Content uploaded by Nadeem Javaid
Author content
All content in this area was uploaded by Nadeem Javaid on Mar 22, 2019
Content may be subject to copyright.
Towards Efficient Energy Management
in a Smart Home Using
Updated Population
Hafiz Muhammad Faisal1, Nadeem Javaid1(B
), Zahoor Ali Khan2,
Fahad Mussadaq3, Muhammad Akhtar3, and Raza Abid Abbasi3
1Comsats University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
3NCBA&E, Multan, Pakistan
http://www.njavaid.com
Abstract. Energy management using demand side management (DSM)
techniques plays a key role in smart grid (SG) domain. Smart meters and
energy management controllers are the important components of the SG.
A lot of research has been done on energy management system (EMS)
for scheduling the appliances. The aim of current research is to organize
the power of the residential units in an optimized way. Intelligent energy
optimization techniques play a vital role in reduction of the electric-
ity bill via scheduling home appliances. Through appliance’s scheduling,
consumer gets feasible cost in response to the consumed electricity. The
utility provides the facility for consumers to schedule their appliances for
the reduction of electricity bill and peak demand reduction. The utility
company is allowed to remotely shut down their appliances in emer-
gency conditions through direct load control programs. A lot of research
has been done on energy management system (EMS) for scheduling the
appliances. In this work, an efficient EMS is proposed for controlling the
load in residential units. Meta-heuristic algorithms have been used for
the optimization of the user energy consumption schedules in an efficient
way. Our proposed scheme is used to minimize the user waiting time.
User waiting time is inversely proportional to the total cost and peak to
average ratio (PAR). Simulation result shows the minimum user waiting
time, however, the total cost is compromised due to the high demand
of the load. In the end, our proposed scheme will be validated through
simulations.
Keywords: Smart grid ·Home energy management system ·
Real time price
1 Introduction
Conventional energy production systems cannot fulfill the user’s need. It gives
rise to a number of different challenges that are faced by the electric power
c
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): AINA 2019, AISC 926, pp. 39–52, 2020.
https://doi.org/10.1007/978-3-030-15032-7_4
40 H. M. Faisal et al.
industry. Approximately, 10–30% energy is wasted in the methods used to sup-
ply the electric power from the source to end consumers. As we know the power
generation and power utilization is a one-way process so, the power generation
system is unable to control and manage the electricity consumption. Tradition-
ally, power generation systems and its demand side management (DSM) methods
are centrally distributed and only focused on industrial customers. Smart grid
(SG) solve maximum problems that are present in conventional energy produc-
tion systems. The bi-directional flow of power between the electricity generation
system and the end consumer allow many factors to be controlled to play a vital
role in energy wastage. The bi-directional communication is not only concerned
by the consumers for electricity price and maintenance schedules of the distri-
bution network, however, also motivate the providers to monitor and analyze
the real time power utilization data. Distributed energy system and information
technology are involved in the SG. Using SG, communication can be handled
and monitored with the proper mechanism. Recent developments of the SG allow
load management methodologies to be implemented more efficiently by permit-
ting them to utilize new technologies. DSM and demand response (DR) are
important components of SG. Between the power grid and consumers, DR play
a vital role. DR schemes are used to reduce the electricity consumption without
affecting the user’s comfort. Two types of DR programs are considered:
•Incentive base DR
•Time based DR
Using the incentive-based DR program, utility can turn ON and OFF the con-
sumers appliances with the term notice. Whereas Time based DR requires the
consumers participation for scheduling their according to the change in price sig-
nals. DR is very helpful in reduction of the consumers electricity bills based their
participation. The core objective of using multiple DR and DSM methodologies
is to alleviate the burden of the electricity bill and energy usage. The objec-
tive of sellers is to increase their profits whereas consumers want their personal
incentives. Smart meters (SM) are installed in the residential area. The rela-
tionship between SM and DSM consist of many different and connected parts
with many features and perspectives to consider. Without regard to exceptions,
the target of SM usage is to minimize energy consumption as well as peak for-
mation without losing the consumers comfort. SM provides the user’s complete
information of demand, supply and price signal. There are some methodologies
including mixed integer linear programming (MILP), linear programming, and
mixed integer non-linear programming (MINLP), etc., which can be applied for
minimization of the energy consumption expanses while managing the efficient
schedules of electricity. DSM itself is not a technique; it is more generally a col-
lection of strategies used to change the user’s energy patterns for obtaining their
suitable power distribution. The collection of strategies (i.e. DSM) encourage
the consumers to monitor and control multiple factors (e.g. power load, appli-
ance management) of power consumption which results in lower power wastage.
