Review of Battery Storage Optimisation in

Distributed Generation
G.Coppez, Non-member, S. Chowdhury, Member, IEEE and S.P. Chowdhury, Member, IEEE

There are a few ways to ensure that there is always

AbstracfN Distributed Generation (DG) in the form of sufficient supply for the load needed. Hybrid systems have a
Renewable Power Generation systems is currently preferred for better chance of being able to supply the load more
clean power generation. However due to their intermittent and consistently as they combine more than one type of renewable
unpredictable nature, energy storage must be used to ensure that technology with different or overlapping times of low
the load is met at all times. There are many possible options for generation and high generation, such as wind and PV hybrid.
energy storage and the most popular and technologically PV generation is dependent on daylight hours and irradiation
matured option, batteries, is the subject of this paper. This paper
levels and wind power generation is independent of irradiation
explores the importance and necessity of batteries within DG
levels, but solely dependent on the wind activity. This
systems, especially with renewable power generation systems.
The paper looks at different varieties of batteries with a specific
distributes the consistency of supply more evenly, but in some
emphasis on lead-acid batteries. To integrate batteries into cases this will not be sufficient.
renewable energy systems in an economical manner, the system Energy storage is introduced in order to maintain a
and the energy storage must be optimised to ensure the most consistent energy balance within the renewable energy system.
effective sizing of each of the system components to ensure the It enables energy to be stored when there is an excess of
reliability of the system whilst minimising the cost. There are generation and supplies additional energy to the loads to
currently many methods of optimising systems with these compensate for the deficit of supply. Apart from ensuring
criteria. These methods optimisation of storage and battery
reliability of supply, energy storage fulfils other functions
components are discussed in this paper.
such as load leveling and enhancing power factor and quality
of supply [13].
Index TermsN distributed generation, renewable power The paper is organised as follows: Section II reviews
generation systems, energy storage, cell battery, lead-acid
different forms of energy storage currently in use. Section III
battery, optimisation
further specifies the operation of batteries and the types of
batteries available for power system applications, outlining
their advantages and disadvantages. Lead acid batteries are
I. INTRODUCTION
further discussed and a model used in optimisation is shown in
enewable power generation systems in the distributed Section IV. Section V discusses various optimization
Rgeneration (DO) context are being increasingly preferred techniques for optimizing energy storage within DO systems.
for power generation. As we come closer to the end of
our finite supply of fossil fuel resources, using them sparingly II. OVERVIEW OF ENERGY STORAGE
as well as finding alternative sources of electricity becomes
increasingly important. At the same time the adverse effects Energy storage is a vital part of DO systems. There are
on the environment of burning fossil fuels have been currently many options in terms of storage. For large scale
acknowledged and the move towards cleaner methods of application, Compressed Air Energy Storage (CAES) and
energy generation is imperative. There are many advantages to Pumped Hydro Storage (PHS) can be used.
this form of power generation especially within remote areas CAES uses excess power generated by the power station to
where access to established utility grids is limited due to compress air during off peak periods. During peak periods this
distance and the cost of extending the grid is too high. air is then decompressed in a compression chamber before
However, there are currently some difficulties associated with being fed to turbines, increasing energy production during
renewable power generation systems which must be peak periods [7].
addressed. Firstly, renewable energy is by nature intermittent PHS uses two water reservoir storage areas, one above the
and unpredictable. Secondly, the supply of energy fluctuates other, to store energy. This is done by pumping water from the
as well as the load needed. This might create energy lower reservoir to the upper reservoir during off-peak periods
imbalances within the system and increase the probability of and then, during peak load hours, allowing the water to flow
load being unmet by supply. from the upper reservoir to the lower reservoir, turning a
generator and converting the energy back into electricity [17].
This ensures availability of water over a whole year for arid
G.Coppez is with the Electrical Engineering Department, University of Cape and semi-arid countries without large river systems.
Town, South Africa (e·mail: gabrielle.coppez@uct.ac.za). Hydrogen fuel cell storage is effective for long and short
S. Chowdhury is with the Electrical Engineering Department, University of term storage and consists of three main stages, an
Cape Town, South Africa (e-mail: Sunetra.Chowdhury@uct.ac.za).
S.P. Chowdhury is with the Electrical Engineering Department, University of
electrolysing stage, a hydrogen storage stage and the fuel cell
Cape Town, South Africa (e-mail: sp.chowdhury@uct.ac.za) stage. In the electrolysing stage, off-peak energy is used to
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electrolyse water to create hydrogen ions. This hydrogen is • Environmental impact D the impact that each component
then stored in the hydrogen storage stage until the peak load and therefore the overall system will have on the area
requires more energy. At this point the fuel cell starts surrounding it. [9][2]
combining the hydrogen with oxygen resulting in a chemical
reaction forming water. The energy from this reaction is To optimise a system based on the criteria listed above, the
