microgrid.pdf

Ipek Cetinbas
Sizing Optimization of Autonomous Microgrids with Meta-Heuristic Algorithms
Department of Electrical and Electronics Engineering
DOCTORAL THESIS
January 2020
Machine Translated by Google

Sizing Optimization of Autonomous Micro Grids Using Meta Heuristic Algorithms
Ipek Cetinbas
DOCTORAL DISSERTATION
January 2020

Graduate School of Natural and Applied Sciences
DOCTORAL THESIS
Prepared as
Pursuant to Graduate Regulations
Sizing Optimization of Autonomous Microgrids with Meta-Heuristic Algorithms
Advisor: Assoc. Dr. Bünyamin Tamyürek
Ipek Cetinbas
January 2020
in Electronics Science
Eskisehir Osmangazi University

: Assoc. Dr. Bünyamin Tamyürek
Approved by the decision numbered .........................
Second Advisor : -
prof. Dr. Hurriyet ERSAHAN
Doctoral Thesis Defense Jury:
director of the institution
Member : Assoc. Dr. Bünyamin Tamyürek
APPROVAL
Member : Prof. Dr. Hasan Huseyin Erkaya
ÿpek Çetinbaÿ, PhD student of Electrical and Electronics Engineering Department
Member : Assoc. Dr. Mehmet Demirtas
This study, titled "Sizing Optimization of Autonomous Microgrids with Meta-Heuristic Algorithms", which was
prepared as a PhD thesis, was accepted by our jury as a part of the postgraduate regulation.
Member : Prof. Dr. Nihat Ozturk
It was evaluated in accordance with the relevant articles and accepted unanimously.
Member : Assoc. Dr. Sinan Kivrak
Counselor
of the Board of Directors of the Graduate School of Natural and Applied Sciences.

I have obtained it in accordance with scientific ethical principles and rules; I used in my thesis
According to the thesis writing guide of Eskiÿehir Osmangazi University Institute of Science and Technology,
that I have cited and cited all of the works and that the information, documents and results are scientifically
Assoc. Dr. "Sizing Optimization of Autonomous Microgrids with Meta-Heuristic Algorithms", which I prepared under the
consultancy of Bünyamin Tamyürek.
I declare that I present it in accordance with ethical principles and rules. 06/01/2020
that my PhD thesis is an original work; scientific at all stages of my thesis work.
Ipek Cetinbas
that I act in accordance with ethical principles and rules; The information and data I have given in my thesis are academic and
ETHICAL STATEMENT

is to provide the load. Also, the most efficient and optimal for the proposed microgrid structure.
A model in MATLAB to run selected sizing algorithms
Keywords: Microgrid, Sizing optimization, Energy management,
A comparative analysis was conducted to implement the energy management strategy.
developed. Energy optimization, reliability and economic evaluation criteria
Particle swarm optimization, Firefly algorithm, Gray wolf optimization algorithm,
Thus, by making optimum use of distributed generation resources,
probability of loss of power supply (LPSP), in line with an objective function that will
Whale optimization algorithm, Salp herd algorithm.
island mode study was carried out. This island mode microgrid model is photovoltaic.
The cost of electrical energy (COE) was taken into account. Conclusion
panels, batteries used as energy storage units, diesel generators and load
vi
as an autonomous microcomponent that meets design specifications with optimally sized components.
SUMMARY
consists of components. Optimum capacities of the microgrid components
network is designed. Moreover, the algorithms exhibit in solving the problem
In this study, autonomous micro-heuristic algorithms were used by using five different metaheuristic algorithms.
particle swarm optimization algorithm for optimal sizing we determined, fire
performances were presented individually, compared with each other and interpreted.
The sizing optimization of the networks has been made. Sizing optimization
beetle algorithm, gray wolf optimization algorithm, whale optimization algorithm and
and sizing optimizations of different algorithms in the designed system.
Its purpose is to supply the electrical energy demanded by the load continuously and reliably with minimum cost.
Metaheuristic algorithms including the salp swarm algorithm were selected and used successfully.
recommendations for its evaluation are presented.

minimum cost. In addition, a comparative analysis has been realized in order to implement
developed to run the selected sizing algorithms in MATLAB. Energy optimization is realized
optimization, Firefly algorithm, Gray wolf optimization algorithm, Whale optimization
the most efficient and optimal energy management strategy for the proposed microgrid
by considering the loss of power supply probability (LPSP) and the cost of energy (COE) in
algorithm, Salp swarm algorithm.
structure. By this way, the optimum use of the distributed production resources is realized
line with a purpose function that ensures the reliability and economic evaluation criteria.
in island mode operation of the micro grid. This island mode microgrid model consists of
Finally, an autonomous microgrid that satisfy the design specifications with optimally sized
vii
photovoltaic panels, batteries to be used as energy storage unit, diesel generator, and load
components has been designed. Moreover, the performances of the algorithms in the solution
SUMMARY
components. For optimal sizing where we determine the optimum capacities of the microgrid
of the problem are presented individually, compared to each other, interpreted, and
In this study, sizing optimization of autonomous microgrids has been made using
components, five meta-heuristic algorithms including particle swarm optimization
suggestions for evaluating the sizing optimizations of different algorithms in the designed
five different meta-heuristic algorithms. The objective of the sizing optimization is to
algorithm, firefly algorithm, gray wolf optimization algorithm, whale optimization
system are presented.
provide the electrical energy demanded by the load continuously and reliably with the
algorithm, and salp swarm algorithm are selected and used successfully. A model was
Keywords: Micro grid, Sizing optimization, Energy management, Particle swarm

I would like to express my gratitude to my precious family who have always been by my side.
My dear advisor, Assoc. Dr.
viii
I would like to thank the technopark management and technical staff.
To Bünyamin Tamyürek, my dear teacher Assoc. Dr. To Mehmet Demirtaÿ and my education life
THANKS
Gazi University for their contributions during my thesis studies.

INDEX OF TABLES ................................................. .................................................................. .. xiii
4.1. Optimal design in Microgrids .................................................. ........................ 20
CONTENTS
1. INTRODUCTION AND OBJECTIVE ............................................. .................................................................. ............. one
3.2.1.3. Hybrid AC/DC microgrid systems.................................... ...... 16
4.1.2. Evaluation criteria of hybrid renewable energy systems
3. MICRO NETWORKS ................................................. .................................................................. .
SUMMARY................................................. .................................................................. ................................ vi
3.2.2.1. Urban microgrids .................................................. ........................ 18
4.1.3. Selection of sizing methods of hybrid renewable energy systems. 22
INDEX OF FIGURES ................................................. .................................................................. ......... xii
3.2.1. Microgrids by power system ......................................... ............ 13
4.2.1. Particle swarm optimization algorithm ....................................... ... 25
THANKS................................................. .................................................................. ................... viii
3.1. Microgrid Concept .................................................. ............................................ 10
3.3. Comparison of Microgrids with Classical Grids................................... 18
4.1.1. Classification of hybrid renewable energy systems ................................ 21
ix
INDEX OF ICONS AND ABBREVIATIONS .................................................. ..................... xiv
SIZING OPTIMIZATION WITH ALGORITMS .................... 20
3.2.1.2. DC microgrid systems ................................................. ..................... 15
Page
2. LITERATURE RESEARCH .................................................. ........................................ 4
3.2.2. Microgrids by location ............................................... ........................ 17
determination ................................................. .................................................................. .22
3.2.2.2. Rural/remote microgrids ............................................. ............ 18
SUMMARY.................................................. .................................................................. ...................... vii
10
4.2. Sizing optimization with metaheuristic algorithms in microgrids.. 23
3.2. Basic Structure and Classification of Microgrids ................................................ 12
4. OPTIMAL DESIGN AND META INTUITIVE IN MICRO NETWORKS
CONTENTS................................................. .................................................................. ............ ix
3.2.1.1. AC microgrid systems ................................................. ...................... 13

5.2. Components of Microgrid .................................................. ................................... 37
6.7. COE and LPSP Outputs .................................................. ............................................. 68
Page
5.2.2. Battery energy storage unit.................................................... ........................ 41
6. FINDINGS AND DISCUSSION ............................................. ........................................ 52
6.9. Changing Battery Capacity ................................................. .............................. 75
5.2.4. Load................................................. .................................................................. .............. 43
4.2.3. Gray wolf optimization algorithm .................................................. .............. 28
6.2. Optimization Results of System Cost ......................................... ............ 58
6.12. Evaluation and Comparison of Algorithms...................................... 81
5.1. Design of a Proposed Island-Mode Microgrid .................................................. .. 36
5.4.1. Economic evaluation criterion ................................................. ..................... 49
RESOURCES INDEX ................................................. .................................................................. ... 86
4.2.5. Salp swarm algorithm ................................................. ................................. 33
5.3. Energy Management Strategy of MicroGrid ............................................. ............... 46
6.4. Change of Objective Function and Statistical Results ................................. 60
6.8. Variation of PV Power ....................................... .................................................. 72
x
5.2.1. PV power system.................................................... ................................................. 37
6.6. Convergence Graphs ................................................. .............................................. 64
5.4.3. Objective function ................................................. ............................................. 50
4.2.2. Firefly algorithm ............................................... ............................. 26
5.2.3. Diesel generator ................................................. .............................................. 42
6.1. Consequences of Sizing Optimization....................................... .......... 54
6.10. Variation of Diesel Generator Power .................................................. ........................ 78
6.3. Distribution of Energy Production by Resources ............................................. ............ 59
5.2.5. Inverter ................................................. .................................................................. .......... 45
4.2.4. Whale optimization algorithm ................................................. ............... 31
7. CONCLUSION AND RECOMMENDATIONS ............................................. .............................................
5.4. Economic and Reliability Evaluation Criteria and Objective Function .. 49
6.5. Time Usage and Statistical Results ............................................. ............... 62
84
5. METHOD................................................. .................................................................. ...................... 36
5.4.2. Reliability evaluation criterion ................................................. ............. 50

xi
CURRICULUM VITAE ....................................... .................................................................. ..................... 90

