Transilvania University of Brașov Dissertation [611811]

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

1

FIȘA LUCRĂRII DE ABSOLVIRE/ LUCRĂRII DE LICENȚĂ / PRO IECTULUI DE DIPLOM Ă/ DISERTAȚIE
Universitatea Transilvania din Brașov
Lucrare de absolvire/ lucrare de licență/
proiect de diplomă/ disertație nr. ……….
Facultatea
Inginerie Electrică și Știința Calculatoare lor
Departamentul
Inginerie Electrică și Fizică Aplicată Viza facultății
Programul de studii
Sisteme Electrice Avansate Anul universitar
2018 -2019
Candidat: [anonimizat]. Ciprian HANGANU Promoția
2017 -2018
Cadrul didactic îndrumător
Conf. Dr. Ing. Luminița BAROTE

LUCRARE DE ABSOLVIRE/ LUCRARE DE LI CENȚĂ/ PROIECT DE DIPLOM Ă/
DISERTAȚIE
Titlul lucrării:
ENERGY STORAGE IN ELECTRICAL MICROGRIDS WITH RENEWABLE ENERGY SOURCES
Problemele principale tratate:
1. Indroduction
2. RES m odel description
3. System modeling
4. Simulation results
5. Conclusions
Locul și dur ata practicii: corp N, laboratorul POWERELMA – sala N I 6, 90 ore.
Bibliografie:
1.1 Donadee, J. (2013). Optimal operation of energy storage for arbitrage and ancillary service
capacity: The infinite horizon approach. In North American Power Sympo -sium (N APS), 2013, 1 –6.
1.2 Hanley, C.J. et al. (2008). Solar energy grid integration systems –energy storage (SEGIS -ES). Sandia
Report, SAND2008 -4247.

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

2
1.3 Holbert, K.E. and Chen, Z. (2015). Contemplating a residential battery system for the
Southwestern US. In North American Power Symposium (NAPS), 2015, 1 –6.
Aspecte particulare:
Simulari in Matlab SimuLink

Primit tema la data de:
10.12.2017

Data predării lucrării:
20.06.2019

Director departament , Cadru didactic îndrumător,
Conf. Dr. I ng. Lia Elena ACIU Conf. Dr. Ing. Luminița BAROTE

Candidat: [anonimizat]. Ciprian HANGANU
LUCRARE DE ABSOLVIRE/ LUCRARE DE LICENȚĂ / PROIECT DE DIPLOM Ă/
DISERTAȚIE – VIZE
Data
vizei Capitole/ problemele analizate Semnătura cadrului didactic
îndrumător
15.03.2018 Indroduction
14.10.2018 RES m odel description
18.02.2019 System modeling
21.04.2019 Simulation results
14.06.2019 Conclusions

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

3
APRECIEREA ȘI AVIZUL CADRULUI DIDACTIC ÎNDRUMĂTOR

(aprecierea lucrării / proiectului se face prin raportare la cerințele privind elaborarea și redactarea
stabilite pe PS/ facultate; pot fi utilizate instrumente de eval uare, grile cu criterii unitare de apreciere
adoptate de facultate / departament )

Data:
ADMIS pentru susținere/
RESPINS
CADRU DIDACTIC ÎNDRUMĂTOR
Conf. Dr. Ing. Luminița BAROTE

AVIZUL DIRECTORULUI DE DEPARTAMENT
Data: ADMIS pentru susținer e/
RESPINS Director departament
Conf. Dr. Ing. Lia Elena ACIU

SUSȚINEREA LUCRĂRII DE ABSOLVIRE/ LUCRĂRII DE LICENȚĂ /
PROIECTULUI DE DIPLOM Ă/ DISERTAȚIE I
Sesiunea

Rezultatul
susținerii PROMOV AT cu media:

RESPINS cu refacerea lucrării

RESPINS fără refacerea lucrării

PREȘEDINTE COMISIE
Prof. Dr. Ing. Danut ILEA

TRANSILVANIA UNIVERSITY OF BRAȘOV
FACULTY OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
DEPARTMENT OF ELECTRICAL ENGINEERI NG AND APPLIED PHYSICS
MASTER STUDY PROGRAM: ADVANCED ELECTRICAL SYSTEMS

Eng. Ciprian HANGANU

Energy storage in electrical microgrids with
renewable energy sources

Scientific coordinator:

Conf. u niv. dr. eng. Luminița BAROTE

BRAȘOV
2019

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

2
Contents

1. Introduction ………………………….. ………………………….. ………………………….. ………………………….. … 3
1.1. Types of batteries used in energy storage ………………………….. ………………………….. ……………. 4
1.2. Microgrids generalities ………………………….. ………………………….. ………………………….. ………… 6
1.3. Renewable energy sources microgrids ………………………….. ………………………….. ……………….. 7
1.3.1. Solar microgrids ………………………….. ………………………….. ………………………….. ………….. 8
2. Project goals ………………………….. ………………………….. ………………………….. ………………………….. .. 10
3. Model description ………………………….. ………………………….. ………………………….. …………………… 11
3.1. The working principle of photovoltaic and its characteristic ………………………….. ……………. 11
3.2. DC-DC Converter ………………………….. ………………………….. ………………………….. ……………… 14
3.3. Energy storage systems ………………………….. ………………………….. ………………………….. ……… 16
3.3.1. Lead acid battery (LAB) ………………………….. ………………………….. ………………………….. 17
3.3.2. Lithium – Ion (Li -Ion) Battery ………………………….. ………………………….. …………………. 19
3.4. Inverter for the mocrogrid presented ………………………….. ………………………….. ………………… 22
3.5. AC loaders ………………………….. ………………………….. ………………………….. ……………………….. 24
4. System modeling ………………………….. ………………………….. ………………………….. …………………….. 30
5. Simulation results ………………………….. ………………………….. ………………………….. …………………… 35
5.1 Case 1 ………………………….. ………………………….. ………………………….. ………………………….. …. 35
5.2. Case 2 ………………………….. ………………………….. ………………………….. ………………………….. …. 43
6. Coclusions ………………………….. ………………………….. ………………………….. ………………………….. ….. 57
7. Refere nces ………………………….. ………………………….. ………………………….. ………………………….. ……… 58

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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1. Introduction