harnessed and converted to electrical energy [8]. reliability, efficiency, technical maturity and life span would
For high power efficiency application, flywheels, super­ be maximised and the cost and environmental impact of the
capacitors and superconducting magnetic energy storage system minimised. However all of these parameters do not
(SMES) can be used. need to be used to optimise the system; the parameters used
Flywheels use off-peak energy to rotate a rotor attached to a for optimisation of the system are decided depending on the
wheel within a vacuum. Energy is then conserved in kinetic project and the needs of the project stake holders.
form until it is needed. When electrical energy is in high
demand, the kinetic energy is then used to generate power A.2 Multi Criteria Decision Making (MCDM)
[13]. Multi Criteria Decision Making Management (MCDM) can
Super-capacitors are conventional capacitors with increased be used to aid in system selection. This is done by looking at
surface area and a double layer of charge which enables a various criteria of proposed systems such as those outlined
higher energy density than conventional capacitors to be above. These criteria are assigned priority order in terms of
stored. Super capacitors have a high power density, but what is most important for the system. The criteria are
relatively low storage ability when compared with other forms separated into qualitative and quantitative components.
of energy storage such as batteries [13]. Qualitative components are ratings assigned by the system
SMES uses off peak energy to pass DC current through a designers to the different relative attributes of the systems
coil made of superconductive wire. This then creates a being evaluated such as environmental impact and
magnetic field around the coil in which energy is stored. The technological maturity. Quantitative components are
coil can charge and discharge very quickly giving a quick components that have concrete data associated with them such
response to the system needs [7]. as efficiency and life span. These tables are then combined to
Chemical storage or battery is the most popular and assess each system being proposed in the order of priorities
frequently used method of energy storage. There are many given before [2].
types of storage within this category with the two main types With regard to DG systems such as a PV-wind generation
being flow batteries and normal cell batteries. Flow batteries system, important aspects are efficiency, maturity and
are used for large scale applications. The electrolytes are kept therefore reliability of the storage method as well as cost.
separately in reservoir tanks and moved into the Looking at the possible energy storage methods, SMES has
electrochemical cell using pumps. When the electrolytes flow the highest efficiency of the methods discussed. However,
through the electrochemical cell, the chemical energy is SMES is a relatively new technology and is very expensive
converted to electrical energy [5]. Power delivered is due to the use of superconductive wiring in the coil. Hydrogen
dependent on the rate at which the electrolytes enter the storage and super-capacitors, as relatively immature methods
electrochemical cell and are converted. These batteries are of storage, are not preferred for applications where support is
deemed 75-85% efficient and have a long life span. As the not readily available. The efficiency of hydrogen storage also
electrolytes are stored separately, very little self discharge does not qualify the cost of the system. For a small power
occurs. Flow batteries are quite costly storage solution as they application, the use of CAES as well as PHS is not justified as
involve other elements, such as pumps to move the there are large initial costs involved with the systems,
electrolytes between the reservoirs and the electrochemical especially in the case of PHS. In terms of CAES, whilst being
cell [17]. a relatively cheap form of energy storage, the location of the
There are many types of cell batteries which are discussed system is limited by the presence of underground compressed­
in more detail in Section III. air storage. While flywheels are efficient and low cost
systems, their self discharge rate is high and the energy
A.I Storage Method Selection density that they can supply is low. This makes them
The selection of the sizing of a system is based on many unsuitable for these types of applications. Chemical storage in
different criteria. Some criteria which can be used for the form of batteries is the ideal solution for Distributed
optimisation are: Generation systems as there are no auxiliary systems which
• Reliability D the ability of the system to meet the load at need to be run in conjunction with batteries. In addition,
all times. batteries are a very mature form of storage and can yield high
• Efficiency Dthe ability to use the components in a way as energy density at low cost. The following section focuses on
to minimize losses. the different types of cell batteries available.
• Cost Dthe lifecycle cost of the system including the initial
investment plus running costs over the lifespan of the III. OVERVIEW OF CELL BATIERY
system.
• Technical maturity D commercial availability and proven Cell batteries are currently the most used form of energy
reliability of the technologies used storage in DG. Cell batteries come in various forms and types.
• Life span Dthe length of time that the system will be able Important comparison criteria for different types of batteries
to operate are: possible depth of discharge of the battery, cost, number of
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charge/discharge cycles the battery can tolerate, efficiency, a very high self-discharge rate, making them less ideal for
self-discharge, maturity of the technology and energy density. long term energy storage. Zinc bromine batteries are relatively
This section provides an overview of different types of immature in their technology and use, so still have to be
commercially available cell batteries.
Table 1. Key Battery Attributes Comparison

