed. However, the user comfort (UC) and appliances waiting time have not been discussed.
The authors in [
10
], present a compact survey of the current trends in HEMS. The challenges in the
implementation of HEMS are discussed and give the insights on current literature regarding DR,
DSM, appliance scheduling, and on single or multiple objective optimizations in HEMS. However,
the integration of RESs and ESSs into residential sector, their impacts on electricity bill and PAR have
not been addressed.
In [
11
], the authors discussed the importance of energy management and planning in smart cities
(SC). The paper presents a review on the planning and optimization of SC energy system. Four areas of
SC energy system: generation, storage, transportation and end user are addressed in detail. Although,
an ESS is integrated to supports residential load in grid events. But, the scheduling of appliances or
relocating of load and ESS in response to dynamic pricing of electricity market has not been discussed.
The authors in [
12
], give insights regarding the ways and manners that facilitate the integration of
RESs and DG in the SG and the concept of SCs have been comprehensively addressed. The obstacles
to the integration of DG into the existing distribution network have been highlighted. The effect
of DG on voltage stability and control at low and medium voltages are also discussed. Moreover,
the impact of DG on power quality, power system stability, and related events of voltage sag and
swell due to the failure of distributed sources are addressed in a comprehensive way. Although,
the authors discussed the impact of DGs and RESs integration on power quality and power system
stability. However, the integration of RESs and ESSs into residential sector and their role in DSM
programs have been ignored.
In [
13
], the authors present a detailed review of evaluating trends in smart homes (SHs) and SG.
They investigate the effectiveness of various communication technologies: Zig-Bee, Z-Wave, Wi-Fi and
wired protocols. The authors point out the merits and demerits of existing technologies, and products
available in the market. Moreover, the barriers, challenges, benefits, and future trends regarding the
communication technologies and their role in the SG and SHs are also discussed. The authors in [14],
demonstrate a residential energy monitoring system (REMS). Three SHs powered by an in-house
RES (i.e., PV system) are considered for demonstration. The SH is equipped with data logger system
to measure and record the electricity production and demand patterns. The internal and external
temperature, as well as the humidity is recorded to accurately schedule the operation of heating
ventilation and air conditioning (HVAC) system. However, this paper did not address the integration
of ESS to utilize the RE more efficiently.
Energies 2017,10, 549 5 of 35
A. Mehmood et al. [
15
], present an in-depth review of load furcating (LF), current LF techniques
in existing power system, future trends, and its importance for the implementation of future SG is
discussed. They elaborate the two major types of LF: mathematical modelling and artificial intelligence
based computational models and their subcategories as well. The authors also present a comparative
study of various dynamic pricing schemes: realtime pricing (RTP), Time of Use pricing (ToUP) and
critical peak pricing(CPP).
Lee et al. [
16
], present linear programming (LP) based REMS for reduction of electricity
cost and PAR by charging the ESS from utility in off-peak hours and is discharged in peak hors.
The integration of RESs into residential sector is not considered and the charging of ESS from utility
is not an economical solution. In [
17
], the authors propose an ILP based HEMS with integrated
RES for appliances scheduling to shift the shiftable appliances from peak hours to off-peak hours.
However, the authors did not consider the UC and ESS integration. The authors in [
18
], discuss the
scheduling of home appliances for designed objective of electricity cost minimization or optimization
of electricity consumption pattern. MILP is used to schedule the household appliances and the RES.
An in-house RES not only reduces the electricity bills but the surplus energy is also sold to the utility to
generate revenue. Although, RES with HEMS is fruitful for both the utility and the prosumer, however,
the installation of RES may not be feasible for a single/small domestic consumer.
In [
19
], the authors proposed a hybrid technique of lighting search algorithm (LSA) and artificial
neural network (ANN) to generate an optimal operation patterns of the appliances. Four appliances
are modeled and developed in MATLAB/SIMULINK, with pre-defined preferences of the consumer.
To optimize the appliances ON/OFF time, and to complete the assigned tasks with minimum cost,
a hybridization of LSA and ANN is used. The proposed technique significantly reduces the electricity
cost and outperforms the hybrid of particle swarm optimization (PSO) based ANN. However, RES and
ESS has not been taken into account to more efficiently minimize the electricity bill. In addition,
the reduction of PAR and its impact on power system has not been discussed.
A comparative study of WDO and PSO is done in [
20
]. Household appliances are considered for
scheduling with the objectives of cost minimization and UC maximization. Moreover, Knapsack-WDO
(K-WDO) is also studied for the same objective functions. Results illustrate that K-WDO perform
better than WDO and PSO. However, in the proposed model the integration of RES is not considered
and an ESS is used to store energy from grid in off-peak hours which is not a viable solution.
Z. Weng et al. [
21
], demonstrate a fully automated energy management system (EMS) using
reinforcement learning (RL) techniques. The energy management and appliance scheduling problem
is solved by observe, learn and adapt (OLA) algorithm which adds more intelligence to EMS.
The proposed mechanism significantly reduce the cost and PAR, but UC is compromised.
In [
22
], the authors discuss the problem of peak demand during certain hours. The concept
of clustering and smart charging has been introduced to maximize the benefits in term of cost
reduction and UC. A GA-based EMS is designed which efficiently utilizes energy within the clusters by
scheduling the ESS and appliances. Results show that by appropriate scheduling of ESS and appliances
in RTP environment customer can get maximum saving over electricity bill. The authors in [
23
]
present the comparison of GA and PSO in terms of computational cost and computational efforts.
Results show that PSO needs less computational effort and computational cost to reach an optimal
solution as compared to GA. In [
24
], the authors demonstrate an efficient HEMS architecture for
implementation of DSM in the residential sector. They combine RTP scheme with ToUP because the
use of only RTP signal may shift the peak demand to off-peak hours. To eliminate the creation of
new peaks, an objective function is properly formulated and solved by using GA. Results illustrate
that the hybridization of RTP and ToUP schemes is effective for reduction of bill and PAR. Moreover,
an acceptable trade-off between the UC and cost reduction is also achieved.
Energies 2017,10, 549 6 of 35
Table 2. Recent Trends: State of the art.
Technique Domain Desired Objective Findings Remarks
LP [16] REMS Reduction of electricity
bill and PAR
Objectives are achieved
via charging ESS from
grid in off-peak hours
and then discharging it
in peak hours
RES has not
been utilized
ILP [17]
Appliances
scheduling and
integration of RES
Reduction of electricity
bill and peak load
A significant reduction
bill and peak loads is
achieved via RES
integration and
optimizing energy
consumption pattern
UC and ESS is
not considered
MILP [18]
HEMS,
and integration as
well as grid
interconnection of
RES
Reduction of cost and
PAR via RES utilization
Cost reduction is
achieved
Infeasible for
small scale
residential
consumers
LSA-ANN, and
PSO-ANN [19]
HEMS,
and appliances
scheduling
Cost reduction and
comparison of LSA-ANN
and PSO-ANN
LNA-ANN outperformed
the PSO-ANN, and
significantly reduce the
electricity bill
UC and PAR
reduction is not
considered
PSO, WDO, and
K-WDO [20]
Appliances
scheduling
Minimization of
electricity cost and
maximization of UC
A favorable trade-off
between the cost and UC
is achieved, and K-WDO
performs better than
other algorithms
RES is not
exploited
RL [21]Fully-automated
EMS
Optimization of
appliances operating
time, and reduction
of PAR
Peaks formation is
avoided, and cost
reduction is achieved
UC and RES is
not considered
GA [22] REMS Electricity cost and
PAR reduction
New peaks formation is
avoided by dividing the
appliances into clusters
UC is
compromised,
and RES is not
considered
GA, and PSO [23]Appliances
scheduling
Optimization of
appliances operating time
to pay minimum
electricity bill
GA-based scheduling
achieves the desired
objectives with less
computational cost and
efforts
UC is
compromised
at achieving the
minimum
electricity bill
GA [24] HEMS Reduction of electricity
bill and PAR
Hybrid of RTP and ToUP
is ued to avoid the peaks
formation
UC is not
considered
Greedy iterative
algorithm [25]
EMS,
and optimization
of grid operation
Optimization of
consumers energy
consumption pattern for
grid station stability
RTP signal is used an
invisible hand to
optimized the energy
consumption pattern
UC is
compromised
IPSO [26]Grid station
stability
Reduction of load in
peak hours
The desired objective is
achieved by rejecting
extra load requests in
peak hours
Only passive
appliances are
considered,
and UC is
compromised.