The need for a Battery Energy Storage System (BESS) to ser ve as a buffer for
electric energy is palpable for micro -grid systems that have a large penetration of intermittent
renewable energy sources. A BESS may be economical for both islanded microgrids and a for
grid-connected system, as a BESS increases reliabi lity during outages and provides revenue or
grid services such as peak shaving, voltage regulation, and arbitrage power trading during
normal operation [1.5].
Applications of a BESS can be found in various settings to assist with renewable
power integrati on. It has been applied to the problem of harmonic distortion, generally known
as voltage regulation, which may occur in stan -dalone operation (islanding) of a microgrid .
Specifically, a BESS can be used to reduces the effects of Photo Voltaic (PV) and wi nd
energy production variability by different control strategies such as a rule -based control and a
model predictive control (MPC). A BESS in conjunction with PV and demand forecasting can
help shift renewable generation to times of higher power demand or lower electricity price via
an MPC technique. A mathematical model for a large BESS system was performed in as a
reduced four state space equations to model the relation between the bulk power grid and a
BESS [1.8].
The benefits of BESS in coping with va riable renewable energy production are
evident, but the costs associated with financing and installing BESS are often prohibitive. In
particular for residential settings , a BESS may not produce sufficient revenue from energy
arbitrage to achieve investmen t payback without government incentives to fund the BESS. At
the same time, BESS costs are anticipated to drop in the near future and investment banks are
expecting the payback time for unsubsidized investment in electric vehicles (EV) combined
with roofto p solar and BESS [1.3] to reduce to around six to eight years. Also, the economies
of scale due to the adoption of EV and rapid improvement of battery technologies will likely
reduce BESS prices. As a result, the projected reduction in pricing of BESS is e xpected to
lead to a return on investment within a time frame of three years by 2030 [1.4-1.6].
Motivated by the need to find the optimal BESS in vestment as a function of time
considering capital and O&M costs , as well as operational revenues , this pape r proposes a
stochastic optimization approach that leverages mixed integer and real (convex) optimization
to formulate financially optimal BESS sizing solutions . The stochastic optimization isused to

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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address the variability in prediction and forecasting o f energy and BESS pricing to determine
when is the optimal time to invest in a BESS. The convex optimization isused to compute
globally optimal solutions for BESS sizing parameters , given the operational model and the
price variability in the day-ahead market [1.2] . The paper is outline das follows. First, the
problem formu lation and the system topology for financial optimization are summarized in
Section 2. The mathematical framework is summarized in Section 3, explaining the
optimization techniques, objective functions and the constraints. Different operating scenarios
are discussed and compared in Section 4 to cover cases of extreme high/low power variability
in solar, wind and demand patterns. In Section 5, different BESS installation cases and
optimal BESS sizing for a case study of a real microgrid are presented.

1.1. Types of batteries used in energy storage

Li-ion batteries leverage the high reactivity of elemental lithium, the lightness of
the metal, and its low density to create a lightweight, energy dense battery. While the overall
cell and system design varies from manufacturer to manufacturer, the use of lithium
compounds creates a high average cell voltage and high power and energy densities. Different
Li-ion chemistries attempt to optimize energy density and lifecycle considerations through
aspects such as more moderate thermal operating conditions. Stability and thermal runaway
are important concerns for Li -ion battery systems. A common attribute across Li -ion
chemistries is the use of graphite fo r the anode (except in lithium titanate spinel technology),
which helps the battery systems achieve a more level discharge curve. The cathode is
typically made from one of three materials: a layered oxide, such as lithium cobalt oxide; a
polyanion, such as lithium iron phosphate; or a spinel, such as lithium manganese oxide. Li –
ion batteries are also high efficiency and have decent lifecycle expectations. Controlling
thermal runaway in Li -ion battery systems is a consistent problem, although grid storage
applications have considerably fewer operating variables that can impact the thermal
management of these systems. In contrast to the constrained spaces and mobile operating
qualities of transportation applications, utility applications are stationary and can use a variety
of thermal management considerations, from system architecture to advanced air or liquid
cooling systems.

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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Lead -acid batteries , deployed extensively for backup power, have a more mature
technology base than other advanced batteries, particula rly for utility -scale applications.
However, a shift toward advanced lead -acid batteries is occurring as environmental safety
concerns mount globally. Additionally, advanced lead -acid batteries offer weight and
operational advantages. Innovation in advance d lead -acid batteries has focused on the
addition of carbon to the negative electrodes of the battery cell. This provides several benefits.
The addition of carbon, either in a split electrode or as a replacement electrode, enhances the
battery’s higher cha rge and discharge power characteristics by increasing the surface area on
which the electrochemical reaction can take place. Furthermore, these batteries can operate on
a broader depth -of-discharge range, increasing the functionality of the cells. The carb on
replacement also enhances lifecycle qualities by reducing the development of extraneous
materials, which precipitate out from the standard electron exchange reactions. This
precipitation encourages self -discharge and diminishes capacity. These enhanced
specifications have increased the interest in advanced lead -acid batteries for grid -scale
applications. Advanced lead -acid batteries offer high efficiency rates – on average around
90% – and these rates are enhanced with carbon additives in the cell design . What this
technology offers in power density it lacks in energy density, particularly when compared to
Li-ion batteries. As a result, although advanced lead -acid batteries offer significant
improvements in performance, durability, and longevity over trad itional lead -acid, this
technology is typically best suited for energy -intensive applications, such as spinning reserves
or load following.
Flow batteries are unique in the field of advanced batteries because of the use of
liquid anolytes and catholytes. These are stored in tanks and are then pumped across a
membrane to charge and discharge the battery. This design permits significant power and
energy benefits as well as efficiency and reliability. The design and operation of flow batteries
is more similar to regenerative fuel cells than traditional advanced battery systems. Power and
energy capacity are decoupled by the design of the system. The power (kilowatts) of the
system is determined by the size of the storage tanks and can be augmented relatively e asily.
Likewise, the energy storage capacity (kilowatthours) is determined by the volume and
concentration of the electrolyte; this too can be scaled up to provide systems with a few hours
or several days’ worth of storage capacity. These components are co mbined in a simple

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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platform of cells and stacks, creating a modular design that is also intended to accommodate
quick response.

1.2. Microgrids generalities

Microgrids are small self -reliant electricity grids that produce and distribute power
across a limited area, such as a village or industrial complex. Microgrids can be grid -tied,
where the system is able to connect with a larger traditional grid, or standalone systems where
there is no outside electrical connection. The Energy Systems Model and this paper focus only
on standalone systems. Standalone microgrids have traditionally been a very niche market,
appropriate only for applications where less expensive traditi onal grids could not operate
[1.2]. But continual improvements in the performance and cost of microgrid technologies (ex.
PV, small wind, and batteries) are making microgrids a more attractive option, particularly in
developing or remote areas that have not yet invested in tra ditional grid infrastructure [1. 3].
Microgrids have several advantages o ver traditional grids: they are scalable and do
not require large capital investment, they are generally environmentally superior to traditional
generation, and they can be tailored to the particular needs of a community [ 1.4– 1.6]. But
cost is always an i mportant factor, and any serious discussion of microgrid technology must
effectively address that issue . on of microgrid technology must effectively address that issue.
Evaluating microgrid systems involves significant complexity and uncertainty. This
complexity and uncertainty relates to both the design of the microgrid system (such as the
scale of different energy sources and quantity of storage to purchase and install) and to the
operation of an existing system (such as dispatch algorithms for storage a nd generation) [ 1.2].
Furthermore, the design and operation are somewhat interdependent: the optimal design
depends on the way that the system will be operated and the optimal operation depends on the
system design. This optimization is made more complex b y uncertainty in actual load and
renewable resource as well as price uncertainty for variable costs such as diesel fuel.
Fortunately, the objective in microgrid design is normally very simple: to meet load with the
lowest levelized cost of electricity (LCO E) or lowest net present cost (NPC) through the
expected lifetime of the system. However, even this is complicated by questions of reliability:
a lower LCOE can be attained if you are tolerant of more frequent power outages. For all of
these reasons, one c annot expect to achieve the perfectly optimal microgrid system design or

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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operation. Rather, several simplifying assumptions must be made and an acceptable
compromise chosen from a more constrained set of options .