This can be achieved by applying various artificial intelligence algorithms. In
Towards Efficient Energy Management 41
DSM strategy consumers perform load management and shifting the load into
different time duration.
2 Related Work
Using MILP in [1] as a design technique, the main objective of this method
is to minimize the peak to average ratio (PAR) value and the total electricity
bill paid by consumers. The authors also focuses on balance load management
but the user comfort is not considered in MILP. There is a trade-off between
conventional systems and today’s renewable energy sources (RES). SG is a two-
way communication of utility and consumers and by saving this 10–30%, we cover
a lot of energy wastage problem. Cost minimization is a big challenge for today’s
researchers in SG. Using a genetic algorithm (GA) technique in the research
article [2], cost minimization is achieved at a low level with the integration of RES
and stored energy. When price and demand are higher than the stored energy
is helpful in specific time changes. Deployment and maintenance cost of storage
devices and RES has been ignored in this technique. Majority of researchers
have focused on residential areas only. Balancing the load in commercial and
residential areas is a big problem. However, using GA-DSM [3] algorithm in peak
hour, electricity consumption is reduced by 21% in an industry which is very
remarkable. The authors neglected the PAR value and user comfort feasibility.
The authors in [4] proposed scheme of MINLP is used to solve the cost min-
imization under the price tariff ToU. Although the cost minimization at the
peak hour is achieved, however, the author disregarded the PAR value which is
another important factor in SG. Dynamic programming [5] technique was used
for cost minimization and by scheduling the gadgets for various duration. This
will be done by the integration of RES and energy storage systems (ESS) with
SG. Residents have the capability to produce the electricity from RES. A con-
sumer can sell the additional electricity to the neighbours. The important factor
of installation and maintenance has been ignored in RES. The novel schedul-
ing model [6] of the combination of GA and binary partial swarm optimization
(BPSO) algorithms. The goal of this technique is electricity bill and PAR min-
imization. In DSM, the user can manage their home appliances by shifting the
load to another time so the load demand is a key factor in this regard. By shifting
the load and by using cuckoo search algorithm (CSA) [7] algorithm peak load
has been reduced by 22%. Balanced load curve generated by the CSA algorithm
shows the user preference for appliance usage, load shifting can then be done
by using this curve. For real-time schedule controller, new binary backtracking
search algorithm (BBSA) [8] was proposed. By using the load limit home appli-
ances are shifted from peak hour and electricity price reduced 21% per day in
comparison to PSO algorithm. Huang et al. [9] developed the two point esti-
mation method embedded with PSO method for reducing the computational
complexity in a home energy management systems (HEMS). This scheme is
intelligent enough in comparison to GPSO-LHA in the context of computational
burden. Author [9] did not consider the cost of electricity and PAR value which
42 H. M. Faisal et al.
are the important factor in HEMS. The author in [4] proposed using MINLP
technique cost minimization is achieved under the price tariff of ToU however,
PAR is not considered in this technique. Utilizing DSM, 30% power utiliza-
tion can be minimized without knowing the usage on the user side. The overall
objective of load management is to schedule a load during high demand to low
demand intervals. This can be done by the combination of the GA algorithm and
bacterial foraging (BF) in [10]. Hybrid technique [10] minimizes the electricity
bill and PAR using the load management shifting. Hybrid scheme uses the RTP
signal for reducing the electricity bills and PAR.