P. Chavali et al. [
25
], present a distributed mechanism for REMS and grid optimization using
the greedy iterative algorithm. In the proposed technique, the electricity price is used as an invisible
hand to optimize the appliances scheduling and energy consumption. The proposed scheme did not
consider the user comfort in the problem formulation. In [
26
], the authors proposed an improved-PSO
(IPSO) for the solution of cost minimization problem. Results illustrate that the proposed IPSO brings
the user load curve near to the objective curve, where the objective curve and electricity price have
Energies 2017,10, 549 7 of 35
an inverse relationship. One of the objective functions is power system stability, and the proposed
scheme compromise on the UC by rejecting the load in peak hours.
3. Problem Statement
The rapid increase in household electronic appliances significantly increase the electricity demand
of residential sector. Almost 40% of the generated electricity is consumed by the residential sector [
27
].
Currently, power generation is heavily dependent on fossil fuels and the hour of need is to fulfil the
inevitably increasing electricity demand with minimal emissions of greenhouse gases. Therefore,
scientists have worked to figure out new means of electricity generation i.e., RSERs. In this context,
the integration of RSERs and ESSs become lucrative for the researchers. Moreover, the conventional
power grid is already vulnerable to instability due to heavy loaded conditions and will be unable
to maintain its stability if the integration of RESs is done at a large scale. So, the research is
going on to implement the SG in a distributed manner, and HEMS is an integral part of SG to
optimize the energy consumption of household appliances. Keeping this objective in mind, we seek
to develop an OHEMS. Our objective is twofold: (i) integration of RES and ESS into residential
sector; (ii) energy management through appliances and resource scheduling. In order to achieve the
above-mentioned objectives, and to design an efficient HEMS, various algorithms such as LP [
28
],
ILP [
29
], MILP [
30
], dynamic programming (DP) [
31
] and convex programming (CP) [
32
] have been
proposed. However, these techniques have very slow convergence rate, and in some cases, they are
unable to handle a large number of appliances. So, the heuristic algorithms such as GA [
33
], BPSO [
34
],
WDO [
35
] and BFO [
36
] are introduced to overcome these problems. The heuristic optimizations are
used where it is very difficult to find the exact optimal/feasible points. For example, in case of LP,
it is understood that the optimal solution must lies within the solutions points in that pool. However,
in heuristic optimization, there might be infinite or more solution points in that solution space and
optimal solutions should be any one among those. Moreover, in the aforementioned techniques (LP, ILP,
MILP, DP and CP) based HEMSs, the integration of RESs, maximization of UC and adaptability with
dynamic pricing are ignored. Therefore, in this paper, not only the heuristic algorithms (GA, BPSO,
WDO, BFO, and HGPO) are used to design an OHEMS but their performance is also evaluated in
terms of energy consumption pattern, electricity bill and PAR reduction.
4. Problem Formulation
In this section, the mathematical models and constraints of photovoltaic (PV) system, ESS and
appliances are presented. Based on these descriptions, the optimization problem is formulated.
4.1. Energy Generation Model of PV System
The house of our smart prosumer is equipped with a rooftop PV system (i.e., RES). Although,
the RSERs mainly include solar, wind, tidal, geothermal, biomass, and biogass energies, however,
among them the most abundant and almost free of cost (i.e., having small operation and maintenance
costs) is solar energy and is available everywhere and to everyone. According to [
37
], the Earth receives
174,000 terawatts (TW) of incoming solar radiation at the upper atmosphere. Approximately 30% is
reflected back to space while the rest is absorbed by clouds, oceans and land masses. Most of the
world’s population live in areas with insolation levels of 150–300 watts/m
2
or 3.5–7.0 kWh/m
2
per day.
In this regard, the proposed OHEMS tries to maximize the benefits from PV system and to minimize
the electricity bill, carbon emissions and PAR. The output power
EPV (t)
of PV system in kW at time
t
is calculated by Equation (1) [38],
EPV (t) = ηPV ·APV ·Ir(t)·10.005(Ta(t)25)t(1)
where,
ηPV
is energy conversion efficiency of the PV system (%),
APV
is the area of the generator (m
2
),
Ir(t)
is the solar irradiance (kW/m
2
) at time
t
, 0.005 is temperature correction factor [
39
],
Ta(t)
is
Energies 2017,10, 549 8 of 35
the outdoor temperature (
C) at time
t
and 25 is standard room temperature (
C). The distribution of
hourly sun irradiation usually complies with a bimodal distribution that can be considered as a linear
blend of two unimodal distribution functions. The unimodal distribution functions could be modeled
by Weibull probability density function as shown in Equation (2) [38],
f(Ir(t)) = ζα1
β1Ir(t)
β1(α11)e(Ir
β1)α1+ (1ζ)α2
β2Ir(t)
β2(α21)e(Ir
β2)α2, 0 <Ir(t)<(2)
where,
ζ
is a weighted factor,
α1
and
α2
are shape factors, together with
β1
and
β2
which are
scale factors.
4.2. Energy Storage Model
A small capacity ESS is used to store a portion of electricity generated by PV system, it is mainly
for exploiting the PV energy more efficiently. The ESS stores the energy only when its storage level is
lower then upper charge level. Electricity stored in the ESS at time
t
is presented by Equation (3) [
38
],
and the electricity charged, the electricity discharged and the self-discharging rate is taken into account.
The charging and discharging of ESS would lose some electrical energy, so turn-around efficiency of
ESS is considered.
ES(t) = ES(t1) + κ·ηESS ·EPCh (t)κ·EPDch (t)
ηESS t(3)
where
ES
is stored energy (kWh) at time
t
,
κ
is time slot duration (hour),
ηESS
is ESS efficiency,
EPCh
is
the electric power (kW) supplied to ESS from RES at time and
EPDch
is is the electric power (kW)
supplied to the load from ESS at time t.
In order to maintain the storage and avoid overcharging/deep discharging, charge and discharge
rate of electricity, and energy stored in ESS should not exceed the limits defined by the manufacturer.
EPCh(t)EPCh
UB (4)
EP(t)D ch EPDch
LB (5)
ES(t)ESCh
UB (6)
where
EPCh
UB
is upper charge limit of ESS charge rate,
EPDch
LB
is lower limit of ESS discharge rate and
ESCh
UB is upper limit of ESS stored energy.
4.3. Energy Consumption Model
Let us suppose that the smart prosumer has two sets of appliances i.e.,
M
and
N
. The set
of shiftable (i.e., can be shifted to low price slots) appliances
M={a1
,
a2
,
a3
,
. . .
,
am}
and
the set of non-shiftable (i.e., will start operation on the time defined by the user) appliances
N={b1
,
b2
,
b3
,
. . .
,
bn}
over a scheduling horizon of
t={
1, 2, 3, 4, 5,
. . .
, 24
}
. The daily energy
consumptions of shiftable and non-shiftable appliances are given by Equations (7) and (8) respectively,
Ea=
24
t=1 m
M=1
Ea
t,meM!={Ea
t1,meM+Ea
t2,meM+. . . +Ea
t24,meM}(7)
Eb=
24
t=1 n
N=1
Eb
t,neN!={Eb
t1,neN+Eb
t2,neN+. . . +Eb
t24,neN}(8)
Energies 2017,10, 549 9 of 35
where
Ea
t1,meM
,
Ea
t2,meM
,
. . .