4.1. Optimal design process in microgrids .................................................. .......... 20
5.2. Irradiation data ................................................. .................................................................. ............. 38
INDEX OF FIGURES
4.3. Evaluation criteria of hybrid renewable energy systems....................... 22
5.4. Load data ................................................. .................................................................. ................... 44
6.8. Variation of the number of PV panels according to the number of iterations. ......... 73
4.5. The place of metaheuristic algorithms in optimization methods ................................ 24
Page
6.1. The effect of changing the capacities of the components on COE and LPSP................................ 56
6.10. Variation of the number of diesel generators according to the number of iterations ................................. 79
3.4. General structure of a hybrid AC/DC microgrid .................................................. ............. 17
5.1. Proposed island-mode microgrid system .................................................. ........................ 36
3.2. General structure of an AC microgrid ............................................. .............................. 14
4.7. Hunting methods of humpback whales ................................................. ........................ 31
6.3. Examining the minimum, maximum and average of the trial outputs ................. 61
6.7. COE and LPSP outputs of algorithms .................................................. .......................... 69
xii
4.2. Classification of hybrid renewable energy systems ................................................ 21
6.5. Variation of convergence graphs according to the number of iterations ............................................. 64
5.3. Temperature data ................................................. .................................................................. .......... 40
Shape
4.4. Sizing methods of hybrid renewable energy systems................................. 23
5.5. Energy management strategy ................................................. .............................................. 47
6.9. Variation of the number of autonomous working days according to the number of iterations ................................. 76
6.2. Distribution of energy production by resources .................................................. ...................... 59
3.1. Classification of microgrids ................................................. .............................. 13
4.6. Hierarchical structure in gray wolves ................................................. ............................................. 29
4.8. Behavior patterns of Salp herds .................................................. ............................... 34
6.4. Examining the minimum, maximum and average of trial outputs ................. 64
3.3. General structure of a DC microgrid ............................................. .............................. 16
6.6. Pareto optimal solution.................................................... .................................................................. .. 69

5.1. Technical and economic features of the PV power system ............................................. ............ 38
5.3. Technical and economic features of diesel generator ............................................. ............ 43
INDEX OF TABLES
5.2. Technical and economic features of the battery energy storage unit ............................ 42
5.4. Technical and economic characteristics of the inverter ............................................. .......... 45
6.6. Time use ................................................. .................................................................. ......... 63
chart
6.7. Comparison of algorithms ................................................. .................................... 82
6.1. Individual input parameters, common parameters, and search space of algorithms...... 53
6.8. Comparison of the advantages and disadvantages of algorithms...................................... 83
Page
xiii
6.5. Variations of the objective function and statistical results ........................................ 60

abbreviations
Salp herd algorithm
firefly algorithm
BOA
INDEX OF ICONS AND ABBREVIATIONS
Whale optimization algorithm
GKOA
photovoltaic
Possibility of power supply loss
COE Cost of energy
Explanation
SSA
IN
Particle swarm optimization algorithm
AA
Direct current
Alternative current
PV
LPSP
xiv
Gray wolf optimization algorithm
ABA
PSOA

Today, energy consumption rates of users are in line with energy production.
are the main directions.
1. INTRODUCTION AND OBJECTIVE
In contrast to the speed of the formation process of fossil fuels in nature, the rate of use of these resources
Problems such as the increase in the distance between the points of energy production and transmission
resources and backup systems that can provide instant power, such as a diesel generator.
personal and global risks such as pollution, global warming, climatic and environmental problems.
It is considered as an indispensable energy source. Besides daily use
Solutions to all these problems are sought with microgrids. different energy sources
management, as well as its controlled consumption, have been the subject of significant scrutiny.
reducing the costs of energy management practices to more economical levels.
in many important private and public areas such as defence, education, housing, security and transportation.
The use of renewable energy sources has gained importance. Different alternative
have strategies to work independently from the grid when desired, energy storage
For example, renewable energy to work with energy storage systems
one
not watching. On the basis of this problem, it is the primary energy provider.
issues that we encounter and whose solutions require important mathematical modeling and analysis
Having energy production systems at a central point, production and consumption
the overlap lies. The intensity of the use of fossil fuels in the production process; weather
Electrical energy is no longer human, since it is used in every moment of daily life.
It causes significant losses and control problems during operation. Stated
use of distributed power systems with each other and with renewable resources,
the need for growth for industrialization to increase the use of electrical energy, health,
brings with it. It will eliminate these problems of fossil fuels and alternative
the use of energy sources together, ensuring the continuity of energy and energy
and integration of backup systems to these resources in microgrid applications
Due to reasons such as the significant increase in the consumption of electrical energy,
is evaluated as.

load of backup energy systems, if any, with the most cost-effective and uninterrupted
This factor is used in the calculation of the continuity and cost of the energy due to the variability of the
the instantaneous power consumption profile of the consumer and the energy to be supplied to the loads.
To be considered for sizing optimization of microgrids
In this study, sizing of an island-mode microgrid
reliability and reliability for determining the most suitable capacities of the components to be used in the network.
is used. The aim here is to use the most efficient and efficient optimization algorithm.
Similarly, in a system that can operate independently of the grid, different energy
intended. Reliability and economic evaluation in the designed optimization model
The main purpose in optimization and sizing is the energy production and storage resources and
the use of electrical energy produced from these sources according to meteorological data.
to meet the demand of the consumer and to design the most suitable system for the use of the consumer.
directing the energy resources to the load in a way that will give the fastest response in the
A method that takes into account (loss of power supply probability)
micro-component consisting of a diesel generator and load to be used as an instant backup energy source.
2
provides energy to the group.
Energy optimization was carried out with the algorithm.
meteorological data needs to be taken into account.
criteria for continuity.
Many different algorithms for evaluating constraints and mathematical expressions
Developing and designing a model to achieve optimization
A problem has been identified and defined by economic evaluation criteria.
COE (cost of energy) and LPSP, in line with an objective function that will meet the criteria
the variable instantaneous power of the consumer continuously by using its resources together and in coordination.
Realizing the design of the continuous system, variable load and meteorological environment
is to provide. Alternative energy sources such as solar or wind in the systems to be designed
has been applied. Five meta-heuristics selected to test the designed model
is a new field of study considered as energy optimization. Energy
Photovoltaic (PV) power system, batteries to be used as energy storage unit,

presented, compared with each other, interpreted and different algorithms
details are defined and the proposed energy management structure is presented. After that
3
performance of algorithms when applied to sizing optimizations
examined and the current situation at the point reached was revealed. Then your problem
recommendations are presented.
optimization was evaluated and meta-heuristics used as a tool in this optimization.
A literature review was conducted on the subject and studies conducted by other researchers
for the evaluation of sizing optimizations in the designed model.
As a result, the sizing of the microgrid, which is the main purpose of this thesis,
A solution to the problem was sought with five metaheuristic optimization algorithms. First stage
with meta-heuristic algorithms determined on this structure created and designed.
A solution has been produced to the sizing problem of the microgrid.

Energy management and sizing with classical methods in microgrids
Optimal function of an objective function determined by the constraints defined in microgrids.
designing a micro-grid that will fully meet the energy demand of the load in the grids
In another study, Zolfaghari et al. (2019) running cost of microgrids and
2. LITERATURE RESEARCH
formulated with mixed integer linear programming. Scenarios in this direction
was defined and the most suitable battery capacity was investigated in terms of cost criteria.
Mashayekh et al. (2017) micro-organisms with more than one energy generation source
solutions were compared and smaller busbar development error was observed.
This process is called optimization. Optimization process in microgrids
optimum technology portfolio, optimum technology placement and optimum distribution
Worked on sizing the storage unit. Will work without network connection
have addressed the issue. For this, in a microgrid with different energy sources,
estimation of input variables to minimize or maximize
battery energy with an analytical cost-based approach to reduce the total system cost.
they have designed. This design is for optimal placement of distributed generators in microgrids.
analyzed in three categories.
physical constraints and operating constraints, integer linear for electricity and heat transfer network
generator components and the problem defined for this determined problem.
It is an important issue and is related to energy management and sizing optimization.
literature review classical methods, software tools and applications of artificial intelligence methods
with mixed integer linear programming, an optimization model that determines
Microgrid PV system, battery, wind turbine, fuel cell and diesel
4
Various studies on optimization have been examined.
tested with nodal and multi-node approaches. Power flow with tested model
includes the formulation of the model. The work of this optimization model developed is only

In another study using classical methods, Jiménez et al. (2019) a
Cetinbas et al. (2019) Hospital located in Eskiÿehir Osmangazi University
In addition to the analytical approach, Cardoso et al. (2018) a study that includes different energy sources.
They sought a solution with a linear linear programming model. in the operation of this network
The PV power system is a hybrid consisting of batteries, diesel generators and loads.
The HOMER software was successful in finding the optimal solution.
The proposed model was tested using the PV power system as the output of the model.
carried out and described the aging and deterioration pattern of the battery. Optimal PV
As a result of performance analysis and optimization, PV power system, diesel generator and
revealed that it has important effects on system planning and decision making.
they have been used.
and investigated the effects of system costs on battery aging.
In addition to the sizing optimization with classical methods, the software
contributed to the use of renewable energy.
The results obtained from this system operating in island mode were compared with HOMER, and the method and
5
new mixed integer location and sizing problem in the mini-grid
modified electricity consisting of solar energy, wind energy, batteries and loads
Hybrid Optimization of Multiple Energy Resources (HOMER) software for the
dimensioning model of the microgrid using mixed integer linear programming.
aimed to minimize electrical power losses. Actual production and consumption data
micro-grid is designed. using one-year real consumption data.
The optimum power values of the converters and the optimum capacity of the battery were determined.
With the proposed microgrid, the cost of electrical energy has been reduced, reliability has been increased and
optimum location and capacity information was obtained.
system and storage capacity, optimal distributed resource utilization, operating strategy
tools have been developed and used in the planning and evaluation of microgrids.
In another study using HOMER software, Zahboune et al. (2016)
The results suggest that battery degradation should be taken into account when sizing microgrids.
A hybrid power generation system based on the system cascade analysis method has been designed.