In HEMS, SG has a significant functionality to minimize the users’ cost using
DSM. Meta heuristic scheme is designed in [11] for the reduction of cost and PAR
value. Applying the combination of GA and CSA, we achieved the minimized
cost and PAR value as well, under the RTP price tariff, as compared to other
techniques with the desirable user waiting time. In DSM user performed pri-
orities that are set to schedule the appliances in HEMS. The authors proposed
[12] evolutionary accretive comfort algorithm (EASA) which is comprised of four
postulations. These postulations are defined according to the time and device
bases priorities. Based on the input values EASA generates optimal energy solu-
tion pattern which satisfies the user budget. The author defines three different
user budgets to find the absolute solutions. Ma, Yao, et al. [13] defines discom-
fort function for two different type of gadgets. First category is flexible starting
time and the other is flexible power devices. Authors in [13] considered a multi-
objective function for user comfort and cost parameters.
The proposed bat algorithm in [14] can be applied to obtain the optimum
result. By applying this algorithm energy consumption can be reduced which
is simply a non-linear optimization problem. The main goal of current work is
to decrease the power usage and increase consumer comfort standards in the
residential area. In Al Hasib [15], the author considered bidirectional energy
exchange between SG and small residential building. The main goal of this paper
is to maintain the balance between electricity cost and user comfort. Here the
appliances load was categorized into three categories. Based on a declining block
rates (DBR), the author proposed a comfort demand function. The authors in
[16] recommended a min max load scheduling (MMLS) algorithm used to reduce
the PAR while optimizing the operational comfort level (OCL) of user’s. It is
important to note the difficulty faced by user’s, under the control of HEMS when
reducing power consumption. For residential demand response, the author has
proposed an intelligent algorithm which analyze the effect of HEMS operations
on the use’r comfort [18].
The authors in [19], smart homes are integrated with SG to purchase/sell
electricity in peak load demand. The proposed scheme objective is to minimize
the cost and PAR along with the increase in earning profits. In this model two
optimization techniques, CSA and strawberry algorithm (SA) are used with RES:
wind turbine (WT), photovoltaic panels (PV) and ESS. The simulations results
show that the proposed scheme efficiently reduce cost and PAR with maximiz-
ing earning’s. CSA optimization technique outperforms than SA to minimizing
Towards Efficient Energy Management 43
cost and PAR during peak load demand. Many techniques and models in [20–25]
were addressed using SG and micro SG with standalone and connected-grid with
HEMS are the emerging research areas in the last few years. In [26,27], many
authors have proposed scholastic programming models however, dynamic pro-
gramming schemes in [28,29] were proposed. These static and dynamic models
need precise tuning in their algorithm to manage the parameters and to control
them. The authors in [30] proposed DSM model, where RESs are connected.
The proposed model consist of three layers: the utility, the customer and the
DR aggregator. The role of the DR aggregator has been defined as a mediator
that communicate with both customers and the utility. The experimental out-
comes demonstrate that consumers can get profit from the proposed design: the
DR aggregator can make the profit by providing DR services; the utility can
reduce the generation cost; customers can save money on their monthly elec-
tricity bill. Evolutionary algorithms are used for load shifting in order to reduce
the cost of the customers [32]. All service sides have data sets, where schedul-
ing problem have been managed to solve the efficiency problem, the industry
faced more problem because of big power consumption appliances. Due to high
load user’s need to use the energy more intelligently in both residential and
commercial sector.
3 Problem Formulation
DSM techniques are proposed to handle the irregular consumption of electric-
ity which is the complex task to tackle. Consumers require more electricity at
certain time intervals, so, there is a possibility for peak formation and electric-
ity blackouts. In this situation, intelligent algorithms are required for EMS to
help user’s for scheduling the power from high demand intervals to low demand
intervals in an effective manner. Most of the techniques have been designed to
reduce the peak formation, electricity cost and user’s discomfort [1,33,35]. How-
ever, there is always a trade-off between PAR, electricity bill and user comfort
standards. RES integration is lacking in [1] for enhancing the comfort standards
of the residents. In [33], appliance priorities are not considered in an automatic
fashion. The study in [35] prioritizes the appliances manually; however, they need
automatic priority specifications for controlling the whole system efficiently. So,
there is need to design an EMS which can optimize the energy consumption of
the residential sector consumers efficiently. Meta-heuristic algorithms are used
for the optimization of the energy consumption schedules defined by user’s.
