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Earth Worm Optimization for Home Energy Management System in Smart Grid


Abstract and Figures

Smart grid based energy management system promises an efficient consumption of electricity. For optimized energy consumption, a bio inspired meta-heuristic algorithms: Earth Worm Algorithm (EWA) and Bacterial Foraging Algorithm (BFA) are presented in this paper. In this work, we targeted residential area. Our aim is to reduce the electricity cost and Peak to Average Ratio (PAR). We have used the Critical Peak Pricing (CPP) scheme for calculating electricity bill. Through simulations, we have compared the results of EWA, BFA and unscheduled appliances. After implementing our techniques, EWA based energy management controller gives more efficient results than BFA in term of cost, while for PAR reduction , BFA performs better than EWA.
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Earth Worm Optimization for Home Energy
Management System in Smart Grid
Mudabbir ALi1, Samia Abid1, Asad Ghafar1, Nasir Ayub1,Hafsa Arshad1, Sajawal
Khan1and Nadeem Javaid1,
Abstract Smart grid based energy management system promises an efficient con-
sumption of electricity. For optimized energy consumption, a bio inspired meta-
heuristic algorithms: Earth Worm Algorithm (EWA) and Bacterial Foraging Algo-
rithm (BFA) are presented in this paper. In this work, we targeted residential area.
Our aim is to reduce the electricity cost and Peak to Average Ratio (PAR). We
have used the Critical Peak Pricing (CPP) scheme for calculating electricity bill.
Through simulations, we have compared the results of EWA, BFA and unscheduled
appliances. After implementing our techniques, EWA based energy management
controller gives more efficient results than BFA in term of cost, while for PAR re-
duction, BFA performs better than EWA.
Smart Grid, Meta heuristic techniques, EWA algorithm, BFA algorithm, Critical
Peak Point, Home Energy Management System, PAR
1 Introduction
Energy management is one of the interesting area for scientific research. The de-
mand of energy is increasing with the passage of time due to increased number
of homes, industries and other commercial buildings. Increasing demand of any-
thing requires a manageable production in order to facilitate the consumer with the
best satisfaction. Similarly, the generation of energy also needs to be managed. For
smart management of electricity distribution among consumers, Smart Grids (SGs)
are introduced. The main functions of SG are energy controlling that it gather, dis-
tribution and act on information about the behavior of all participants including
industries, homes and other buildings. Demand Side Management (DSM) facilitates
1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
2 Mudabbir Ali et al.
more proficient and reliable grid tasks in SG. Moreover, SG increases the connectiv-
ity, coordination and automations between transmission and consumption of energy.
Fig.1 represents the abstract architecture of SG framework. Energy Management
Controller (EMC) is a device, through which we can adjust our appliances in home.
EMC is connected via Wide Area Network (WAN) that communicates with the SG
domain for electricity management. In Home Energy Management System (HEMS)
all the components perform their task, i.e. Smart Meters (SM), EMC, appliances
etc. Different pricing schemes are provided by the utility that are used for calculat-
ing the electricity bills in different scenarios like hourly base, daily base etc. The
main focus of this paper is to reduce electricity cost and PAR for HEMS through the
implementation of optimization techniques.
Rest of the paper is organized as follows: Section 2 contains the brief descrip-
tion of related work. Next, Section 3 explains our proposed solution. Simulation
results are discussed in Section 4. Finally, the conclusion of our work is described
in Section 5 by pointing out the future work.
Fig. 1: Energy Flow with Smart Grid
2 Related work
From decades, number of research have been done globally on SG and energy man-
agement system. Different optimization techniques have a key role in scheduling of
appliances in order to manage electricity consumption. Different researchers pro-
posed different types of technique on energy management, i.e. heuristic algorithms
Earth Worm Optimization for Home Energy Management System in Smart Grid 3
based, dynamic programming algorithms, divide and conquer technique based etc.
In this regard, few research papers are discussed below.
In [1], EMC general architecture for Home Area Network (HAN) is proposed.