PV power system, diesel generator and electricity using also biomass gasification source
proposed an off-grid hybrid energy system. Energy requirements of the load
In another microgrid design made with HOMER software, Halabi et al. (2017)
The most optimal system configuration of the hybrid system that can operate in off-grid modes
performed using the configuration. The technical and economic aspects of the designed system
They proposed a strategy to minimize the total cost in the system. PV
evaluated. As a result of the research, the energy cost of the grid-connected system
modeled two decentralized power generation facilities. Various
evaluated in terms of This system is technically and economically viable.
Unlike other work with the HOMER software, Rajbongshi et al. (2017)
Combining PV power system with biomass for an agricultural farm and residential area in Pakistan
evaluated. Total net present cost and energy in meeting the load demand
concluded.
With artificial intelligence methods of sizing optimization in microgrids
Yahiaoui et al. (2017) hybrid power generation located in an isolated rural village.
6
A hybrid power generation system consisting of a power grid is designed. connected to the grid and
population-based and metaheuristic algorithms with swarm intelligence popular applications
design of the system to meet the hybrid system consisting of PV power system and biomass
PV power systems, diesel generators, energy storage units and converters
Researched using the HOMER simulator and in terms of various load levels.
The analysis was done with HOMER software and this design is the net present cost and energy cost.
concluded that.
For these scenarios, these two stations were analyzed from technical, economic and environmental perspectives.
It is cheaper and the biomass gasification system gives better output than the PV system.
In another study using biomass source, Shahzad et al. (2017)
implementation is noteworthy. Among these methods, developed by being inspired by living things,
Satisfactory results have been obtained.
Successful results are obtained with these algorithms.

The minimization was carried out with the gray wolf optimization algorithm (GKOA). GKOA
SSA and power loss sensitivity algorithm together
In terms of minimization of active power loss and characteristic speed of convergence, a successful
The operation of the microgrid is planned for situations where the
The cost of this hybrid power system consisting of panels, diesel generator, batteries and load
A grid-connected hybrid renewable energy system with a hydroelectric system
they have suggested. Optimum power flow control and minimization of production cost
The performance of the hybrid algorithm created for sizing optimization was examined.
They shared the sharing with the modified PSOA. specific and indefinite load
Among metaheuristic algorithms, PSOA is a popular algorithm and Azaza and Wallin
have been compared. Fast convergence of the solution of the GKOA problem according to the PSOA algorithm.
compared and the proposed algorithm is more successful in minimizing the production cost.
performed.
The performance of the algorithm is compared to the particle swarm optimization algorithm (PSOA).
intended. With PSOA, genetic algorithm and memory-based genetic algorithm
The locations of the points and the capacities of the components were investigated. One-year weather conditions
(SSA) and hybrid renewable energy to be integrated into their distribution systems
Technical and economic analyzes as a result of the energy management carried out with the simulation
conducted under the simulation revealed the development potential of the rural areas of Sweden.
Lingamuthu and Mariappan (2019) with pumped storage in addition to other systems
and at lower cost.
In addition to the GKOA algorithm, Abdel-mawgoud et al. (2019) salp swarm algorithm
(2017) evaluated the investments to be made in hybrid microgrid systems with this algorithm.
Potential for Swedish power plant installation
performed.
7
have done using it. Considering the effect of annual load increase,
power between distributed generators in a microgrid consisting of a combined heat and power plant
In another study with PSOA, Gholami and Dehnavi (2019) found wind, sun,

BOA in the optimal placement and sizing of the energy storage unit
intended to be accomplished. The main purpose of sizing optimization is production,
they aimed. With the proposed system design using the SSA and the cuckoo algorithm
In addition to the BOA algorithm, Sanajaoba (2019) used the ABA algorithm in his study.
By increasing environmental and social sensitivity, uninterrupted, cost-effective and
A problem is defined by the criteria of reliability and economic evaluation.
They worked on optimal sizing. Remote where there is no access to electrical energy
Wong et al. (2019) another meta-heuristic algorithm, whale optimization
idle and unused capacities in microgrids brought together
was created. By applying five metaheuristic algorithms to this model,
algorithm (ABA) and the performance of PSOA and BOA algorithms were compared. Battery
sizing optimization of the grid with metaheuristic algorithms.
They worked on the determination of the capacity of the distribution system and tried to reduce the losses in the distribution system.
applicability and configuration were evaluated.
plans can disrupt energy continuity. Microgrids
to determine the most suitable capacities of the components to be used in the microgrid.
8
performed better than the algorithms.
utilizing the regional potentials of energy resources, storage and backup energy
microgrids consisting of storage and backup energy systems are reliable, economical,
maximum power is obtained and the production cost is minimized.
used a hybrid energy system consisting of solar energy, wind energy and battery.
is to provide clean energy transfer. Random without sizing optimization
Energy management model by removing the necessary mathematical expressions for sizing
and maintenance costs, as well as incorrect or instantaneous capacity
Optimal layout of the battery energy storage system using an algorithm (BOA) and
a system that can be installed in a region has been examined in terms of energy cost,
In this study, unlike previous studies, an autonomous micro
capital will be properly channeled, renewable
they aimed. Battery location and size estimated with BOA, firefly
Maximum benefit will be obtained from microgrids supported by Therefore

The advantage of the structure is that the desired algorithm can be applied to the system and the capacities are optimal.
9
The outputs of the sizing optimization are obtained. This designed energy management sub
prediction can be made.

It can work independently of the networks as well as together (Alhelou and Hayek,
Microgrid: PV panels, inverter, and power supply to the customer's electrical loads
3.1. Microgrid Concept
Microgrid: from localized electrical energy sources and load group
3. MICRO NETWORKS
Microgrid: small-scale electrical grids and other small-scale
It is a controllable entity from which it comes (Kumar, 2018).
has been defined as follows.
Microgrid: distributed generation resources, energy storage units and local
autonomously separate from the power grid for economic and physical reasons.
2019).
The general outlines of the microgrid concept are presented in different sources from different perspectives.
consists of. Microgrids that are connected to the classical electricity grid and can operate simultaneously
Microgrid: a group connected together within defined electrical boundaries
They are structures that can operate autonomously (Alhelou and Hayek, 2019).
control system consisting of distributed generation source and loads and viewed from the grid side
Microgrid: power, consisting of local electrical energy sources and loads
connected to the mains grid or separate from the power grid for economic and physical reasons.
They are small networks consisting of loads and connecting these components (Kumar et al., 2018).
are structures that can work (Diaz et al., 2019).
10
small local electricity grid (Alhelou and Hayek, 2019).
Microgrid: combining different renewable energy sources and local loads
They are structures that act as a single entity (Shi, 2019).

energy consisting of storage units and loads and called microgrid
Expectations from microgrids the place or end of the microgrid will be implemented
Based on these definitions, microgrids, regionally existing
It can also be applied in independent local and remote areas. Classic large-scale power
While places such as institutions focus on energy security, energy for other users
In terms of energy with micro-grids, with the increasing integration of energy sources into the grids,
The past period when the resources were not used and large power generation plants were available.
a single structure that is established, works and is controlled in a way to produce and consume on-site
where access to energy is limited or the generation of electrical energy from fossil sources
production and use from the most suitable local energy source, giving flexibility to users.
is a critical issue. For this reason, renewable energy sources, distributed generators,
Koltsaklis et al., 2018; Boudoudouh and Maâroufi, 2018; Cetinbas et al., 2017).
(Arup, 2019).
renewable energy sources and these sources for their scenarios and targets.
replaced by microgrids, individuals both as consumers and on a small scale
can be advantageous. Microgrids are safe, reliable, sustainable, low-carbon,
it can work. Therefore, microgrids are secure and reliable. Renewable energy
11th
solutions emerged. These systems can work in connection with the mains as well as from the mains.
It can isolate itself against undesirable effects in case of malfunctions,
It varies according to the user's expectations. Data centers or finance
near loads of power generation facilities using renewable energy sources
Microgrids have many advantages over systems. Renewable energy
The issues of sustainability and reducing carbon footprint may be important. Moreover
the trend is towards sustainable and low carbon resources. Microgrids are the latest
turning to affordable renewable energy sources when it is not economical
are defined as small-power networks that behave as Medium and long term energy
Individuals can only take part in the electrical energy market as consumers. This situation
combination, storage and most cost-effective system balance.
takes place in the energy market as a producer with its productions. (Mengelkamp et al., 2018;
described as affordable and profitable. In grid-connected operation, on-grid
incorporating the potential of electricity into electrical energy production systems today's energy sector
as long as renewable energy sources and/or fuel are available

generation, storage, monitoring, measurement, such as electronic converters and energy management unit
The energy production of the PV systems that produce electricity can be estimated. Wind power
3.2. Basic Structure and Classification of Microgrids
Considering local energy potentials and local grid structure, micro
load group takes place in a wide range from critical loads to adjustable loads.
according to the status of the system, alternating current (AC) micro grid, direct current (DC) micro
configurable. Resource group of microgrids can be easily controlled, intermittent or
energy in its structure, which is presented as an alternative to the various disadvantages of production and transmission
These loads that can be taken have a controllable mechanism. Medical devices, emergency services
renewable energy sources, energy storage units, generators, grid, power
With the variability of conditions, there is also a change in energy production. Intermittent and variable energy
regional consumption, on-site production and on-site consumption, and reduction of transmission losses and
It is in the controllable resources group because it can adjust according to the load demand. PV systems
institutions that include the applications of the critical load group, depending on personal preferences.
Microgrids are divided into two groups, based on power system and location. Strength
12
and control components.
exemplary loads. In microgrids, storage is the demand for load.
systems are less predictable than solar energy. Microgrids
Microgrids, which are small-scale grids, are centralized energy grids of classical grids.
networks can be configured in various ways and
It can be disabled or reactivated by intervening at specified periods.
network and hybrid AA/DC microgrid are examined in three classes.
and life support units, loads that are subject to the health sector, data centers and financial
the regional realization of the supply of electricity, the electricity produced locally with its own resources.
creates uncontrollable resources. Fuel cells and diesel generators produce energy
It produces energy depending on meteorological conditions such as radiation and temperature, and this
Loads such as lighting and ambient temperature control can be adjusted
efficient use of resources. Microgrids in general
It provides backup energy to meet critical loads (Arup, 2019).