4SystemModel
We have developed a home energy management (HEM) method for controlling
the energy consumption load and price of the smart homes. Initially, this algo-
rithm starts with a single smart home and 15 appliances in it. These appliances
are categorized into two main categories: schedulable and non-schedulable appli-
ances. Smart meter decides the operation time of the appliances according to
44 H. M. Faisal et al.
their power rating and defined pricing tariff from the utilities. The power rating
varies for each appliance and scheduling of these appliances is done in such a
way to achieve the optimum solution from the designed objective function. In
this work, two pricing tariffs are used: RTP and CPP for checking their impact
on the customers electricity bills. DR and DSM used in SG provide more sta-
bility and reliability in grid operation. The aim of this work is to reduce the
PAR, energy consumption and cost, and to enhance the consumers preferences
according the consumers standards. Main architecture of this system is visual-
ized in Fig. 1. The electricity bill is conveyed to consumers through the smart
meter. HEM controller decides which appliances should be turned on using the
defined pricing signals during the peak and off-peak hours. The core objective
of this study is to reduce the power utilization, PAR and electricity cost while
maximizing the user comfort. However, there is always a trade-off which occurs
between electricity bill and consumers preferences. Total energy utilization is
formulated using Eq. 1. Equations 2and 3is used to calculate the PAR and cost
respectively.
Load =
24
t=1
(PR∗S(t)),S(t)=[1/0] (1)
PAR =(Max(Load)/Av g (Load)) (2)
Cost =
24
t=1
(PP ∗PR∗S(t)),S(t)=[1/0] (3)
Fig. 1. System model
Towards Efficient Energy Management 45
5 Optimization Algorithms
Traditional optimization algorithms, which belongs to mathematical techniques,
are not working satisfactorily if a large number of devices exists. Computa-
tionally power is also slow and time consuming. Behind this reason, we apply
heuristic schemes grey wolf optimization (GWO) and Jaya algorithm to obtain
our objectives. We proposed a JGO algorithm, which is discussed in details in
the subsection below.
5.1 GWO
GWO is a novel meta-heuristic algorithm. It consists of four types of wolves:
alpha, beta, delta and omega. There are three main phases of hunting in GWO.
Alpha is used as the most fittest solution between beta, delta and omega. List
of main steps of GWO is given.
1. Encircling the prey
2. Hunting
3. Exploitation: It is also called attacking the grey
4. Exploration: It is also known as search for prey.
5.2 Jaya Algorithm
Suppose f(x) is the target function to be minimized or maximized. Assume that
there are “m” number of design variable and “n” number of candidate solutions,
at any iteration i, where k = 1, 2, 3, 4.....n. Let the best value of f(x) (i.e.
f(x)best) is obtained by the best candidate names as “best” and vice versa for
the worst candidate (i.e. f(x)worst). If the value for jth variable during the ith
iteration for kth candidate is Xj,k,i then this value is modified as per the following
equation given below:
´
Xj,k,i =Xj,k,i +r1,j,i (Xj, best, i..........) (4)
Where the value of j variable, for the “best” candidate is Xj, best, i and vice
versa for the worst candidate, where the updated value of Xj,k,i and r1,j,i and
r2,j,i which are the two random number for the jth variable is X’j,k,i in the range
of [0,1].
5.3 Updated Population
In this section, we described our proposed scheme. The population update is
performed in GWO, the updating is totally dependent on the placement of the
primary three accurate candidates. So in GWO, we tend to note initial 3 good
solutions, oblige the other wolves or search agents to change and update their
locations on the idea of the placement of the most effective search agent. Here,
Eq. 5is used to find the effective search agent location.
X(t+1)= X1+X2+X3
3(5)
46 H. M. Faisal et al.
Hence, we are able to say that Jaya population update strategy is good as
compared to the GWO as a result of here, our aim is to search out the more
effective result as possible and also the a lot of optimized result may be possible
by having a large random and various population. Where initialization of GWO
is better than Jaya initialization. So, we selected Jaya based population update
strategy and GWO based initialization strategy, so that, we proposed a new
proposed algorithm.