,
Ea
t24,meM
denote the energy consumption of shiftable appliances and
Eb
t1,neN
,
Eb
t2,neN
,
. . .
,
Eb
t24,neN
represent the energy consumption of non-shiftable appliances at time
t
.
The total daily energy consumption Etotal of the prosumer load is calculated as,
Etotal =
24
t=1 m
M=1
Ea
t,meM+
n
N=1
Eb
t,neN!(9)
4.4. PAR
PAR is a ratio of peak load consumed in a time slot
t
and the average of total load consumed
over the scheduling horizon, i.e., from
t=
1 to
t=
24. PAR tells us about the energy consumption
behaviour of the consumer and the operation of utility peak plants have a direct relationship with the
consumers PARs. So, it is beneficial for the utility and consumer to reduce PAR so that power supply
and demand balance can be maintained. For single user it is calculated as follow,
PAR =maxEtotal (t)
1
TT
t=1Etotal(t)(10)
For multiple users N it can be calculated as,
PAR =maxEtotal (t,n)
1
TN
n=1T
t=1Etotal(t,n)(11)
4.5. Energy Pricing Model
Numerous electricity tariffs are available to define the energy pricing over a day. Such as ToUP,
DAP, peak pricing (PP), CPP and RTP [
40
]. In most of the appliances scheduling schemes, the pricing
of electricity is assumed to be DAP or ToUP, because the RTP increase the communication complexity.
Moreover, in ToUP the pricing horizon is divided into different blocks and a fixed price is defined for
each block. In this model, we use DAP in which the price of electricity changes on the hourly basis
and remains constant in an hour. The daily electricity bill of shiftable appliances
Ea
P
and non-shiftable
appliances Eb
Pis calculated by Equations (12) and (13) respectively,
Ea
P=
24
t=1 m
M=1Ea
meM(t)×Xa
meM(t)×PDA P(t)!(12)
Eb
P=
24
t=1 n
N=1Eb
neN(t)×Xb
neN(t)×PDA P(t)!(13)
Etotal
P=Ea
P+Eb
P=
24
t=1 m
M=1Ea
meM(t)×Xa
meM(t)×PDA P(t)(14)
+
n
N=1Eb
neN(t)×Xb
neN(t)×PDA P(t)!
Xa
meM(t) = (1 if shiftable appliance is ON
0 if shiftable appliance is OFF (15)
Xb
neN(t) = (1 if non-shiftable appliance is ON
0 if non-shiftable appliance is OFF (16)
Energies 2017,10, 549 10 of 35
where
Xa
meM(t)
represent the
ON/OF F
state of a shiftable appliance
M
,
Xb
neN(t)
represent the
ON/OF F
state of an non-shiftable appliance
N
and
PDA P(t)
is DAP in the particular time slot
t
.
The electricity bill
EP(t)
an any time slot
t
after taking RES and ESS into consideration is calculated as,
EP(t) = Ea(t) + Eb(t)EPV (t)ES(τ)×PDAP(t)(17)
where,
τ
is a specific time slot between
t20
and
t24
that having highest bill. As shown in Figure 10 that
the RE is not available in those slots, so, the ESS is discharged to reduce the prosumer’s electricity bill.
4.6. Appliance Scheduling Problem
The main objectives of this work are: to minimize the electricity bill and PAR of the prosumer
by optimization the energy consumption pattern. The reduction in consumer PAR is beneficial
for utility and all connected consumers, because it reduces the operations of utility peak plants:
standby generators. Let us suppose that a utility grid provides supply to
N
number of users and all
of them have HEMSs to optimize their energy consumption patterns and PARs which will definitely
results in an optimized grid operation. In this paper, the appliances scheduling problem of a single
prosumer is formulated as an optimization problem by using multiple knapsack problems (MKP).
MKP is a combinational optimization problem: to select the appliances for a particular hour from
a given set of appliances (
M
and
N
as defined in Section 4.3). Each appliance has a value
X
which
represents the ON/OFF status of it, and a weight which shows the power rating of the appliance.
The number of the appliances to be selected (remain ON) for a particular hour depends on the objective
function and the constraints i.e., to pay as minimum electricity bill as possible and the total weight
(energy consumption) of the appliances must satisfy the constraints given by Equations (19) and (20).
The optimization problem is defined as,
Objective function:
min Ea(t) + Eb(t)EPV (t)ES(τ)×PD AP (t)!(18)
Subject to:
Etotal (t)Egrid (t) + EPV (t) + ES(τ),1t24 (19)
Etotal (t)Emin
unsch (20)
τ0τsch τmax (21)
where,
Egrid (t)
is the sanction load that an end-user can import from the utility grid at time
t
,
Emin
unsch
is
the minimum energy consumed in unscheduled scenario,
τ0
represents the lower limit of scheduling
horizon, τsch indicates the scheduling time and τmax shows the upper limit of scheduling horizon.
4.7. Feasible Region
An area enclosed by a specific set of points (constraints of optimization problem) is called feasible
region of the solution. Here, the objective function of our optimization problem is the minimization
of the electricity bill as given by Equation (18). It illustrates that the electricity bill depends on two
factors: amount of scheduled load and electricity price in a particular time slot. As the price signal is
defined by the utility and we have no control over it, therefore, to minimize the electricity bill we can
only modify the shape of energy consumption pattern. The electricity price (DAP signal) we used has
range from 8.1 to 27.35 cents/kWh, and the four possible cases of the electricity bill using this DAP
signal and unscheduled load are as given in Table 3.
Based upon the values given in Table 3and maximum electricity bill in unscheduled scenario,
the constraints of the feasible region are,
Energies 2017,10, 549 11 of 35
C1:EMin(unschduled)Etotal(t)EMax(unschduled)
C2:EP(scheduled)(t)EP(Max,unscheduled)
C3:24
t=1Etotal
P(scheduled)24
t=1Etotal
P(unscheduled)
where the constraint
C1
shows that in any time slot
t
the scheduled load must be greater than
minimum unscheduled load and less than maximum unscheduled load,
C2
shows that in any time
slot
t
the electricity bill must be less than maximum electricity bill in unscheduled scenario and
C3
represents that in scheduled scenario the total electricity bill must be less than the total electricity bill
in unscheduled scenario.
Table 3. Possible cases.
Case Discerption
1Loadmin ,Pricemin
2Loadmin ,Pricema x
3Loadmax,Pricemin
4Loadmax,Pricem ax
5. Proposed System Architecture
In SG, DSM and DR ensure more stable and reliable grid operation. The main objective behind
the design and implementation of an HEMS is the reduction of electricity bill and PAR. From the utility
point of view, its two main benefits are the management of energy resources and reduction of PAR.
While from the consumers point of view, it minimizes the electricity bill. These objectives are achieved
only with the modification in energy consumption pattern of shiftable appliances in response to DAP
signal of electricity market defined and broadcasted by the utility.
In the proposed OHEMS, it is assumed that in future SG each prosumer will have a HEMS.
To meet the energy demand with minimum electricity bill smart prosumer utilizes an in-house RES
and an ESS along with grid energy. HEMS of the grid friendly prosumer schedules the appliances and
the ESS to reduce the electricity bill and PAR in dynamic pricing environment of the electricity market.
In this context, the prosumer’s appliances are divided into two categories: shiftable (i.e., their start
time can be shifted to low price slots and would not be interrupted once they start operation) and
non-shiftable (i.e., their start time can not be deferred and would not be interrupted during operation)
appliances, as shown in Table 4.
Table 4. Load categorization.
Shiftable Loads Non-Shiftable Loads
Washing machine Personal computers
Air conditioner Security cameras
Clothes dryer Microwave oven
Water heater Refrigerator
Dish washer Television
ESS Lights
The proposed system architecture shown in Figure 1, mainly includes AMI, SM,
smart scheduler (SS), master controller (MC), PV system, direct current (DC)/alternating current
(AC) inverter, ESS, and appliances. In Figure 1the energy flow is represented by solid lines while
information flow is shown by dotted lines.