Hybrid AC/DC microgrids
AA microgrids
by location
Urban microgrids
DC microgrids
Classification of microgrids
Rural microgrids
According to the power system
grouping is given in figure 3.1 (Hossain et al., 2019).
Figure 3.1. Classification of microgrids
The connection of the generation, consumption and storage components of the microgrid to the AC bus is power
networks are grouped into urban and rural microgrids. Microgrids
units, generators, loads and all components in the system are connected to an AC bus.
general structure is given. DC generators and DC loads can also be connected to AC microgrids.
Microgrids AC microgrid, DC microgrid according to the state of the power system
carried out by electronic converters. In Figure 3.2, an AC microgrid
3.2.1. Microgrids by power system
3.2.1.1. AC microgrid systems
AC microgrids representing the AC power system in the distribution network are special
networks can operate both as grid-connected and island-mode. routine work
and hybrid AC/DC microgrid.
DC generators and loads make the connection via inverter and converter. AA micro
13
can be connected. Distributed generators such as solar and wind energy systems, energy storage
In these cases, the AC microgrid and the power grid are connected via common connection. your load
easily from the common port to the mains without the need for

~
~ ~
~
= ~
=
=
~
~
point
DA/MM
Connection
AA/AA
Main Power System
Storage System
DA/MM
AA/AA
AA Busbar
Transformer
Wind Energy System
Downloader
DA Load
AA Load
Partner
DA/MM
Solar Energy System
can easily be included in the system. DC generator and loads to AC microgrid
passes (Çetinbaÿ et al., 2017; Justo et al., 2013). Classical energy grid generation being AC and
In this case, the micro-grid is disconnected from the mains and becomes an island-mode isolated operation.
energy needs are met from the microgrid. Energy demand from microgrid
Figure 3.2. General structure of an AC microgrid
AA has a developed infrastructure at the point of application of protection and control equipment.
cannot be met, energy is supplied from the power grid to the micro grid. Micro
The most important advantage of these microgrids is that they can be directly applied to microgrids.
of inverters and converters for multi-stage energy conversion when it needs to be connected
In case of excess energy in the grid, energy is exported to the main power grid. Routine
The necessity of connecting the AA microgrids is the disadvantage of the microgrids.
14
forms. Generators that originally produced AC energy, such as wind turbines
the occurrence of any malfunction in the main power grid other than the operation

energy loss with less energy conversion with power electronics equipment
Connections are made with directional converters. PV systems and fuel cells are environmentally friendly energy
Components that require charging and discharging functions, such as batteries and electric cars, are double
3.2.1.2. DC microgrid systems
production sources DC energy generation and computers, various household appliances, fluorescent
In DC microgrids, components are connected to the DC busbar via converters. A
DC energy of load and user equipment such as lamps and variable speed drives
Reducing efficiency and increasing efficiency are among the advantages of DC microgrids.
The general structure of DC microgrid is given in figure 3.3. PV power systems DC/DC and
(Çetinbaÿ et al., 2017; Justo et al., 2013).
15
is a requirement of the need for DC microgrid. energy in demand
wind energy power systems follow the AC/DC converter connection procedure.

=
=
=
=
=
=
=
~
=
~
=
~
Main Inverter
point
DA Load
Wind Energy System
AA Load
DA/DA
Solar Energy System
Storage System
Main Power System
DA/MM
Step-down Transformer
DA/DA
DA/MM
Partner
DA/DA
DA Busbar
Connection
Coexistence of DC and AC distribution network and these two busbars are bidirectional to each other.
16
and load components can be connected. The most important advantage of hybrid microgrids is AA or
3.2.1.3. Hybrid AC/DC microgrid systems
Figure 3.3. General structure of a DC microgrid
Since DC producing and consuming components can be connected to the busbar where the energy type is produced.
hybrid microgrid structure is given. Generation, storage of microgrid to DC and AA busbar
A hybrid micro-grid structure was obtained by connecting it through a converter. In Figure 3.4
energy conversion processes are minimized. energy conversion devices and interface

~
~
=
~
~
=
=
=
~
=
=
=
=
~
~
DA/DA
Wind Energy System
Partner
AA Busbar
Connection
Solar Energy System
=
point
AA/DA
AA/AA
Storage System
=
DA busbar
Bidirectional
Solar Energy System
AA Load
Storage System
Converter
AA/AA
Main Power System
DA/DA
DA Load
AA/DA
Step-down Transformer
DA/DA
3.2.2. Microgrids by location
productivity will also be increased (Çetinbaÿ et al., 2017; Hossain et al., 2019).
17
is studied in class.
Figure 3.4. General structure of a hybrid AC/DC microgrid
while reducing energy consumption devices has a positive effect on reducing energy costs.
Microgrids by location can be divided into urban and rural microgrids.

can be built (Hossain et al., 2019).
It is separated from classical networks with the principle of on-site consumption. Also micro
3.2.2.1. Urban microgrids
Rural microgrids are connected to the main power grid for reasons such as geographical location.
differs from conventional networks. In terms of the power range to which it is applied
can be customized. Thus, at all levels, from production to consumption, micro
These micro-grids operating under the rules that urban micro-grids are obliged to comply with.
exchange networks. These microgrids, which are generally connected to the grid,
Since the principle of energy is adopted at very large powers, micro-grids are used in energy production.
for university campuses, shopping malls, and other industry and community applications
It is an important opportunity to be presented.
In such cases, it can change the network-connected operating mode to islanding.
Economical with the use of local energy solutions
in the selection; different in microgrids depending on the geographical location and meteorological conditions.
networks can be individualized and designed according to the energy demand and needs profile.
18
3.2.2.2. Rural/remote microgrids
In addition, microgrids are provided to regions with consumers selected as the target audience.
Microgrids are affected in terms of power range, energy source and choice of installation location.
Urban microgrids can be connected to the utility grid and powered by the main grid.
It works in island mode in remote applications where there is no access. Autonomous
when evaluated; centralized generation and distribution from a single point in conventional networks
smaller power sources or micro sources are used. Distributed energy source
power quality deterioration and unusual failure in the main power grid
(Hossain et al., 2019). Clean energy for remote and hard-to-reach isolated areas
support and revitalize activities and energy for the individual without access to electricity
renewable energy sources and the combination of these sources can be selected. On-site production
Urban microgrid applications in an area covering residential and commercial areas, hospitals,
3.3. Comparison of Microgrids with Classical Grids

In this section, microgrids are defined as a concept. The basic structure examined
It is aimed to make optimum use of the networks. Grid-connected micro
microgrids are classified according to the power system and location, and
networks are able to isolate themselves when any fault occurs on the network side.
have been compared.
and can work in island mode. In addition, microgrids allow access to the electricity grid.
It plays a critical role for the energy access of remote regions where it is not available.
19

Classification
Determination
of evaluation criteria
renewable
energy systems
Optimal
sizing
hybrid
optimal
design
Optimal
mathematical
model
optimal structure
Determining
the sizing method
Optimal design is important in order to benefit from the networks in the most effective way. this section
As a result of these stages, the optimal design process is completed by obtaining the
optimal structure, optimal mathematical model and optimal sizing. In the following sections
Optimal design in microgrids and sizing with meta-heuristic algorithms
This design process is explained in detail in the article.
Optimization has been examined under two sub-headings.
4.1. Optimal design in Microgrids
Optimal design approach in microgrids hybrid renewable energy systems
20
4. OPTIMAL DESIGN AND SIZING OPTIMIZATION IN MICRO NETWORKS WITH
META-INTUITIVE ALGORITHMS
classification, determination of evaluation criteria and sizing
Microgrids of renewable or non-renewable distributed generators
It is carried out in three stages, including the selection of methods/algorithms. this design
As a result of energy production by combining it under the roof of the network, economic, political
The process is given in Figure 4.1.
and many environmental benefits. Obtaining these benefits and
Figure 4.1. Optimal design process in microgrids

Classification of hybrid
renewable energy
systems
Hydro power, PV, wind,
energy storage, diesel
generator, etc.
Hybrid renewable energy
systems without hydroelectric
power or pumped storage hydro power
Hybrid renewable energy
systems with hydroelectric power or
pumped storage hydro power
PV, wind, energy
storage, diesel generator,
etc.
creates the structure. While creating these components, urban or rural microgrid structure
is important. Urban microgrids are powered by the mains power grid. but far
meteorological analysis of renewable energy systems in regional and isolated microgrid applications.
Depending on the conditions, the energy production is random, variable and interrupted.
21
electrical energy may not be produced at the desired level. Therefore
4.1.1. Classification of hybrid renewable energy systems
Figure 4.2. Classification of hybrid renewable energy systems
get maximum efficiency from renewable energy sources and overcome these problems
PV power systems, wind power systems, energy storage units, diesel generators,
Hybrid renewable energy systems with and without hydroelectric power
hybrid energy sources together with energy storage solutions to come
The situation is divided into two classes. The classification of these energy systems is given in figure 4.2.
Combinations of hydroelectric and pumped storage hydro power generation plants
used as.
(Lian et al., 2019).
Optimal solution that includes the energy generation and storage components of the microgrid by combining

Social
indicators
indicators
Economic Environmental
Evaluation criteria of
hybrid renewable energy
systems
indicators indicators
Reliability
Figure 4.3. Evaluation criteria of hybrid renewable energy systems
4.1.3. Selection of sizing methods of hybrid renewable energy systems
Reliability, economic, environmental and social indicators ensure optimal sizing
The main purpose of optimization techniques is practical, economical and environmentally optimal.
It plays an important role in reflecting the criteria needed to determine the mathematical model.
obtaining a system. Sizing methods transfer of energy to the user
is playing. The reliability indicators show the energy demand of the designed system and the load.
the most suitable way to ensure that the systems operate in the most efficient and reliable way during
22
Economic indicators are concerned with the availability of hybrid energy systems.
are mathematical and software applications used to find the method. hybrid
4.1.2. Determination of evaluation criteria of hybrid renewable energy systems
It deals with the ability to meet the load with acceptable system costs. In addition to this
sizing methods of renewable energy systems are given in figure 4.4 (Lian, J.,
With the determination of the evaluation criteria, which is the second stage of the optimal design,
As environmental indicators, the status of pollutants formed at the output of the hybrid system
et al., 2019).
the optimal mathematical model is obtained. hybrid renewable energy systems
social indicators are evaluated, while policies, sustainable energy and social indicators are evaluated.
evaluation criteria are given in figure 4.3. (Lian et al., 2019).
It deals with the effects of developments on the system.