6 Pseudo Code of the Proposed Scheme
1. Generate initial search agents
Gi(i=1,2, ...., n) (6)
2. Initialize the vector’s
3. Calculate the fitness value
4. Iter = 1
5. repeat
6. for i = 1:
7. end for
8. Calculate the fitness value of all solutions
9. Update the value
10. Update the vectors
11. Iter = Iter+1
12. until Iter ≥maximum number of iterations
13. output best solution.
7 Simulation and Reasoning
In this section, we demonstrate simulation results and evaluate the performance
of the proposed algorithm. The load, cost and user’s waiting time for each appli-
ance are represented in terms of hours, cents and kWh. By applying RTP signal
in a smart home, we achieve maximum user comfort time, however, at some level
cost and PAR values are maximized. There is always a trade-off between PAR,
cost and user comfort. In RTP price signal tariff, electricity price varies during
different time slots of a single day. Figure 2shows the complete details of price
per hour in 24 h. In afternoon, the price rate is two times higher. Per hour cost is
increased due to unbalancing of the price at peak time. This is shown in Fig. 3.
Figure 4shows the hourly based energy consumption pattern in both scheduled
and unscheduled scenarios.
Figure 5represents the PAR value of GWO, Jaya and proposed algorithm. It
demonstrates that proposed technique result is better than the GWO algorithm,
however, Jaya algorithm is better than our proposed algorithm. PAR value of
proposed algorithm is 50% less than the GWO so as compared to GWO we
achieves PAR goal in a smart home, however; Jaya technique is suitable for the
Towards Efficient Energy Management 47
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
8
10
12
14
16
18
20
22
24
26
28
Time (hour)
Price (cent/kWh)
RTP
Fig. 2. Total cost
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
50
100
150
200
250
Time (hour)
Per hour cost (cent)
Unscheduled
GWO
JGO
Jaya
Fig. 3. Per hour cost
reduction of PAR in RTP signal. Figure 6shows the total cost of GWO, Jaya and
proposed algorithm. It is shown that the overall cost of the algorithm is higher
as compared to GWO and Jaya. This effects the overall cost per day in the RTP
signal. Reduction of electricity cost is the core objective of DSM in SG. With
the comparison of Jaya and GWO, cost of our proposed technique is higher due
to higher user demand. Figure 6shows the total cost of three different schemes
in terms of cents. Complete load details of unscheduled load, Jaya algorithm,
GWO algorithm and proposed algorithm are shown in Fig. 7. It can be observed
that all values are approximately equal. Graphical representation of the load is
useful for calculating the overall cost in the RTP signal.
The user waiting time (hour) is shown in Fig.8. The consumer satisfaction
level is measured in terms of waiting time. In our proposed scheme, user’s satis-
48 H. M. Faisal et al.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
2
4
6
8
10
12
Time (hour)
Per hour load (kWh)
Unscheduled
GWO
JGO
Jaya
Fig. 4. Per hour load
Unscheduled GWO JGO Jaya
0
1
2
3
4
5
6
7
8
PAR
Fig. 5. PAR
Unscheduled GWO JGO Jaya
0
200
400
600
800
1000
1200
1400
1600
Total cost (cent)
Fig. 6. Total cost
Towards Efficient Energy Management 49
Unscheduled GWO JGO Jaya
0
20
40
60
80
100
120
Total load (kWh)
Fig. 7. Total load
GWO JGO Jaya
0
1
2
3
4
5
6
Waiting time (hour)
Fig. 8. User waiting time
faction is the time limit a user waits for a particular appliance to turn on. So user
waiting time is inversely propositional to cost and PAR. We achieved minimal
user waiting time for high load and cost in DSM. Our proposed scheme achieved
minimum user waiting time however, cost and PAR values are compromised.
Proposed scheme of user waiting time is low as compared to GWO and Jaya.