1.3. Renewable energy sources microgrids

The electric energy generation industry is headed towards reducing its dependence
on fossil fuels through the integration of technologies that are friendlier to the environment.
This is due, in part, to the integration of distributed systems based on renewable
energy sources. This strategy seeks to address new requirements of generation, operation and
energy supply, and provoke the modification of the current structure of the transmission and
distribution system, in order to change it from a centralized to a de centralized structure [1 .2].
A particular case of these systems is the smart electrical microgrids, where a
consumer with in situ generation can also act as a small -scale generator due to bidirectional
power flow.
The microgrids are characterized by a sche me of electric energy supply based on
multiple sources of energy, known as hybrid generation.
The selection of energy generation systems depends on geographical and climatic
conditions, availability of conventional energy sources at the site, as well as th e overall
financial characteristics of the project.
Its operation is based on energy supply management from monitoring the demand
and generation, which allows: the mitigation of the impact on the environment by prioritizing
renewable generation over conven tional generation, the reduction of the energy consumption
peaks of the grid by making use of local generation, and the increasing of the reliability of
energy supply to avoid the effect of interr uptions in transmission grids [ 1 .2], among others.
For the design of an electric microgrid, information about the demand to be supplied
and the energy potential for in situ generation is required. Then, the system configuration is
set, from which the dimensioning of the components and electrical installations is m ade, and
finally, the energy and financial analysis can be done.

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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1.3.1. Solar microgrids

Energy storage systems (ESSs) have an important and diverse role in microgrids.
Solar PV and other renewable distributed generation (DG) technologies require a voltage
source in order to synchronize. This has typically been done with a backup generator; an ESS
provides a similar voltage source but without the emissions of a diesel generator.
Recent advances in microgrid automation systems, however, have made ESSs less of
a necessityin partially renewable -based microgrids. According to industry leader ABB,
microgrids with as much as 50 percent of load coming from renewable sources do not need an
ESS. This is 10 percent higher than previously believed.
Despite this, microgrids without some form of storage are not likely to become the
norm, as ESSs provide a number of other advantages aside from being a voltage source. Peak
shaving, smoothing power flow, and volt ampere reactive (VAR) support are just a few of the
supplemental f unctions an ESS frequently serves. Islanding and black -start assistance further
support the case for storage use in renewable DG microgrid systems.
The concept of the electrical grid in the United States dates back to the 1920’s when
utility companies join ed forces to contribute to peak load coverage and provide facilities with
a stable source of power. The utility -grade electrical grid that is present today works as an
interconnected system of power plants that generates electricity which is then sold to
facilities.
While this system has worked for almost a century, facility owners are now subject
to fluctuating utility prices due to the costs of non -renewable fuel sources and environmental
regulation. Microgrids are localized energy grids that help facilit y owners take control back
from the traditional grid, maintain a stable flow of power, and lock in utility rates. Microgrids
work well for facilities that are 75,000 square feet or larger. Here are some of the basics about
microgrids [1.11]
Local, self -generation of power can be enticing for owners who want control over
their energy costs, but how do microgrids actually work? In order to meet a long -term goal of
lower operating and utility costs, microgrids integrate reliable renewable energy resources
like solar power with high -efficiency gas powered generation systems and possible energy
storage. This creates a viable replica of the conventional utility grid which is owned and
controlled by the building owner.

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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The microgrid system incorporates interconnect ed production from both a renewable
energy source and low -cost natural gas generators. Owners can use this system in two
configurations. First, owners who face high demand costs from utility companies can use the
microgrid system as a reliable demand reduc tion energy source. In this configuration, the
owner will stay connected to the grid and use a renewable energy source like solar power to
reduce kWh consumption from the grid (see Configuration 1). During peak demand times, the
gas generators will work wi th the solar power to reduce grid consumption. During the night,
when utility costs are low, the owner will continue to purchase low-cost power from the
utility [1.9] .

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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2. Project goals

The purpose of the work is to achieve the electricity required for a hom e, produced by
a system with photovoltaic panels. The stored energy will be used to maintain consumers in
operation when the photovoltaic panels do not produce sufficient energy .
The basic problem of a distributed power system is that the power (consumpti on)
demand differs from that available to the generators. In this case, there is a regime of
imbalance which, if not rapid disposal, produces negative effects that endanger consumers or
even generators.
This imbalance is eliminated with storage systems. Du ring the energy deficit period
storage devices can inject power into the system and can be achieved through appropriate
design of their capacity at any time with consumer requirements.

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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3. Model description

The system considered in the present stud y consists of a PV generator, a Li-Ion battery
banks (V b = 120 V), a single -phase inverter used to interface the dc -link to the AC loads . The
block diagram of the proposed system is shown in Fig 3.1.

Fig. 3.1. Block diagram of the projected system

3.1. The working principle of photovoltaic and its
characteristic

Photovoltaic cell has photovoltaic effect, after the crystal silicon cell absorbs the solar
light, pairs of positive charges and negative electrons will come into being in the PN
section, when the circuit is closed, there will be current in it, at the same time the solar
energy change into electric energy directly. A photovoltaic generating electricity system is
composed of photovoltaic cell and controller and inverter, its equivalent circuit is sho wn

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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in Fig. 3.2 . IL: photovoltaic current; Id: diode current; Rsh: PN section equivalent
resistance; Rs: photovoltaic cell inner resistance.

Fig. 3.2 Photovoltaic cell equivalent circuit.

The model of the PV Technical Caracteristicle analyzed by type o f Circuit equivalent
of a photovoltaic cells are :
– The maximum power Pmax= 250 [Wp] ;
– Voltage at pmax, Ump= 30,75 [V];
– the current to Pmax, I=8,34 [A];
– the current short, the Isc= 8,83 [A];
– Voltage in open circuit, Uoc= 38,32 [V];
– the temperat ure coefficient for the Isc, α=(0.065 ± 0.015)%/°C;
– the temperature coefficient for Uoc, β= -(80±10)mV/°C.
A model for the equivalent of the PV to be analyzed is shown in Figure 3.2 and is
characterised by the following equations below.






−


−= 1 exp 1
21
ocref
sc refUCUC I I
(3.1.1)
where:




−



−=
ocmp
scmp
UCU
IIC
21 exp 1 (3.1.2)

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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


−


−
=
scmpocmp
IIUU
C
1ln1
2 (3.1.3)

refTTT−= (3.1.4)

sc
ref refIGGTGGI



−+



= 1  (3.1.5)

IRT Us−−= (3.1.6)

U U Uref nou += (3.1.7)

I I Iref nou += (3.1.8)

Using the formulas above, the model of PV in sim ulink was made, as shown in the
figure 3.3 :

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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Fig. 3.3 Model Simulink of PV

3.2. DC-DC Converter

Next we will talk about the type of converter needed in the design of the energy
storage micro network produced by photovoltaic panels.
One challenge when inte rfacing devices to PV modules is that PV modules be have
like a current source at high output current and a voltage source at low output current. This
special output characteristic of PV modules requires devices that interface with them to have
maximum pow er point tracking (MPPT) capabilities in order to maximi ze the efficiency of
PV modules . Field testing MPPT converters with real PV modules is challenging.
An alternative to field testing a PV system is to replace the PV module with a PV
simulator, which i s a device that has the same output characteristic of a real PV module, and
can be used within a lab environment at any time of the year. The method used in this paper to
simulate a real PV module is to digitally implement the current -voltage (IV) characte ristic of
a PV module through a look -up-table (LUT) that resides in the memory of the microcontroller
and convert the digital signal to power output though a dc -dc converter. The major advantage