Economic Hybrid
Environmental software
tools
methods
indications
Reliability
Sizing methods of hybrid
renewable energy systems
Social
indicators
Classical Artificial
intelligence
methods indicators methods
indications
is used. The purpose of these algorithms is to operate the distributed generators with the most appropriate capacity.
can be classified as Classical methods that can find the optimum point in a few tries
they use mathematical approaches and functions and need a set of rules.
and to ensure that the design conditions are within limits. The operation of microgrids
Artificial intelligence methods, on the other hand, take the various situations in the human and universe as a reference.
single-purpose or multi-purpose
aims to make simulations. Larger and more complex than conventional methods
problems need to be optimized. Optimization methods and
23
can solve problems. With discrete problems and incomplete datasets
algorithms have been developed. Optimization algorithms; mathematical and computer
they can work. Multiple methods are brought together and a hybrid method approach is used.
programming is divided into two. Among these optimization methods mentioned, meta
can be used while sizing can also be done using software tools.
the location of heuristic algorithms is given in figure 4.5 (Twaha and Ramli, 2018).
Figure 4.4. Sizing methods of hybrid renewable energy systems
4.2. Sizing optimization with metaheuristic algorithms in microgrids
These methods are classical methods, artificial intelligence methods, hybrid methods and software tools.
Various methods and algorithms for the optimization of microgrids

Linear
optimization
Physics or chemistry based
algorithms
Dynamic
optimization
Swarm intelligence
based algorithms
dynamic
programming
Algorithms not
based on swarm intelligence
Meta-heuristic
methods
numeric
Optimization methods
techniques
Mathematical
optimization methods
Meta-heuristic methods
Computer programming
optimization methods
Nature-inspired algorithms
Combinatorial
optimization
Algorithms inspired by
living things
• The structure of metaheuristic algorithms inspired by evolution and nature is simple and
can be learned quickly.
provides flexibility in application.
There are many reasons. If a few of them are important to be specified;
• Solve other problems without making a major change in the overall structure of the algorithm.
24
Preferring metaheuristic algorithms among optimization algorithms
• These algorithms, which are available for development, allow hybridization.
Figure 4.5. The place of metaheuristic algorithms in optimization methods

It has been provided to be applied in the model put forward for optimization.
and the community in which these particles coexist is called swarm. in a particular herd
• With its derivative-independent mechanism, the problems are stochastic, that is, randomly.
avoids optimal points better than these individual algorithms (Twaha and Ramli,
PSOA was developed by James Kennedy and Russell Eberhart in 1995.
It moves in that direction by taking advantage of the particle's information. Best next location
and provides an advantage in its application to real problems.
Different swarm intelligence of the energy optimization model developed in this thesis study
It is an optimization algorithm that deals with solution development and is built on this basis.
algorithms have many advantages over individual algorithms. With individual algorithms
can avoid.
In order to evaluate the results of different algorithms that can be applied
various movements interactively with each other while ensuring safety in situations
examined. Mathematical expressions and general usage logics of these algorithms
25
in algorithms, a number of results are improved, more functions are evaluated, and local
It has been seen that it provides easier and faster access. Each individual particle in the swarm
4.2.1. Particle swarm optimization algorithm
each particle in the position is based on past experience and the one with the best position in the swarm.
solves it. This means that the derivative of the search space is not calculated.
2018).
PSOA is inspired by fish and insects moving in swarms.
It aims to update according to the currently active widget. consisting of the specified number of particles
• Since the solution search structure is based on a stochastic structure, it is one of the local optimums.
to ensure that the algorithms are tested and by testing the accuracy of the model,
Animals that are in the herd and are constantly on the move, are in search of food and risky.
Five optimization algorithms: PSOA, ABA, GKOA, BOA and SSA
exhibits. It has a purpose determined by the effects of these movements on other individuals in the herd.
Swarm intelligence inspired by nature and living things within meta-heuristic algorithms
a result is improved and fewer functions are evaluated. Collective like herd intelligence

+1 2
+1
+1
2
+1
+1
one
= +
representing the cognitive learning factor, and 2 is the difference between one particle and the other particles in the swarm.
random positions and velocities are assigned in the swarm. The fitness of each particle in the swarm
(4.2)
parameters are usually chosen with values close to two.
process is imitated. Fireflies use flashes of biological content to mate for reproduction.
The intensity of light and the attraction of fireflies are formulated. a certain light
are evaluated in terms of results and the global best value is determined. speed and position
ÿ
the current position of the particles and the position of the next iteration,
are numbers.
beetles' rhythmic lights, and when distance is correlated with the inverse square law, the light
= ÿ + [ 1
Al-Saedi, 2012).
best particle solution
and
There is an inverse relationship between density. fireflies at a distance
26
(4.1)
1 particle to act according to its own experience
4.2.2. firefly algorithm
social learning parameters that enable them to act according to their experiences. This
beetles produce sudden light in the form of flashes and affect other living things around them.
A solution was sought and more vivid and brighter rhythmic lights were used as the objective function of FA.
value is calculated and local best results are determined. Then all the best local
ÿ
here and
random ranging between 1 and 2 [0,1]
They use it to trap them in their selection and hunting processes. Fire
It was accepted that it was attracted by other fireflies in direct proportion to its brightness.
The light intensity at the distance from the source was calculated by Equation (4.3). Practically
updates are made with Equation (4.1) and Equation (4.2), respectively (Sigarchian et al., 2016,
is the current speed and the speed information in the next step.
The light intensity decreases with distance from the source. distance and light
and it is the best global solution.
to optimization problems through interaction, attraction and communication relations.
ÿ ( ÿ ) +
ABA was proposed by Xin-She Yang in 2008. shoot with this algorithm
orientation tendency is used. In this algorithm, fireflies have no gender and the light
ÿ ( ÿ )]

ÿ
2
=1
ÿ
0
0
0
(ÿ 2 ) ( ÿ )+ÿ (
0
0
2
2
+1
2
one
ÿ
0
(ÿ )
one
(4.7)
r of the attractive firefly
(4.11)
The absorption of light as it propagates is accomplished by adding a constant light absorption coefficient to the equation.
The characteristic related to the length depending on its constant value is given. ÿ as a measure of length
ÿ 1
(4.9)
The positions of two fireflies and the distance between them when considered as
Equation (4.4) is used and the gaussian distribution is used. Here, the light source,
=
,
= | ÿ | = ÿÿ ( ÿ )
The updated new position is calculated by Equation (4.11).
(4.3)
denotes attractiveness. ( ) if
( ) =
Equation (4.6) was used in terms of Here
ÿ =
27
( ) =
(4.8)
shows the attractiveness value at a distance and is calculated by Equation (4.7). In Equation (4.8)
(4.6)
= +
Also, if the light intensity and distance are zero, Equation (4.3) will be undefined.
can be used and is given in Equation (4.9).
It is calculated by Equation (4.10).
=
(4.10)
With the attraction of fireflies towards the brighter firefly,
)
distance and represents a constant light absorption coefficient.
The attractiveness of fireflies is given by Equation (4.5) and the ease of calculation
= ÿ( ÿ )
ÿ
distance between two fireflies is zero
(4.5)
ÿ 1, ÿ ÿ
( ) =
=
+ ( ÿ )
(4.4)
1+ 2
2
ÿ1
2
0
2

they have. This hierarchical structure is given in figure 4.6. Place at the first level of the hierarchy
inspired by living gray wolves. Gray wolves' pursuit of prey, tracking, approach,
Here
elderly people whose information is used, hunters who hunt for the herd, caregivers who care for the elderly and sick
A value of 0 is usually accepted as 1. Express the random parameter ÿ and random numbers
Gray wolves have a hierarchical structure consisting of four classes: alpha, beta, delta and omega.
In dangerous situations, scouts warn the herd, guards guarding the herd, alpha and beta.
2016; Othman et al., 2016).
The orders of these wolves are carried out by the pack. Capable of managing the herd
wolves, on the other hand, are at the bottom of the herd in all aspects, including the order of eating.
Alpha wolves, who can be male or female, take on many tasks as the pack leader. Raid
values in the range of [0,1] (Kaabeche et al., 2017; Alomoush et al., 2018; Satapathy et al.,
The delta consists of wolves. Omega is the last step of the hierarchical structure.
They are responsible for the basic tasks of the donor. Beta, which is in the second step of the hierarchical structure,
It was proposed in 2014. For GKOA, those at the top of the food chain and in flocks
wolves counsel alpha wolves, follow orders, and retrieve
They perform many tasks such as
4.2.3. Gray wolf optimization algorithm
GKOA by Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis
Alpha wolves, who are alpha wolves, make many decisions such as deciding to hunt, sleeping places and waking up.
It was observed that there was confusion in the herd that lost the omega wolves. These wolves are babysitting
28
It was developed by being inspired by surrounding siege, intimidation and hunting behaviors.
They are wolves who are in the position of assistants of alpha wolves in many respects. Following the borders
they pass notifications to alpha wolves. Beta wolves, who are candidates to become alpha wolves, are like decision making.

