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Optimum Sizing of PV-WT-FC-DG Hybrid Energy System using Teaching Learning-Based Optimization


Abstract and Figures

In a hybrid energy system, fulfilling the user's electricity demand cost-effectively is an imperative task. In a stand-alone environment, diesel generator (DG) and renewable energy sources (RESs), including photovoltaics (PVs), wind turbines (WTs), and fuel cells (FCs) provide an effective and reliable solution to fulfill a household load. To achieve the economic and environmental aspects of hybrid RESs (HRESs), optimal sizing of components is indispensable. In this paper, the PV-WT-FC-DG hybrid system is considered, optimally sized, and modeled in terms of environmental emissions and total annual cost (TAC) values for Hawksbay, Pakistan. For optimal sizing, the teaching learning-based optimization (TLBO) algorithm is proposed that does not require any algorithm-specific parameter for its execution. The results reveal that TLBO fulfills the household load at minimum TAC.
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Optimum sizing of PV-WT-FC-DG hybrid energy
system using teaching learning-based optimization
Asif Khan and Nadeem Javaid
COMSATS University Islamabad, Islamabad 44000, Pakistan
Abstract—In a hybrid energy system, fulfilling the user’s elec-
tricity demand cost-effectively is an imperative task. In a stand-
alone environment, diesel generator (DG) and renewable energy
sources (RESs), including photovoltaics (PVs), wind turbines
(WTs), and fuel cells (FCs) provide an effective and reliable
solution to fulfill a household load. To achieve the economic
and environmental aspects of hybrid RESs (HRESs), optimal
sizing of components is indispensable. In this paper, the PV-
WT-FC-DG hybrid system is considered, optimally sized, and
modeled in terms of environmental emissions and total annual
cost (TAC) values for Hawksbay, Pakistan. For optimal sizing,
the teaching learning-based optimization (TLBO) algorithm is
proposed that does not require any algorithm-specific parameter
for its execution. The results reveal that TLBO fulfills the
household load at minimum TAC.
Index Terms—Unit sizing, stand-alone system, renewable en-
ergy sources, energy storage system, fuel cells, optimization.
The power demand of the world is increasing day by day at
an escalating pace which cannot be entirely fulfilled by tradi-
tional grids and conventional methods due to limited resources
[1], [2]. Traditional grids are mostly dependent on fossil fuels
(FFs) including oil, coal, gas, and other sources. According
to a study, 75% of the energy produced by power systems
throughout the world is generated by different means of FFs
[3]. The energy generated by FFs results in environmental
pollution causing acid rains, global warming, and smogs. The
FFs are subjected to the scarcity problem and their varying
and increasing prices are also drawing the people’s attention
to shift towards new technologies like smart grids, microgrids,
and renewable energy sources (RESs).
The RESs: photovoltaic (PV), wind turbine (WT), biogas,
biomass, and so forth are emerging alternatives to the FFs.
These alternatives provide green energy and also play a great
role in reducing environmental problems. However, at the
present stage, approximately 15% to 20% of the world’s
total energy demand is met by RESs [3]. Since, RESs are
clean, ecological, and inexhaustible by nature, therefore, sev-
eral countries are taking initiatives to use these sources. For
instance, California Governor Brown has recently signed a bill
with electricity retailers to serve 60% of their load with RESs
by 2030 and 100% by 2045 [4].
The RESs display intermittent and variable nature. Due
to varying weather characteristics, RESs cannot be relied
upon 24 hours (hrs) of the day, when considering a single
generation source [5]. So, the reliability of RESs is a vital
challenge because these sources are directly dependent on
natural conditions: solar irradiation, wind speed, and temper-
ature [6]. Hybrid RESs (HRESs) are proposed to overcome
the unreliability issue in a stand-alone (SA) environment. The
hybrid system consists of two or more RESs for fulfilling
the user’s load. Further, diesel generator (DG); a conventional
energy source is also integrated into HRESs for eliminating
the disadvantages of one-another. Moreover, the integration
of energy storage systems (ESSs) like batteries, fuel cells
(FCs), and many more to HRESs increases the reliability of
the system to a higher level.