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

15
of digitally implemented PV simulators is that it provides a controlled environment where
users can set conditions such as temperature, insolation level. Moreover, digital control of 2 a
pulse -width modulation (PWM) converter has the advantage of high noise immunity,
immunity to analogue component variations, fast d esign process and programmability for
multiple applications, as compared to an analogue counter part .
The converter chosen for th is theme is the boost converter.
The boost converter, also known as the step -up converter, is another converter that
works in s witching and has the same components as the buck converter, but it produces a
stabilized output voltage higher than that of the input to an unstabilized / stabilized feed
source.
The ideal boost converter (without parasitic components) is built from five b asic
components: a semiconductor power switch, a diode, an inductor, a capacitance, and a PWM
controller. In the boost converter, the components are different than the buck converter. The
basic circuit is shown in the figure 3.4 .

Fig. 3.4 Basic circuit of boost converter

The basic operating principle of the circuit in Figure 3.4 is to close and open the
switch (/ transistor) . When the switch (or transistor) is ON (in conduction or closed), the
current through the inductor increases and the energy stored in the inductance increases.
When the switch is open, the current in the inductance continues to flow through the diode D,
the RC group and back to the source. The energy from the inductor is discharged to the load
via diode D; the diode is polarized correctly, so the inductor terminal connected to the diode
anode is more positive than the terminal connected to the power supply . On the diode anode
we have a higher voltage than the one on the cathode, ie the output voltage is equal to the

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

16
input voltage plus the voltage generated by the inductance, ie: the output voltage is greater
than the input voltage. From here the converter i s said to be boost .
The equivalent circuit during switch on and off condition of the switch S is shown
in Fig .3.5 and Fig. 3.6 respectively.

Fig. 3.5 Boost converter circuit when switch S is on (Mode -I)

Fig. 3.6 Boost converter circuit when switc h S is off (Mode -II)

Maximum Power Point Tracking system(MPPT): Since the output of the PV is less
efficient, a MPPT system is adopted to enhance the PV power. This paper emphasizes on the
Perturb and Observe algorithm to optimize the performance. This is achieved by varying the
duty cycle of the buck boost converter so that the source impedance can be matched with load
impedance. Each time the output power of the buck boost converter is compared with the
previous power of the module and the duty cycle of the converter is adjusted accordingly to
track maximum power point. This process continues until the power of the PV reaches the
maximum value (close to MPP).

3.3. Energy storage systems

Energy storage systems play the important role of unifying, distributin g and enhancing
the capabilities of alternative and renewable energy -distributed generating systems. The

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

17
centralized power generation is large enough not to be affected by changes from residential
and commercial loads, while the distributed generation is e xposed to these changes. The
storage systems are capable of smoothing out the load fluctuations as well as to react to fast
transient power quality.
Energy storage is a well established concept yet still relatively unexplored. As the
electricity sector is undergoing a lot of change, energy storage is starting to become a
realisticoption for:
• restructuring the electricity market;
• integrating renewable resources;
• improving power quality;
• aiding shift towards distributed energy;
• helping network operate under m ore stringent environmental requirements.
To store the energy produced by photovoltaic panels, a battery pack is required, so we will
consider two types of batteries: Lead acid and Li -ion.

3.3.1. Lead acid battery (LAB)

This is the most common energy storage de vice in use at present. Its success is due to
its maturity (research has been ongoing for an estimated 140 years), relatively low cost, long
lifespan, fast response, and low self -discharge rate. These batteries are can be used for both
short -term applicati ons (seconds) and long -term applications (up to 8 hours).
Both the power and energy capacities of lead -acid batteries (LABs) are based on the
size and geometry of the electrodes. The power capacity can be increased by increasing the
surface area for each e lectrode, which means greater quantities of thinner electrode plates in
the battery. However, to increase the storage capacity of the battery, the mass of each
electrode must be increased, which means fewer and thicker plates. Consequently, a
compromise mu st be met for each application.
LABs can respond within milliseconds at full power. The average DC -DC efficiency
of a LAB is 75% to 85% during normal operation, with a life of approximately 5 years or 250 –
1,000 charge/discharge cycles, depending on the de pth of discharge.
A model of LAB commonly used is shown in Fig.3.7 and consists of a controlled
voltage source ( Eb) in series with an internal resistance ( Rint) and the battery voltage
represented by Vb. This voltage can be determined by measuring it at no load, while the

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internal resistance can be deduced by connecting a load and measuring both the voltage and
current at full load.

Fig. 3.7 Equivalent electric circuit model of the LAB

Internal voltage Eb depends on several factors, such as state of c harge,
temperature, battery type and can be expressed by a general relation as follows:

(3.3.1 )

where:
– Eb0: the voltage corresponding to no load at nominal load, [V];
– K: the bias voltage, [ V];
– Q: the battery capacity, [Ah];
– Ib: the discharge current of the battery, [A].
The state of charge (SOC) expressed in equation (2) is another important parameter
of the battery and is used as input to the charging / discharging controller of the bat tery.

(3.3.2 )

where: n Q is the battery rated capacity.

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In the Fig. 3.8, the Simulink implementation for the LAB model used is shown:

Fig. 3.8 The Simulink implementetion of the LAB model
3.3.2. Lithium – Ion (Li -Ion) Battery

Li-Ion battery is the electrochemical cell, based on lithium -metal oxid (LiMO2) as
cathode and graphite as anode separated and connected by an electrolyte. The electrolyte
conducts ions but is an insulator to electrons. In a charged state, the anode contains a high
concentration of intercalated lithium while the cathode is depleted of lithium. During the
discharge, a lithium ion leaves the anode and migrates through the electrolyte to the cathode
while its associated electron is collected by the current colle ctor to be used to power an
electric device .
Li-Ion battery systems consists of a series of cells, each capable of storing a fixed
amount of energy. The energy capacity of a Li -Ion battery is expressed in Ah, which
corresponds to its discharge capacity at the rated current, for 4 -hours. The total power
available is thus dependent on the rated output voltage and current. The Li -Ion battery is one

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type of storage device of high interest for wind energy systems. It appears to be well suited
for small wind ins tallations, such as the ones found in remote communities.
The lithium battery model, which is shown in Fig. 3.9 , estimates the model to be a
simple electrical circuit. The transients are represented by a capacitor (C), while the losses are
separated into two internal resistances (R1, R2). The internal voltage source, representing the
equilibrium potential, is expected to vary base on the State of Charge (SOC) of the battery.

