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An Efficient Scheduling of User Appliances Using Multi Objective Optimization in Smart Grid


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Electricity is the basic demand of consumers. With the passage of time this demand is increasing day by day. Smart grid (SG) trying to fulfill the demand of customers. When demand increases then load is also high. To maintain load from on peak hours to off peak hours, consumer needs to manage their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customer’s needs. In this paper, scheme is proposed which is used to minimize the electricity cost and also maximize the user comfort. The proposed scheme is performed better than existing meta heuristic techniques. The proposed scheme is used real time price (RTP) price signal. Simulation results shows that the algorithm has met the objective of DSM. Moreover, the proposed algorithm outperforms earth worm algorithm (EWA) and single swam optimization (SSO) in terms of electricity cost and user comfort.
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An Efficient Scheduling of User
Appliances Using Multi Objective
Optimization in Smart Grid
Hafiz Muhammad Faisal1, Nadeem Javaid1(B
), Umar Qasim2, Shujaat Habib3,
Zeshan Iqbal4, and Hasnain Mubarak4
1Comsats University Islamabad, Islamabad 44000, Pakistan
2Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada
3Air University, Multan, Pakistan
4NCBA&E, Multan, Pakistan
Abstract. Electricity is the basic demand of consumers. With the pas-
sage of time this demand is increasing day by day. Smart grid (SG) trying
to fulfill the demand of customers. When demand increases then load is also
high. To maintain load from on peak hours to off peak hours, consumer
needs to manage their appliances by home energy management system
(HEMS). HEMS schedule the appliances according to customer’s needs. In
this paper, scheme is proposed which is used to minimize the electricity cost
and also maximize the user comfort. The proposed scheme is performed
better than existing meta heuristic techniques. The proposed scheme is
used real time price (RTP) price signal. Simulation results shows that the
algorithm has met the objective of DSM. Moreover, the proposed algo-
rithm outperforms earth worm algorithm (EWA) and single swam opti-
mization (SSO) in terms of electricity cost and user comfort.
Keywords: Smart grid ·Home energy management system ·
Real time price
1 Introduction
Energy is one of the most important resource, and energy demand is grow-
ing every day. Service providers are facing many problems to fulfill the energy
demand in residential building and industrial sectors. There are two ways to
solve this problem.
1. Produce additional energy and find new resources to produce energy
2. Excellent usage of existing resources
The first approach is costly and time consuming, as compared to the sec-
ond approach that is more efficient and inexpensive. Information technology
evolved and many schemes are introduced for energy consumption optimization.
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 371–384, 2019.
372 H. M. Faisal et al.
The bi-directional communication is not only concerned by the consumers for
electricity price and maintenance schedules of the distribution network, however,
also motivate the providers to monitor and analyze the real time power utiliza-
tion data. Smart meters (SM) are installed in the residential area. SM provides
the user’s complete information of demand, supply and price signal. The energy
consumption is increasing at a rapid rate in residential areas, hence the effi-
cient use of energy is a big issue in the residential sectors. DSM has two main
functions; load management and demand response (DR). DR is one of the core
function of DSM. DR can be termed as the series of steps taken by customer
in reaction to the changing price rates announced by utility. Due to rapidly
changing grid conditions demand level can also be changed. This varying change
causes a mismatch between demand and supply. This mismatch is dangerous for
the integrity of grid that is spread over a large area. That is the reason DR is
used as it provides flexibility at relatively low rates. DR always try to adjust
the power demand of consumers. The DR scheme helps the customers to save
electricity bills when the prices are high in peak hours. Customers can shift the
usage of their own appliances into off peak hours. Many researchers proposed
different schemes in the literature. DR is divided into incentive based programs
and price based programs. The utility can control the appliances of the user
and provides the financial incentives for demand reduction. However, privacy is
compromised by directly accessing the appliances of customers. In price based
programs, end users change the power consumption in their houses according to
the price schemes which is provided by the utility. The power generation and
power utilization is a one way process so, the power generation system is unable
to control and manage electricity consumption. SM provides two way communi-
cation between user and utility. Smart home (SH) and SM are very important
in residential building for reducing energy consumption due to information com-
munication technology advancement. In this paper, DSM technique implements
for scheduling the appliances in residential sectors. The main aim of proposed
scheme is to decrease the cost of electricity, minimization of peak to average
ration (PAR) and to obtain maximum user comfort. In this scheme, we consid-
ers a single home in which 15 appliances are present. Two Meta heuristic tech-
niques: earth worm algorithm (EWA) and single swarm optimization (SSO)are
proposed and implemented with different price signals. Proposed scheme gives
better results as compared to EWA and SSO.
