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Ant Colony Optimization Based Energy Management Controller for Smart Grid


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In this paper, we introduce a generic architecture for demand side management (DSM) and use combined model of time of use tariff and inclined block rates. The problem formulation is carried via multiple knapsack and its solution is obtained via ant colony optimization (ACO). Simulation results show that the designed model for energy management achieves our objectives; it is proven as a cost-effective solution to increase sustainability of smart grid. The ACO based energy management controller performs more efficiently than energy management controller without ACO based scheduling in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
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Ant Colony Optimization based Energy
Management Controller for Smart Grid
Sahar Rahim1, Zafar Iqbal2, Nusrat Shaheen1, Zahoor Ali Khan3,4,
Umar Qasim5, Shahid Ahmed Khan1, Nadeem Javaid1,*
1COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan
2UIIT, Pir Mehr Ali Shah Arid Agriculture University, Rawalpind 46000, Pakistan
3Faculty of Engg., Dalhousie University, Halifax, NS B3J 4R2, Canada
4CIS Higher Colleges of Technology, Fujairah 4114, United Arab Emirates
5Cameron Library, University of Alberta, Edmonton, AB, T6G 2J8 Canada
Corresponding author:,
Abstract—In this paper, we introduce a generic architecture
for demand side management (DSM) and use combined model
of time of use tariff and inclined block rates. The problem
formulation is carried via multiple knapsack and its solution is
obtained via ant colony optimization (ACO). Simulation results
show that the designed model for energy management achieves
our objectives; it is proven as a cost-effective solution to increase
sustainability of smart grid. The ACO based energy management
controller performs more efficiently than energy management
controller without ACO based scheduling in terms of electricity
bill reduction, peak to average ratio minimization and user
comfort level maximization.
Keywords: Smart Grid; Demand Side Management; Multiple
Knapsack Problems; Time Of Use Tariff; Inclined Block Rate;
Renewable Energy Sources.
TRADITIONAL electrical power system is inadequate to
meet modern power grid challenges such as reliability,
stability, robustness, etc. [1]. Thus, a new infrastructure is
needed to smartly meet these challenges and reduce pressure
on global environment. In this regard, smart grid (SG) in-
tegrates communication technologies, computational abilities,
control systems and sensors with existing grid and enables two
way flow of information between utility and end users. Main
aims of SG are to enhance efficiency, sustainability, capacity
and customer engagement [2].
One of the important aspects of SG is demand side man-
agement (DSM) which is the best way to maintain balance
between demand and supply. Two main functions of DSM are
load management and demand response (DR). Load manage-
ment focuses on the improvement of energy efficiency [3],
while DR is a responsive action taken by a customer against
dynamic price models [4]. The common objectives of SG are
electricity bill reduction, minimization of aggregated power
consumption and minimization of both electricity bill and
aggregated power. To achieve these objectives, many DSM
techniques and algorithms are proposed in the previous years;
integer linear programming [5], mixed integer linear program-
ming [6], mixed integer non-linear programming [7], convex
programming [8], etc. However, these techniques can not
tackle large number of different household appliances having
unpredictable, non-linear and complex energy consumption
patterns due to randomness in human behavior. Moreover, to
attain electricity cost minimization objective, they ignore user
comfort level and their electricity pricing model is also not
compatible with real scenarios.
In this paper, ant colony optimization (ACO) technique is
used for DR (in DSM) due to its exceptional characteristics;
flexibility for specified constraints, ease of implementation,
low computational complexity and low computational time
[9]. We first design an energy management controller (EMC)
model smart homes using multiple knapsack problem (MKP)
and then apply ACO to get feasible solution for designed
objective function. To calculate electricity bills, we use time
of use (TOU) tariff model with inclined block rate (IBR), so
that peak formation is avoided. Effectiveness of the designed
EMC model is shown via simulations where unscheduled and
ACO based schedules cases are compared in terms of energy
consumption pattern, electricity bill, peak to average ratio
(PAR), user comfort level, and execution time.
The rest of the paper is organized as follows. Section II
briefly describes related work. Section III explains motivation
for proposed work. Section IV describes system model and
section V deals with problem formulation. Simulation results
are discussed in section VI. Finally, paper is concluded in
section VII by pointing out the future work.