Energies 2017,10, 549 12 of 35
Figure 1. Proposed smart home (SH).
The integration of advanced ICTs into the conventional power grid for metering and
communication is known as AMI. It works as a backbone of the SG and enables the two-way
communication between the utility and the consumers. Furthermore, AMI is responsible for the
collection and transmission of energy consumption data from SM to the utility as well as for the
relaying of exogenous grid signals to the SM. The exogenous grid signals may be the price signal,
ambient temperature, solar irradiance or DR signal. The SM works as a communication gateway
between the SH and the utility. SM is typically installed between the AMI and EMC. The main
functions of SM are reading, processing and sending of energy consumption data to the utility via AMI
as well as the receiving and processing of DR and pricing signals from the utility. RSERs are considered
as a real alternative to fossil fuel power generation. RSERs mainly includes PV system, wind turbines,
small scale hydro-turbines and fuel cells. In the proposed scheme only PV system is utilized due
to its easy installation and small capital investment. A DC/AC inverter is used to convert the PV
system generated DC electricity into AC. An ESS works as a source and sink is regarded as a promising
solution for the integration of RSERs into distribution networks/residential sector. Therefore, in the
proposed OHEMS a small capacity ESS is used to efficiently exploit the PV system, and to reduce the
electricity bill in peak hours. An SS installed between SM and MC is programmed using heuristic
algorithms. The designed SS not only generates the optimal energy usage pattern for all appliances but
also sends it to the MC for execution. MC is the core of proposed OHEMS and controls the operation
of appliances and ESS according to the generated schedule by SS.
6. Scheduling Algorithms
The appliances scheduling problem formulated in Section 4is solved by using GA, BPSO, WDO,
BFO, and HGPO algorithms. Although, appliances scheduling problem has been solved by different
classical optimization techniques: LP, ILP, MILP and DP. However, these techniques can not handle
large number of appliances and face a lot of difficulties in convergence. Moreover, most of the classical
algorithms do not have the global perspective and often converge at the local optimum solution.
Energies 2017,10, 549 13 of 35
In contrast, the evolutionary algorithms: GA, BPSO, WDO, BFO, and HGPO algorithms give alternate
methods to solve complex problems, and outperform the classical optimization techniques.
6.1. GA
GA is an iterative optimization algorithm inspired by the natural genetic process of the living
organisms. Rather than working on a single solution, GA deals with different possible solutions in
each iteration [
41
]. GA begins its search with randomly initialized binary coded chromosomes.
The chromosomes pattern of GA represent the
ON/OF F
state of appliances, and the length of
chromosomes shows the number of appliances.
Length of chromosomes = L (22)
where, Lis the number of household appliances.
Once the population (a set of solutions that shows the status of each appliance in a particular
time slot) is created, fitness function of each possible solution is evaluated according to the objective
function of the optimization problem. Here, the fitness of each population is evaluated using
Equation (18). Then, a new population is created by applying the natural genetic operators: crossover
and mutation. The GA parameters with their values on which it gives optimal results are listed
in Table 5.
Table 5. Genetic algorithm (GA) parameters.
Parameters Value
Number of iterations 500
Populationsize 200
Pm0.1
Pc0.9
n11
The working flow of GA is shown in Figure 2. In each iteration, a new population is produced
through crossover and mutation. In the crossover step, two binary strings are crossover to create
a new
off spring. Crossover probability says how often crossover will be performed. If there is
no crossover, offspring is exact copy of parents. If there is a crossover, offspring is made from
parts of parents’ chromosome. If crossover probability is 100%, then all offspring is made by
crossover. If it is 0%, a whole new generation is made from exact copies of chromosomes from old
population [
42
]. Moreover,
a larger
crossover rate avoids premature convergence to the sub-optimal
solutions, so, the best crossover rate selected for optimization problems is 90% as given by,
Pc=0.9 (23)
To create randomness in the results so that the repetition of a population could be avoided we
use mutation process. It changes one or more principles gene in a chromosome from its initial state.
In natural genetic process, the probability of mutation is very low, so, an optimum mutation rate for
optimization problems is,
Pm=1Pc(24)
Once crossover and mutation are done, again a population is generated and fitness is evaluated
and compared with previous population.
Energies 2017,10, 549 14 of 35
Figure 2. Working flow of GA.
6.2. BPSO
PSO is a nature-inspired optimization algorithm for finding an optimal solution within the search
space. The PSO algorithm inherently exists in the continuous domain. However, it can be modified
into the discrete domain, and its variants for the discrete domain is BPSO. The working flow of BPSO
shown in Figure 3, mainly depends on four factors; initial position, initial velocity, particle own best
position and global best position among all the particles. In this algorithm, a population is randomly
initialized and scattered in the search space. The initial positions and velocities of the particles are
represented by
xi
=
x1+x2+· · · +xI
and
Vi
=
V1+V2+· · · +VI
respectively. The particles update
their velocities in each iteration by using Equation (25) [43],
24
t=1
I
i=1
Vt+1
i=
24
t=1
I
i=1
(wVt
j(j) + c1r1(Xlbest,i(j)) xt
i(j)) + c2r2(Xgbest,i(j)xt
i(j)) (25)
where
Vt+1
i
is the velocity of particle (appliance) in upcoming time slot,
w
is inertia factor,
Vt
j
is current
velocity,
r1
and
r2
are random numbers,
c1
and
c2
are local and global pulls respectively,
xt
i
is the
particle current position,
Xlbest
is local best position and
Xgbest
global best position. The velocities of
the particles are mapped between 0 and 1 by using sigmoid function given by Equation (26),
sig(Vt+1
i(j)) = 1
1+ex p(Vt+1
i(j)) (26)
Energies 2017,10, 549 15 of 35
The random values assigned to each particle in the population are compared with the sigmoid
function to generate a binary coded population.
xt+1
i=(1sig(Vt+1
i(j)) <rij ,
0otherwise.(27)
In each iteration, particles record their positions with respect to neighbors. The local best positions
found by the particles are represented as Xlbest =Xlbest1+Xlbest2+· · · +Xlbest N . The local best values
are then compared with each other to find the global best position. The
gth
particle among the particles
is said to be the global best position if it satisfy the objective function, and the global best positions
are represented as
Xgbest
=
Xgbest1+Xgbest2+· · · +XgbestN
. The main reason behind the use of global
best value rather than local best value is its faster convergence to the optimal solution. The global best
value is a binary coded string and represents the optimal
ON/OF F
status of the appliances. The fitness
function of each particle is evaluated and compared with the corresponding cost of global best value,
and the string with minimum fitness function is selected. The BPSO parameters and their values on
which BPSO gives optimal results are given in Table 6.
Figure 3. Steps involved in BPSO algorithm.
Energies 2017,10, 549 16 of 35
Table 6. Binary particle swarm optimization (BPSO) Parameters.
Parameters Values
Number of iterations 300
Swarmsize 200
vmax 4
vmin 4
wi2
wf0.4
c12
c22
n11
6.3. WDO
WDO is an another nature-inspired optimization algorithm based on the atmospheric motion
of air parcels. In this algorithm, infinitely small air parcels move in N-dimensional search space.
The major difference between the WDO and other heuristic algorithms is the use of four different
forces to control the motion of air parcels in the atmosphere. These forces include pressure gradient
force, friction force, gravitational force, and Coriolis force. The pressure gradient force moves the air
parcels in the forward direction, while the frictional force resists their motion in the forward direction.
Furthermore, the gravitational force is a vertical force in three-dimensional search space that attracts
the air parcels towards the origin, and the deflection of air parcels in the atmosphere is due to the
Coriolis force. Mathematically, all four forces are represented as [44].