To design a reliable, efficient, and an economic model of
HRESs to satisfy the household load, an optimum sizing of
RESs is essential. The optimum sizing of RESs that leads to
an optimal solution is called unit sizing. The unit sizing is
considered vital as oversizing of RESs’ components, which
leads to a higher cost while undersizing results in an energy
deficit situation; where generation sources are unable to fulfill
the user’s load. The unit sizing of RESs is a complex task
because each component of the system needs to be modeled
and optimally sized [7]. In this context, several optimiza-
tion techniques like hybrid optimization model for electric
renewable (HOMER) [8], [9], iterative method [10], meta-
heuristics [11]-[14], and other schemes are reported in the
literature for unit sizing and energy management problems.
However, HOMER software cannot optimize and formulate
multi-objective problems, intra-hour variability support, depth
of discharge of the battery. Further, meta-heuristic algorithms
proposed in [11]- [14] require algorithmic-specific parameters,
which, if not tuned properly may halt in local optimum or
result in a high computational time [15]-[17]. Therefore, a
meta-heuristic scheme that does not consider any algorithmic-
specific parameters for their execution is essential to be
considered. The contributions of this paper are given below.
Various sizing components of the PV-WT-FC-DG hybrid
system are elaborated using an informative HRES model.
A non-algorithmic-specific parameters scheme known
as teaching learning-based optimization (TLBO) is em-
ployed to find an optimum solution. The yearly solar
irradiation, ambient temperature, and wind speed real data
obtained from Hawksbay, Pakistan are utilized.
The yearly results of the PV-WT-FC-DG hybrid system
are discussed with perspective to highlight the economic
and environmental benefits. Further, plots are shown for
four seasons, including spring, summer, fall, and winter.
This rest of the paper is organized as follows. The system
model is presented in Section II. In Section III, the objective
function based on total annual cost (TAC) minimization along
with some constraints is given. The TLBO is elaborated in
Section IV. In Section V, the results obtained for the PV-WT-
FC-DG hybrid system are discussed. Conclusion and future
work are given in Section VI.
The HRESs add and exploit several readily available RESs
along with the ESS. The RESs considered in this paper are
the PVs and WTs. A non-renewable energy source DG is also
integrated into the system model. The ESS consists of FCs.
The system model diagram of the studied HRES is depicted in
Fig. 1. In this paper, DC bus and AC bus are used. The DC bus
combines the output/input of the FC, WTs, and PV panels as
shown in Fig. 1. The AC bus combines the output/input of the
DG and household AC load. The inverters are used to perform
electricity flow from DC to AC and also supply AC to home.
AC Load
AC BusDC Bus
Wind Turbines
PV Panels
Diesel Generator
Fuel Cell
Fig. 1: System model of PV-WT-FC-DG hybrid system
The ESS is composed of FCs, electrolyzer, and H2tanks.
In comparison to other ESSs, the FCs are highly environment-
friendly and efficient [7]. The electrolyzer is an electrochemi-
cal device that utilizes the electrical power to dissociate water
into oxygen and hydrogen. Finally, H2tanks are utilized to
store compressed H2gas.
In the deficit situation, where energy provided by RESs is
less than the user’s load, then the power stored in the H2tank
is utilized. Here, the FC generates power to help the HRESs in
fulfilling the household load. In a situation, when there is no
energy left in the H2tanks, then the deficit energy is fulfilled
by DG.
In this paper, the objective function is minimization of
the TAC (ζtac), which is composed of three different costs,
including annual capital cost (ζcpt), annual maintenance cost
(ζmtn), and DG annual cost (ζdg ). So, minimization of ζtac
is given by Eq. 1.
Minimize ζ tac =ζcpt +ζmtn +ζdg .(1)
The constraints given below should be satisfied for the PV-
WT-FC-DG hybrid system.