In [15], authors investigate the problem of household appli-
ance scheduling to enhance energy efficiency of electrical grid
and provide benefits to end users. They proposed a solution
that optimally schedules a set of appliances. To minimize
customer electricity bills and maintain energy consumption
within a limit, they use day-ahead variable peak pricing model
and map their problem by using MKP. By limiting the energy
demand within certain capacity, problem of load shedding
can be removed. Results show that this model effectively
Fig. 1: SG architecture
reduces utility electricity bills while keeping power consump-
tion within pre-defined limits. Another model of home energy
management controller for residential users is proposed in
[16]. Objective function is formulated by knapsack problem
and dynamic programming approach is used to solve problem
and to set consumer preferences for each appliance. These
priorities were the value of appliances that are used to schedule
the appliance to satisfy their operational time constraints to
avoid peak formation and to reduce electricity cost.
In [10], authors present an efficient model of DSM that
reduces PAR and electricity bills for residential, industrial
and commercial users. Scheduling problem is formulated as
a minimization problem and then problem is evaluated by
using heuristic evolutionary approach. Heuristic algorithms
show better results because of their flexible nature that allow
the implementation of individual load pattern in order to
minimize inconvenience. Proposed model is beneficial for
both utilities and customers in a way that PAR reduction
causes minimization in the number of peak power plants
while incentive based model helps consumer to reduce their
electricity bills. Simulation results show that the proposed
DSM strategy achieves significant savings, while reducing the
peak load demand of the smart grid.
In [11], authors discuss an efficient architecture for energy
management system by using home area network (HAN) for
residential users. They combine real-time pricing (RTP) tariff
model with the IBR because when only the RTP is adopted,
there is a risk that most of the appliances operate during the
hours of lowest electricity price that cause peak formation. To
strengthen the stability of electricity system, peak formation
must be avoided. To solve these issues in an optimized way,
objective function is formulated. As this kind of optimization
problem is non-linear, therefore they use GA to optimize their
problem. Simulation results shows that proposed model is very
effective to reduce PAR and electricity cost.
Another DSM model is proposed in [12] for residential users
to reduce PAR and electricity bill minimization. GA is used
to get optimal start time of each appliance in each time slot
while satisfying its operational constraints. There is a tradeoff
between electricity cost and waiting time. When waiting time
of an appliance is zero, its electricity cost is increased and vice
versa. Combined model of RTP with IBR is used to avoid peak
formation. Simulations are carried out for single and multiple
users. Results show the effectiveness of proposed DSM model
for both single and multiple user scenarios.
An efficient heuristic approach is presented in [17] for
scheduling of smart appliances in residential area. The pro-
posed algorithm is evaluated by comparing the electricity cost
and computational time with an exact algorithm. Variable
energy price model is used for scheduling of appliances.
Hourly prices for electricity, the operation start times of set of
appliances are optimized to reduce cost of energy consumption
while satisfying the operational and peak power constraints.
Results show that electricity cost obtained by heuristic algo-
rithm is within 5% of the optimal cost of exact algorithm
whereas computational time is reduced by exponential factor.
In SG, DSM enables more efficient and reliable grid op-
erations. Its two main functions are energy management and
demand side control activities for end users. In residential area,
every smart home is equipped with EMCs and smart meters
to make stable and reliable bi-directional communication be-
tween utilities and customers. All elements, such as electri-
cal appliances, sensors, local generation and energy storage
systems (ESSs) give their information to EMC through HAN
and EMC controls scheduling of appliances. After collecting
all information, EMC sends it to SG domain through WAN.
There are various wireless solutions for communication links
between the smart meters and the EMCs such as ZigBee, Z-
Wave, Wi-Fi, or a wired (HomePlug) protocol [1]. Simple
architecture of DSM is shown in fig. 2. In residential area
Distributed RESs
Smart devices
Residential area
SG domain
Service provider
Two way communication
One way communication
Fig. 2: Components of DSM
based DSM, we consider Nsmart homes and Msmart
appliances. In this model, all smart homes have smart metering
system and EMC. End users change their energy usage accord-
ing to incentive based schemes offered by utilities. In each
home, consumer inputs different parameters of appliances to
appliances scheduler and then appliance manager gives signal
to various appliances about their on/off status. For electricity
pricing model, TOU tariff is used to calculate electricity bill
against the energy consumption cost per day. In order to
design the optimization model for home energy management,
we have categorized the load according to the characteristic
of appliances and life style of end users as discussed in the
following section.