The rest of the paper is categorized as follows: Sect. 2defines the related
work. Section 3discuss the problem statement. In Sect. 4, explains the system
model. Section 5, we discusses the proposed scheme. Computational results are
shown in Sect. 6. Paper findings are presented in Sect. 7(Table 1).
An Efficient Scheduling of User Appliances 373
Table 1. List of acronyms
DSM Demand side management
GWO Gray wolf optimization
BPSO Binary particle swarm optimization
EMC Energy management controller
DR Demand response
TOU Time of use
IBR Inclind block rate
SG Smart grid
MILP Mixed integer linear programming
GA Genetic algorithm
DP Dynamic programming
CSA Cuckoo Search algorithm
FP Fractional Programming
HSA Harmony search algorithm
EDE Enhance differential evaluation
LOT Length of operational time
PAR Peak to average ratio
RTP Real time pricing
CPP Critical peak pricing
RES Renewable energy sources
AMI Advance metering infrastructure
EDE Enhanced differential evaluation
HEMS Home energy management system
2 Related Work
Mixed integer linear programming (MILP), method is used in [1], to mini-
mize the total electricity bill paid by consumers is the main purpose of this
method. Authors worked on balance load management however, the user com-
fort is neglected in MILP. It shows an exchange between conventional systems
and today’s renewable energy sources (RES). A two-way communication between
utility and consumer through SM, a lot of energy wastage problem covered by
saving this 10–30%. In SG, the big challenge for researchers is cost minimization.
The genetic algorithm (GA) technique used in the paper [2], with the integration
of RES and stored energy a low level of cost minimization achieved. In particular
time changes when electricity price and user demand are higher than the stored
energy is helpful. Authors neglected the deployment and maintenance cost of
storage devices and RES in this technique. One of the big problem is to balance
the load in commercial and residential areas (Table 2).
374 H. M. Faisal et al.
Table 2. Related work
Technique Achievements Limitations
MILP [1]Reduction in PAR and
total electricity cost
Comfort preferences are
not considered
GA [2]Cost minimization Deployment and
maintenance cost of
storage devices and RES
are ignored
GA- DSM [3]Electricity consumption
Neglected the PAR value
and user comfort
MINLP under time Of
use (ToU) [4]
Cost minimization Neglected the PAR
DP [5]Cost minimization Installation and
maintenance is ignored
Combination of GA and
binary partial swarm
optimization BPSO
Algorithms [6]
Cost and peak
Ignored comfort
preferences and focus
only residential area
CSA [7]Shifting the load in
another time Interval
and peak load reduced
Neglect the electricity
BBSA [8] Shifting the load and
electricity price reduced
Consider specific time
interval in a day and
hardware and software
installation expense
Two point estimation
method embedded with
PSO based method [9]
Compute load burden Neglected cost of
electricity and PAR
FP [10]Electricity cost reduction Neglect the PAR and
user comfort
HSA [11]HSA algorithm is
structure, and
Real time
implementation is not
Single knapsack [12] Energy consumption
optimization considering
six layer architecture
Harder architecture in
time scenario
(EDE and HSA) [13]RESs startup and
generation cost
Computational time is
GWO [16]Solving non-linear
economic load dispatch
The user has to come up
with ways of handling
the constraints
Greedy algorithm [17]Minimized cost and user
PAR is ignored
An Efficient Scheduling of User Appliances 375
Though, by using GA-DSM [3] algorithm in maximum hour, 21% of electricity
consumption is reduced in an industry which is very noteworthy. The PAR value
and user comfort feasibility ignored by authors. In [4] authors proposed scheme
of MINLP to solved the cost minimization under the price tariff ToU. Even
though at the peak hour, cost minimization is achieved, however, authors don’t
considered PAR. Cost minimization and the scheduling of gadgets for various
duration achieved by using dynamic programming [5] technique. Authors in [5],
focused only on residential consumption. This achieved by the integration of
RES and ESS’s with SG. Residents are capable of producing the electricity from
RES. An additional electricity could be sold by consumers to their neighbors.