While, increasing the cost and PAR, the waiting time increases, which is shown
in Fig. 8. Our proposed scheme tries to achieve the maximum trade off between
user waiting time and cost.
8 Conclusion
In this paper, we evaluate a load management problem in a smart home for
different electrical appliances. These appliances are scheduled using the meta
heuristics techniques according to their consumption pattern. We evaluate two
50 H. M. Faisal et al.
meta heuristic algorithms performance on the parameters of cost, PAR and user
comfort. Simulation result shows the tradeoff of cost and user waiting time.
Results show proposed updated population scheme is effective as compare to
GWO and Jaya in term of waiting time. In future, we will combine RES into SG
for PAR reduction.
References
1. Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appli-
ances via MILP. Energies 8(1), 217–232 (2014)
2. Mary, G.A., Rajarajeswari, R.: Smart grid cost optimization using genetic algo-
rithm. Int. J. Res. Eng. Technol. 3(07), 282–287 (2014)
3. Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side
management for smart grid. Wireless Pers. Commun. 93(2), 481–502 (2017)
4. Setlhaolo, D., Xia, X., Zhang, J.: Optimal scheduling of household appliances for
demand response. Electr. Power Syst. Res. 116, 24–28 (2014)
5. Samadi, P., Wong, V.W.S., Schober, R.: Load scheduling and power trading in
systems with high penetration of renewable energy resources. IEEE Trans. Smart
Grid 7(4), 1802–1812 (2016)
6. Ullah, I., Javaid, N., Khan, Z.A., Qasim, U., Khan, Z.A., Mehmood, S.A.: An
incentive-based optimal energy consumption scheduling algorithm for residential
users. Procedia Comput. Sci. 52, 851–857 (2015)
7. Cakmak, R., Altas, I.H.: Scheduling of domestic shiftable loads via Cuckoo Search
optimization algorithm. In: 2016 4th International Istanbul Smart Grid Congress
and Fair (ICSG), pp. 1–4. IEEE, April 2016
8. Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real
time optimal schedule controller for home energy management system using new
binary backtracking search algorithm. Eng. Build. 138, 215–227 (2017)
9. Huang, Y., Wang, L., Guo, W., Kang, Q., Wu, Q.: Chance constrained optimization
in a home energy management system. IEEE Trans. Smart Grid (2016). https://
doi.org/10.1109/TSG.2016.2550031
10. Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z.A., Qasim, U.: Demand side
management using hybrid bacterial foraging and genetic algorithm optimization
techniques. In: 2016 10th International Conference on Complex, Intelligent, and
Software Intensive Systems (CISIS), pp. 494–502. IEEE (2016)
11. Aslam, S., Javaid, N., Khalid, A.: An efficient home energy management scheme
using cuckoo search. In: International Conference on P2P, Parallel, Grid, Cloud
and Internet Computing 3PGCIC 2017: Advances on P2P, Parallel, Grid, Cloud
and Internet Computing, pp 167–178 (2017)
12. Khan, A., Javaid, N., Khan, M.I.: Time and device based priority induced comfort
management in smart home within the consumer budget limitation
13. Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand
response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)
14. Mohan, N., Sivaraj, R., Pyira, R.D.: A comprehensive reviews of bat algorithm and
its application to various optimization problems. Asian J. Res. Soc. Sci. Humanti.
6, 676–690 (2016)
15. Al Hasib, A. Nikitin, N., & Natvig, L. (2015) Cost comfort balancing in a smart
Residential building with bidirectional energy trading. Sustainable interent and
ICT for sustainability ( SustainIT), 2015 (pp, 1-6)
Towards Efficient Energy Management 51
16. Chen, Y., Lin, R.P., Wang, C., Groot, M.D., Zeng, Z.: Consumer operational com-
fort level based power demand management in the smart grid. In: Proceedings of
the 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe),
Berlin, Germany, 14–17 October 2012, pp. 1–6 (2012)
17. Hassan, N.U., Khalid, Y.I., Yuen, C., Huang, S., Pasha, M.A., Wood, K.L., Kerk,
S.G.: Framework for minimum user participation rate determination to achieve
specific demand response management objectives in the residential smart grids.