A. Load categorization
We classify appliances into three categories; fixed, shiftable
and elastic appliances according to their power consumption
pattern and time of use [18]. Detail of all these categories is
given as follow:
1) Fixed appliances: These are also called regular appli-
ances because their usage or length of operation can not be
modified. For example, lights, fans, clothes iron, microwave
oven, toaster, tv, etc. We represent fixed appliances by Fed and
its power consumption as ν.
2) Shiftable appliances: These are also called burst load
because these are manageable and can be shifted in time
without altering their load profile. For example, washing
machine, dish washer, clothes dyer, etc. We denote shiftable
appliances by Sed and their power consumption by . Each
shiftable appliance is characterized by its length of operation
which is denoted as τsed and it is pre-defined by end users
each day.
3) Elastic appliances: These are also called interruptible
appliances because these are fully controllable in terms of both
usage time and power consumption profile. For example, air
conditioner, refrigerator, water heater, space heater, etc. We
represent elastic appliances by Eed and its power consumption
is denoted by κ. Each elastic appliance eed Eed has power
rating ρeed , power quantity factor λeed, length of operation
τeed , start time αeed and end time βeed . These attributes are
set by the consumer.
B. Energy consumption model
Let A={a1, a2, a3, . . . , am}be the set of appliances
such that a1,a2,a3,· · · ,amare number of appliances that
belong to each category. If tT={1,2,3,· · · ,24 }de-
notes the scheduling horizon, then hourly energy consumption
demand of a appliance is given as,
Ea(t) = {Ea
t3+. . . +Ea
t24 }(1)
where, Ea
t3,· · · ,Ea
t24 denotes energy consumption
demand of each appliance in the respective time slots. The
per day total energy consumption demand for all appliances
is calculated as follows,
t=1 A
C. Energy price model
A number of tariff models are available to define electric
energy prices for a day or for short time duration. Among
these, TOU tariff model is defined for electricity prices depend
on the time of day and are pre-defined in advance. Critical
peak pricing (CPP) is a variant of TOU in which price is
considerably raised in case of emergency situations (e.g. high
demand). RTP based electricity prices can change as often
as hourly, reflecting the utility cost of supplying energy to
customers at that specific time. In our model, we use TOU
with power dependent tariff known as inclined block tariff or
IBR. The energy price at time tis an increasing, piecewise,
linear function of the total energy demand. As E(t)is the total
power consumption of all appliances in a home at each time
slot tand it is calculated as,
E(t) =
t=1 ν(t) + ∆(t) + κ(t)(3)
To calculate electricity bills, energy price for each unit con-
sumed in each time slot is represented by C(t)and according
to IBR model, it is designed as,
C(t) =
C1(t) 0 E(t)E1
th(t)< E(t)
where, E1
th and E2
th are power consumption thresholds and
C1,C2and C3are costs for these particular cases.
D. Residential users
We design our model for three types of users in residential
area; passive, semi-active and active users.
1) Passive users: They only consume electrical energy of
the grid and does not generate or store electrical energy.
They can only shift there load from high peak to low peak
and reduce their electricity bills. The set of passive users is
represented by P.
2) Semi-active users: They have RESs such as solar panels
and wind turbines. They consume energy both from power grid
and RES to reduce their electricity bills. The set of semi-active
users is represented by S.
3) Active users: They take energy from RES and store it in
storage devices such as batteries as well as also take electrical
energy from grid to fulfill their need. The set of active users
is represented by A.
In this work, main objectives are to reduce consumer cost by
optimizing the energy consumption patterns of appliances to
maximize the comfort level of end user. Here, we formulate
our scheduling problem by using MKP. MKP is a resource
allocation problem that consists of “M” resources (capacities)
and set of “N” objects [19]. We take “j” number of knapsacks,
and map our scheduling problem in MKP as follows:
We consider “j” number of knapsacks as power capacities
in each time slot.
Number of appliances as number of objects.
The weight of each object as the energy consumed by
appliances in each time slot. Note that it is independent
of “t”.
The value of object in a specific time slot is the cost of
power consumption of the appliance in that time slot.
The value of binary variable “χ” can be 0 or 1 depending
on the state of electrical appliance.
The total power consumption for all types of appliances
should not exceed maximum power capacity in each hour
denoted as γ(t), we introduce constraint which limits the
power consumption and depends on load profile and its states.