0Npv Nmaxpv,Nmaxpv 200,(2)
0Nwt Nmaxwt ,Nmaxwt 50,(3)
0Nft Nmaxft ,Nmaxf t 200,(4)
where Nmaxpv,Nf tmax , and Nmaxwt denote the maxi-
mum number of PV panels, H2tanks, and WTs, respectively.
In this paper, the minimum and maximum bonds for decision
variables are set at 0 to 200 for PV panels and H2tanks; while
0 to 50 for WTs. The energy storage constraint is given below:
ξminstore ξstore (t)ξmaxstore,(5)
where ξmaxstore and ξminstore represent the maximum
and minimum storage capacity of H2fuel tanks, respectively.
Further, ξminstore is assumed to be zero in this paper. The
components and parameters required for the PV-WT-FC-DG
hybrid system are obtained from [18].
Rao et al. propose a TLBO scheme [19]. The TLBO
does not require any algorithmic-specific parameters for func-
tioning. This algorithm is inspired by a teacher and learner
processes. The algorithm starts by generating a random popu-
lation where rows and columns present learners and subjects,
respectively. A subject presents a decision variable of a given
problem. The total number of subjects for a particular learner
corresponds to a solution. There are two phases of the TLBO
process: teacher phase and the learner phase.
The teacher phase is inspired by the learning process from
the teacher. Here, the mean of learners is calculated as subject
wise and all learners are evaluated via fitness function to
choose a teacher (Jl
teacher) representing the best learner. Latter
algorithm shifts the learners’ mean towards the teacher. Thus,
a new vector formed by current and best mean vectors is added
to the existing population, as presented in Eq. 6 [19].
old(t)+rand ×Jl
teacher (Tf×Ml),(6)
where, rand shows a random number in the range of zero and
one, Tfrepresents a teaching factor having value as either one
or two which is randomly decided with an equal probability
as given in Eq. 7 [19]. The Jl
new in Eq. 6 is accepted if it
provides a better fitness value.
Tf=round[1 + rand ×(2 1)].(7)
The learner phase is associated with interaction among the
learners to share their knowledge. The process initiates by
randomly selecting two learners: Jl
mand Jl
n, from the existing
population, such that (m6=n). Upon the learners’ fitness
values, the population is updated by Eq. 8.
n(t)), if J l
m(t)), otherwise.
Here, Jl
new is accepted if it achieves better fitness value. Also,
the optimization process continues until some termination
criterion is fulfilled.
The mapping steps of TLBO for the PV-WT-FC-DG hybrid
system are given below.
(i) In the first step, input parameters, including hourly input
solar irradiation, ambient temperature, user’s load profile,
and wind speed data are initialized.
(ii) In this step, the power generated by the single PV panel
and WT is calculated.
(iii) In the third step, a random population (Ji) of [50 ×4]
is randomly generated. The 50 rows correspond to 50
solutions (S1,S2, ..., Sx) and 4 columns show decision
variables: Npv,Nwt ,Nf t, and DG which are randomly
generated within the lower and upper bounds. Thus, the
cluster of configurations showing the solution space is
depicted as:
(iv) In the fourth step, each configuration is evaluated via fit-
ness function as given in Eq. 1. The fitness of population
F(Ji)is obtained by the following formula:
F(Ji) = F
The fitness of each configuration F(Sx)represents TAC
value which is obtained by summing of capital, mainte-
nance, and DG costs.
(v) In this step, TLBO equations are applied to update
the first three decision variables: Npv,Nwt , and Nf t.
Further, the learner phase of TLBO is applied and two
solutions: Smand Snare randomly selected from the
population J. Upon fitness criteria, Jnew is updated via
Eq. 8. The Jnew now contains the updated population
(vi) Steps iv-v are repeated until a termination criterion of
100 iterations is fulfilled.
(vii) The best solution among all the iterations based on TAC
value is selected as an optimal solution and the corre-
sponding performance parameters values are returned.
The simulation results are obtained via MATLAB R2018a
software using a system with processor Intel(R) Core(TM) m3-
7Y30 CPU 1.61 GHz with 8 GB of installed memory. The
solar insolation, ambient temperature, and wind speed data
for Hawksbay, Pakistan, are obtained from alternative energy
development board [20]. The Hawksbay has the following
geographical coordinates: 2451’ 25.3” N 6645’ 36.7” E.