In RES, two important factors are: like installation and maintenance has been
Combination of GA and binary partial swarm optimization (BPSO) algo-
rithms proposed in [6]. PAR minimization and electricity cost are the main goal
of this technique. User comfort ignored and it focused only on residential areas.
In DSM, the client can deal with their home appliances by moving the load to
some other time so the load request a key factor in such manner. By shifting the
load and using cuckoo search algorithm (CSA) [7] algorithm, approximately 22%
of peak load has been reduced. The curve of balanced load that is generated by
the CSA algorithm worked on the user partiality for appliance usage, this curve
used then to shift the load.
New binary backtracking search algorithm (BBSA) [8] was proposed for real-
time schedule controller. In comparison to PSO algorithm, home appliances
shifted from peak hour and electricity price reduced 21% per day by using the
load limit.
The two point estimation methods embedded with partial swarm optimiza-
tion (PSO) method is developed by Huang et al. [9] for dropping the computa-
tional complexity in a HEMS. In contrast, this scheme is intelligent enough than
GPSOLHA in the perspective of computational burden. However in HEMS the
cost of electricity and PAR value has not been considered by Author [9]. For
residential appliances, the authors proposed an improved model for HEMS in
[10]. The main goal is to minimize the cost by shifting the appliances. Using the
RTP tariff and DER, fractional programming implemented for HEMS.
The authors discussed the harmony search algorithm (HSA) algorithm in [11].
Authors also defined the searching criteria of different techniques. The primary
steps of HSA is adaptation and used in different fields.
Authors designed a model in [12] for microgrid systems. The microgrid sys-
tems are integrated with the RESs. The main goal of this model is to minimize
the cost of RESs startup and RESs generation cost. The desired objective is
achieved by the combination of EDE and HAS. PAR value is ignored in the
design model.
Authors proposed a model for HEMS with multiple appliances in [13]. Six
Layered are connected with each other to perform better results for the reduc-
tion of PAR and cost. The author in [4] proposed utilizing MINLP method cost
minimization to be accomplished under ToU price signal. The main goal of this
376 H. M. Faisal et al.
technique is cost minimization. Customers can save maximum energy cost using
MINLP algorithm. Scheduling the load is the core objective of load management
during high demand to low demand time. Evolutionary algorithms are used for
load shifting [15]. All service sides have data sets, where scheduling problem have
been managed to solve the efficiency problem, the industry faced more problem
because of big power consumption appliances. Due to high load users need to use
the energy more intelligently in both residential and commercial sector. Authors
in [15] proposed an algorithm for load management. The main goal of this paper
reduces the electricity cost. All service sides have data sets, where the scheduling
problem have been managed to solve the efficiency problem, the industry faced
more problem because of big power consumption appliances. Customers require
high load in efficient way and more intelligently in the residential and commer-
cial area. Authors proposed a model for cost minimization in [17]. Using an
intelligent decision system (IDSS), minimum cost and minimum PAR problems
were solved. IDSS provides better result communication between the user and
utility. Authors discussed the EDE algorithm in [18]. An updated version of ED
was used. Authors used five trial vectors instead of one. Using three different
random vectors, a new population was created. The mutant and trial vectors
were generated by the fitness function. The authors proposed [19] evolutionary
accretive comfort algorithm (EASA) which is comprised of four postulations.