B.1 Lead Acid Batteries Attributes Lead Acid Li Ion NaS Ni-Cd Zn-Br

Within the cell battery group, lead acid batteries are the Depth of 75% 80% 100% 100% 100%
Discharge
cheapest and most popular. They can tolerate a depth of
Cost Low Very High High and High High
discharge of 75% and have a life span of 1000-2000 cycles auxillary
on this depth of discharge. Lead acid batteries are 72-78% heating
efficient and are currently the most matured battery systems
needed
technology [5].
Lifespan 1000 3000 2500 3000 2000
(Cycles)
B.2 Lithium Ion (Li Ion) Batteries Efficiency 72-78% 100% 89% 72-78% 75%
Lithium ion batteries are mostly used within portable Self- Average Negligible Negligible High Negligible
electrical equipments, such as laptops. This is because they discharge
have a very high efficiency of almost 100%. In addition, Maturity of Mature Immature Mature Mature Immature
Technology
they have a lifespan of 3000 cycles at a depth of discharge of
80% [5]. However, lithium ion batteries are very expensive
and therefore not currently considered for larger applications proved in their application. This paper will focus on lead
where a higher energy density is needed. acid batteries, which have been proven in their use in isolated
power systems and which remain the cheapest option in terms
B.3 Sodium Sulphur (NaS) Batteries of cell batteries. The need for maintenance and their short
Sodium sulphur batteries are efficient batteries which work lifespan is still an area where improvements need to be made,
well with the pattern of daily charge and discharge. Sodium but when compared with other battery types, specifically for
sulphur batteries have a lifespan of 2500 cycles for a depth of an isolated hybrid renewable energy system, where low costs
discharge of 100%, 4500 cycles for a depth of discharge of are very important, lead acid batteries are still considered the
90%, and 6500 cycles for a depth of discharge of 65% [18]. best option and are discussed in more depth.
Sodium Sulphur batteries have an efficiency of 89% but must
be kept at a temperature of 300°C. While these batteries IV. LEAD-ACID BATTERY