Constraints show that power consumption is predefined,
t=1 E(t)×χ(t)γ(t)(5)
Here, γ(t)is the power capacity in each hour that is available
from grid and χ(t)[0,1] denotes the states of appliances.
Total power consumption in each hour must be limited to this
capacity factor.
A. Objective function and its solution via ACO
The overall objective function of our scheduling problem is
to minimize electricity bill with optimal use of power from
grid and to minimize waiting time (to avoid frustration of
end users). Additionally, optimal integration of RESs is also
a key point to reduce green house gas (GHG) emission. We
formulate our objective function as an optimization function
and is modeled as,
t=1 a1·
(Ea(t)×Ca(t))+a2ϕa(t) (6)
where, Cais the electricity cost in each time slot that must be
minimized while keeping waiting time of shiftable appliances
minimized. a1and a2are weights of two parts of objective
function and their values are a1, a2[0,1] or a1+a2= 1. It
shows that either a1or a2would be 0 or 1. In this work,
our major concern is with electricity cost reduction with
maximizing comfort level of end users. For this purposed
model, we assume waiting time of each shiftable appliance
not greater than 5, if operation start time of an appliance is
greater than our assumption then utility pays penalty.
Algorithm 1 : Improved Algorithm of ACO-EMC
1: Initialize all parameters (αa,βa,τa,ρa)
2: For all users n N do
3: For all appliances a A do
4: For all time slots t T do
5: Randomly generate ant population
6: while Maximum number of iterations and min error not reached
7: For Each individual ant update pheromone refer [21]
8: For Each individual ant evaluate the objective function using
9: if Ea< E1then
10: calculate electricity bill using C1
11: else {E1< Ea< E2}
12: calculate electricity bill using C2
13: else {Ea> E2}
14: calculate electricity bill using C3
15: end if
16: if C(t)is high peak hour then
17: calculate ϕausing (28)
18: else
19: start an appliance
20: end if
21: local update pheromone for each ant refer [21]
22: choose best solution so far
23: global update pheromone for each ant refer [21]
24: repeat until iteration end
25: using Θ(t)when electricity bill is high
26: if E(t)is high then
27: Θ(t)energy
28: else
29: E(t)
30: end if
31: end while
ACO is a meta-heuristic optimization approach that is used
to solve discrete combinatorial optimization problems. It has
unique properties of self-healing, self-protection and self-
organization [13]. In literature, ACO is used for DSM in many
ways. For-example, authors in [14], investigate congestion
management and cost minimization problems. They formulate
their focused problem as a non-linear programming problem
and electricity bill minimization is achieved using ACO. To
our knowledge, ACO implementation in residential area is not
done before. In our work, we use ACO to evaluate the designed
optimization function to get optimized schedules for home
appliances. Our scheme gives novel idea to implement ACO as
optimization tool for DSM in residential area. In [20], linear
programming is used to designed the optimization function.
Refer to [21], we modified its algorithm for our designed
scenario. Algorithm. 3 gives detailed view of ACO based
EMC (ACO-EMC) model. ACO is used to evaluate objective
function (refer eq. 29) and its constraints (refer eq. 29a to eq.
29i) to get feasible operational time for all appliances. Our
proposed model is applicable for single and multiple homes
in residential areas. The improved ACO algorithm is shown
in algorithm 1.
To evaluate different performance metrics of EMC, we
conduct extensive simulations in MATLAB. We use TOU tariff
model of Jemena Electricity Networks (VIC) Ltd [22], [23]
for residential area with IBR. For simulations, we design a
model for residential area in which each home is equipped
with 10 smart appliances and 4 end users. Appliances with
their parametric values that are used in simulations are shown
in table. I, table. II, and table. III, respectively. In table. I, fixed
appliance has only ρaparameter measured in kWh because
these are non-manageable appliances and do not play any role
in load scheduling problem. Whereas, other two categories
TABLE I: Parameters of Fixed Appliances
Appliances ρa(kWh)
Lighting 0.6
Fans 0.75
Clothes iron 1.5
Microwave oven 1.18
Toaster 0.5
Coffee maker 0.8
of appliances; shiftable and elastic appliances are known as
schedulable appliances. As, in table. II, the parameters for
shiftable appliances are αa,βa,ξa,ϕaand ρaare kWh. ϕa
is the unique parameter in shiftable appliance because these
appliances can be interruptible during its length of use. For
elastic appliances, the parameters are αa,βaand ρain kWh
are shown in table. III.