These postulations are defined according to the time and device bases priori-
ties. Based on the input values EASA generates optimal energy solution pattern
which satisfies the user budget. The author defines three different user budgets
to find the absolute solutions. Ma et al. [20] defines discomfort function for two
different type of gadgets. First category is flexible starting time and the other
is flexible power devices. Authors in [20] considered a multi-objective function
for user comfort and cost parameters. The proposed bat algorithm in [21]can
be applied to obtain the optimum result. By applying this algorithm energy
consumption can be reduced which is simply a non-linear optimization prob-
lem. The important goal of bat algorithm is to decrease the power usage and
increase consumer comfort standards in the residential area. In system models,
mathematical problems and scheduling, the bat algorithm used to solve these
3 Problem Statement
Total energy consumption, minimum electricity bill, minimum PAR and maxi-
mum user comfort are the most important problems in SG. DSM schemes are
used to overcome the aforementioned problems in the SG. In [14] purposed
meta heuristic techniques to solved the energy consumption problem according
to electricity bill and user comfort. Some authors used mathematical solutions
to solve these issues. Some authors [46] proposed RES with the integration of
ESS to tackle the energy demand problems. The main aim of all techniques is to
reduce the electricity bill with maximum user comfort. In some hours, when user
demand is higher than the total electricity production. It creates a peak load in
An Efficient Scheduling of User Appliances 377
the smart home so HEMS is faced difficulty to maintain the balance. To avoid
such problems, proposed algorithm, which shifts the home appliances from on
peak hours to off peak hours, and thus achieve the minimum cost and maximum
user comfort.
In SG, DSM controller is used for managing the user demand according to the
consumer demand. DSM provides more reliability and proficiency in user tasks.
Different meta heuristic techniques are implemented in DSM to control the user
appliances. DSM techniques manages the load to consume the electricity at off-
peak hours instead on peak hours. Initially, considering single home with 15
appliances. These appliances are categorized into two main categories: schedu-
lable and non-schedulable appliances. First category is further categorized into
two sub types: interruptible and non-interruptible. Non-interruptible appliances
are those appliances that cannot be shifted when they are running and cannot be
switched on as per the user’s requirements. Where interruptible appliances are
the those appliances which can be allocated to various time intervals. Every single
home contains SM. SM decides the operation time of every appliance according
to the power rating. These 15 appliances are taken from [14] details of appli-
ances are shown in Table 3. SM provides the two-way communication between
consumers and service provider. Different price signals (CPP, ToU, RTP) are
used to find the electricity bills. The service provider provides the electricity
price signal. ToU used as pricing unit to calculate the electricity cost. The main
objective of our study is to minimize electricity consumption in order to reduce
the cost and PAR, however, the tradeoff will occur between cost and user com-
fort. Equation 1is used to calculate the PAR. Cost value is calculated by the
Eq. 2. Total load formula given in Eq.3. Main architecture of system model is
shown in Fig. 1.
PAR =max(loads)
Cost =
Rate PApp
Rate) (2)
Load =Papp
Rate App (3)
378 H. M. Faisal et al.
Fig. 1. System model
Table 3. Appliances used in simulation
Appliances Power (kW) Category
Vacuum cleaner 1.2 Interruptible
Sensors 0.01 Interruptible
PHEV 3.5 Interruptible
Dish washer 1 Interruptible
Stove 3 Interruptible
Microwave 1.7 Interruptible
Other occasional loads 1 Interruptible
Clothes washer 1Non-interruptible
Spin dryer 2.5 Non-interruptible
Oven 5 Base
TV 0.6 Base
PC 0.3 Base
Laptop 0.1 Base
Radio/player 0.2 Base
Coffee maker 0.8 Base
5 Proposed Scheme
Mathematical optimization algorithms try to solve the energy consumption prob-
lems however, with large number of smart devices its harder to present the sat-
isfactory solutions. Behind this problem, use meta heuristic techniques to solve
An Efficient Scheduling of User Appliances 379
the energy consumption problem and reduce the cost. The main objective of
EWO algorithm is to reduce the electricity bill and shift the appliances into off
peak hours. SALP algorithm provides the facility to manage the appliances using
different price signals. Different electricity price signals are discussed to define
the cost of electricity for a complete day. In our scheme, we consider RTP tariff.