themselves are not expensive, maintaining the battery at Lead acid batteries are currently the form of batteries most
300°C requires energy which decreases the overall efficiency found in power application. While they have a good energy
of the storage system and increases the cost [5]. density, their power density is limited and therefore the
amount of energy that can be supplied to the system and the
B.4 Nickel Cadmium (NiCd) Batteries time taken to charge the battery is significant. However, lead
Nickel cadmium batteries have efficiency between 72 and acid batteries are still the best option for their combination of
78%. They can store up to 27MW of power which makes them performance and cost [7]. Lead acid batteries have a relatively
very useful. Nickel Cadmium batteries have a lifespan of 3000 short lifespan and therefore need to be replaced periodically.
cycles with a depth of discharge of 100% and are thus well They are therefore still the limiting factor in isolated power
suited to the daily discharge and charge of a renewable energy generation effectiveness.
system. They have high self-discharge rate losing between 5 Due to the inherent unpredictable nature of renewable DG
and 20% of charge held per month [5]. However, they are also systems and the absolute dependence of the power generated
expensive and toxic [13]. on climatic conditions, it is very important to simulate all
components of the system together before implementation to
B.5 Zinc Bromine (ZnBr) Batteries ensure that the power supply needed for the load is met at all
Zinc bromine batteries have an efficiency of 75% and times. The battery forms a crucial part of this modeling as the
negligible self-discharge. They have high power and energy battery increases the output predictability of the system by
density [5]. The technology related to zinc bromine batteries is compensating for energy deficit in times when insufficient
still relatively new and therefore not as technologically mature renewable energy has been generated and by storing the
as others. In addition, these batteries are toxic [13]. excess energy in times when plentiful renewable energy has
Within these types of batteries, as seen from table 1, lead been generated. Therefore the extent to which the battery can
acid and nickel cadmium are the most technologically store and supply energy is very important within a standalone
advanced [5]. The ease of use of lead acid batteries as well as system and must be simulated under all conditions to ensure
their low cost make them the preferred type of energy storage. that the system will be able to meet its need.
Lithium ion batteries, whilst having high efficiency and Modelling of lead acid batteries can be done in a number of
lifespan at high depths of discharge, are currently too ways depending on the necessary accuracy of the simulation
expensive to be used in large applications. Sodium sulphur as well as which parameters need to be taken into account.
batteries are expensive and there is additional need to maintain Some methods of battery modelling need experimentation to
it at 300°C for optimal use. While Nickel cadmium batteries ascertain the characteristics and plot response curves for the
have a good lifespan with 100% depth of discharge, they have battery, by measuring voltages and currents during the charge
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and discharge processes. These are effective in gaining Construction, Probabilistic Methods, Adaptive Neural
technical knowledge on the battery, but not very helpful in Network and Genetic Algorithm Approaches. [21] These are
actual simulation for optimisation and system behaviour outlined below.
analysis purposes [21]. Another way of modelling the battery
is by using an electrical equivalent circuit to represent the C J Graphical Construction
various parameters and characteristics of the battery and Graphical construction is used to optimise in terms of two
ascertain their values or equations to represent their values at criteria. This is an accurate way of optimising the system;
different points in the process. however, it restricts which parameters can be used. [21]
For optimization purposes, the battery is modeled Graphical construction techniques require time series
dependant on the other sources in the system. Koutroulis et al chronological data for the systems involved. This enables the
[10] use the following equation to model batteries for these graph to be plotted using this information. For example in a
optimization simulation purposes: Hybrid renewable power generation system, where wind
t power generation, solar generation and battery storage are
Ci (t) = Ci (t - 1) - ns ?k( ) IJ.t used, one could only choose two parameters, such as the size
l .gus of the battery storage and the size of the solar panels to be
Where C i ( t) and C i (t - 1) are the available battery optimised in terms of one parameter, for this instance,
(Ah) at the specified times t and t-J of day i and ns· is the probability of Loss of Load. This method would therefore not
battery efficiency, which is taken at 80% for charging and take into account other factors, such as how many wind
100% for discharging. [10] turbines are included, the angles of the wind turbine blades,
This equation is then used to calculate the battery capacity the height of the blades above ground, etc.
as it charges and discharges depending on the value of the load This is therefore useful for simple systems with few