Demand Response (DR) is used in SG to indicate the electricity price in real time
and to transform it to EMC. Through Real Time Pricing (RTP) scheme it is not
possible to handle PAR balancing, therefore authors integrated RTP and Inclined
Block Rate (IBR) pricing scheme to reduce PAR. Genetic Algorithm (GA) is used
as optimization technique for scheduling the appliances. The main target is to re-
duce electricity cost and PAR. Authors successfully reduced electricity cost after
implementing their technique. They also reduce the delay time of appliances’ op-
eration which means User Comfort (UC) has also taken under account. So, they
proved that integrated technique (IBR and RTP) provides better results than alone
RTP. According to paper, security issues are not addressed. Authors used five heuris-
tic techniques in [2], and compared the results in order to achieve the optimal
solution for HEMS. The mentioned techniques are GA, Binary Particle Swarm
Optimization (BPSO), Bacterial Foraging Optimization Algorithm (BFOA),Wind-
Driven Optimization (WDO) algorithm and their proposed Hybrid Genetic Wind-
Driven (GWD) algorithm. Through statistical analysis, results depicted that Hybrid
GWD reduces cost 10% percent by GA and 33% by WDO while rest of other results
are far behind from their proposed technique. So, overall Hybrid GWD results are
better in reducing bill as compared to GA, BPSO, BFOA and WDO. The limitations
are that the priority based scheduling is not intelligently installed and did not com-
pare with other techniques for better exploration.
In [3], authors have presented an EMC for avoiding the peak formation, the elec-
tricity bill reduction and maintaining the acceptable UC by formulating problem as
Multiple Knapsack Problem (MKP). Authors also integrated Renewable Energy Re-
sources (RES) in HEMS. They used two pricing schemes for bill calculation; Time
Of Use (TOU) and IBR. In paper three heuristic algorithms GA, BPSO, and Ant
Colony Optimization (ACO) are used to achieve the optimal solution for their de-
signed model. The results show the validity of the proposed solution that it reduces
the electricity bill and PAR. As there is a trade-off between cost reduction and UC,
UC is compromised. New meta-heuristic technique inspired by the nature of earth-
worm reproduction system is proposed in [4]. That reproduction is categorized into
2 types (Reproduction 1 and Reproduction 2). Reproduction 2 gives the optimized
solution after applying 9 crossovers functions then they applied cauchy mutation
for the extraction of most optimized value. Authors just designed the technique in
the paper but did not apply on SG for its performance exploration. According to
[5], DSM is introduced to handle the issue of energy demands plus to utilize the
energy efficiently. Authors proposed the hybrid technique and compared their pro-
posed technique with GA, BFA and unscheduled appliances. After simulation and
practical implementation, they successfully achieved the goal they want by reducing
electricity cost and maintained UC with the comparison of unscheduled appliances
but, still limited to some extent as BFA shows better result in term of cost reduction.
In [6], authors focused on scheduling of load and power trading with RES. They
have adopted dynamic programming scheme to schedule operation of appliances
4 Mudabbir Ali et al.
and game theoretic approach to interact between different users with excess energy
generation. Users are encouraged to avoid reverse transferring of excess energy to
utility using RES. After simulations load is scheduled, electricity cost is reduced
and user with excess energy generation is able to sell the electricity to local users
cheaper than energy provider. As a result there is a reduction in electricity payments
and proposed system provides the gateway to earning revenues. Moreover, no secu-
rity issue is mentioned regarding to protection of RES for trading the excess energy
The consumption of energy is managed in residential SG network according to [7].
Each house is configured by 2 types of demands, flexible and essential demands, the
subcategory of flexible demands are delay-sensitive and delay-tolerant demands.
Service of delay-sensitive demand is considered to be more important than delay-
tolerant demands. Meanwhile, to decrease the waiting time of delay-tolerant de-
mands, these demands need to be upgraded for high priority queue. A centralized
algorithm based on dynamic programming is proposed by authors to optimize the
solution. As an addition, a distributed algorithm is proposed for implementation and
the neural network. Simulation results show that the usage of electricity in house-
hold is managed and for flexible demands operation delays are reduced as well.
There is an issue regarding to inconsideration of parameters tuning.
Authors in [8], target the Peak Power Consumption (PPC) minimization. To ad-
dress such issue, authors proposed the family plan approach that divides users into
schedule groups. User’s appliances are scheduled for PPC minimization through
clustering scheme. Their aim is to achieve the reduction in payments, PPC and fuel
cost. PPC is reduced by scheduling the jobs of each controllable family. They also
balance the PAR and PPC by incorporating divide and conquer rule. User has to
stay connected as a group member as mentioned in paper. Although their simula-
tion work well however, limitations are still for research analysis that their system
cannot handle interruptible jobs. In [9], user aware management approach is used
that manages load by user preferences. Problem is trade-off between cost and UC.