The RTP is updated for every one hour. Two-way communication requires to
interact with the user for RTP.
5.1 EWO
The reproduction of earthworms states multiple optimization issues, the repro-
duction steps of earthworms are following:
Each earthworm have the capacity to reproduce off springs and every earth-
worm individual have two kinds of reproduction,
Every child of earthworm contains all the genetic factor of parents,
Singular earthworm is moved on next generation, and cannot be changed by
5.2 SALP
Salps belongs to the family of salpidae. Swarming behavior is one of the most
interesting behavior. In salps, population has two groups: 1. Leader 2. Followers.
The leader is at the front of the chain and rest of the salps are attached behind
it followers. Equation 4is used to update the position of the leader.
5.3 Updated Population Scheme
Our proposed algorithm provides the facility for consumers to schedule the appli-
ances and reduce the cost while considering the user comfort. The population
size is 30. In our proposed algorithm, the step of reproduction contains two types
of reproductions: Reproduction 1 and Reproduction 2. In our research, we have
implemented a new updated population scheme for scheduling the home appli-
ances. The first step is the initialization of all parameters with the maximum
generation and constant value. A fitness function is defined for choosing the
best solution. After applying the fitness function, two types of reproductions are
applied. The main purpose of the updated population algorithm is to obtain the
maximum solution of appliances. For complexity, mutation and crossover steps
are taken in the proposed algorithm. The major contribution of the proposed
algorithm updates the population according to their fitness function.
380 H. M. Faisal et al.
Algorithm 1. SALP Algorithm
1: Initialization the salp population xi(i=1,2,......n) considering ub and lb
2: while (end condition in not satisfied ) do
3: Calculate the fitness of each agent (salp)
4: Set Fas the best search agent
5: Update civalue
6: for every salp (xi)) do
7: if (i==1) then
8: Update the position of the leading salp
9: else
10: Update the position of the follower salp
11: end if
12: end for
13: Update the salps
14: end while
15: Return
6 Simulation and Reasoning
In this section, simulation results are briefly discussed. The implementation of
the proposed has been done in MATLAB. The main objective of DSM in the
smart home achieved by proposed scheme. The objective of DSM is maximize
user comfort, minimum electricity cost and minimum PAR. Proposed scheme
results are better as compared to EWO and SALP algorithm. For experimenta-
tion, we considered 15 appliances in the SH. By applying the ToU/CPP price
signal in the smart home, proposed scheme achieved the minimum cost of elec-
tricity and user comfort, however, PAR value is compromised. Figure 1defines
the electricity cost per hour in cents for the 24 h. It clearly defines the electricity
cost of unscheduled, EWA algorithm, SSO algorithm and updated population
proposed algorithm. Figure 2shows the total load of aforementioned algorithms
in (kwh), in a day. Figure 3shows the PAR values of unscheduled, EWA, SSO
and proposed scheme. It clearly shows that the EWA and SSO algorithm outper-
formed the proposed scheme in terms of PAR. EWA and SSO performed better
in terms of PAR as compared to own algorithm. However, there will be a tradeoff
between PAR and user waiting time. The PAR is reduced 49.27%, 50.24% and
42.94% by EWA, SSO and proposed algorithm respectively with the unsched-
uled case. Figure 4represents the comparison of EWA, SSO algorithm and the
proposed scheme in the regard of electricity cost. In peak hours electricity cost
of appliances increases quickly. To overcome this, scheduling the appliances is
done on, on-peak hours to off-peak hours. The cost of appliances is reduced due
to scheduling. Figure 4shows the cost values of unscheduled, EWA, SSO and the
proposed algorithm. Proposed algorithm performs better as compared to EWA
and SSO. The user comfort is reduced by 1.75%, 10.23% and 11.76% by the
EWA, SSO and proposed scheme respectively. Considering the total electricity
cost in the form of cents. In Fig. 5, the user waiting time shown. The user wait-
ing time is calculated in terms of user comfort. User waiting time is inversely
An Efficient Scheduling of User Appliances 381
proportional to user comfort. By applying the price signal user comfort value
of proposed scheme is low, as compared to EWA and SSO. The user comfort is
reduced by 76.36%, 75.36% and 86.16% by the EWA, SSO and proposed scheme
respectively (Fig. 6).