TABLE II: Parameters of Shiftable Appliances
Appliances αa
Washing machine 8 16 5 0.78
Dish washer 7 12 5 3.60
Clothes dyer 6 18 5 4.40
TABLE III: Parameters of Elastic Appliances
Appliances αa
Air conditioner 6 24 1.44
Refrigerator 6 24 0.73
Water heater 6 24 4.45
Space heater 6 24 1.50
TABLE IV: ACO parametric list
Parameters Values
Ant quantity 10
Pheromone intensity factor 2
Visibility intensity factor 6
Evaporation rate 5
Trail decay factor 0.5
Stopping criteria Max. iteration
Max. iteration 600
Simulation parameters of ACO-EMC are given in table. IV,
A. Electricity bill reduction
The maximum value of electricity bill in unscheduled model
is 266.3492 cent as shown in fig. 3. It is reduced to 114.2536
cent in ACO-EMC. During peak hours (16-22), sufficient
electricity cost reduction is shown for the designed ACO-EMC
model. ACO-EMC acts more effectively than the unscheduled-
EMC BPSO-EMC in achieving our designed objective of
electricity cost reduction due to its characteristics of local and
global exploration.
Fig. 3: Electricity bill (cent)
Performance of the designed model (ACO-EMC) with re-
spect to PAR reduction is shown in fig. 4. It shows that PAR
is significantly reduced for ACO-EMC as compared to the
unscheduled case because these are designed to avoid peak
formation in any hour of a day. Results prove that our proposed
model effectively tackle the peak formation problem. PAR
curves for ACO-EMC describe that power consumption of
appliances is optimally distributed in 24 hours without creating
peak in peak hours (16-22) of a day. We have used combined
model of TOU and IBR for electricity billing to avoid peak
formation via giving information to consumers.
C. Waiting time
User comfort is related to both electricity bill and waiting
time of an appliance. In order to achieve lower electricity
bills, smart users must operate their appliances according
to optimal schedule of EMC. During scheduling horizon of
shiftable appliances, operational time is not fixed due to price
variation in dynamic pricing models. Generally, it is observed
that electricity cost reduction and waiting time show inverse
relationship. By applying waiting time constraints on the
objective function, we have enhanced the performance of EMC
in terms of user comfort and electricity bill reduction. In fig. 5,
it is shown that electricity bill is high if rate of waiting time
is zero and it is low with increase in rate of waiting time for
the proposed model.
Fig. 4: PAR curve
Fig. 5: Possible trade off between electricity cost and waiting
In this paper, we have presented an efficient DSM model
for residential energy management system in order to avoid
peak formations while decreasing the utilities electricity bill
by preserving user comfort level within acceptable limits. We
used ACO to solve our objective function and used combined
pricing models, TOU tariff and IBR model for electricity bill
calculation. From the results, it is clearly justified that our
proposed model works efficiently in terms of electricity bill
reduction, and minimization of PAR while considering user
In future, we will focus on human behavior to achieve
comfort level of consumer and to minimize frustration cost
and improve security and privacy issues between end user and
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... Based on TOU and IBR, Rahim et al. employed ACO to decrease energy usage at the residential load. The recommended approach may dramatically lower peak load, PAR, and energy expenditures without affecting customer satisfaction (Rahim et al. 2016a). ...
Full-text available
Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efciency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown signifcantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difcult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the diferent challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identifed as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefy discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefts it can ofer for an energy management system. This comprehensive review of DSM will assist all researchers in this feld in improving energy management strategies and reducing the efects of system uncertainties, variances, and restrictions.
... This optimization method is developed based on the behavior of a colony of ants. The paradigm used in this optimization is based on the communication of biological ants through pheromone-based communication [19], [20]. The ants travel in search of food and leave trails of pheromones and attract other ants to follow the trails, which is why ants always travel in a line. ...
... The results show that this technique provided a better solution than the genetic algorithm in terms of the global optimum solution and the time of computation. Apart from these two well-known solution approaches, the genetic algorithm and PSO methods of the EMS, there are other approaches, such as differential evolution [134], gray wolf optimization (GWO) [135], ant colony optimization (ACO) [136], etc. ...