So, Game theoretic based energy optimization technique is used. It is different from
other demand management systems, that it allows the user to prioritize the user pref-
erences and savings. Load is distributed by means of game theoretic approach which
has successfully reduced the electricity cost. Authors also analyze the performance
of algorithm that approaches to 96% closer to optimal solution. As limitations, their
proposed technique is not perfect in the presence of multiple energy resources.
Integer Linear Programming (ILP) technique is proposed in [10], for minimizing
the peak load and scheduling the load in SG. Moreover, another aim is to sched-
ule the power for power shift-able appliances and to optimize the operation time
for time shift-able appliances. Authors are able to schedule both optimal operation
time, optimal power and accomplished effective electricity consumption. Again, au-
thors did not address the UC issue. Authors proposed a power scheduling strategy
for the residential consumers in [11], to achieve a trade-off between the electricity
cost and discomfort. Day ahead pricing signal is used in this paper. The result shows
that the authors achieved low electricity price, reduction in PAR and achieved the
limited user comfort because of the trade-off between cost and user comfort. As lim-
Earth Worm Optimization for Home Energy Management System in Smart Grid 5
itation for this paper authors did not provide incentive based DR load adjustments.
As [12], elaborates that DR is essential for reducing electricity costs and loads. Au-
thors focused on developing strategy for Heating, Ventilating and Air-Conditioning
(HVAC) to respond to real time pricing for reduction of peak load. Proposed tech-
nique Dynamic Demand Response (DDR) changes the temperature’s set point to
control HVAC loads depending on electricity retail price after each 15 minute then
shift the loads of appliances partially. When DDR changed the set point of tem-
perature, discomfort level decreases. Simulation showed DDR in residential HVAC
systems can reduce peak load and electricity bill. Electricity cost for both hot and
cold months are reduced to 31% and 29% respectively.
3 Proposed Solution
In residential areas, every home is considerably being equipped with SM that pro-
vide two-way communication between customer and utility, shown in Fig. 2. In our
proposed system, we have categorized 13 appliances into 3 classifications; inter-
ruptible, non-interruptible and fixed appliances. We have scheduled the appliances
according to [6]. Classified appliances are mentioned below in Table1 .
Table 1: Classification of Appliances
Non-Interruptable Interruptable Fixed
Washing Machine Air Conditioner Light
Dish Washer Refrigerator Fans
Cloth Dryer Water heater Cloth iron
Space heater Microwave oven
Coffee maker
Interruptible appliances are those, that can be turned off any time during opera-
tion. Non-interruptible appliances, that cannot be interrupted during operation time,
while Fixed appliances are considered as non-manageable or non-modified appli-
ances because they are regular appliances that cannot affect much on load schedul-
ing issues. End users are allowed to customize the parameters of appliances ac-
cording to their will by interacting with EMC to establish connection with utility.
Moreover, end users are remained updated about the pricing scheme announced by
the utility through DSM. The aim of utility is to educate users about the efficient
energy consumption. Fig.2 elaborates the generic work flow of DSM for HEMS.
Peak and off peak hours are the time slots, where load of energy varies during the
whole day. In this work we have assumed that 11 to 18 hours are the slots for peak
hours during the whole day. Hence, the parameters are initialized in advance by end
user using DSM. Parameters for non-interruptible appliances are shown in Table2,
6 Mudabbir Ali et al.
Fig. 2: DSM Work Flow
Table3 for interruptable appliances and Table4 represents the parameters for fixed
Table 2: Non-Interruptable Appliances
Appliances α β γ (kWH)
Washing Machine 8 16 5 0.78
Dish washer 7 12 5 3.60
Cloth dryer 6 18 5 4.40
Table 3: Interruptable Appliances
Appliances α β (kWH)
Air conditioner 6 24 1.44
Refrigerator 6 24 0.73
Water heater 6 24 4.45
Space heater 6 24 1.50
According to Table2, αrepresents the starting time for the appliances, βrepre-
sents ending time of appliances in hours. For specific appliance, γrepresents the
number of operational hours in which energy is utilized at any time slot between
αand β, while represents the load of specific appliance in kWH. Similarly, in
Table3 for interruptible appliances, α,βand are same as described for Table2
however, there is no constraint of operational hours in which appliances have to op-
Earth Worm Optimization for Home Energy Management System in Smart Grid 7
Table 4: Fixed Appliances
Appliances (kWH)
Lighting 0.6
Fans 0.75
Clothes iron 1.5
Microwave oven 1.18
Toaster 0.5
Coffee maker 0.8
erate for limited hours between αand β. On the other hand in Table4, parameters for
fixed appliances are initialized in which appliances can be operated independently
at any hour during a whole day.