The dataset contains data, which are recorded every 10 minutes
per day (min/day). Thus, the mean values of solar irradiation,
ambient temperature, and wind speed (at a height of 10 meters
(m)) data are calculated on an hourly basis for the year 2010,
which are shown in Figs. 2a, 2b, and 2c, respectively. The
user’s load profile of 10196 kilowatts (kW) for a year (8760
h) is plotted in Fig. 2d. From the analysis of input data,
including insolation watt per square meter (W/m2) and wind
speed meter per second (m/s), it is obvious that the proposed
site is suitable for electricity generation from the RESs.
TABLE I: Summary of mean, standard deviation, best perfor-
mance, and worst performance of the TLBO algorithm over
10 independent runs for the hybrid systems
Hybrid systems Index TLBO TAC ($)
PV-WT-FC-DG Mean 27948
Std. 18.8236
Best 27933
Worst 27986
TABLE II: Summary of the results for the hybrid system
achieved by TLBO
Hybrid systems PV-WT-FC-DG
Npv 47
Nwt 6
DG 1
PV cost ($) 814.6227
WT cost ($) 1468.50
FC cost ($) 6019.50
Electrolyser cost ($) 6019.5
Hydrogen tank cost ($) 1460.40
DG cost ($) 5316.40
Fuel cost ($) 4989.2
Inv./Conv. cost ($) 1845.10
TAC cost ($) 27933
In Pakistan, the year is generally divided into four seasons,
including spring, summer, autumn, and winter. As per me-
teorological definition, the 1st day of the month considering
equinoxes’ and solstices’ counts as the beginning of seasons
[21]. The spring period starts from 1st March and ends on
31st May. Summer runs from 1st June to 31st August. The fall
season starts from 1st September and ends on 30th November.
Finally, the winter season runs from 1st December to 28th or
29th (leap year) February. As the input data vary during each
season, therefore, the seasonal plots are also given for the first
ten days of each season’s beginning.
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
Insolation (W/m2)
(a) Solar irradiance hourly data during a year
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
Temperature (°C)
(b) Ambient temperature hourly data during a year
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
Wind speed (m/s)
(c) Wind speed hourly data profiles during a year
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
Power (kW)
(d) Load profile hourly data during a year
Fig. 2: Hourly solar irradiance, ambient temperature, wind speed, and load profiles data during a year (8760 h)
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
PVs (kWh)
(a) Produced PVs power during a year
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
WTs (kWh)
(b) Produced WTs power during a year
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
Energy storage tanks (kWh)
(c) Energy stored in H2tanks during a year
1 1001 2001 3001 4001 5001 6001 7001 8001 8760
Time (h)
Diesel power (kW)
(d) Produced DG power during a year
Fig. 3: Hourly PVs’ power, WTs’ power, energy stored in H2tanks, and produced DG power during a year (8760 h) for
PV-WT-FC-DG hybrid system
1 24 48 72 96 120 144 168 192 216 240
Time (h)
Energy (kWh)
(a) Hourly scheduling of energy production, consumption, and H2
storage status for the first ten days of Spring
1 24 48 72 96 120 144 168 192 216 240
Time (h)
Energy (kWh)
(b) Hourly scheduling of energy production, consumption, and H2
storage status for the first ten days of Summer
1 24 48 72 96 120 144 168 192 216 240
Time (h)
Energy (kWh)
(c) Hourly scheduling of energy production, consumption, and H2
storage status for the first ten days of Fall
1 24 48 72 96 120 144 168 192 216 240
Time (h)
Energy (kWh)
(d) Hourly scheduling of energy production, consumption, and H2
storage status for the first ten days of Winter
Fig. 4: Hourly scheduling of energy production, consumption, and H2storage status for the first ten days of four seasons for
PV-WT-FC-DG hybrid system
In this paper, the four design variables: Npv, N wt, N ft ,and
DG are all integer decision variables. In this study, one DG
is considered for supplying electricity during an energy deficit
situation, so its value is always set as one. Due to randomness
involved in the meta-heuristic algorithms, this study runs the
TLBO scheme for ten times. The summary of mean, standard
deviation, the best performance, and the worst performance of
the proposed algorithms over the hybrid systems is reported
in Table I.