Fpg =ρδν (28)
FC=2×µ(29)
FG=ρδν ×g(30)
FF=ραµ (31)
where
Fpg
is pressure gradient force,
is pressure gradient,
ρ
is air density,
δν
is finite volume of
the air,
FC
is Coriolis force,
represents the rotation of the earth,
µ
is velocity vector of the wind,
FG
vertical force directed toward the earth’s surface,
g
is acceleration of gravity,
FF
is friction force,
and αis friction coefficient.
In each iteration, the position and velocity of the air parcels are updated by [44],
νnew = ((1α)νold gxold + [|Pmax
Po ld
|RT(xmax xold )] cνold
Po ld
(32)
and
xnew =xold + (νnew ×t)(33)
In Equation (32), the
((
1
α)νold )
represents the opposite force continuously pushing the air
parcels towards their previous positions. Whereas the
gxold
shows the gravitational force which attracts
the air parcels towards the center of the earth. The motion of the air parcels towards the high-pressure
point/global best position is described by
[|Pmax
Pold |RT(xmax xold )]
and
cνold
Pold
explains the Coriolis force
which controls the motion of the air parcels.
WDO creates random solutions from the
n
number of air parcels. After evaluating the fitness
function, and updating velocities a new population is generated. The fitness function of the new and
old air parcels are compared to obtain an optimal appliance scheduling pattern. The pressure term
used in WDO is just like fitness term used in GA, BPSO and HGPO. The WDO parameters and their
Energies 2017,10, 549 17 of 35
values that give optimal results are given in Table 7and the main steps involved in WDO algorithm
are shown in Figure 4.
Table 7. Wind driven optimization (WDO) parameters.
Parameters Values
Number of iterations 500
Populationsize 200
dimMin 5
dimMax 5
vmin 0.3
vmax 0.3
RT 3
n 11
g 0.2
α0.4
Figure 4. Main steps of WDO algorithm.
Energies 2017,10, 549 18 of 35
6.4. BFO
BFO is a new addition to the family of nature-inspired optimization algorithms. BFO algorithm is
inspired by the social foraging behaviour of Escherichia coli. Since its inception, BFO has drawn the
attention of researchers from diverse fields of knowledge. In the BFO algorithm, the bacteria swim in
search of nutrients and select the best nutrients (solutions) to maximize its energy. The parameters
used in BFO and the values on which it gives optimal results are listed in Table 8.
Table 8. BFO parameters.
Parameter Value
Maximungeneration 500
Ne24
Nr5
Nc5
Np30
Ns2
Ci0.01
Ped 0.5
θ0.5
BFO algorithm consists of four steps; Chemotaxis, Swimming, Reproduction, and
Elimination-dispersal. BFO algorithm starts its search with parameters initialization, and after
initialization of parameters, under the chemotaxis step, initial states of appliances are evaluated
and then our system computes the new positions of bacteria (solution matrix). Mathematically the
chemotaxis steps of a bacterium can be represented by Equation (34) [45],
θi(j+1, k,l) = θi(j,k,l) + C(i)(i)
pT(i)(i)(34)
where
θi
(
j
,
k
,
l
) represents
i
-th bacterium at
j
-th chemotactic,
k
-th reproductive and
l
-th
elimination-dispersal step.
C(i)
is the size of step taken in the random direction specified by the
tumble, and indicates a vector in the random direction whose elements lie in [1, 1].
In next step, swimming loop is initialized to find the current best state of appliances.
The swimming step is represented by Equation (35) [46],
Jcc(θ,P(j,k,l)) =
S
i=1
Jcc(θi,θ(j,k,l)) =
S
i=1
[dattractant exp(wattractant
n
m=1
(θmθi
m)2)] (35)
+
S
i=1
[hrepell ant exp(wrepell ant
n
m=1
(θmθi
m)2)]
where
Jcc
(
θ
, P( j, k, l)) is the objective function,
S
is the total number of bacteria,
n
is the number of
appliances, and
θ
=[
θ1
,
θ1
,
· · ·
,
θn
]
n
is a n-dimensional search space. The
dattractant
,
wattractant
,
hrepell ant
and wrepell ant are different coefficients that should be selected properly [47].
When swimming steps complete, iterations of reproduction loop start in which only feasible
solutions are recorded to produce next generation. Then, the elimination-dispersion steps discard
the least feasible solutions, and new random samples are inserted with a low probability. This is
a very important process because infeasible solutions are eliminated, and the chances of repetition are
avoided. The working flow of BFO algorithm is shown in Figure 5.
Energies 2017,10, 549 19 of 35
Figure 5. Steps involved in BFO algorithm.
6.5. HGPO
Our proposed HGPO algorithm combines the features of BPSO and GA to effectively reduce the
electricity bill and PAR. The GA and BPSO are chosen for hybridization because the GA is good in PAR
reduction, and BPSO is effective in bill reduction. Working procedure of HGPO consists of two stages,
first, all steps of BPSO are followed, then, the crossover and mutation operators of GA are applied
to the current global best position
Xt
gbest
and the previous global best position
Xt1
gbest
found by BPSO
algorithm. The application of crossover and mutation operators to the best positions give better results
than their application to the random population. The proposed HGPO algorithm is shown in Figure 6.
Energies 2017,10, 549 20 of 35
Figure 6. Steps involved in HGPO algorithm.
7. Results and Discussions
In this section, simulation results of the proposed OHEMS are presented. In the proposed scheme,
the integration of RES and ESS, as well as the performance of various heuristic algorithms (GA, BPSO,
WDO, BFO, and HGPO), is evaluated via two stages simulations. In the first case, the integration
of RES and ESS into the residential sector are evaluated in terms of energy consumption pattern,
and electricity bill as well as PAR reduction. While in the second case, the same performance metrics
(energy consumption pattern, and electricity bill as well as PAR minimization) are used to evaluate the
effectiveness of GA, BPSO, WDO, BFO and HGPO algorithms for HEMSs. For simulations, we have
used MATLAB installed on Intel(R) Core(TM) i3-2370M CPU @ 2.4GHz and 2GB RAM with Windows 7.
The computational time of all heuristic algorithms is given in Table 9.
Table 9. Computational time.
Algorithm Computational Time (Sec)
GA 2.21
BPSO 1.86
WDO 2.84
BFO 3.59
HGPO 1.71
Energies 2017,10, 549 21 of 35
To demonstrate the proposed OHEMS, an end user with 11 passive appliances and an ESS
which works as a source and sink is considered. The descriptions of the appliances are shown in
Table 10, the column III shows the power rating of the appliances and column IV shows the length
of operation time of the corresponding appliance. In this paper, the length of operation time is
arbitrarily taken and remains the same in both scenarios (i.e., unscheduled and scheduled) to have
a fair comparison. It is assumed that the utility power supply is available around the clock to
support the prosumer’s load. Moreover, the utility has AMI to get the forecasted data of weather
conditions, ambient temperature, and solar irradiance from the metrological department, and to
broadcast it to the prosumers. The exogenous grid signals (DAP signal, forecasted ambient temperature,
and solar irradiance) used in the proposed OHEMS are shown in Figures 79respectively. The DAP
shown in Figure 7is defined by the utility operator and the normalized form of solar irradiance and
temperature data obtain from METEONORM 6.1 for Islamabad region of Pakistan is presented in
Figures 8and 9.
Table 10. Description of the appliances.
Load Type Appliances Power Rating (kW) Daily Usage (Hours)
Shiftable
ESS 3
Washing machine 0.8 5
Air conditioner 1.3 10
Clothes dryer 0.7 4
Water heater 1 8
Dish washer 0.2 3
Un-shiftable
Personal computers 0.2 18
Security cameras 0.1 24
Microwave oven 0.5 7
Refrigerator 0.9 20
Television 0.2 8
Lights 0.1 6
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
5
10
15
20
25
30
Time (hours)
Cost (Cents/kWh)
DAP
Figure 7. DAP signal.