Fig. 3.9 Li-Ion battery model

Li-Ion batteries are constructed using a series of cells which makes them easily
scalable. The battery internal stack voltage is directly related to the SOC of the battery based
on the following equations:
(3.3.3 )
For n cell stacks, V eq would be equal to n ⋅ V cell . The k factor was found to be
0.1829 for regular room temperature :

(3.3.4 )

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where:
– T – the temperature impact on battery operation;
– R – the Li -Ion battery internal resistance;
– F – the Faraday’s number.
One way to estimating the Li -Ion state of charge is to update the SOC variable from
one time step to the next, based on the power that goes through the cell stack. The change in
SOC for a Li -Ion battery is modelled as follows:

(12)

(3.3.5 )

In Fig. 3.10 below shows the Li -ion battery model for implementation in matlab :

Fig. 3.10 The Li -Ion model for implementation in M atlab

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The theme was chosen for the Li -Ion battery version required to store PV energy.
To make this system the battery has bee n parameterized as follows:

Item 12v 100ah lifepo4 battery pack
Brand Sordioens
Type Lithium Iron Phosphate
Voltage Nominal 12.8V (Customized 3.2V 12V 24V 36V 48V)
Capacity Nominal 100AH
Max Discharge Current 100A Or Customized
Max Charge Current 60A Or Customized
Operating Temperature Range Charge:0 to +60°C
Discharge: -20 to +60℃
Life Cycle More than 2000times (100%DOD)

3.4. Inverter for the mocrogrid presented

The inverter in power electronics refers to a class of power conversion circuits that
operate from a dc voltage source or a dc current source and con vert it into a symmetric ac
voltage or ac current. It does reverse of what ac -to-dc rectifier does. The input of the inverter
is a direct dc source or dc source derived from an ac source. For example, a primary source of
input power may be utility ac volta ge supply that is converted to dc by an ac – dc rectifier with
filter capacitor and then ‘inverted’ back to ac using an inverter. Here, final ac output may be
of a different frequency and magnitude than the input ac of the utility supply.
If the dc input i s a voltage source, the inverter is called a Voltage Source Inverter
(VSI). Similarly a Current Source Inverter (CSI) is that where the input to the circuit is a
current source. The VSI circuit has direct control over the output ac voltage, whereas the CSI
directly controls the output ac current.
One of the simplest dc voltage source for a VSI may be a battery bank or a solar
photovoltaic cells stack. The ac voltage supply, after rectification into dc can also serve as a
dc voltage source. A voltage source is called stiff, if source voltage magnitude does not
depend on load connected to it. All the voltage source inverters assume stiff voltage supply at
the input. Output of the voltage waveforms of the ideal inverters should be sinusoidal.

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However the pract ical inverter waveforms are non sinusoidal and contain certain harmonics.
For low and medium power applications quasi square wave or square wave voltages are
acceptable.
A variable voltage can be obtained by varying the input dc voltage and maintaining the
inverter gain constant. On the other hand, if dc input voltage is fixed then variable output
voltage can be obtained by varying the inverter gain. This can be accomplished by Pulse
Width Modulation -PWM control on the inverter. PWM means that the width of the square
pulse in positive and negative halves can be adjusted according to the rms of the output
required. The inverter gain may be defined as the ratio of the ac output (rms) voltage to dc
input voltage. In Square Wave PWM technique the output ac rms v oltage is fixed when the
input dc voltage is fixed.
The structure of PV grid -connected inverter is as shown in figure 3.13 . The system is
mainly composed of the former stage of DC – DC converter, intermediate DC bus and the
level of DC – AC inverter. The DC – DC converter boosts the DC voltage. Firstly, sample the
photovoltaic cells output voltage and current, and compute the collected signals based on
improved conductivity incremental control algorithm. And then control the switch state of the
DC booster circuit to realize maximum power.
The DC – AC inverter adopts double PI control. The outer loop keep DC bus voltage
stable, inner loop is used to control the stability of the output current.

Fig. 3.11 Block diagram of single phase inverter.

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3.5. AC loader s

This micro -grid was sized to produce energy and store the unconsumed energy needed
by the following AC consumers :
Household consumers are divided into two tables that determine the load curves for
two seasons (winter and summer).

0 1 2 3 4 5 6 7 8 910 11 12 13 14 15 16 17 18 19 20 21 22 23
Nr. ConsumerQuantity
(pieces)Power
(W)
1Incandescent 8 60 240 360 480 480
2Fluorescent 6 26 78 78 156 156
3Blender 1 250 63
4Esspresor 1 2000 500
5Iron 1 1000 500 500
6Refrigerator (NEW) 1 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180
7Microwave oven 1 1500 375 375 375
8Washing machine
(horizontal)1 300 300
9Vacuum cleaner 1 600 300
10jacuzzi 1 800 200
11Power Plant 9kw 1 1700 1700 1700 1700 1700 1700 1700
12Radiant panel 4 350 700 700 700 700 700 700 700
13Air conditioning 1 1000
14TV LED 42 " 2 120 120 120 240 240
15Home cinema 1 300 150
16Wi-fi system 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
17Desktop Computer 1 350 350 350
18Ink Jet Printer 1 30 15
19Laptop Computer 1 70 70 70 70
180 181 180 180 180 180 2580 3086 3024 186 186 186 701 486 561 186 186 1406 1544 3589 3812 3662 186 180Table 1: Household consumers divided into hours on a winter day
Hourly functioning of consumers
Lighting
Appliances
air conditioning
communications

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The load curve for any day in winter is shown in figure 3.12 .
As you can see, variations in consumer power are very high in certain time slots.

0 5 10 15 20 2505001000150020002500300035004000Load curve for the winter
time [hour]Power [watt]

Fig. 3.12 Load curve for a winter day

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0 1 2 3 4 5 6 7 8 910 11 12 13 14 15 16 17 18 19 20 21 22 23
Nr. ConsumerQuantity
(pieces)Power
(W)
1Incandescent 8 60 480
2Fluorescent 6 26 156
3Blender 1 250 63
4Esspresor 1 2000 500
5Iron 1 1000 500 500
6Refrigerator (NEW) 1 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180 180
7Microwave oven 1 1500 375 375 375
8Washing machine
(horizontal)1 300 300
9Vacuum cleaner 1 600 300
10Jacuzzi 1 800 200
11Power Plant 9kw 1 1700 850
12Radiant panel 4 350
13Air conditioning 1 1000 1000 1000 1000 1000 1000
14TV LED 42 " 2 120 120 120 240 240
15Home cinema 1 300 150
16Wi-fi system 1 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
17Desktop Computer 1 350 350 350
18Ink Jet Printer 1 30 15
19Laptop Computer 1 70 70 70 70
180 180 180 180 180 180 180 686 624 186 186 186 1701 1486 1561 186 186 1406 526 1751 1776 2112 186 180Table 2: Household consumers divided into hours on a summer day
Lighting
Appliances
Air conditioning and heating
CommunicationsHourly functioning of consumers

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In one summer day, the p ower consumed is much lower than the power of winter
consumers without exceeding 2500 watts at certain times of the day.

0 5 10 15 20 2505001000150020002500
time [hour]Power [watt]Load curve for the summer

Fig. 3.13 Load curve for a summer day

After carrying out the above mentioned load curves, the solar radiation
characteristics in t he Matlab Simulink were realized, being considered the PV system in the
work and having the dimensions and the above -described characteristics being divided into 2
cases.
a. Case 1: A winter month.