ÿ
ÿ
Figure 4.6. Hierarchical structure in gray wolves
It is expressed by Equation (4.12)-(4.16). Here,
(4.12)
(+1)
It is modeled mathematically in three stages by simulation. Get the best result
stands for vectors. decreases from 2 to 0 during the iteration. ÿ1 and ÿ2 [0,1]
(4.14)
= 2 ÿ ×
ÿ
ÿÿÿÿÿÿÿÿÿÿÿ
The third best result is beta and delta, respectively, and the remaining results are omega.
= | × ( )
(4.16)
29
= ( )
ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿ
distance between prey and gray wolf,
ÿ ( )
Gray wolves have the ability to recognize prey's position while hunting. the hunting process
they represent the second and third solution. The three best solutions obtained are saved and omega
In the GKOA algorithm, social hierarchy consists of prey siege and hunting behaviors.
the position of the prey, the position of a gray wolf, the current iteration, and the coefficient
(4.13)
= 2 × × ÿ1 ÿ
It has been likened to the social hierarchical order of wolves. The best result is alpha, second and
are random numbers that vary in the range.
(4.15)
ÿ ×
ÿÿÿÿÿÿÿÿ |
= 2 × ÿ2
has been named. Gray wolves surround their prey for their food. The approach to siege their prey
alpha wolf rules and alpha wolf offers the best solution, while beta and delta wolves are the best, respectively.
ÿÿÿÿ
2

one
(4.17)
ÿÿÿÿ
×
ÿ
the importance of the hunt is reduced.
alpha, beta, and
ÿ
The hunting process of wolves is expressed by Equation (4.17)-(4.23). Here
= | 2
attack the prey when it is available, and gray wolves move away from the prey in other cases.
are the position vectors of delta wolves. (+1) denoting the next position
ÿÿÿÿÿ
(4.20)
ÿÿÿÿ
ÿÿÿÿ
In the GKOA algorithm, the search process is started by generating a random population. During iteration,
alpha, beta, and delta wolves predict the predicted position of prey. Each
Other wolves, including wolves, update their positions to the best solution. Grey
ÿÿÿÿ
ÿ
ÿÿÿÿ
of
and
ÿÿÿÿ
= | 3
=
is forced. Emphasizing the distance between the prey and the gray wolf, which can take values in the range of [0, 2]
and
(4.21)
for alpha,
×
×
the candidate solution updates itself based on its distance from the prey. With the parameters used
ÿÿÿÿÿ
ÿÿÿÿÿ
ÿ |
ÿÿÿÿ
ÿÿÿÿ
ÿÿÿÿ
=
ÿ
30
,
ÿÿÿÿ
ÿ |
ÿ
,
(4.22)
solutions of beta and delta wolves are averaged and other search agents calculate their positions.
The for parameter is used, a weight is added to the av and from the first iteration to the last iteration.
convergence or search state is decided and when the termination criterion is reached, the algorithm
ÿ
ÿÿÿÿ
×
ÿÿÿÿ
ÿÿÿÿ
Hunting results in attacking the prey. Capable of taking values in the range of [-1,1]
ÿÿÿÿ
ÿÿÿÿ
(4.18)
×
(+1)
ÿ |
ÿÿÿÿ
updates according to this information.
(4.23)
=
=
search continues. When >1 the importance of the catch is emphasized and when it is <1
terminated (Mirjalili et al., 2014; Miao et al., 2020; Saxena et al., 2020).
= | one
ÿÿÿÿ
×
ÿÿÿÿ
ÿÿÿÿ
As a result of decreasing from 2 to 0, it changes in the range of [ÿÿÿÿÿÿ2ÿÿÿ , 2ÿÿÿ ÿ ]. A is less than 1
distance to prey of alpha, beta, and delta wolves, respectively, and
(4.19)
ÿÿÿÿ
ÿÿÿÿ
,
2
one
2
3
3
ÿÿÿ ÿÿÿ
3
ÿÿÿ
one
+ 3
+ 2

BOA was proposed by Seyedali Mirjalili and Andrew Lewis in 2016.
Bubble feeding with two maneuvers, upward spiral and double loop.
is likened.
4.2.4. Whale optimization algorithm
they are fed. They form bubbles at intervals in a circle or nine shape.
humpback, which is one of the largest mammals that can behave and is in the category of hunter
They begin to form bubbles in a spiral shape and float to the surface. narrated and
they do. In an upward spiral, whales dive around 12 meters and prey around
BOA can think, learn, make decisions, communicate and be emotional
Figure 4.7. Hunting methods of humpback whales
people's social life by having a family and living with this family structure for life.
inspired by whales. Whales that can live alone or in groups
behavior is similar to the social behavior of humans. some whale species
Hunting models of humpback whales inspired by the algorithm are given in figure 4.7.
31
Humpback whales eat a special diet called bubble-net feeding.

ÿ ( ) ÿ ×
(4.24)
parameter changing in the range of [0,1] assuming that it will move in a spiral fashion
Mathematical work in BOA algorithm inspired by hunting humpback whales
the best position vector obtained so far and updated at each iteration,
(4.26)
×
Whales tend to recognize and encircle prey. If there is no foreknowledge about hunting
decreases from 2 to 0 during the iteration. It is a random number that changes in the range [0,1].
(4.28)
= 2 ×
ÿÿÿÿÿÿÿÿ
has been done.
ÿ
| ÿ ( ) ÿ ( )
After the good solution has been identified, the other agents update their positions. this update
ÿ
ÿ
It is carried out by two approaches as an update. narrowing siege mechanism
× (2 × × ) ×
32
=
= 2 × × ÿ
ÿÿÿÿÿ
(4.25)
(4.30). The distance between the whale and the prey is calculated and for the next step.
is a random number that changes.
represents the position vector, the current iteration, and the coefficient vectors.
It is modeled as prey containment, bubble net feeding and hunting prey.
(4.27)
= 2 ÿ ×
ÿ ÿÿÿ
(4.29)
=
The whale optimization algorithm is defined as the current best candidate solution target prey. Most
ÿÿÿÿÿÿÿÿ
bubble net feeding shrinkage mechanism and spiral position
(+1)
ÿÿÿÿ
= | ÿ ( ) ÿ ( )
carried out by a decision. In the approach of the whales to the hunt, 50% narrowing approach and 50%
= | ×
(+1)
by reducing the parameter. Spiral position update Equation (4.29)-
ÿ ( )
|
has been added. This random decision by whales is simulated using equation (4.31).
operation is performed by Equation (4.24)-(4.28). Here,
helical movement is performed. Here it is a constant coefficient in the range of [-1,1]
(4.30)
Narrowing encirclement mechanism and spiral position update instantaneously in whales
distance between prey and whale,
ÿ
ÿ
2
×

parameter is used.
= { ÿ ( ) ÿ ×
When using the best results for the update, when is greater than 1
(4.33)
updates. decrease of the parameter from 2 to 0 and
and it is an algorithm structure inspired by sea snails that eat plankton. A big
< 0.5
ÿ ( ),
a position update is made according to a random whale. Update Equation (4.32)-(4.33)
by narrowing the parameter
They feed and grow together. For mathematical modeling of Salp chains
,
in order to pass
×
= | ×
(+1)
2016; Qais, et al., 2020, Guo, et al., 2020).
33
(4.32)
ÿ
ÿÿÿÿ
position when less than 1
=
their positions according to the chosen agent or the best solution obtained so far.
ÿ
× (2 × × ) ×
instead of the best solution found so far to allow global search.
It guides the algorithm in searching for global solutions.
SSA, Seyedali Mirjalili, Amir H. Gandomi, Seyedeh Zahra Mirjalili, Shahrzad
SSA barrel-shaped transparent fish that move by passing water through feeding filters.
ÿ 0.5
performed with.
ÿ
It acts as a decision maker about encirclement or spiral movement (Mirjalili and Lewis,
Salps living in the group link their bodies together in chains and
The population is divided into two groups, leaders and followers. The leader is at the very front of the spur chain.
the remaining salps are called followers. Behavior of SSA-related salps
Instead of focusing on a single point in the search for prey, it goes beyond the reference whale.
ÿ |
ÿ ×
4.2.5. Salp herd algorithm
modeling is given in figure 4.8 (Mirjalili et al., 2017).
(+1)
BOA starts with a random set of solutions. At each iteration, the agents are either randomly
Saremi was recommended by Hossam Faris and Seyed Mohammad Mirjalili in 2017.
(4.31)
ÿ ×

ÿ((4× )ÿ )
2 and 3
3
one
2
one
one
2
one
2
3
the position of the food source, lower limit,
< 0
ÿ 0
ÿ
(4.36)
ÿ1 )
is in the range of [0,1]
+ ), + ),
are random numbers that change. 1 salp is the most important parameter of the swarm algorithm. This
= 2 ×
=
is the maximum number of iterations. Position of followers using Equation (4.36)
is updated.
makes position updates according to the food source. Here is the position of the leading salp,
(4.34)
Figure 4.8. Behavior of Salp herds
The position of the lead sling is updated by Equation (4.34)-(4.35) and only the lead sling
With the parameter, a balance is achieved between convergence and research. Here is the current iteration and
(4.35)
× ( +
34
upper limit,
× (( ÿ ) × × (( ÿ ) ×
= { +
follower salp
swing chain
leader salp
direction of movement

The theoretical and mathematical backgrounds of five selected metaheuristic algorithms are given.
In this section, the operational stages of the optimal design process of microgrids
Position update based on food source named best result achieved
35
has been explained. Sizing is the most important part of this design process.
it does. Followers update their positions relative to each other and gradually salp the leader.
convergence is prevented (Mirjalili et al., 2017; El-Sehiemy, et al., 2020; Tubishat, et al.,
PSOA, ABA, GKOA, BOA and SSA, focusing on optimization
SSA records the best solution obtained and identifies it as a food source.
they act right. With this gradual convergence, catching on local optimum points and early
2020; Neggaz, et al., 2020).
In the algorithm, leader salp explores the balance between convergence and search, and only leader salp