Energies 2017,10, 549 22 of 35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
5
10
15
20
25
30
35
Time (hours)
Temprature (°C)
Forecasted ambient temprature
Figure 8. Forecasted outdoor temperature.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Time (hours)
Solar irradiance (kW/m2)
Solar irradiance
Figure 9. Solar irradiance.
The electricity generation by PV system modeled in Equation (1) mainly depends on energy
conversion efficiency of the solar generator, the effective area of the generator, solar irradiance,
and ambient temperature. Ninety percent of the estimated RE in any time slot of the scheduling
horizon is considered for consumption. This uncertainty of 10% is included to cater for the disparity
between the estimated and the actual generation. Moreover, 30% of the 90% of estimated RE in
each time slot is used for the charging of ESS as long as the charging level of ESS is between 10–90%.
The estimated RE, RE after taking 10% uncertainty, remaining RE after the charging of ESS and charging
level of ESS system are presented in Figures 10 and 11 respectively. The ESS is charged only from the
PV system in the day time. After the ESS charged fully, it is utilized in a high price time slot i.e.,
τ
as
given by Equation (17).
Energies 2017,10, 549 23 of 35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
0.2
0.4
0.6
0.8
1
1.2
Time (hours)
RES Generation (kW/h)
Estimated RE
90% of Estimated RE
Remaing RE after ESS charging
Figure 10. Estimated renewable energy.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
0.5
1
1.5
2
2.5
3
Time (hours)
Battery Storage Level (kW)
Battery charging level
Figure 11. Charging level of energy storage system (ESS).
7.1. Case 1: Integration of RES and ESS
In this case, the benefits of RES and ESS integration into the residential sector are highlighted
in terms of energy consumption pattern, and electricity bill as well as PAR reduction. The load
distribution among the utility, RES and ESS is shown in Figure 12. It illustrates that in second scenario
a portion of the prosumer load shifted from utility to RES, and in the third scenario an ESS is also
integrated to support the prosumer’s load in peak hours.
Energies 2017,10, 549 24 of 35
Unscheduled Unscheduled+RES Unscheduled+RES+ESS
0
10
20
30
40
50
60
Load distribution(kW)
Load on ESS
Load on RES
Load on utility
Figure 12. Load distribution.
7.1.1. Energy Consumption
To explain the behavior of the energy consumption pattern, we define some arbitrary thresholds.
The descriptions of load and thresholds are given in Table 11.
Table 11. Thresholds of energy consumption.
Load Range (kW)
High peak 5 and above
Peak 4–4.9
Moderate 2–3.9
Minimum 1–1.9
Negligible 0.1–0.9
The energy consumption pattern of the prosumer load is shown in Figure 13. Results illustrate
that in the first scenario (unscheduled load without RES and ESS), the energy consumption pattern has
a high peak load of 5.95 kW in time slot 1, and peak loads of 4.7 kW and 4.2 kW in time slots 21 and 9
respectively. Moreover, the energy consumption pattern shows a moderate behaviour in time slots 2–8,
10, 13–14, 17–20 and 22–24, and minimum energy is consumed in time slots 11–12 and 15–16.
The energy consumption pattern of the second scenario (unscheduled load with RES), shows
that the energy consumption remains the same in time slots 1–6 and 20–24. This is because the RE is
available from the PV system as shown in Figure 10. This illustrates that the in-house RES generates
electricity only in the time slots 7–19. Therefore, in time slots 7–19, the energy consumption is reduced
by the corresponding amount of RE available in that time slot.
In the third scenario (unscheduled load with RES and ESS), we also integrate an ESS to the
proposed SH. After the integration of ESS, energy consumption remains same in time slots 1–6 and
20–24; however, in time slots 7–8 and 10–19 the energy consumption becomes higher than the second
scenario. This increase occurs because 30% of the RE available in time slots 7–19 is used for the charging
of ESS. While, in time slot 9, the ESS is discharged and load on the utility is reduced by 73.60% and
67.16% as compared to first and second scenarios respectively.
Energies 2017,10, 549 25 of 35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
1
2
3
4
5
6
7
Time (hours)
Energy consumption (kWh)
Unsch hourly load
Unsch+RES hourly load
Unsch+RES+ESS hourly load
Figure 13. Energy consumption in case 1.
7.1.2. Electricity Cost
The corresponding electricity bill of the energy consumption is shown in Figure 14.
Results illustrate that in time slots 1–6 and 20–24 the electricity bill of all the three scenarios remains
the same. While, in time slots 7–19, the RE is available and significantly reduces the electricity bill.
Particularly, in time slot 9, the RE reduces the electricity bill by 19.55%, and after the discharge of ESS,
the total reduction of 71.41% is achieved in the electricity bill.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
20
40
60
80
100
120
Time(hours)
Electricity Cost (Cents)
Unsch hourly cost
Unsch+RES hourly cost
Unsch+RES+ESS hourly cost
Figure 14. Electricity cost in case 1.
Energies 2017,10, 549 26 of 35
7.1.3. Total Cost
The total electricity bill of case 1 is shown in Figure 15. Results illustrate that the investment of a
small capital cost on the integration of RES reduces the electricity bill by 17.25%, and after adding EES
too, the bill is reduced by 19.94%. The reduction in electricity bill due to RES and ESS is summarized
in Table 12.
Figure 15. Total electricity cost of the prosumer in case 1.
Table 12. Comparison of case 1 cost.
Scenario Cost (Cents) Difference (Cents) Reduction (%)
Unscheduled 862.66 - -
Unscheduled + RES 713.78 148.88 17.25%
Unscheduled + RES + ESS 690.63 172.03 19.94%
7.1.4. PAR
The PAR of the prosumer load is shown in Figure 16. Results illustrate that the integration
of RES reduces the PAR by 15.68% and after incorporating the ESS as well, the PAR is reduced by
21.55%. The reduction in PAR in all three scenarios is shown in Table 13. This reduction in PAR not
only enhances the stability and reliability of the power system but also reduces the electricity bill of
the prosumer.
Table 13. Comparison of case 1 PAR.
Scenario PAR Difference Reduction (%)
Unscheduled 3.0482 - -
Unscheduled + RES 2.5702 0.478 15.68%
Unscheduled + RES + ESS 2.3912 0.657 21.55%
Energies 2017,10, 549 27 of 35
Unscheduled Unscheduled+RES Unscheduled+RES+ESS
0
0.5
1
1.5
2
2.5
3
3.5
Case 1 PAR
Figure 16. Case 1 PAR.
7.2. Feasible Region of the Objective Function
The feasible region of the objective function (electricity bill) formulated in Equation (18) is shown
in Figure 17. In Figure 17, the trapezoid of
P1(
0.1, 0.81
)
,
P2(
0.1, 2.735
)
,
P4(
6, 48.6
)
and
P6(
6, 164.2
)
represents the overall region of electricity bill. Where, the point P1(0.1, 0.81)represents the electricity
bill when minimum possible load (0.1 kW) is scheduled in minimum price slot (21) of RTP signal,
and if the energy consumption is 0.1 kW in the maximum price slot (9) of RTP signal, the electricity
bill will be represented by the point
P2(
0.1, 2.735
)
. Moreover, the points
P4(
6, 164.2
)
and
P6(
6, 48.6
)
represent the bill when all appliances are in ON status in minimum and maximum price slots of RTP
signal respectively. Equation (18) shows that the objective function depends on two factors: amount of
load and electricity price in that time slot. The price signal is defined by the utility and we have no
control over it; therefore, to minimize the electricity bill we can only modify the shape of the energy
consumption pattern. Furthermore, in scheduled load scenarios, the electricity cost in any time slot
must not exceed the maximum cost of the unscheduled scenario, i.e., 59.5 cents. After applying the
limit of 59.5 cents the feasible region of scheduled load electricity bill is shown by the pentagon of
P1(
0.1, 0.81
)
,
P2(
0.1, 2.735
)
,
P3(
2.13, 59.5
)
,
P5(
6, 59.5
)
and
P6(
6, 48.6
)
. Where, the point
P3(
2.13, 59.5
)
shows that in maximum price time slot of RTP signal the scheduled load must not exceed than 2.13 kW,
and point
P5(
6, 59.5
)
shows that the maximum possible load (6 kW) must not be scheduled in time
slots having price greater than 9.92 cents.