Figure 3.1 4 shows the characteristics of solar radiation in the Harman area of Brasov
for December:

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0 5 10 15 20 2500.050.10.150.20.250.30.350.40.45Solar radiation for December
Time [hours]Radiation [kW/mp]

day radiation
Fig. 3.14 The iradiation characteristic in Harman , Brasov for December

In order to be able to perform simulations in matlab it is necessary to know the
average iradiation power per square meter. Thi s figure shows the Gaussian feature fo r the
December radiation points .

0 5 10 15 2000.050.10.150.20.250.30.350.4
time [hours]Radiation [kW/mp]The characteristic of Gaussian for december

December data
Gaussian

Fig. 3.1 5 The characteristic of Gaussian for December in Harman, Brasov
Analyzing these features, it can be seen that in a winter month the average iradiation is
between 250 -300 W / mp.

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b. Case 2: A summer month

After analyzing the summer load curve, it was chosen in July, thus representing the
radiance characteristic in the Harman area, Brasov for this month:
0 5 10 15 20 2500.20.40.60.811.21.4Solar radiation for July
Time [hours]Radiation [kW/mp]

day radiation

Fig. 3.16 The iradiati on characteristic in Harman , Brasov for July

The following figure shows the Gaussian iradiation pattern for July.
0 5 10 15 2000.20.40.60.81
time [hours]Radiation [kW/mp]The characteristic of Gaussian for July

July data
Gaussian

Fig. 3.17 The characteristic of Gaussian for July in Harman , Braso v

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4. System modeling

The electrical model for storing the electrical energy produced by a P V system is
shown in Figure 4 .1. The S impower system library was used to implement the scheme.

Fig. 4.1 The general scheme of the model in Matlab S imulink for the PV storage system of
energy.

The electric model of the DC boost converter in the structure of the overall project
scheme. The converter was implemented in matlab based on the theoretical data from cap. 3
and was used for this by the SimPowerSystem library within the matlab software .

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Fig. 4.2 DC -DC Converter model

The electric model of the inverter in the structure of the general scheme of the project.

Fig. 4.3 Inverter average -value model

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The use of lithium ion, li -ion batteries has grown significantly in recent years. They
offer some distinct advantages and improvements over other forms of battery technology.
However, like all technologies, lithium ion batteries have their advantages and
disadvantages.
To gain the best from the li -ion battery technology, it is necessary to understand not
only the advantages, but also the limitations or disadvantages of the techn ology. In this way
they can be used in a manner that plays to their strengths in the best way.
Lithium ion battery advantages
There are many advantages to using a li -ion cell of battery. These li -ion battery
advantages include:
• High energy density: The h igh energy density is one of the chief advantages
of lithium ion battery technology. With electronic equipment such as mobile phones needing
to operate longer between charges while still consuming more power, there is always a need
to batteries with a much higher energy density. In addition to this, there are many power
applications from power tools to electric vehicles. The much higher power density offered by
lithium ion batteries is a distinct advantage. Electric vehicles also need a battery technology
that has a high energy density.
• Self-discharge: One issue with many rechargeable batteries is the self
discharge rate. Lithium ion cells is that their rate of self -discharge is much lower than that of
other rechargeable cells such as Ni -Cad and NiMH forms . It is typically around 5% in the first
4 hours after being charged but then falls to around 1 or 2% per month.
• Low maintenance: One major lithium ion battery advantage is that they do
not require and maintenance to ensure their performance. Ni -Cad cell s required a periodic
discharge to ensure that they did not exhibit the memory effect. As this does not affect lithium
ion cells, this process or other similar maintenance procedures are not required.
• No requirement for priming: Some rechargeable cells n eed to be primed
when they receive their first charge. There is no requirement for this with lithium ion cells and
batteries.
• Variety of types available: There are several types of lithium ion cell
available. This advantage of lithium ion batteries can m ean that the right technology can be
used for the particular application needed. Some forms of lithium ion battery provide a high

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current density and are ideal for consumer mobile electronic equipment. Others are able to
provide much higher current levels and are ideal for power tools and electric vehicles.

Lithium ion battery disadvantages
Like the use of any technology, there are some disadvantages that need to be
balanced against the benefits. The li -ion battery disadvantages include:
• Protection require d: Lithium ion cells and batteries are not as robust as some
other rechargeable technologies. They require protection from being over charged and
discharged too far. In addition to this, they need to have the current maintained within safe
limits. Accord ingly one lithium ion battery disadvantage is that they require protection
circuitry incorporated to ensure they are kept within their safe operating limits. Fortunately
with modern integrated circuit technology, this can be relatively easily incorporated into the
battery, or within the equipment if the battery is not interchangeable. Incorporation of the
battery management circuitry enables li -ion batteries to be used without any special
knowledge. They can be left on charge and after the battery is fully charged the charger will
cut the supply to it.
• Ageing : One of the major lithium ion battery disadvantages for consumer
electronics is that lithium ion batteries suffer from ageing. Not only is this time or calendar
dependent, but it is also dependent upo n the number of charge discharge cycles that the
battery has undergone. Often batteries will only be able to withstand 500 – 1000 charge
discharge cycles before their capacity falls. With the development of li -ion technology, this
figure is increasing, but after a while batteries may need replacing and this can be an issue if
they are embedded in the equipment. When a typical consumer lithium cobalt oxide, LCO
battery or cell needs to be stored it should be partially charged – around 40% to 50% and kept
in a cool storage area. Storage under these conditions will help increase the life.
• Transportation: This li -ion battery disadvantage has come to the fore in
recent years. Many airlines limit the number of lithium ion batteries they take, and this means
their transportation is limited to ships. For air travellers, lithium ion batteries often need to be
in carry -on luggage, although with the security position, this may change from time to time.
But the number of batteries may be limited. Any lithium ion battie ries carried separately must
be protected against short circuits by protective covers, etc.
• Cost: A major lithium ion battery disadvantage is their cost. Typically they
are around 40% more costly to manufacture than Nickel cadmium cells. This is a major factor

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when considering their use in mass produced consumer items where any additional costs are a
major issue.
• Immature technology: Although lithium ion batteries have been available for
many years, it can still be considered an immature technology by s ome as it is very much a
developing area. This can be a disadvantage in terms of the fact that the technology does not
remain constant. However as new lithium ion technologies are being developed all the time, it
can also be an advantage as better solution s are coming available.
Li-ion battery technology has very many distinct advantages. Accordingly the
technology is widely used, and this is only set to increase. Understanding the advantages as
well as the disadvantages or limitations enables the best use to be made of the battery
technology.
Because this battery model is in great demand, it has been chosen Li -Ion to continue
simulations in matlab simulink
The battery electric model for storing energy in the structure of the overall project
scheme. Li -Ion b atteries were used to store energy .

Fig. 4.4 Li -Ion battery model

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5. Simulation results

Because the 24 -hour simulation time was too long, it only used 5 seconds to check
the variations at certain points
Next, we will simulate 2 cases for this model.