In Table I, the TAC value achieved by TLBO at best index
is 27933$ for PV-WT-FC-DG hybrid system. The worst index
value obtained by TLBO is 27986$. The mean and standard
deviation values achieved by the TLBO scheme are 27948$
and 18.8236$, respectively. In Table II, the optimum solution
obtained by TLBO along with each component’s cost and
TAC is given for the PV-WT-FC-DG hybrid system. The TAC
achieved by the PV-WT-FC-DG system is 27933$.
The hourly PVs power, WTs power, energy stored in H2
tanks, and produced DG power during a year for PV-WT-FC-
DG hybrid system are presented in Figs. 3a-3d, respectively.
In the PV-WT-FC-DG hybrid system, the optimum number
of PV panels, WTs, and H2tanks are found 47, 6, and
26, respectively. The hourly scheduling of energy production,
consumption, and H2storage status for the first ten days
of four seasons: spring, summer, fall, and winter are shown
in Figs. 4a-4d, respectively, for the PV-WT-FC-DG hybrid
system. In Fig. 4a, most of the household load is fulfilled
by PVs, WTs, and ESS. The DG power is utilized only when
RESs and ESS are unable to fulfill the required load. The
DG is less and more frequently used during the summer and
the winter seasons, which is shown in Fig 4b and Fig. 4d,
respectively. In winter, DG is more utilized as compared to
other seasons because of the less amount of power is available
from the RESs.
The breakdown of TAC achieved by TLBO for the PV-WT-
FC-DG hybrid system is given in Fig. 5. Fig. 5a depicts that
the ESS in the PV-WT-FC-DG hybrid system accounts for the
largest TAC of 48%. The cost breakdown of ESS is further
given in Fig. 5b that accounts for 45%, 45%, and 11% for
the FC, electrolyzer, and H2tanks, respectively. The 11% is
caused since the value is rounded to the nearest integer. The
DG accounts for 37% of the system cost which comprises 51%
and 49% for DG with maintenance and fuel costs as shown
in Fig. 5c.
Pollution emission is an important factor, which should also
be considered while analyzing different hybrid systems. There-
fore, CO2, sulfur dioxide S O2, and nitrogen dioxide N O2are
considered and regarded in this paper for measurement of the
pollutant emissions. The pollution emission during a year for
the proposed PV-WT-FC-DG hybrid system is summarized in
PV: 3%
WT: 5%
ESS: 48%
DG: 37%
Inverter: 7%
(a) Breakdown of TAC for PV-WT-FC-DG hybrid
FC: 45%
Electrolyzer: 45%
H2 tanks: 11%
(b) Breakdown of ESS for PV-WT-FC-DG
hybrid system
DG+Maint.: 51% Fuel: 49%
(c) Breakdown of DG for PV-WT-FC-DG
hybrid system
Fig. 5: Breakdown of the TAC by TLBO for PV-WT-FC-DG hybrid system
Table III. The total annual pollution comprising of CO2,SO2,
and NO2for the PV-WT-FC-DG are achieved as: 6082.97,
77.24, and 115.87 kilograms (kgs), respectively.
TABLE III: Emission comparisons of proposed hybrid system
Hybrid systems PV-WT-FC-DG
Fuel consumption by DG in a year (l) 1931.1
CO2(Kg) 6082.97
SO2(Kg) 77.24
N O2(Kg) 115.87
In this paper, the TAC analysis of the PV-WT-FC-DG hybrid
system is performed for SA household load, located in Hawks-
bay, Pakistan. To achieve the minimum TAC, an economic
model is introduced along with an efficient algorithm-specific
parameter-less scheme TLBO, which is used to optimally size
the HRESs. The TLBO algorithm finds the optimum number
of HRESs to satisfy the household load with minimum TAC.