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The grid integration of microgrids and the selection of energy management systems (EMS) based on robustness and energy efficiency in terms of generation, storage, and distribution are becoming more challenging with rising electrical power demand. The problems regarding exploring renewable energy resources with efficient and durable energy storage systems demand side management and sustainable solutions to microgrid development to maintain the power system's stability and security. This article mainly focuses on the overview of the recent developments of mi-crogrid EMS within the control strategies and the implementation challenges of the microgrid. First, it provides energy management strategies for the major microgrid components, including load, generation , and energy storage systems. Then, it presents the different optimization approaches employed for microgrid energy management, such as classical, metaheuristic, and artificial intelligence. Moreover, this article sheds light on the major implementation challenges of microgrids. Overall, this article provides interactive guidelines for researchers to assist them in deciding on their future research.
... "Smart meter has communications with the CSU and antithesis. Also, A distributed (EMC) unit in each user's smart meter [6]". ...
Electrical energy management (EEM) is an object that has proceeds appointed importance in the 21th- century in order to its assistance to economic development and ecological ascertainment. “EEM” may be perfected on the supply side “(SS)” or demand side “(DS)”. On the supply side, “EEM” is cultivated when: There is an outgrowth desire “(demand requirement is higher than supply)”. “EEM” assists to suspend the design a resent generation station. On the “DS”, “EEM” is used to minimize the cost of electrical energy consumption and the interrelated forfeitures. The technique utilized for “EEM” is demand side load management that plan at ending valley filling, peak clipping and strategic preservation of electrical systems [1]. Seeming new inventions like “distributed generation (DG)”, “distributed storage (DS)” and “DSLM” will modify the method we use and generate energy. A smart grid (SG) is an electrical network that manages electricity demand in an unstoppable sustainable, reliable and economic manner. A smart grid uses smart net meters to overcome the sickliness of traditional electrical grid. “(DSM)” is a vital advantage of “(SG)” to progress power efficiency, minimize the peak average load and minimize the cost. From basic purposes of DSM is shifting load from peak hours to off-peak hours and reducing consumption during peak hours. Generally, a deregulated grid system is considered where the retailer purchases electricity from the electricity market to cover the end users’ energy need. In this research, Demand Side Management (DSM) techniques (load shifting and Peak clipping) are used to maximize the profit for Retailer Company by reducing total power demand pending peak demand periods and achieve an optimal daily load schedule using linear programming method and Genetic Algorithm. This method is performed on the 69-bus radial network. Also, a short term Artificial Neural Network technique is used to get forecasted wind speed, solar radiation and forecasted users load for date 15-Aug-2019. The neural network here uses an actual hourly load data, actual hourly wind speed and solar radiation data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for Retailer Company. Then comparison between profit using linear programing and genetic algorithm are made. The optimized DSM succeeded to maximize the profits of the company.
... This escalating trend makes the conventional power grid infrastructure inadequate to accommodate the load demand in the succeeding decades. Furthermore, the majority of power generation resources in the existing power grids architecture S. Rahim and P. Siano strategies, enhances power quality, provides resilience, and promotes the employment of renewable energy sources (RESs) integrated with energy storage banks (ESBs) (Rahim et al., 2016). In order to bolster the theory of decentralized power system, the microgrid (MG) is emanated as an adequate small-scale power grid having low computational complexity, operating in dual modes (isolated and grid-connected), consisting of distributed generation (DG) units, fortified with limited power production capacity through fossil fuels and RESs, and equipped with distributed ESBs (batteries and electric vehicles (EVs)) (Karagiorgos & Siozios, 2019). ...
The power grid infrastructure encounters multiple uncertainties such as unprecedented energy generation from non-dispatchable resources, erratic load, intensifying energy demand, the transition towards electric mobility, and the electricity market that exaggerate the decision-maker’s difficulties in the power system. How to deal with massive real-time uncertain data is a pressing and challenging issue. To date, the art to tackle contingencies and ambiguous events has globally advanced and attained great assiduity, whereas, substantial work has been conducted on the optimization problems under uncertainties. In this regard, a comprehensive review of contemporary research is presented to identify future research trends. Moreover, the literature on the state-of-the-art uncertainty modeling methods is scrutinized, whilst a comparative assessment is stated to provide a broader overview evidencing that, so far, there is no particular preeminent uncertainty handling technique. The presented work may be adopted for the selection of the most suitable methodology in each application. In comparison to traditional approaches, robust optimization is one of the recent and adaptive uncertainty handling techniques for optimization problems owing to its salient features. Furthermore, its contributions in five crucial categories of power grid optimization problems are reviewed to highlight additional challenges and the scope of future research in the context of envisioned power networks.