5.1 Case 1

For first case , a summer day was chosen in which the radiation reached maximum
values ranging between 600 -700 W / m2 and presented in the following descriptions.

a. AC Load Variations

Features for a 1000 watts power consumption plus a 1500 watts po wer consumption
over a time span.
Figure 5.1 shows the PV system power variation, reaching a value of approximately
1800 W, depending on the solar radiation value:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50500100015002000The power characteristic for PV
timePower [watt]

Fig. 5.1 PV p ower variation

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The power consumed on that day is about 1000W, but in a tim e it may exceed 1500W
as shown in Figure 5.2:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-500050010001500200025003000
timePower [watt]The power characteristic for consumers

Fig. 5.2 AC loads power variation

Figure 5.3 shows the variation in battery power, whic h shows that in the interval [2 –
4s] the network takes over the battery power demand.

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-1500-1000-50005001000The power characteristic for battery
timePower [watt]

Fig . 5.3 Li-ion battery power variation

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The power variation within this system is shown in Figure 5.4:

1 2 3 4 5-1500-1000-500050010001500200025003000
timePower [watt]The power characteristics

Power PV
Power Consumers
Power Battery

Fig. 5.4 The power balance of the analyzed RES system
The current variation is negative in the time range [2 -4s], which means that the battery
is discharged, as shown in Figure 5 .5:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-12-10-8-6-4-20246The current characteristic for Battery
timeCurrent [A]

Fig. 5.5 The Li-ion battery current variation

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Figure 5.6 shows the voltage variation on the battery, and as there is a slight decrease
in the battery discharge rate:
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5020406080100120140
timeVoltage [V]The vboltage chracteristic for battery

Fig . 5.6 The Li-ion b attery voltage variation

Figure 5.7 shows that during the [0 -2s] and [ 4-5s] interval the battery is being charged
and in the interval [2 – 4s] it is discharged:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.80.80.80.80.80.80.80.80.80.80.8
timeThe SOC characteristic for Battery

Fig. 5.7 The SOC of Li-ion battery variation

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b. Constant AC loads.

Characteristics for a power consumed throughout the 2000 W day . Figure 5.8 shows
the PV system power variation:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50500100015002000
timePower [watt]The power characteristic for PV

Fig. 5.8 The PV power variation

The consumer power variation is constant because it was considered a constant power
throughout the simulation day as shown in Figure 5.9:

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0.5 1 1.5 2 2.5 3 3.5 4 4.5 505001000150020002500
timePower [watt]The power characteristic for consumers
Fig. 5.9 AC loads power variation

As shown in Figure 5.10, the battery injects power into the network as the power of
the consumer is constant :

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-2500-2000-1500-1000-5000The power characteristic for Battery
timePower [watt]

Fig. 5.10 The Li-ion b attery power variation

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Figure 5.11 shows the power balance characteristics of the analyzed case system .

1 2 3 4 5-3000-2000-10000100020003000The power characteristics
timePower [watt]

Power PV
Power Consumers
Power Battery

Fig. 5.11 The power b alance variation of analayzed system

The current variation on the battery in figure 5.12 is negative, it means that the battery
is in the discharge mode :

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-20-15-10-50The current characteristic for Battery
timeCurrent [A]

Fig. 5.12 The Li-ion battery current variation

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Considering that the power consumed is constant, it can be seen in Figure 5.13 that the
battery voltage is constant:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5020406080100120140The voltage characteristic Battery
timeVoltage [V]

Fig. 5.13 The Li-ion battery voltage variation

In figure 5.14 it is indicated that the battery is continuously discharged because the
power consumed is constant and the PV system doe s not produce the required power:

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0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.79990.79990.80.80.80.80.8
timeThe SOC characteristic for Battery
Fig. 5.14 The SOC of battery variation

5.2. Case 2

Case 2 shows the characteristics for winter when the radiation is very low reaching
200-250W / mp.
This will show the characteristics for the different power consumed:
a. For a permanent power of 1000 watts plus a power of 1500 watts consumed at
different time intervals per day.

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0.5 1 1.5 2 2.5 3 3.5 4 4.5 50100200300400500600700800
timePower [watt]The power characteristic for PV

Fig. 5.15 The power for PV variation

For the winter season when the radiation is low, but the consumption increases
compared to the summer seaso n, it is still considered a power consumed up to 2500 in a time
span:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-500050010001500200025003000
timePower [watt]The power characteristic for consumers

Fig 5.16 The power consumers variation

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Figure 5.17 shows the variation in battery power in this case:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-2200-2000-1800-1600-1400-1200-1000-800-600-400-200
timePower [watt]The power charcteristic for Battery

Fig. 5.17 The Battery power variation

Figure 5.18 shows the power var iation in the system presented in this study:

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1 2 3 4 5-2500-2000-1500-1000-50005001000150020002500
timePower [watt]The power characteristics

Power PV
Power Consumers
Power Battery
Fig. 5.18 The powers variation

Negative portion of Figure 5.19 is that the battery is in discharge mode :

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-20-18-16-14-12-10-8-6-4-20
timeCurrent [A]The current characteristic for Battery

Fig. 5.19 The Battery current variation

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Figure 5.20 shows the variation in battery voltage w hich shows a decrease in the
discharge mode:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5020406080100120140
timeVoltage [V]The voltage characteristic for Battery

Fig. 5.20 The Battery voltage variation

In Figure 5.21, the various SOC on the battery shows the charge / discharge mode:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.79990.79990.79990.79990.79990.79990.80.80.80.80.8
timeThe SOC characteristic for Battery

Fig. 5.21 The SOC of Battery variation

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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b. For a power consumed without interruption of 2000 W we hav e the
following characteristics:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50100200300400500600700800
timePower [watt]The power characteristic for PV

Fig. 5.22 The power of PV variation

The power consumed is constant around 2000W as shown by the variation in Figure
5.23:
0.5 1 1.5 2 2.5 3 3.5 4 4.5 505001000150020002500The power characteristic for consumers
timePower [watt]

Fig. 5.23 The power consumers variation

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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The power in the battery s hould be constant, but given that the solar radiation varies, it
shows an increase without being conditioned by the power consumed, see Figure 5.24 :
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-2500-2000-1500-1000-5000
timePower [watt]The power characteristic for Battery

Fig. 5.24 The power Battery variation

Figure 5.25 shows the power variation in the energy storage syst em:
1 2 3 4 5-2000-1500-1000-50005001000150020002500
timePower [watt]The powers characteristics

Power PV
Power Consumers
Power Battery

Fig. 5.25 The powers variation

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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In Figure 5.26 is the battery current, even if the battery is negative and the battery is
discharged, it tends to a positive point:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-20-18-16-14-12-10-8-6-4-20
timeCurrent [A]The current characteristic for Battery

Fig. 5.26 The Battery current variation

Figure 5.27 shows the voltage variation that is constant over time:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5020406080100120140The voltage characteristic for Battery
timeVoltage [V]

Fig. 5.27 The Battery voltage variation

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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Figure 5.28 shows that while we have a constant power consumption and the PV
system output is low the battery is in the discharge mode, the power needs to be s upported by
the power g enerator :

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.79980.79990.79990.8
timeThe SOC characteristic for Battery

Fig. 5.28 The SOC of Battery variation

c. Characteristics for 3000 watts power consumed in certain time stamps and
1000 watts permanently consumed.
0.5 1 1.5 2 2.5 3 3.5 4 4.5 50100200300400500600700800
timePower [watt]The power characteristic for PV