From the simulation results, it is found that the PV-WT-FC-
DG hybrid system results in a TAC of 27933$ to satisfy the
household load. In the future, Jaya and hybrid meta-heuristic
algorithms will be used to find the optimum size of the
proposed system. The results obtained by these algorithms will
be compared with the TLBO.
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... The sizing of a PV/WT/FC/Diesel system is elaborated in Hawksbay, Pakistan [70]. The optimum search is conducted using teaching learning-based optimisation, whose advantage is that it does not require any specific parameters for its execution. ...
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In a stand-alone environment, a system comprising of non-renewable source, renewable energy sources (RESs), and energy storage systems like fuel cells (FCs) provide an effective and reliable solution to fulfill the user's load. In this paper, a diesel generator (DG), pho-tovoltaics (PVs), wind turbines (WTs) and FCs are modeled, optimally sized, and compared in three scenarios: PV-WT-FC-DG, PV-FC-DG, and WT-FC-DG in terms of environmental emission and total annual cost (TAC) for a home, located in Hawksbay, Pakistan. The optimal size of hybrid RESs and their components is achieved using a novel TAC minimization algorithm (TACMA). The TACMA achieves superior results in terms of TAC when it is compared to two algorithm-specific parameter-less schemes: Jaya and teaching learning-based optimization. Further, the PV-WT-FC-DG and PV-FC-DG hybrid systems are found as the most economical and nature-friendly scenarios, respectively.
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Optimal sizing of single micro-grid faces problems such as high life cycle cost, low self-consumption of power generated by renewable energy, and disturbances of intermittent renewable energy. Interconnecting single micro-grids as a cooperative system to reach a proper size of renewable energy generations and batteries is a credible method to promote performance in reliability and economy. However, to guarantee the optimal collaborative sizing of two micro-grids is a challenging task, particularly with power exchange. In this paper, the optimal sizing of economic and collaborative for two micro-grids and the tie line is modelled as a unit commitment problem to express the influence of power exchange between micro-grids on each life cycle cost, meanwhile guaranteeing certain degree of power supply reliability, which is calculated by Loss of Power Supply Probability in the simulation. A specified collaborative operation of power exchange between two micro-grids is constructed as the scheduling scheme to optimize the life cycle cost of two micro-grids using genetic algorithm. The case study verifies the validity of the method proposed and reveal the advantages of power exchange in the two micro-grids system. The results demonstrate that the proposed optimal sizing means based on collaborative operation can minimize the life cycle cost of two micro-grids respectively considering different renewable energy sources. Compared to the sizing of single micro-grid, the suggested method can not only improve the economic performance for each micro-grid but also form a strong support between interconnected micro-grids. In addition, a proper price of power exchanges will balance the cost saving between micro-grids, making the corresponding stake-holders prefer to be interconnected.
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A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.
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Ensuring continuous power supply in the hybrid renewable energy and battery system is important because power utilization is fundamental to economy and human well-being. Earlier studies have been concerned with the ability to supply a desired amount of power in a long-term static condition in the context of system reliability. Meanwhile, short-term dynamic behavior and its relevant vulnerability in a hybrid system associated with a continuous power supply has been given less attention due to the modeling complexity of the problem. This study uses a parametric approach to assess the dynamic transition of vulnerabilities potentially leading to disruptions in electricity supply throughout a given day to identify a specific vulnerable time. The methodology of quantifying the dynamic vulnerability of power source components during a given day in a hybrid system is developed. Then, the temporal dynamic vulnerability and overall vulnerability in the system are presented by changing the capacity size in the case of a fictitious standalone house. Through the analysis, the approach developed in this study would potentially highlight the greater contribution of the battery to continuous power supply, compared with the solar PV in the hybrid system.