We have used CPP scheme for electricity bill calculation. The main purpose to use
this pricing scheme is that it provides the accurate information to customer about the
cost of electricity, so that customer can make decisions that how and when to use
electricity. CPP pricing scheme awares the customer about the peak hours during the
day. CPP rate offers lower prices during all other times. Therefore, the customer can
have the opportunity of better assessement and thus reduces the overall energy costs.
A bio-inspired meta-heuristic algorithm; EWA [4], is used for optimizing the elec-
tricity consumption. In EWA, there are two kinds of reproductions; Reproduction
1 and Reproduction 2. In nature, Reproduction 1 generates only 1 offspring either
from male or female, while Reproduction 2 may generates more than one offspring
at a time. Multiple crossover operators are used in order to improve the version of
crossover head, addition to this Cauchy mutation is implemented to extract the best
value after iterations. An abstract representation of natural behavior of earthworm
is mentioned in Fig. 3.
Fig. 3: Earth Worm Natural Behaviour.
8 Mudabbir Ali et al.
3.1 EWA Algorithm
The reproduction quality of earthworm performs multiple optimization steps, pro-
duction steps of earthworm is optimized by the following scenarios:
Every earthworm in population has the ability to regenerate its offspring in na-
ture and ever individual earthworm has the capability of 2 reproductions.
Every child of earthworm singular generated holds the entire genetic factor
whose length is equivalent to parental earthworm.
The earthworm singular with the fitness permits on straight next generation,
and can not be altered by operators. This can be an assurance that population of
earthworm can not fail in the increment in generations.
Steps of EWA algorithm are mentioned in Algorithm 1
Algorithm 1 EWA
1: procedure START
2: Initialization: Generates counter of t = 1; Set P as population of NP individual earthworm
which is randomly distributed in search space; numbers of kept earthworm are set as nKEW,
maximum generation MaxGn,αas similarity factor, proportional aspect β, constant γ= 0.9.
3: Evaluation of Fitness: each earthworm is evaluated individually according to its position
4: While till best solution is not achieved or t <MaxGen
5: All the earthworms in population are then sorted according to their fitness values
6: for i = 1 to NP (all earthworms) do
7: Generate offspring xi1 through Reproduction 1
8: Generate offspring through Reproduction 2
9: Do crossover
10: if i <nKEW then
11: set the number of particular parents (N) and the produced off springs (M); Select the N parents
using method i.e. roulette wheel selection; Generate the M offspring; Calculating xi2 according
to offspring M generated
12: else
13: Randomly an individual earthworm as xi2
14: end if
15: Update the location of earthworm end for i
16: for j = nKEW + 1 to NP (earthworm individuals non-kept)
17: do Cauchy mutation
18: end for j
19: Calculate the population according to the newly restructured positions;
20: t = t + 1.
21: Step 4:
22: end while
23: Step 5:
24: Best solution is extracted
25: End.
Our next task is to compare EWA with BFA [5]. For BFA, we have used same
appliances classification and parameters as mentioned in Table 3, Table 4 and Table
5. Then we merged both techniques in order to depict the clear justifications. BFA
Earth Worm Optimization for Home Energy Management System in Smart Grid 9
is also a bio-inspired technique in which animals with poor foraging strategies are
eliminated and replaced by healthy ones in nature. The main focus of this algorithm
is to allow cells to randomly transmit towards the optimal solution by means of
chemotaxis, swarming, reproduction and elimination-dispersal steps. Such behavior
involve different steps as shown in Algorithm 2.
Algorithm 2 BFA
1: procedure START
2: Step 1:
3: Chemotaxis: number of chemotactic steps measures the length of bacteria’s life time.
4: if favorable environment = true
5: bacteria = continue swimming
6: else
7: bacteria = direction changed
8: do swarming
9: bacterias attraction complete.
10: Step 2:
11: Reproduction: for optimized value
12: Calculate the reproduction of bacteria quality
13: then bacteria reproduce the generation
14: fitness value = found
15: creat next generation.
16: Step 3:
17: Elimination-dispersal: Cells are eliminated and new random samples are inserted for local
18: reproduction created for chemotaxis
19: End.