... The power grid network has been transmogrifying over time with deterioration in fossil fuels consumption and optimal penetration of an augmented share of RESs (sunlight, wind, vibration, heat, biogas, and biofuel), one of the potential and scalable remedies against austere environmental deviations [1,4]. On the global scale, several renewable energy reinforcement programs and policies have been introduced to obey the deep decarbonization agreements (intended to limit average temperature below 2 • C) which is the subject of research for the past few years. ...
Over the past decade, escalating trends toward renewable energy resources, electrified transportation, magnifying load demand, erratic energy reserve capacities, dynamic demand response programs, and fast ubiquitous connectivity models have amplified the complexity of energy predicaments owing to the acute intensification of operational and technical uncertainties in the energy grid’s evolutionary phases, one of the pressing challenges that need to be properly addressed. For risk hedging and circumventing the impact of ambiguous parameters, several experts and decision-makers have developed uncertainty modeling approaches for optimization problems under uncertainty. This article first offers a generic overview of traditional uncertainty modeling techniques (such as probabilistic techniques, possibilistic techniques, hybrid probabilistic–possibilistic methods, information gap decision theory, and interval-based analysis) to highlight the significance of robust optimization (RO) method, a state-of-the-art deterministic set-based uncertainty methodology used to optimize a system having uncertain inputs. For this reason – a most popular and pertinent uncertainty modeling technique, the RO approach is precisely introduced to study and recapitulate its remarkable features, decisive modules, and shortcomings. Next, the preceding research on the RO’s contributions in the domain of the power grid is reviewed over three key enablers: Decentralization, Decarbonization, and Digitalization. More specifically, the literature on microgrid, virtual power plant, co/trigeneration, renewable energy with storage units, electric mobility, demand response, and security threats are covered in this survey. Finally, a rigorous discussion on foremost prospects, research gaps, and future directions is quantified. This strategic study can be utilized by researchers, engineers, and power industrialists to anticipate open research areas and unprecedented opportunities related to the application of the RO uncertainty handling method in the futuristic power grid.
Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions.
Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization techniques called the Meta heuristic methods. Recent advancements introduced smart optimization techniques powered by Artificial Intelligence (AI). This article elucidates the applications of AI-based optimization techniques and their advantages over other methods. Various Machine learning (ML) and Deep Learning (DL) algorithms and their utilization for HEMS are discussed in brief.
Nowadays, energy plays a prominent role in all aspects of our life. So far, unclean and non-renewable energy, which has severe economic and environmental impacts, dominant the worldwide energy market. Energy researchers from all over the world are focusing on two approaches: reducing consumption especially for residential loads and diversifying energy sources to include renewables. The constant expansion and competitiveness of renewable energy technologies call for a better approach to grid management. Hence, Home Energy Management System (HEMS) using renewables and integrated into a Smart Grid (SG) scheme provides a solution for monitoring and scheduling appliances' operational activities, which helps reduce consumption and increase energy efficiency. This work mainly aims to provide a comprehensive literature on HEMS in the SG and reviews it as a closed-loop control system. In addition, the architecture of HEMS integrated into a SG is studied, including HEMS functionality, renewable energy sources in a SG, smart energy management system center controller, smart appliances classification, most advanced HEMS monitoring devices used today, sensing, and measuring devices, and HEMS communication and networking system. Demand Side Management (DSM) and Demand Response (DR) programs in HEMS are discussed, with a classification of different DR programs. Several HEMS scheduling methods, including mathematical, metaheuristic, and artificial intelligence optimization techniques, will be reviewed. Some HEMS challenges are also briefly discussed.
Conference Paper
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Demand-Side-Management (DSM) is one of the key factors in the future smart grid, utilizing the consumer side as an additional degree of freedom for planning and controlling the grid. Caused by the growing dissemination of distributed energy resources (DERs), the classical top-down oriented modus operandi transforms towards new operational models. While some DER-types are dependent on environmental conditions and thus, rely on forecasts and heuristic predictions, optimizing and scheduling these dynamic loads in smart grid applications adaptively is still a matter of concern. We will present a distributed implementation of the Ant Colony System as an optimization algorithm combined with a MAPE-K feedback loop that optimizes loads towards dynamic changes of the generation conditions adaptively. Thus, the self-optimizing system uses the available energy more efficiently.
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The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM), which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.