=
~
=
=
~
=
=
=
=
=
=
It consists of generator and load components. PV power system and batteries DC/DA
In this thesis, a microgrid-connected and island-mode micro
In this section, we propose an island-mode hybrid microgrid for optimal design.
5. METHOD
This microgrid; PV power system, batteries used as energy storage unit, diesel
design, components of microgrid, energy management strategy of microgrid, economic
In the AA busbar, there are diesel generator and load components. of renewable energy source
connected to the DC bus through the converter. DC bus is connected to AC bus with inverter.
design is given. In this context, this chapter is about a proposed island-mode microgrid.
diesel generator to ensure the energy continuity of the system in the island mode microgrid
5.1. Design of a Proposed Island Mode Microgrid
and autonomous for worst-case scenarios where there is no access to electrical energy.
Figure 5.1. Proposed island-mode microgrid system
and reliability evaluation criteria and objective function.
examined.
The electrical energy produced varies according to seasonal conditions. Therefore
36
network is proposed and designed. The designed microgrid system is given in figure 5.1.
batteries with working capability are used.
AA Busbar
pfv
AA Load
DA/DA
DA Busbar
Battery Energy Storage
pbat
Diesel Generator
PV Power System
DA/MM
DA/DA
= pinv_load

has values.
Here,
5.2. Components of Microgrid
( ) =
if the standard test
The investment cost shown in the table will be used in the economic evaluation of the PV system. Operation
and maintenance cost has been determined as 20% of the investment cost.
consists of. The components in the microgrid and the technical and economic aspects of these components
×
is a constant temperature coefficient of 25°C and ÿ3.7 × 10ÿ3 (1/°C) respectively
has been selected. Current prices were used for the investment cost of PV panels and 500 $/kW
It depends on many parameters such as and is given in Equation (5.1).
( ) Output power of the PV module (W),
regulator or solar charge controllers. PV given investment cost
5.2.1. PV power system
37
The technical and economic characteristics of the PV power system are given in Table 5.1.
Standard test of PV module
temperature and
the ayes have it.
(5.1)
Microgrid PV power system consists of battery, diesel generator, load and inverter components.
cell under standard test conditions
PV power systems are long-lasting project investments and the lifespan for this operation is 24 years.
properties are examined in the sub-headings.
is the ambient temperature (°C) [Homer, 2019].
has been determined. PV energy from the PV power system to the energy storage units
It is the nominal power (W) under conditions, ( ) solar radiation (W/m2 ) and
1000 W/m2 .
The efficiency of the regulator varies between 94% and 97%, but 95% for this study.
The electrical energy produced from the PV power system is the output power of the module, radiation and temperature.
( )
+(0.0256× ( )))ÿ
_ ×[1+ ×( ]
_

Component
$
Investment cost
(c) Radiation data for day 3
Table 5.1. Technical and economic characteristics of the PV power system
PV regulator cost 1500
(b) Irradiation data for day 2
Value Unit
95 %
(d) Irradiation data for day 4
PV regulator efficiency
Parameter
Figure 5.2. Irradiation data
The energy produced from the PV power system varies with the change of radiation
and temperature with time. In the analyzes made in this thesis study, Gazi University Technopark
Year
Five-day real radiation and temperature data from the meteorological station in
Life time 24
PV
38
500 $/kW
(a) Irradiation data for day 1
used.

Figure 5.2. Irradiation data (continued)
The data were used by converting them to one hour time period. 0 W/m2 to 984.33 W/m2
The five-day radiation data used in the microgrid, varying between
The resulting temperature data were converted into one hour time period and used. 15°C to 31.3°C
This five-day data set is repeated 73 times over the course of a year.
39
Given in 5.2(f).
A one-year data set was created using Global recorded at 10-minute intervals
(e) Irradiation data for day 5
In addition to the radiation data, the temperature data recorded at 10-minute intervals
The radiation data are given in Figure 5.2(a)-(e). Radiation consisting of 10-minute periods
(f) Five days of radiation data
and the 10-minute change on the basis of each other is given in Figure 5.3(a)-(e). 10 minute periods

(a) Temperature data for day 1
(e) Temperature data for day 5
Figure 5.3. temperature data
(b) Temperature data for day 2
40
(c) Temperature data for day 3
The five-day temperature data used in the microgrid, varying between
(d) Temperature data for day 4
Given in 5.3(f).

status, energy production and load consumption of the system. Regarding battery capacity
and
limitations are given in Equation (5.2) and Equation (5.3).
(5.2)
Here is the current energy level of the battery.
(5.3)
, the battery allowed
41
< <
with minimum and maximum energy storage capacities.
(f) Five-day temperature data
between
(5.4)
=
Figure 5.3. Temperature data (continued)
takes place. The minimum storage capacity of the battery is related to the depth of discharge (DOD).
5.2.2. Battery energy storage unit
= (1 ÿ
The capacity of the battery in the system depends on the energy demand of the load, the number of autonomous working days, the discharge.
The state of the battery at any time ( ) is charged at the previous time ( ÿ 1).
) ×
It is determined by Equation (5.4) depending on the depth and efficiency of the inverter and battery.
_
_
.
_
_
u .
_
.
_

Technical and economic characteristics of the battery energy storage unit in Table 5.2
Diesel generator secondary energy source for remote microgrid applications
the energy storage capacity of the battery (kWh), the energy demand of the three load
economic features. The parameters given in the equation are the consumption parameters obtained from
the fuel curve of the diesel generator. The first parameter is the power produced by the diesel generator
has been determined. When the batteries are evaluated in general, the discharge depth is 80% and
discharge depth
It is given by Equation (5.5).
The lifespan is chosen as 5 years.
the number of days the battery can operate autonomously (3-5 days in general), DOD discharge
Battery
( ) + 0.08415 ×
and
Life time
(5.5)
depth,
Component
Yield
and provides energy continuity. Energy production of diesel generator
42
given. The investment cost shown in the table will be used in the economic evaluation of the battery.
Operation and maintenance cost 20% of investment cost
5.2.3. diesel generator
Year
power (kW),
Here,
efficiency was taken as 85%. The investment cost of these batteries is 280 $/kW.
( ) is the rated power (kW). In Table 5.3, the technical and
80%
( ) = 0.246 ×
shows the hourly fuel consumption per kW of power output proportional to the second
Investment cost $280/kW
(kWh),
Table 5.2. Technical and economic features of battery energy storage unit
( )
Parameter Value Unit
85%
where ( ) the fuel consumption of the diesel generator (L/h),
are the efficiency of the inverter and the battery, respectively.
( ) produced by the diesel generator
5

Table 5.3. Technical and economic features of diesel generator
on a similar course from the beginning to the end of the shift, except for lunch hours.
parameter is obtained by dividing the no-load fuel consumption by its nominal capacity [Homer,
Parameter Value Unit
and given in figures 5.4(a)-(e). The load data used in the microgrid is in figure 5.4(f)
was found to be minimal.
30000 Hours
The investment cost shown in the table will be used in the economic evaluation of the diesel
generator. Operation and maintenance cost 20% of investment cost
translated and used by scaling 1ÿ10. This five-day dataset is one year
costs were researched and the cost per kW was determined as $1000.
Load consumption and load profile in microgrids are important in the design of the storage unit and
the whole system. Optimum performance of the components, especially during peak energy consumption
and changes in the user's energy consumption.
The life expectancy of diesel generators is considered to be between 10000 and 30000 hours according to their adequacy.
has created. While the minimum load power is 9.38 kW, the maximum load power is 30.
There is a steady consumption and after the end of the working hours, the load consumption
43
Component
It follows an increasing course at the beginning of the working hours and decreases similarly at the end of the working hour.
As with the temperature data set, the load data were recorded at 10-minute intervals.
2019].
Life time
given. Load data consisting of 10-minute periods is transferred to one hour time period.
A one-year data set was created by using it repeatedly 73 times throughout the
has been determined. The power range in which it is used and the performance of maintenance procedures
diesel generator
5.2.4. Load
is kW. When the load consumption is examined, it is seen that the consumption decreases in the noon hours.
is done. In this study, the investment of a diesel generator with a working life of 30000 hours
plays an important role in determining their capacity. In this thesis study, real-time load data recorded in
Gazi University Technopark was used. radiation and

(c) Load data for day 3
(a) Load data for day 1
(e) Load data for day 5
Figure 5.4. load data
(d) Load data for day 4
(b) Load data for day 2
44

The technical and economic characteristics of the inverter are given in Table 5.4. seen in the chart
Life time
investment cost will be used in the economic evaluation of the converter. Operation and maintenance
cost has been determined as 20% of the investment cost. Inverters
24
operating efficiency ranges from 90% to 95% and is 92% for this study.
Year
determined. For the investment cost, the actual selling prices of the inverters were researched and $60/kW
inverter
45
has been determined.
Yield
(f) Five days of load data
Table 5.4. Technical and economic characteristics of the inverter
92 %
Figure 5.4. Load data (continued)
Component
5.2.5. inverter
Value Unit
The PV power system and the battery are connected to the AC bus via a bidirectional converter.
Parameter

and accordingly, minimum fuel consumption from the diesel generator.
is taken. With this strategy, which is designed to consist of four situations, energy generation from the PV system,
If the battery is not sufficient, a diesel generator is used.
intended. In line with these determined purposes, the energy management given in figure 5.5
charging the battery, discharging the battery, generating energy from the diesel generator and
Case 4: The PV generation system is insufficient to meet the load demand and the battery is empty.
strategy was created. In Figure 5.5(a) the initial conditions of the system
The processes of meeting the energy demand of the load are organized.
In this case, the diesel generator, which has a secondary priority of use to supply energy to the load
The main flow diagram is given. Working state in this main flow diagram
Case 1: The energy produced from the PV power system is sufficient to meet the load demand. produced
used.
46
if disclosed; While energy is produced from the PV system, the excess energy demand of the load is a production.
Excess energy is stored in the battery.
5.3. Energy Management Strategy of MicroGrid
value occurs, the designed model follows the path given in figure 5.5(b) and the batteries are charged.
Case 2: In addition to Case 1, the energy produced from the PV power system is the demand of the battery and the load.
When the energy demand of the load cannot be met with the energy produced from the PV system,
The energy developed for the microgrid designed by obtaining the computer model
If it is more than that, this energy is spent on external loads.
coordinating the load flow between the management strategy and the components of the microgrid,
The algorithm provides energy by discharge from the batteries given in figure 5.5(c), and the batteries are
Case 3: Renewable energy source is insufficient to meet the load demand. In this case
meeting the energy demand of the load, maximizing the benefit from the PV power system
if it is not sufficient, the energy from the diesel generator can be obtained by following the path given in figure 5.5(d).
The battery, which has the primary usage priority, is used to provide energy to the load.