4 Simulation Results
For simulations, we have evaluated the performance of different techniques in MAT-
LAB. Scheduling of appliances are managed according to [6]. Different parameters
of appliances can be defined through customers also. We have conducted the simu-
lations on the basis of energy consumption, peak to average ratio, waiting time and
electricity cost.
In Fig 4, the numbers of hour are mentioned on the x-axis and load (kWhs) of
electricity is given on y axis of graph. After simulations, the load is maintained for
BFA scheduled appliances between 1 to 6 hours. BFA shows almost the constant
load of 2kWh at 1 to 6. While EWA permits a little bit higher load between same
time slots (1 to 6) that is almost 3 kWhs. On the other hand unscheduled appliances
are showing the consumption of electricity slightly higher than BFA at 1 to 3 hours
but less than EWA during the time slots of 3 to 5 hours. Load of BFA gradually
increases during time slots of 6 to 8 with the variant resultant load of 6 to 17 kWHs.
For EWA, in 6 to 10 hours, load increases from 3 to 18 kWHs. On the other hand
unscheduled appliances also depict the same behavior with different results having
10 Mudabbir Ali et al.
maximum load of 19 kWHs at 8 hour. After 11 hour, load of appliances starts de-
creasing for EWA and BFA while for unscheduled appliances, load decreases after
8 hour. We can not totally neglect the consumption of electricity in peak hours how-
ever; we at least accomplished to optimize the consumption to some extent using
BFA and EWA algorithms. In case of unscheduled appliances, we can clearly visu-
alize that the consumption is not smart. In peak hours, load is higher at peak hours
because no smart technique is used. We can see the effect of shifting the loads that
in peak hours, unscheduled appliances are not efficient while our proposed scheme
works well, another reason is that we have classified different appliances into three
categories. Overall, the loads of appliances are same during 24 hours but the con-
sumption of energy is optimized in order to avoid higher electricity cost.
Time (hours)
1 2 3 4 5 6 7 8 9101112131415161718192021222324
Load (kWh)
EWA Scheduled
BFA Scheduled
Fig. 4: Electricity Load for 24 Hours.
As PAR is concerned, we have utilized our techniques for optimization. PAR is
reduced which is beneficial to balance the power supply and demand. In case of PAR
reduction we have given more priority to interruptible and non- interruptible appli-
ances than fixed appliances. In Fig 5, we can see that there is a difference regarding
the PAR reduction, that EWA technique reduces PAR to almost 5% and BFA to 7%
as compared to non scheduled appliances. So therefore, regarding to PAR reduc-
tion BFA performs better than EWA. For avoiding peak formation, we have used
CPP pricing scheme that provides information to user about rates of electricity bills.
Hence reduction in electricity bills are accomplished. Moreover, EWA algorithm’s
quality of multiple crossovers assists to achieve best solution for optimizing the
problem. Unscheduled appliances show poor results because, they do not tackle the
peak formation problems while our scheduled schemes are designed to avoid peak
Earth Worm Optimization for Home Energy Management System in Smart Grid 11
formation in any hour of the day because appliances are optimally distributed for 24
Unscheduled EWA Scheduled BFA Scheduled
14 Peak to Average Ratio
Fig. 5: Peak to Average Ratio.
Time (hours)
1 2 3 4 5 6 7 8 9101112131415161718192021222324
Electricity Cost (cent)
EWA Scheduled
BFA Scheduled
Fig. 6: Electricity Cost.
Fig 6 elaborates that the result of scheduled appliances are optimal as compared
to unscheduled appliances in term of electricity cost reduction. For 24 hours, maxi-
mum electricity bill in case of unscheduled appliances are 1998 cents at peak hours
12 Mudabbir Ali et al.
Unscheduled EWA Scheduled BFA Scheduled
Total Cost
Fig. 7: Total Electricity Bill.
between 11 to 12 hours, while for BFA scheduled appliances we can see that during
11 to 12 hours the maximum cost lies almost at 1200 cents and in case of EWA
technique, the results are more efficient. During peak hours, the cost is reduced to
450 cents in same time slots (11 to 12). From simulation results, we can extract
the information in chunks that, during 1 to 6 hours both scheduled and unscheduled
appliances have almost same electricity cost. When we slightly go further from 7
to 17 hours we can see the drastic change that our proposed optimization technique
EWA is forming very little peak in peak hours, i.e. 200 to 450 then 450 to 130 cents.
On the other hand BFA shows slightly higher peak formation as compared to EWA
but less then unscheduled. Such accomplishment was achieved due to the BFA pa-
rameters (reproduction and elimination) and EWA’s two reproduction formations.