Int. J. Electri. Power Energy Syst. 74, 91–103 (2016)
18. Kuzlu, M.: Score-based intelligent home energy management (HEM) algorithm for
demand response applications and impact of HEM operation on customer comfort.
IET Gener. Transm. Distrib. 9, 627–635 (2015)
19. Aslam, S., Javaid, N., Khan, F.A., Alamri, A., Almogren, A., Abdul, W.: Towards
efficient energy management and power trading in a residential area via integrating
a grid-connected microgrid
20. Abhinav, S., Schizas, I.D., Ferrese, F., Davoudi, A.: Optimization-based AC micro-
grid synchronization. IEEE Trans. Ind. Inform. 13, 2339–2349 (2017)
21. Dehghanpour, K., Nehrir, H.: Real-time multiobjective microgrid power manage-
ment using distributed optimization in an agent-based bargaining framework. IEEE
Trans. Smart Grid 2017. 2708686
22. Liu, J., Chen, H., Zhang, W., Yurkovich, B., Rizzoni, G.: Energy management
problems under uncertainties for grid-connected microgrids: a chance constrained
programming approach. IEEE Trans. Smart Grid 8, 2585–2596 (2017)
23. Luo, Z., Wu, Z., Li, Z., Cai, H., Li, B., Gu, W.: A two-stage optimization and
control for CCHP microgrid energy management. Appl. Therm. Eng. 125, 513–
522 (2017)
24. Moga, D., Petreus, D., Muresan, V., Stroia, N., Cosovici, G.: Optimal generation
scheduling in Islanded microgrids. IFAC-PapersOnLine 49, 135–139 (2016)
25. Chen, J., Chen, J.: Stability analysis and parameters optimization of islanded
microgrid with both ideal and dynamic constant power loads. IEEE Trans. Ind.
Electron. 65, 3263–3274 (2017)
26. Su, W., Wang, J., Roh, J.: Stochastic energy scheduling in microgrids with inter-
mittent renewable energy resources. IEEE Trans. Smart Grid 5, 1876–1883 (2014)
27. Shuai, H., Fang, J., Ai, X., Tang, Y., Wen, J., He, H.: Stochastic optimization
of economic dispatch for microgrid based on approximate dynamic programming.
IEEE Trans. Smart Grid 2018 (2018). 2798039
28. Razmara, M., Bharati, G.R., Shahbakhti, M., Paudyal, S., Robinett III., R.D.:
Bilevel optimization framework for smart building-to-grid systems. IEEE Trans.
Smart Grid 9, 582–593 (2016)
29. Esfahani, M.M., Hariri, A., Mohammed, O.A.: A multiagent-based game-theoretic
and optimization approach for market operation of multi-microgrid systems. IEEE
Trans. Ind. Inform. (2018)
30. Li, D., Sun, H., Chiu, W.-Y., Vincent Poor, H.: Multiobjective Optimization for
Demand Side Management in Smart Grid
31. Macedo, M.N.Q., Galo, J.J.M., De Almeida, L.A.L., Lima, A.D.C.: Demand side
management using artificial neural networks in a Smart Grid environment. Renew.
Sustain. Energy Rev. 41, 128–133 (2015)
32. Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart
grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)
33. Elham, S., Shahram, J.: Optimal residential appliance scheduling under dynamic
pricing scheme via HEMDAS. Energy Build. 93, 40–49 (2015)
52 H. M. Faisal et al.
34. Adika, C.O., Wang, L.: Smart charging and appliance scheduling approaches to
demand side management. Int. J. Elect. Power Energy Syst. 57, 232240 (2014)
35. Jovanovic, R., Bousselham, A., Bayram, I.S.: Residential demand response schedul-
ing with consideration of consumer preferences. Appl. Sci. 6(1), 1–16 (2016)
36. Shirazi, E., Zakariazadeh, A., Jadid, S.: Optimal joint scheduling of electrical and
thermal appliances in a smart home environment. Energy Convers. Manage. 106,
181–193 (2015)