Fig. 2. Electricity cost
Fig. 3. Load
382 H. M. Faisal et al.
Fig. 4. PA R
Fig. 5. Total cost
Fig. 6. Waiting time
An Efficient Scheduling of User Appliances 383
7 Conclusion
In this paper, proposed algorithm is used for shifting the appliances. The schedul-
ing is based on real-time data of price signal. Proposed scheme results are better
as compared to EWO and SALP algorithm. It is clear that the algorithm intro-
duced works efficiently as compared to EWO and SALP with the parameters of
cost, PAR and user comfort. With the proposed algorithm derived from EWO
and SALP proposed algorithm achieved minimum cost and maximum waiting
time of our proposed scheme. In the future, we will integrate renewable energy
system and ESS with more then one SH in order to minimize cost and maximize
user comfort.
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... Authors in the literature have used their own classification for scheduling of home appliances. Faisal et al. [61] classified fifteen appliances into seven interruptible, two non-interruptible, and six base appliances. The interruptible appliances include the vacuum cleaner, sensors, PHEV, dishwasher, stove, microwave, and other occasional loads. ...
... In [61], a meta-heuristic scheme named updated population scheme is proposed to minimize EC while maximizing the US level. The proposed scheme uses fifteen home appliances for scheduling. ...
Full-text available
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer's flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.
Full-text available
The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.
In this paper, the information gap decision theory is proposed as a solution model for reliable planning in residential smart building by considering the nondeterministic prices of the market and solar thermal storage system. In this model, combined heat and power, energy storage system, boiler, smart home appliances, and solar thermal storage system have been considered. The information gap decision theory is independent from the model size. Therefore, apartment smart building's energy management systems (also known as small scale loads) are able to utilize information gap decision theory in order to make better-informed conclusions against nondeterministic behavior seen in prices. In the suggested approach, two main functions are exist as reliability and opportunities. Optimum scheduling of smart residential building risk prevention approach is modeled based on reliability function while opportunity function is used to model risk-taking approach of apartment smart building's optimum scheduling. In this work, two scenarios have been considered for evaluating the impact of the solar thermal storage system which are being assessed yielding some impactful outcomes, which will mark the suggested model's validity. In obtained results, the STSS is in our consideration, the overall operation cost of the example smart residential building is decreased by 22.23%.
Swarm Intelligence (SI) is referred to the social conduct emerging within decentralized and self-organization of swarms. These swarms are summarized as the well-known examples such as bird groups, fish schools, and the most social in insects species for instance bees, termites, and ants. Among those, Salp Swarm Algorithm (SSA), that has been successfully utilized and held in different fields of optimization, engineering practice, and real-world problems, so far. This review carries out a extensive study for the present status of publications, advances, applications, variants with SSA including its modifications, population topology, hybridization, extensions, theoretical analysis, and parallel implementation in order to show its potential to show its potential to overcome many practical optimization issues. Further, this review will be greatly useful for the researchers and algorithm developers analyzing at Swarm Intelligence, especially SSA to use this simple and yet very efficient approach for several tough optimization issues.