0123456
0
20
40
60
80
100
120
140
160
Energy consumption (kW)
Electricity Cost (Cents)
Feasible Region
P2 (0.1, 2.735)
P1 (0.1, 0.81)
P5 (6, 59.5)
P4 (6, 164)
P6 (6, 48.6)
P3 (2.13, 59.5)
Figure 17. Feasible region of objective function.
Energies 2017,10, 549 28 of 35
7.3. Case 2: OHEMS with Integrated RES and ESS
The distribution of the prosumer load among the utility, RES and ESS is illustrated in Figure 12.
It shows that in the second scenario of case 1, an RES is integrated to the SH while, in the third scenario,
an ESS is also utilized to support the prosumer’s load.
Here, in this section, the simulation results of the heuristic algorithms (GA, BPSO, WDO, BFO and
HGPO) based OHEMS are presented. In this case, the cost of unscheduled load with RES and
ESS is taken as a reference to validate the effectiveness of the heuristic algorithms for HEMSs.
The performance of heuristic algorithms (GA, BPSO, WDO, BFO and HGPO) is evaluated by using
the same performance metrics (energy usage pattern, and electricity bill as well as PAR reduction) of
case 1. The designed SS generates an optimal energy usage pattern for shiftable appliances in response
to grid signals and a large portion of load is shifted toward off-peak hours. This relocating of load
significantly reduces the electricity bill as well as the PAR. Simulation results of the proposed scheme
are as follows:
7.3.1. Energy Consumption
The energy consumption patterns of scheduled and unscheduled loads with RES and ESS
are shown in Figure 18. In unscheduled load with RES and ESS scenario, the prosumer’s energy
consumption pattern has a high peak of 5.95 kW in time slot 1 and followed by a peak load of 4.8 kW
in time slot 21. A moderate behaviour of load is recorded in time slots 2–8, 13–14, 17–20, and 22–24.
Moreover, the energy consumption is found to be minimum in time slots 9–10, and negligible in time
slots 11–12 and 15–16.
GA-based scheduling creates no high peaks, and a peak load of 4.75 kW is consumed in time
slot 21; 4.12 kW in time slot 20; 4.07 kW in time slot 22; and 4.01 kW in time slot 24. The energy
consumption pattern is found to be moderate in time slots 1–6, 16–19 and 23. Furthermore, the energy
consumption is minimum in time slots 7–8, 10 and 13–15, and negligible in time slots 9 and 11–12.
The comparison of GA-based scheduled and unscheduled load shows that the maximum load in
GA-based scheduling is 20.16% less than the maximum load in the unscheduled scenario.
BPSO-based scheduling also does not create high peaks, and the energy consumption pattern
has peak load of 4.96 kW in time slots 2 and 22; 4.9 kW in time slot 21; 4.81 kW in time slot 20;
4.76 kW in time slot 19; 4.67 kW in time slots 17 and 19; 4.2 kW in time slot 3; and 4.12 kW in time
slot 16. The energy consumption is found to be moderate in time slots 5, 15 and 23. Additionally, a
minimum amount of energy is consumed in time slots 1, 4, 6 and 18, and negligible in time slots 7–14.
The comparison of BPSO-based scheduled and unscheduled load shows that the maximum load in
BPSO is 16.63% less than the maximum load in the unscheduled scenario.
In WDO-based scheduling, a high peak load of 5.75 kW is consumed in time slot 19 and 5.12 kW
in time slots 22–23. The energy consumption pattern has peak load of 4.95 kW in time slot 21; 4.08 kW
in time 20; and 4 kW in time slots 2 and 24. In addition, a moderate amount of energy is consumed
in time slots 3, 5, 16 and 17. The energy consumption is minimum in time slots 1, 4, 6, 15 and 18,
and negligible in time slots 7–14. The comparison of WDO-based scheduled and unscheduled load
shows that the maximum load in WDO is 3.36% less than the maximum in the unscheduled scenario.
The BFO algorithm neither creates high peaks nor peaks in energy consumption pattern.
The energy consumption is moderate in time slots 1–5, 17 and 19–24, and minimum in time slots
6–8, 11, 13–16 and 18. Moreover, the energy consumption is negligible in time slots 9–10 and 12.
The comparison of BFO-based scheduled and unscheduled load shows that the maximum load in BFO
is 36.13% less than the maximum load in the unscheduled scenario.
The proposed HGPO-based scheduling creates no high peaks, and a peak load of 4.7 kW is
consumed in time slot 19 and 4.61 kW in time slots 21–23. The energy consumption is moderate in time
slots 1–2, 4–6, 17 and 18. Moreover, minimum load is scheduled in time slots 3, 10, 13, 16, 20 and 24,
and negligible in time slots 7–9, 11–12 and 14–15. The comparison of HGPO-based scheduled and
Energies 2017,10, 549 29 of 35
unscheduled load shows that the maximum load in case of HGPO is 22.52% less than the maximum
load in the unscheduled scenario.
Figure 18 illustrates that the load shifting pattern of HGPO is balanced, and uniformly distribute
the load over the scheduling horizon. On the other hand, the other heuristic algorithms either
completely shift the load towards off-peak hours or consume higher energy in peak hours. The HGPO
outperforms the GA, BPSO, WDO and BFO algorithms by allowing the appliances to complete their
tasks with minimum delay and avoids peak formation as well.
Figure 18. Hourly energy consumption in case 2.
7.3.2. Electricity Cost
The corresponding electricity bill of unscheduled and scheduled load with RES and ESS is shown
in Figure 19. Results illustrate that the electricity bill of heuristic algorithms (GA, BPSO, WDO,
BFO and HGPO) based scheduled load remains within the feasible region. In GA-based scheduling,
the maximum electricity bill is 39.7 cents in time slot 7. The electricity bill in case of BPSO has
a maximum value of 43.6 cents in time slot 2 which is also less than the maximum bill (59.5) of
unscheduled scenario. In the case of WDO, the maximum electricity bill is 47.3 cents in time slot 21,
and the bill in all other slots also remains within the feasible region. The BFO-based scheduled load
has maximum bill of 49.8 cents in a time slot 8. In the proposed HGPO algorithm, the electricity
bill satisfies all the limits of the feasible region, and a maximum bill of 43.6 cents is recorded in time
slot 6. Moreover, in peak hours the electricity bill of scheduled load is comparatively less than the
unscheduled load, and the comparison of total electricity bills of the heuristic algorithms shows that
the performance of HGPO-based HEMS in term of bill reduction is better than GA, BPSO, WDO and
BFO based HEMS.
Figure 19. Hourly electricity cost in case 2.
Energies 2017,10, 549 30 of 35
7.3.3. Total Cost
The comparison of overall electricity bill of the unscheduled and scheduled load with RES and
ESS is shown in Figure 20. The total electricity bill in unscheduled, and in scheduled load scenarios
using GA, BPSO, WDO, BFO and HGPO algorithms are 690.63, 622.97, 555.32, 584.24, 581.56 and
517.15 cents respectively. The comparison of total electricity bill shows that GA, BPSO, WDO, BFO
and HGPO algorithms based HEMS reduces the electricity bill by 9.80%, 19.60%, 15.40%, 15.80%,
and 25.12% respectively. The changes in electricity bill due to scheduling are listed in Table 14. In peak
hours the electricity charges of heuristic algorithms based scheduled load scenarios are significantly
less than the unscheduled scenario. However, when the overall cost reduction is concerned the HGPO
gives better results than the other optimization algorithms. Moreover, the electricity consumption
pattern of HGPO is uniform, while the GA, BPSO, WDO and BFO algorithms create new peaks in
off-peak hours that degrade their overall performance.