Then total electricity bill reduction is mentioned in Fig 7 to almost 45% for BFA
and 55% for EWA, the fact is that we have delayed the operation of some appliances
according to designed objective functions.
Waiting time of appliances in Fig 8 directly affects the cost of electricity bill and
User Comfort. Waiting time is increased because we have reduced the electricity
cost by shifting the load of appliances from peak to off peak hours. According to
Fig 8 BFA is better than EWA in term of waiting time. Waiting time factor has an
important role for user satisfaction, increasing waiting time User Comfort also sac-
rifices. The relationship between Electricity cost and waiting time is inversely por-
tion. So, in Fig 8 simulation, waiting time for EWA has increased but bill decreased.
EWA has sacrificed the user comfort and user has to wait for specific appliances to
be operated.
Earth Worm Optimization for Home Energy Management System in Smart Grid 13
BFA Scheduled EWA Scheduled
waiting time
5waiting time
Fig. 8: Waiting Time.
5 Conclusion
We have proposed the techniques for optimized electricity consumption in HEMS.
Through simulations we have deduced the results that EWA performs better than
BFA in term of cost and in term of PAR reduction BFA have a better results. Using
CPP pricing scheme PAR reduction is achieved. We are able to accomplish the op-
timal results after shifting the load of appliances from peak hours to off peak hours.
As there is a trade-off between cost reduction and UC, therefore in our work we have
compromised the UC. In future we will focus on reducing the waiting time to meet
the acceptable UC and concentrate on contribution of Renewable energy resources.
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... Ali et al. [74] have presented a simulated model for smart grid energy management and optimization using earthworm algorithm [75] and bacterial foraging algorithm [76,77]. Authors have used a pricing scheme so that the consumers get an idea of cost and use electricity accordingly and avoid usage of energy during peak hours to avoid the extra cost of electricity. ...
... For shorter-range communications, Bluetooth is preferred in smart homes [120,121]. In the smart home's network, the energy management controller controls the electronic devices [74]. The electronic devices consume considerable energy depending on their nature, as every smart home has an air conditioner for cooling, a heater for heating, purifiers for air quality, and light for illumination. ...
Full-text available
In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.
... In order to recognize apple image better, it is necessary to find a simpler and more efficient way to get the optimal threshold of image. In recent years, the successful application of swarm intelligence optimization algorithm in the field of science and engineering plays a positive role in the application of agricultural image segmentation, such as Fruit Fly Optimization Algorithm (FOA) [48], Preaching optimization algorithm [56], Fireworks algorithm [34], Interactive Search algorithm [43], Selection and Boundary search algorithm [61], Locust Swarm (LS) algorithm [5], Moth-Flame Optimization (MFO) algorithm [41], Shuffled Frog Leaping Algorithm (SFLA) [17], Harmony Search Algorithm (HSA) [21] and Artificial Bee Colony algorithm (ABC) [33], Grey Wolf Optimizer (GWO) [42], Human Mental Search (HMS) [44], Monarch Butterfly Optimization (MBO) [19], Slime Mould Algorithm (SMA) [35], Moth Search Algorithm (MSA) [53], Hunger Games Search (HGS) [60], Harris Hawks Optimization (HHO) [25], Earth Worm Optimization Algorithm (EWA) [1], Elephant Herding Optimization (EHO) [27], etc. However, all the above algorithms have the defect of easy convergence to local optimum. ...
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Considering that apples are susceptible to physiological diseases, an effective image segmentation method is a key point for the fruit diseases detection. It is necessary to develop a novel and effective segmentation algorithm for apple skin disease images. Aiming at the shortcomings of noise interference and over-segmentation of the maximum class variance (OTSU) method in image segmentation, an image segmentation algorithm with memory-based Fruit Fly Optimization algorithm and adaptive weighting factor were proposed and applied in the apple skin disease images. The iterative memory step size was applied to the updating process of the individual position of the flies after one-time optimization to achieve fast global convergence. Combining the improved algorithm with the classical Otsu method, the segmentation algorithm with memory-based fruit fly optimization algorithm was formed. The algorithm encodes and processes the apple skin disease images, selects the inter-class variance of the image as its fitness value, and then, the improved Otsu method was used to segment skin disease of apple images. The convergence of speed and accuracy of memory-based fruit fly optimization algorithm are significantly superior to the other six algorithms. Three types of apple skin disease images including black spot disease under strong illumination, black spot disease under medium illumination and bitterness disease under weak illumination conditions were selected for segmentation experiments. Compared with other region segmentation methods and edge segmentation methods, the results indicate that the Otsu’s segmentation algorithm with memory-based fruit fly optimization algorithm has much better effect in the segmentation experiments of apple skin disease images. The benchmark apple images test results show that the proposed method achieves better segmentation effect than other two segmentation methods. The results show that the proposed algorithm achieves better segmentation effect and higher performance stability.