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Electricity usage at electricity rush hour (peak hour) may vary from each and every service area such as industrial area, commercial area and residential area. Equalizing the power consumption in industry may lead to the utilization of power in other service areas in an efficient way. Although industries have comparably lesser number of power consuming device types than other service areas the power consumption is quite high. To meet the demands rising in the industry, shiftable loads (devices) can be redistributed equally to all the working time slots based on the average power utilization. It can be done in a flexible manner by shaping the loads using Demand Side Management (DSM) technique in Smart Grid. The main objective is to minimize the power utilization during the electricity rush hour by effectively distributing the power available during off-peak hour. Evolutionary algorithm can be well adapted to problems where optimization is the core criteria. Any maximization or minimization problem can be solved efficiently using evolutionary algorithm. Hence, to obtain the optimized fitness function of load redistribution in industry Genetic Algorithm in Demand Side Management (GA-DSM) is chosen and it has benefited with an overall reduction of 21.91% which is very remarkable. In addition to this the evaluation of the fitness function using GA-DSM is carried out in other two industrial dataset models (steel plant and wind power plant) which is unavailable so far in the literature.
Conference Paper
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Electrical power systems have been developed to more advanced structures called as “smart grids”. Among many other issues, demand side management (DSM) is one of the important instruments of the smart grids. DSM techniques increase the efficiency of the grid, and modify consumer’s electrical demand via demand response (DR) programs using financial incentives. In this study, shiftable domestic loads scheduled by Cuckoo search algorithm (CSA) to ensure balanced load curve as much as possible while considering the consumers’ preferences. The main motivation of this paper is scheduling the usage hours of the shiftable household appliances in a neighborhood by applying the recently developed metaheuristic CSA. This paper proposes an approach to increase the efficiency and lifetime of utility network by scheduling and operating shiftable domestic loads while considering consumer’s demand in terms of financial benefits. Proposed CSA based scheduling mechanism is compared with that of Genetic algorithm (GA). The results reveal positive effects of the scheduling and the performance of the CSA over GA.
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Demand Side Management (DSM) through optimization of home energy consumption in smart grid environment is now one of the well-known research areas. Appliances scheduling has been done through many different algorithms to reduce peak load and consequently the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliances scheduling options and enhanced load categorization in smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with embedded MATLAB code. Single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns.} Simulation results prove the effectiveness of the proposed model.
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Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.
Bio-inspired algorithms are emerging very rapidly and are applied to different kinds of problems. The recently developed Bat algorithm is one of its kinds which is gaining popularity because of its simplicity and efficiency. The algorithm imitates the behavior of bats and serves as a meta-heuristic optimization algorithm. This paper provides a comprehensive survey of different applications of bat algorithm.
With constructions of demonstrative microgrids, the realistic optimal economic dispatch and energy management system are required eagerly. However, most current works usually give some simplifications on the modeling of microgrids. This paper presents an optimal day-ahead scheduling model for a microgrid system with photovoltaic cells, wind turbine units, diesel generators and battery storage systems. The power flow constraints are introduced into the scheduling model in order to show some necessary properties in the low voltage distribution network of microgrids. Besides a hybrid harmony search algorithm with differential evolution (HSDE) approach to the optimization problem is proposed. Some improvements such as the dynamic F and CR, the improved mutation, the additional competition and the discrete difference operation have been integrated into the proposed algorithm in order to obtain the competitive results efficiently. The numerical results for several test microgrids adopting the IEEE 9-bus, IEEE 39-bus and IEEE 57-bus systems to represent their transmission networks are employed to show the effectiveness and validity of the proposed model and algorithm. Not only the normal operation mode but also some typical fault modes are used to verify the proposed approach and the simulations show the competitiveness of the HSDE algorithm.
This paper details a proposed demand response (DR) application to optimize the operation of appliances in an indeterminate environment in a home energy management system (HEMS). An indeterminate environment results from forecasted errors of electricity prices and system loads, so a probabilistic analysis of the system performance is of significant interest. Herein, a chance constrained, optimization-based model is formulated to accommodate these uncertainties. The resulting DR application can be easily embedded in resource limited electric devices. To reduce the computational cost, both improved particle swarm optimization (PSO) and a two-point estimate method are presented to solve the chance constrained problem. The improved PSO is used to provide the optimum solution, while the probabilistic assessment of uncertainties is estimated using a two-point estimate method. Numerical comparisons were made to justify the effectiveness of the method. The simulated results obtained using the models indicate that the proposed method can significantly reduce the computational burden while maintaining a high level of accuracy.
This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.