Unsch+RES+ESS GA BPSO WDO BFO HGPO
0
100
200
300
400
500
600
700
Total Cost (Cents)
Figure 20. Case 2 total cost.
Table 14. Comparison of case 2 cost.
Scheduling Technique Cost (Cents) Difference (Cents) Reduction (%)
Unscheduled + RES + ESS 690.63 - -
GA scheduled + RES + ESS 622.97 67.66 9.80%
BPSO scheduled + RES + ESS 555.32 135.31 19.60%
WDO scheduled + RES + ESS 584.24 106.39 15.40%
BFO scheduled + RES + ESS 581.56 109.07 15.80%
HGPO scheduled + RES + ESS 517.15 173.48 25.12%
7.3.4. PAR
The PAR of unscheduled and scheduled load is shown in Figure 21. It illustrates that the proposed
HGPO algorithm minimizes the PAR by 24.88%. The GA, BPSO, WDO and BFO algorithms also
reduce the PAR by 14.09%, 3.30%, 22.10% and 33.54% respectively. The changes in PAR of the heuristic
algorithms based scheduled load are shown in Table 15. Although these algorithms are designed to
reduce the PAR and avoid peaks formation, the BPSO and WDO algorithms shift most of the load
to off-peak hours that creates new peaks. This new peaks formation disturbs the entire operation
schedule of the utility peak plants and utility impose a penalty on the consumer. The HGPO and BFO
based HEMS uniformly distributes the loads over the scheduling horizon and achieves the desired
Energies 2017,10, 549 31 of 35
objective. The performance of the GA, BPSO and WDO algorithms may be enhanced by setting the
proper thresholds of energy consumption in each slot; however, it will affect the UC and will increase
the waiting time of the appliances.
Unsch+RES+ESS GA BPSO WDO BFO HGPO
0
0.5
1
1.5
2
2.5
Case 2 PAR
Figure 21. Case 2 PAR.
Table 15. Comparison of case 2 PAR.
Scheduling Technique PAR Difference Reduction (%)
Unscheduled + RES + ESS 2.391 - -
GA scheduled + RES + ESS 2.054 0.337 14.09%
BPSO scheduled + RES + ESS 2.312 0.079 3.30%
WDO scheduled + RES + ESS 2.350 0.041 22.10%
BFO scheduled + RES + ESS 1.589 0.802 33.54%
HGPO scheduled + RES + ESS 1.796 0.595 24.88 %
8. Conclusions and Future Work
In this paper, an OHEMS is proposed, which incorporates the RES and ESS into the residential
sector. Results illustrate that the integration of RES and EES minimizes the electricity bill by 19.94%
and PAR by 21.55%. After the integration of RES and ESS, the constrained optimization problem is
mathematically formulated by using MKP, and solved by heuristic algorithms: GA, BPSO, WDO,
BFO and HGPO algorithms. The performance of the scheduling algorithms is evaluated in terms of
uniform distribution of load over the scheduling horizon, and reduction of electricity bill as well as
PAR. Simulation results show that, unlike GA, BPSO, WDO, and BFO, the HGPO algorithm uniformly
distributes the load over the scheduling horizon, and further reduces the electricity bill by 25.12% and
PAR by 24.88%. This reduction of PAR enhances the power system stability and ensures the stable
and reliable grid operation. From the above discussion, it is concluded that the HGPO-based HEMS
performs better than other heuristic algorithms and an overall reduction of 40.05% is achieved in cost
and 41.07% in PAR as compared to the unscheduled load without RES and ESS.
In future, we are interested in the coordination among the distributed micro sources, i.e., RES and
ESS to exploit the RSER utilization. This coordination of micro sources in a residential area will not
only enable the exchange of surplus RE among the prosumers but will also reduce the load on utility.
Acknowledgments:
This project was full financially supported by the King Saud University, through Vice
Deanship of Research Chairs.
Energies 2017,10, 549 32 of 35
Author Contributions:
Adnan Ahmad, Asif Khan, and Nadeem Javaid proposed the system model and
classified the appliances. All authors performed extensive simulations for the manuscript. Hafiz Majid Hussain,
and Iftikhar Azim Niaz wrote related work and simulation sections. Wadood Abdul, Ahmad Almogren,
and Atif Alamri wrote rest of the manuscript. Nadeem Javaid and Adnan Ahmad organized and refined
the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Nomenclature
Symbol Description Symbol Description
EPV Available energy from PV system Xgbest Global best value
ηPV Efficiency of PV system Xlbest Local best value
APV Area of PV generator Fpg Pressure gradient force
IrSolar radiation FCCoriolis force
TaAmbient temperature FGGravitational force
α1,α2Shape factors β1,β2Scale factors
ES Stored energy FFFriction force
κDuration of one time slot FGGravitational force
ηESS Efficiency of ESS Pold Pressure at current location
EPCh Charge rate of ESS at time Pmax High pressure point
EPD ch Discharge rate of ESS at time αConstant for update position
EPCh
UB Upper charge limit of ESS ωEarth rotation
EPD ch
LB Lower discharge limit of ESS t Unit step time
ESUB Upper limit of energy storage νnew Updated velocity
MNumber of controllable appliances νold Current velocity
NNumber of un-shiftable appliances RUniversal gas constant
EaEnergy consumption of shiftable appliances Sig Sigmoid function
EbEnergy consumption of non-shiftable appliances nTotal number of appliances
Etotal Total energy consumption PDAP DAP signal
Ea
PElectricity cost of shiftable appliances energyconsumption CiStep size
Eb
PElectricity cost price of non-shiftable appliances energy consumption θPosition of bacteria
EPTotal bill of energy consumption Rotation of earth
Xa
meMON/OFF status of shiftable appliances δν Finite volume of air
Xb
nβeNON/OFF status of non-shiftable appliances wInertia factor
Egrid Available grid energy r1,r2Random numbers
Emin
unsch Minimum energy consumed in unscheduled senario c1Local pull
τ0Lower limit of scheduling horizon c2Global pull
τsch Scheduling time dimMax Upper limit of WDO dimensions
τmax Upper limit of scheduling horizon dimMin Lower limit of WDO dimensions
tTime slots Pressure gradient
LLength of chromosomes µVelocity vector of wind
Vt+1
iParticle upcoming velocity Vt
jParticle current velocity
xt+1
iParticle upcoming position xt
iParticle current position
NeNumber of elimination steps PcProbability of crossover
NcNumber of chemotaxis steps PmProbability of mutation
NpNumber of population steps wiInitial weight constant
NsNumber of swimming steps wfFinal weight constant
NrNumber of reproduction steps vmax Upper limit of velocity
Ped Probability of elimination-dispersal vmin Lower limit of velocity
Energies 2017,10, 549 33 of 35
List of Acronyms
Acronym Description Acronym Description
SG Smart grid SHs Smart homes
SCs Smart cities REMS Residential energy management system
EMS Energy management system HE MS Home energy management system
DR Demand response DSM Demand side management
OH EMS Optimized home energy management system UC User comfort
RESs Renewable energy sources RSERs Renewable and sustainable energy resources
DGs Distributed generation ICTs Information and communication tecnologies
PV Photovoltaic ESS Energy storage system
SM Smart meter AM I Advance metering infrastructure
MC Master controller DC Direct current
AC Alternating current SS Smart scheduler
GA Genetic algorithm PSO Particle swarm optimization
BPSO Binary particle swarm optimization W DO Wind driven optimization
BFO Bacterial foraging optimization HGPO Hybrid GA-PSO
LSA Lighting search algorithm AN N Artificial neural networks
OL A Observe, learn and adopt LF Load forecasting
DP Dynamic programming IPSO Improved particle swarm optimization
CP Convex programming D AP Day ahead pricing
DA P Real time pricing ToUP Time of use pricing
PP Peak pricing CPP Critical peak pricing
LP Linear programming I LP Integer linear programming
MI LP Mixed integer linear programming RES Multiple knapsack problem
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