Size(t) > Size_max
Size(t) = Size_max
Echarge(t) < Size_max -Size(t)
Size(t) = Size_max
rj
Yes
Start
PFV(t) > PLoad(t)/ÿinv
desha
Read entries
Charge
No
Echarge(t) = Pcharge(t)×1 hour
Size(t) = Size(t-1) + Echarge(t)
Yes
Pcharge(t) = PFV(t) - PLoad(t)/ÿinv
Charge
No
Yes
Edump(t) = 0
Edump(t) = Echarge(t) – [Size_max-Size(t)]
Turn back
No
Turn back
Edump(t)=Echarge(t)-(Size_max-Size(t))
Turn back
Figure 5.5. Energy management strategy
(b) Battery charging algorithm in case the produced energy is more than the consumption
(a) Operation of the designed system in initial conditions
47

Size(t) = Size_min
48
(c) In cases where the produced energy is less than the consumed energy, the energy must be met from the batteries.
Figure 5.5. Energy management strategy (continued)
Diesel
Size(t-1)-Size_min > Edischarge(t)
Pdischarge(t) = PLoad(t)/ÿinv - PFV(t)
Turn back
ir Yes
Discharge
Size(t) = Size(t-1)-Echarge
work with diesel generator
hey
Edischarge(t) = Pdischarge(t)×1 hour

ÿ=8760
ÿ=1
Turn back
Size(t) = Size(t-1) + (Pgenerator*ÿinv+PFV(t)-PLoad(t)/ÿinv)
Edump(t) = 0
Turn back
Yes
Diesel
No
Diesel = Pgenerator*ÿinv
ü (ÿ)
ÿ
49
energy supply
COE for sizing optimization of island-mode microgrid
minimization has been made. The cost of energy, which is an economic evaluation criterion, and
(5.6)
Figure 5.5. Energy management strategy (continued)
5.4. Objective Function with Economic and Reliability Evaluation Criteria
The recovery factor of capital is given in Equation (5.6) and Equation (5.7).
($ÿ ÿ) = ×
5.4.1. Economic evaluation criterion
(d) In case of insufficient energy in batteries and PV system, from diesel generator
Size(t) > Size_max
Edump(t) = Size(t) - Size_max
Size(t) = Size_min Size(t) = Size_max

of capital
The aim of the study is to minimize the cost and the probability of power supply loss.
annual reduction is provided. The recovery factor of capital, taking into account the interest rate
It is given in Equation (5.8). In case of faults in the power supply to meet the load, low
=
It is the recovery factor of the principal or capital used for is the actual interest rate, and
(5.7)
It is used as a statistical parameter and ranges from zero to one. This
Here,
is the total net present cost. ü (ÿ) is the hourly energy consumption of the load.
5.4.2. Reliability evaluation criteria
The power produced by the PV power system,
Here,
equal to life span. The reason for this is that other components of PV systems
means not met.
(5.8)
50
is the recovery factor and is the total net present cost multiplied by the system cost.
Minimization of LPSP has been done. LPSP, a reliability evaluation criterion
is the power supplied from the diesel generator to the microgrid.
Systems with more than one decision variable are multi-objective problems. of energy
calculate the present value of system components for a specified period of time
Possibility of power supply loss in renewable energy source generation or technical errors
probability of loss of power supply, power consumed by ü load,
=
It was taken as 12%. indicates the lifetime of the system and generally indicates the life of the PV system.
If the value is zero, the energy demand is fully met and if it is one, it means that the energy demand is met.
It has the longest lifespan compared to
the minimum charging power of the battery and
including investment, cost, operation, maintenance and replacement costs
5.4.3. objective function
For sizing optimization of the microgrid in addition to the cost of energy
ÿ( ü ÿ ÿ
(1+ ) ÿ1
+
ÿ( ü )
)
(1+ )

number of objective functions and ( ) . is the objective function. COE and LPSP in this study
determined as a function. These two decisions determined in the design of the microgrid
has been done. The sum of the relative weights used in the weighted sum method is equal to one.
= 1
means that the energy demand of the load is met only from the diesel generator.
( ) = {ÿ
weight given in Equation (5.9) to make the problem a one-objective problem.
,
is done. Here, ( ) objective function, decision variable, relative importance of each objective,
(5.10)
) × 100
Multi-objective functions with the weighted sum method and the criteria in Equation (5.10).
ÿ (0,1), ÿ {1, … , }
The rate of integration of energy resources into systems is an important issue. for this study
0% of the load where the energy demand is met only from renewable energy sources
51
Assuming that the objectives have the same importance ratio, the relative weights are accepted as 0.5.
The rate of renewable energy use from the PV power system in the energy produced
ÿ
should be.
The objective function is a multi-objective problem. This is multipurpose
= (1 ÿ
In addition to COE and LPSP renewable in remote microgrid applications
The sum method was used.
} × ( ) (5.9)
diesel generator and PV power system are the energy providers of the microgrid. This
(5.11)
weights are added with care and a scalar objective function is obtained.
and is given in Equation (5.11). 100% renewable rate
=1
)
=1
ÿ( ÿ( )

The weights of the LPSP are common parameters for the five algorithms. Sizing
• Optimization results of system cost,
6. FINDINGS AND DISCUSSION
In total, data were collected in 2000 iterations. non-numeric i.e. stochastic
are designed to operate autonomously. In this study, the day when the battery can operate autonomously
• Change of objective function and statistical results,
is to find and compare the results obtained in the experiment. Objective function
solutions to the optimum capacities of these components with PSOA, ABA, GKOA, BOA and SSA
A solution was sought to the problem determined in the direction of the
• Convergence graphs,
affects. The number of particles, the number of iterations, the number of trials of the algorithm, the COE and
has been defined. search space; 0 kW to 100 kW for the power of the PV system, the energy of the battery
parameters, common parameters and search space are defined in Table 6.1. in the chart
It has been reported that both purposes are equally important. Finally, if
optimization was carried out.
• Distribution of energy production by resources,
52
A solution was sought for the optimization with 10 populations and 20 trials in 100 iterations.
• Results of sizing optimization,
has been defined. In island-mode systems, batteries usually last between 3 and 5 days.
The PV power system, battery and diesel generator are the components to be included in the microgrid.
The purpose of multiple executions of these algorithms, which are in the category of algorithms, is
number is defined in the search space in the range of 0 to 5 days. Search with defined upper and lower limits
• Time use and statistical results,
capacities have been determined. Sizing of microgrid with five metaheuristic algorithms
searched. The individual input that these algorithms use during the optimization process
The weights of LPSP and COE constituting 0.5 were chosen and optimization for this system was made.
search space consisting of the declaration of the minimum and maximum capacities of the components
Evaluation of these algorithms run in line with the created model;
The individual parameters seen directly affect the working performance of the algorithms.
0 kWh to 140 kWh for its capacity and 0 kWh to 25 kW for the power of the diesel generator.

Value
rand [0,1]
Lower limit of battery capacity 0 kWh
10 units
of 140 kWh
0.2
• There are eleven sub-sections, including evaluation and comparison of algorithms.
ABA
one hundred
1.4962
rand [0,1]
three
×
limit
1 pc
• COE and LPSP outputs,
one
search space
The bottom of the autonomous working day
one
SSA
20
examined in this section.
1.4962
rand [-1,1]
100 kW
Lower limit of PV system power
limit
• Variation of PV power,
rand [0,1]
The lower of diesel generator power
COE weight ( (10x100=1000) 0.5
53
BOA
0.5
Particle number
Iteration number
Partner
Table 6.1. Individual input parameters, common parameters and search space of algorithms
rand [0,1]
ÿ
Lower limit of the number of PV panels
)
Below the number of diesel generators
• Change of battery capacity,
rand [0,1]
limit
0.5
0.7298
rand [0,1]
LPSP weight I
Number of attempts of the algorithm
parameters
algorithm
[2.0]
rand [0,1]
GKOA
Upper limit of battery capacity
0 kW
• Variation of diesel generator power,
PSOA
limit
10
=
Parameter
[2.0]
Upper limit of PV system power
0 kW
Top 25 kW of diesel generator power
2
2
2
one
0
one
2
3
one

Power of the PV system
0.0534
6.1. Results of Sizing Optimization
calculated. The renewable energy usage rate of this microgrid is approximately 95%.
storage capacity
usage rate
%
determined. In this case, the COE and LPSP are $0.2078/kWh and 0.0634, respectively.
5,2843 5,7003
The capacities of the system, battery and diesel generator were determined by meta-heuristic algorithms and
kW 64.5299 59.0152 58.3161 63.5287 54.0755
93
ABA; 59,0152 kW PV power capacity of the components that make up the microgrid.
results
LPSP
94
Table 6.2. Results of sizing optimization
$/kWh 0.2240 0.1851
7.4953 7.0698
-
54
The energy of the battery
0.0281
of renewable energy
PSOA; The capacities of the components that make up the microgrid are 64,5299 kW PV power.
The PV that the microgrid will use to power the load throughout the life of the project
Power of diesel generator kW 6,2341
Unit PSOA ABA GKOA BOA SSA
95
level.
level.
system as 119,4018 kWh energy storage unit and 5,7003 kW diesel generator.
kWh 130.8693 97.4148 119.4018 130.3328 99.3554
The results of the sizing optimization are given in Table 6.2.
COE
94
system as a 97.4148 kWh energy storage unit and a 5.2843 kW diesel generator.
calculated. The renewable energy utilization rate of this microgrid is approximately 93%.
0.2078 0.2365 0.2182
90
Optimization
The system consists of a 130.8693 kWh energy storage unit and a 6.2341 kW diesel generator.
GKOA; The capacities of the components that make up the microgrid are 58,3161 kW PV power.
0.0415 0.0884 0.0634

microgrid.pdf

Recommended

Recommended

More Related Content

Similar to microgrid.pdf

Similar to microgrid.pdf (20)

Recently uploaded

Recently uploaded (20)

microgrid.pdf