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In power systems, meeting the electricity demand of remote regions is an imperative issue. Considering economic aspects, reliability and pollution concerns, combination of diesel generator and renewable energy sources like wind turbines (WTs), photovoltaic (PV) systems and fuel cells (FCs) can be an effective way to meet the demand of off-grid loads. In a cost-effective hybrid system, it is indispensable to optimize the number of components. In this paper, a PV/WT/FC/diesel system with different fuel prices is considered for electrification to a remote area and is optimized by a discrete simulated annealing algorithm, named DSA. The sizing problem is solved by considering different hybrid systems and two scenarios related to the cost of the diesel fuel.
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Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting tech-nique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolu-tion, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay. A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. Available from: [accessed Mar 26 2018].
Conference Paper
In this paper, Jaya algorithm is used for finding an optimal unit sizing of renewable energy resources (RERs) components, including photovoltaic (PV) panels, wind turbines (WTs) and fuel cell (FC) with an objective to reduce the consumer total annual cost in a stand-alone system. The system reliability is considered using the maximum allowable loss of power supply probability (LP SP max) provided by the consumer. The methodology is applied to real solar irradiation and wind speed data taken for Hawksbay, Pakistan. The results achieved show that when LP SP max values are set to 0% and 2%, the PV-FC is the most cost-effective system as compared to PV-WT-FC and WT-FC systems.
Conference Paper
Renewable energy sources (RESs) are considered as reliable and green electric power generations. Photovoltaic (PV) and wind turbine (WT) are used to provide electricity in remote areas. The optimum unit sizing of hybrid RESs components is a vital challenge in a stand-alone system. This paper presents Jaya algorithm for optimum unit sizing of a PV-WT-Battery hybrid system to fulfill the consumer's load at minimal cost. The reliability of the system is considered by a maximum allowable loss of power supply probability (LP SPmax). The results obtained from the Jaya algorithm show that the PV-WT-Battery hybrid system is the most economical and cost-effective solution for all proposed LP SPmax values as compared to PV-Battery and WT-Battery systems.
Solar and wind energy are the two most available renewable energy resources in the world. In this paper, a high-resolution analysis that allows sizing a hybrid photovoltaic-wind turbine-battery banks has been carried out. The analysis aims to minimize the annualized cost of the systems satisfying two reliability constraints. The solution has been obtained numerically by means of an iterative technique. The decision variables are the photovoltaic area, wind turbine radius, and battery capacity. A high-resolution model, based on fuzzy logic inference system, has been developed to evaluate the number of active occupants and the domestic electricity consumption. In order to allow a more accurate sizing of the system, a new reliability parameter named seasonal loss of load probability ratio that takes into account the seasonality of data has been defined. Seasonal loss of load probability ratio has been used in the iterative process in addition to the most common loss of load probability. Compared with traditional processes, the obtained results demonstrate that the introduction of the new parameter to iterative process causes a meaningful improvement of the system's reliability and a slight increase of its cost on the other hand. The simulation, conducted in MATLAB® environment, has been carried out to supply power for a domestic dwelling located in three different locations of Sicily. Compared to reliability values arising from the traditional procedure, the obtained results show that a reliability improvement of 75% is reached by using the new sizing procedure. Therefore, the proposed methodology gives an important advancement on the current state of the art since it allows at designing renewable plants in a more efficient way.
In this century, problems such as the scarcity of fossil fuel resources and related environmental contamination have led to the emergence of new energy systems based on renewable energy resources. In this paper, an optimal planning approach is proposed based on 100% renewable energy system (RES) for a residential house. In respect to renewable resources potential in the site location and electrical demand, the best combination of resources is chosen based on minimum energy supply cost and maximum reliability. Furthermore, different scenarios are suggested by considering different levels of capacity shortage (CSH) and unmet electricity load (UEL) percentage. As a case study, the real electricity consumption data for a single family household is considered in Hesarak, Tehran, Iran. The final optimal solution for this 100% RES with the objective function of cost minimization and reliability constraint include 4 kW PV, 2 kW wind turbine, 4 kW converter and 6 battery strings. This scenario with CSH of 1.1% and UEL of 0.9% has the net present cost of 20,527 $ that while having low cost, the reliability of this system is also good compared to other scenarios.