... Eva Tuba et al. [16]compared the earthworm optimization algorithm(EWA) with PSO optimization with CEC 2013 bound constrained benchmark functions and the EWA algorithm performed better compared to PSO for the highest mean and standard function deviation values. EWA optimization algorithm was also implemented in smart grid applications for efficient electricity consumption in residential areas that performed better in terms of load(kWh) and electricity cost compared to Bacterial Foraging Algorithm [17]. ...
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Wireless Sensor Networks (WSNs) finds numerous applications in military, industry and civil application for event detection and object tracking. For unattended operation and long lifetime, sensor networks must operate under self configure mode without any central entity. The routing and data communication in WSN should be auto-configurable, dynamic and adaptive to the changes in the network environment. Optimization techniques for energy efficient routing, clustering and data aggregation in WSN is crucial to sustaining the network for long life and for effective utilization of limited bandwidth of the sensor network. In this paper, bio-inspired earthworm optimization algorithm (EWA) is used for optimal cluster head (CH) selection and data aggregation in the WSN and is also compared with other clustering methods like genetic algorithm(GA)-based clustering and particle swarm optimization(PSO) based clustering in terms of delay, throughput, energy and number of live nodes.
The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization.
Revolutions in human activities and lifestyles result in a transition from conventional to intelligent residential building infrastructure. Conventional heating, ventilation, and air conditioning (HVAC), refrigerator and lighting system challenges are addressed without taking into account building heat gains, outdoor illuminance, and temperature. Based on these parameters, a mathematical model for cost estimation of residential building energy consumption, considering indoor heat gains, outdoor temperature, outdoor illuminance, and TOU price has been developed. A total of 46 swarm intelligence based optimization algorithms are used to optimize different building parameters. These swarm intelligence algorithms (SIA) are compared using the convergence curves, statistical and box-plot analysis and the Bald Eagle search (BES) algorithm is found to be the best algorithm among all 46 SIAs. The mean energy consumption costs of the best algorithm, BES and the worst algorithm, fireworks algorithm (FA) are found to be Rs. 8.85 and Rs. 12.98, respectively. In addition, economic analysis has been conducted for the proposed study and it is compared with the existing models with building energy management systems (BEMS) and conventional model (without BEMS). It is observed that, based on this analysis, the cost savings achieved by the proposed study are nearer to 34% and 57% as compared to existing and conventional models.
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The smart grid has given users the ability to regulate their home energy more efficiently and effectively. Home Energy Management (HEM) is a difficult undertaking in this regard, as it necessitates the optimal scheduling of smart appliances to reduce energy usage. In this research, we introduced a meta-heuristic-based HEM system in this research, which incorporates Earth Worm Algorithm (EWA) and Harmony Search Algorithms (HSA). In addition, a hybridization based on EWA and HSA operators is used to optimize energy consumption in terms of electricity cost and Peak-to-Average Ratio (PAR) reduction. Hybridization has been demonstrated to be beneficial in achieving many objectives at the same time. Extensive simulations in MATLAB are used to test the performance of the proposed hybrid technique. The simulations are run for multiple homes with multiple appliances, which are categorized according to the usage and nature of the appliance, taking advantage of appliance scheduling in terms of the time-varying retail pricing enabled by the smart grid two-way communication infrastructure algorithms EWA and HSA, along with a Real-Time Price scheme. These techniques help us to find the best usage pattern for energy consumption to reduce electricity costs. These metaheuristic techniques have efficiently reduced and shifted the load during peak hours to off-peak hours and reduced electricity costs. In comparison to HSA, the simulation results suggest that EWA performs better in terms of cost reduction. In comparison to EWA and HSA, HSA is more efficient in terms of PAR. However, the proposed hybrid approach EHSA gives the maximum reduction in cost which is 2.668%, 2.247%, and 2.535% in the case of single, 10, 30, and 50 homes, respectively as compared to EWA and HSA.