Fig. 5.29 The power for PV variation

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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If we take into account the peak points in Figure 3.12, then the power consumed in an
animated time interval would have values close to 4000 W:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5050010001500200025003000350040004500
timePower [watt]The power characteristic for consumers

Fig. 5.30 The power consumers variation

Being in the winter season, and the PV system has a low efficiency to produce energy,
as shown in Figure 5.31, the power r equirement will be taken over by the current generator:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-4000-3500-3000-2500-2000-1500-1000-5000
timePower [watt]The power characteristic for Battery

Fig. 5.31 The Battery power variation

Transilvania University of Brașov Dissertation
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Master – Advanced Electrical Systems

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The power variation in the system shown is shown in Figure 5.32:

1 2 3 4 5-4000-3000-2000-100001000200030004000
timePower [watt]The powers characteristic

Power PV
Power Consumers
Power Battery

Fig. 5.32 The powers variation

The current variation in Figure 5.33 shows that durin g the time the battery is charging
power in the network, the value is far below zero, which means that if the storage system does
not support this drop it should be helped by a current generator:

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

54

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-35-30-25-20-15-10-50
timeCurrent [A]The current characteristic for Battery
Fig. 5.33 The Battery current variation

Figure 5.34 s hows the voltage variation in batteries:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5020406080100120140The voltage characteristic for Battery
timeVoltage [V]

Fig. 5.34 The Battery voltage variation

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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Figure 5.35 shows the SOC variation for batteries, which shows that the charge regime
is very low compared to the discharge mode:

0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.79980.79990.79990.79990.79990.79990.80.80.80.8
timeThe SOC characteristic for battery

Fig. 5.35 The SOC of Battery varia tion

Analyzing the results presented above, it can be noticed that during the winter when
the radiation is very low, this system does not have the efficiency required for the operation of
all household and electronic equipment. This is why you will cho ose a current generator with
the following technical specifications:

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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ELECTROGEN DIESEL GROUP
-Power (kVA) : 6.0 ;
-Power (kW) : 5,6 ;
-Speed (rpm) : 3000 ;
-Standard voltage (V) : 230 ;
-Power factor (cos phi) : 0.9 ;
-Amp (Amp) : 22 .

Transilvania University of Brașov Dissertation
Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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6. Coclus ions

The simulation of this system was done in matlab for some day using different values
of power consumed as both permanent consumption and variable consumption depending on
the defined time interval.
Of course, considering that photovoltaic panels hav e been used as a renewable energy
source, they only work according to the sun and it will never know the exact value of the
radiation at those times when the power requirements are high.
Thus, for the storage of energy produced by this source, it has been chosen for the Li –
Ion battery because it is a variety of models and is very popular today. These models are also
economic if we relate to the loading / unloading mode because they have a fairly long life.
Because the weather may be unfavorable for a longer period, even when the sun
irradiates in that area but is overwhelmed, a reduction in power consumption will also be
achieved because the batteries can not handle that demand. And to support the required power
in those moments, an automatic starter automat ion generator will be used without the risk of
interruption of the load.

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Master – Advanced Electrical Systems

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7. Refere nces

1. Articles
[1.1]. Donadee, J. (2013). Optimal operation of energy storage for arbitrage and ancillary
service capacity: The infinite horizon approach. In North American Power Sympo –
sium (NAPS), 2013, 1 –6.
[1.2]. Hanley, C.J. et al. (2008). Solar energy grid integration systems –energy storage
(SEGIS -ES). Sandia Report, SAND2008 -4247.
[1.3]. Holbert, K.E. and Chen, Z. (2015). Contemplating a residential battery system for the
Southwestern US. In North American Power Symposium (NAPS), 2015, 1 –6.
[1.4]. Kousksou, T. et al. (2014). Energy storage: Applications and challenges. Solar Energy
Materials and Solar Cells, 120, 59 –80.
[1.5]. Lasseter, R.H. (2002). Microgrids. In Power Engineering Society Winter Meeting,
2002. IEEE, volume 1, 305 –308 vol.1. doi:10.1109/PESW.2002.985003.
[1.6]. Yang, Y. et al. (2014). Sizing strategy of distributed battery storage system wi th high
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[1.7]. Zachar, M. and Daoutidis, P. (2016). Economic dis -patch for microgrids with
constrained external power exchange. IFAC -Pape rsOnLine, 49(7), 833 –838.
[1.8]. Zhang, D. et al. (2015). A state -space model for integration of battery energy storage
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[1.9]. Murthy Balijepalli V, Khaparde SA, Dobariya CV. Deployment of microgrids in
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[1.10]. Katiraei F, Iravani R, Hatziargyriou N, Dimeas A. Microgrids management. IEEE
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[1.11]. Van Broekhoven S, Judson N, Nguyen S, Ross W. Microgrid study: e nergy security
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[1.12]. New York State Energy Research and Development Authority. Microgrids: an
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[1.13]. Celli G, Pilo F, Pisa no G, Soma GG. Optimal participation of a microgrid to the
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Faculty of Electrical Engineering and Computer Science 2019
Master – Advanced Electrical Systems

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[1.14]. Xu Xiaoli, Zuo Yunbo. “Integrated Design of Measurement and Control System for
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[1.15]. Farret, F., Simoes, M. Integration of Alternative Sources of Energy, Published by
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[1.16]. Cao Zuliang, Wa ng Bin, Wang Shuai. “Photovoltaic generation system and
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2. Sites
2.1 https://revistas.unal.edu.co/index.php/dyna/article/view/48586/53625
2.2http://www.scielo.org.co/scielo.php?pid=S001273532015000400013&script=sci_arttext&t
lng=pt

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Master – Advanced Electrical Systems

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DECLARAȚIE PRIVIND ORIGINALITATEA LUCRĂRII DE ABSOLVIRE/
DISERTAȚIE

UNIVERSITATEA TRANSILVANIA DIN BRAȘOV
FACULTATEA de INGINERIE ELECTRICĂ ȘI ȘTIINȚA CALCULATOARE LOR
PROGRAMUL DE STUDII SISTEME ELECTRICE AVANSATE
NUMELE ȘI PRENUMELE HANGANU CIPR IAN
PROMOȚIA 2017 -2018
SESIUNEA DE ABSOLVIRE DISERTAȚIE IUNIE 2019
DENUMIREA LUCRĂRII / PROIECTULUI/ DISERTAȚIEI
ENERGY STORAGE IN ELECTRICAL MICROGRIDS WITH RENEWABLE ENERGY SOURCES
CADRUL DIDACTIC ÎNDRUMĂTOR Conf. Univ. Dr. Ing. BAROTE Luminița

Declar pe propria răspundere că lucrarea de față este rezultatul muncii proprii, pe
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indicate conform normelor etice, în textul lucrării/proiectului, în note și în bibliografie.
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teză/proiect nu încalcă drepturile de proprietate intelectuală ale altcuiva, persoană fizică
sau juridică.
Declar că lucrarea/ proiectul nu a mai fost prezentat(ă) sub această formă vreunei
instituții de învățământ superior în vederea obținerii unui grad sau titlu științific ori
didactic.
În cazul constatării ulterioare a unor declarații false, voi suporta rigorile legii.

Data: 20.06.2019 Nume, prenume, semnătura

Absolvent : Ing. HANGANU CIPRIAN