Conference Paper
The energy demand of residential end users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. In this paper, we describe a scheme to solve multiple knapsack problems (MKP) using heuristic algorithms. Keeping total energy consumption of each household appliance under certain threshold with maximum benefit is regarded as knapsack problem. Here, we design multiple knapsack problems for each hour of a day to schedule different numbers of appliance. To avoid peak hours, load is shifted in low and mid peak hours. Different algorithms are used to schedule household appliances. We use ant colony optimization (ACO) that is one of the meta-heuristic techniques to solve multiple knapsack problems which enables fast convergence rate for scheduling of appliances. Results show that propose scheme is an efficient method for home energy management to maximize user comfort and minimize electricity bills.
Approximation algorithms and in particular approximation schemes like PTAS and FPTAS were already introduced in Section 2.5 and 2.6, respectively. The main motivation in these sections was to illustrate the basic concept of constructing simple approximation schemes. The focus was put on algorithms where both the correctness and the required complexities were easy to understand without having to go deeply into the details of complicated technical constructions. Hence, an intuitive understanding about the basic features of approximation should have been brought to the reader which is a necessary prerequisite to tackle the more sophisticated methods required to improve upon the performance of these simple algorithms.
The demand-side management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users’ cost and per unit cost. The main objective of this research article is to develop a generic demand-side management (G-DSM) model for residential users to reduce peak-to-average ratio (PAR), total energy cost, and waiting time of appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on genetic algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model for both single and multiple user scenarios.
In this paper, we utilize the GA method to optimize the start time units of all the OAAs to achieve our objectives. Since the start time unit is the only variable in our scheme and the constraint parameters are set in the beginning, we assume that the total fitness function is (14). In the selection process, we adopt a roulette selection method in which the individual with a better fitness value has a higher probability to be selected for further processing. In general, the time complexity of the GA process can be represented as O(generation number*(mutation complexity + crossover complexity + selection complexity)). Assume the maximal generation number, the size of the population, and the number of individuals are denoted by g, N, and na, respectively; therefore, the time complexity of our scheme is O(gNna). In this case, the time cost increases as the three parameters become larger, and, usually, the time cost of GA optimization does not satisfy people. However, in our approach, the power scheduling process is implemented at the beginning of the day; therefore, after time parameters are determined, there is enough time for power scheduling, and the algorithm running time problem is not so important. We think a time cost of a few seconds is acceptable. In this paper, the population size is 200; the probability of crossover and the probability of mutation are 90% and 2%, respectively. Finally, when the generation number reaches 1,000, the evolution process will finish. Generally speaking, the relationship between electricity cost and DTRave is a tradeoff. In other words, as the value of DTRave increases, electricity cost decreases. However, the minimum electricity cost value would emerge at a position at which the DTRave value is about 50%, which is not definite, due to the random POE. From the result shown in Fig. 6, at the position that DTRave equals 0, it implies that the major consideration is minimizing the delay time; thus, in this case, ω1=0, ω2=1. However, when the minimum electricity cost is reached, ω1=1, ω2=0.
Demand Side Management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users' cost as well as the per unit cost. The main objective of this paper is to develop a Generic Demand Side Management (G-DSM) model for residential users to reduce Peak-to-Average Ratio (PAR), total energy cost and Waiting Time of Appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on Genetic Algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model both for single and multiple users scenarios.
Conference Paper
This paper describes various smart grid concepts, architectures, and details of associated technological demonstrations implemented worldwide. The survey is based on initiatives taken by EU and IEA (e.g. ETP, EEGI, EERA and IEA DSM) and description of projects conducted in Europe and US (e.g. FENIX, ADDRESS, EU-DEEP, ADINE, GridWise and SEESGEN-ICT). The report presents drivers, visions and roadmaps to develop smart grids worldwide including China and India. The survey encompasses various smart grid concepts, i.e. development of virtual power plant, active demand in consumer networks, DER aggregation business, active distribution network, and ICT applications to develop intelligent future grids. The comparison is carried out on the basis of commercial, technological, and regulatory aspects. In addition, the existing features of smart grid technology and challenges faced to implement it in Finish environment are addressed. As a matter of fact, the implementation of smart grid is consisting of more than any one technology, therefore, this transition will not be so easy. In the end, a fully realized smart grid will be beneficial to all the stakeholders. Smart grid will be an outcome of an evolutionary development of the existing electricity networks towards an optimized and sustainable energy system.
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.