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In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
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In this paper, an energy consumption management is considered for households (users) in a residential smart grid network. In each house, there are two types of demands, essential and flexible demands, where the flexible demands are further categorized into delay-sensitive and delay-tolerant demands. The delay-sensitive demands have higher priority to be served than the delay-tolerant demands. Meanwhile, in order to decrease the delay of delay-tolerant demands, such demands are allowed to be upgraded to the high-priority queue (i.e., the same queue that serves the delay-sensitive demands) with a given probability. An optimization problem is then formulated to minimize the total electricity cost and the operation delay of flexible demands by obtaining the optimal energy management decisions. Based on adaptive dynamic programming, a centralized algorithm is proposed to solve the optimization problem. In addition, a distributed algorithm is designed for practical implementation and the neural network is employed to estimate the pricing or demands when such system information is not known. Simulation results show that the proposed schemes can provide effective management for household electricity usage and reduce the operation delay for the flexible demands.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
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.
Earthworms are essential animals that aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimization algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms in nature. In EWA, the offspring are generated through Reproduction 1 and Reproduction 2 independently, and then, we used weighted sum of all the generated offsprings to get the final earthworm for next generation. Reproduction 1 generates only one offspring by itself that is also special kind of reproduction in nature. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully done by nine improved crossover operators that are an extended version of classical crossover operator used in DE (differential evolution) and GA (genetic algorithm). With the aim of escaping from local optima and improving the search ability of earthworms, the addition of a Cauchy mutation (CM) is made to the EWA method. In order to show the robustness of EWA method, nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed and they are compared between each other through twenty-two high-dimensional benchmarks. The results show that EWA23 (Uniform crossover operator is used in Reproduction 2) performs the best and is further benchmarked on forty-eight functions and an engineering optimization case. The EWA method is able to find the better function values on most benchmark optimization problems than seven other metaheuristic algorithms.
In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.
Minimizing the peak power consumption of electrical appliances under delay requirements is shown to be NP-hard. To address this, we propose a “family plan” approach that partitions users into groups and schedules users’ appliances to minimize the peak power consumption of each group. Our scheme leverages the social network topology and statistical energy usage patterns of users. To partition users into groups with the potential of reducing peak power consumption, our distributed clustering scheme seeks such a partition of users into groups that the total power consumption in each group of users achieves minimum variance. Then, given a set of jobs of users’ appliances to be scheduled in the next scheduling period, we use a distributed scheduling algorithm to minimize the peak power consumption of each group of users. Our simulation results demonstrate that our scheme achieves a significant reduction in user payments, peak power consumption, and fuel cost.
Demand-management programs intend to maintain supply-demand balance and reduce the total energy cost. In this paper, we propose a user-aware demand-management approach that manages residential loads while taking into consideration user preferences. Maximizing users’ savings and comfort can be two contradicting objectives. We identify a trade-off between these two objectives and propose an energy consumption optimization model, as well as a game theoretic approach to take this trade-off into account. User comfort is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort over savings. The proposed game is based on a modified regret matching procedure and borrows advantages of both centralized and decentralized schemes. Through simulations, we show that the proposed approach is scalable, converges in acceptable times, introduces a very limited amount of overhead in the system, achieves very high cost savings, and preserves users’ preferences. Extensive simulations are used to evaluate the performance of the optimization model and the proposed approach.
Demand response and dynamic retail pricing of electricity are key factors in a smart grid to reduce peak loads and to increase the efficiency of the power grid. Air-conditioning and heating loads in residential buildings are major contributors to total electricity consumption. In hot climates, such as Austin, Texas, the electricity cooling load of buildings results in critical peak load during the on-peak period. Demand response (DR) is valuable to reduce both electricity loads and energy costs for end users in a residential building. This paper focuses on developing a control strategy for the HVACs to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller (DDRC) changes the set-point temperature to control HVAC loads depending on electricity retail price published each 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the real-time retail price of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. In addition, a detailed single family house model is developed using OpenStudio and Energyplus considering the geometry of a residential building and geographical environment. This HVAC modeling provides simulation of a house. Comfort level is, moreover, reflected into the DDRC to minimize discomfort when DDRC changes the set-point temperature. Our proposed DDRC is implemented in MATLAB/SIMULINK and connected to the EnergyPlus model via building controls virtual test bed (BCVTB). The real-time retail price is based on the real-time wholesale price in the ERCOT market in Texas. The study shows that DDRC applied in residential HVAC systems could significantly reduce peak loads and electricity bills with a modest variation in thermal comfort.
With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.