power required and the supply power. If the supply power is parameters, however if a more complex system is to be
higher than the load power required, the battery is deemed to optimised, other techniques will prove more useful.
be charging and calculated as such. If the load power required
is lower than the supply power, the battery is deemed to be C2 Probabilistic and Deterministic Techniques
discharging and calculated as such. Probabilistic techniques can be used in situations where
While optimizing the system to ensure that the load is actual hour by hour long-term data is not available and more
always met by the supply, the system can also be optimized general data needs to be used. This method then takes the data
for the lowest cost variation that still ensures that the load is that is available and predicts future values, taking into account
met by the supply. Various methods of doing this are outlined (in the case of a hybrid renewable energy system) the
in the following section. fluctuation of supply and load. The second method is less
computationally intense and requires minimal time series data
V. OPTIMISATION TECHNIQUES
[21]. Roy et al. [14] uses an initially probabilistic approach to
optimize a wind-battery hybrid system by creating a
As the reliability of the supply of renewable DG systems is Probability Density Function (PDF) of the wind speed at the
inconsistent, when designing a hybrid renewable energy site. This is done by gathering information from the site and
system, the reliability of supply of the system must be kept in creating an empirical model of the wind turbine. Belfkira et al.
mind to ensure that the load will be met by the supply at all [3] use statistical model of wind speed and irradiation values
times. There are many different variations of hybrid power as inputs to optimize a wind, PV and battery system.
systems which will ensure that the system is reliable. Both of these approaches then use a deterministic algorithm
However, economically the system must be optimised to t,? optimize the system. This is done by initially creating a
ensure the lowest cost possible whilst maintaining the system <iesign spaceO of feasible solutions which adhere to the
integrity. A common parameter used to measure the system maximum LPSP. This design space is then optimized by
integrity and reliability is Loss of Power Supply Probability minimizing an objective function in the space. The objective
(LPSP). This is the probability that load will encounter an function in each case is a variation of the cost of the system.
insufficient load supply [21]. LPSP must be monitored as the Belfkira et al. [3] use a DIRECT (Dividing RECTangles)
key parameter to ensure that in optimising the system, the algorithm approach to minimizing where a vector of the input
likelihood of the system supply not being able to meet the load parameters such as number of wind turbines, size of PV
at all times is kept very low [4]. Cost of the system can be panels, size of battery storage are input to minimize within the
measured in different ways, but most commonly is measured feasible area restricted by the power input and requirements.
by the total annualised cost of the system or the total cost of Further constraints can be added to this optimization process,
the system. such as to only use the battery within the recommended State
Annualised cost is made up of annual capital cost, annual of Charge (SOC) and to use the battery as little as possible.
maintenance and operation cost and annual replacement This appears to be an effective method of optimizing the
cost[15]. The total cost of the system is generally calculated system and is non-restrictive in terms of the constraints that
over 20 years and therefore includes the capital cost of the can be used to optimize the system.
system, the maintenance cost of the system over 20 years and
the installation costs [3][1 0]. C3 Genetic Algorithm (GA)
Some techniques used for optimising [state in terms of Genetic Algorithm (GA) technique uses the biological
what.] DG systems are as follows in terms of LPSP: Graphical principles of genetics, namely, crossover, recombination and
 5

mutation in the optimisation procedure. This occurs as the

system solves values of entered parameters for certain
conditions. Different solutions of the values of the s�t of
parameters are grouped together to form a @hromosomeOand
then these chromosomes are crossed over and recombined
until the optimal solution is found. These different sets of All Generations finished? Yes /Output & Store
'

Optimal Parameters
)
optimal solutions are then recombined to create even more
optimal solutions. The selection of the chromosomes for cross­
over and advancing to the next generation is generally
controlled by a fitness factor. These fitness factors are
assigned to the chromosomes based on the accuracy of the
solution for the scenario.
GA is very useful for solving non-linear problems, in this Constraints Evaluation & Chromosome Repair
case for situations where DG systemG supply fluctuates and
the load fluctuates randomly [21].
A sample set of parameters that can be considered for
optimizing a wind-PV-battery hybrid system is as follows: Constraints Evaluation & Chromosome Repair
Number of wind turbines, angle of turbine blades, height
above ground of the blades, number of solar panels with a
Fig. 1 GA Optimisation Flowchart [10]
specified size, size of battery storage. The conditions that must
be met are that the system LSLP must be minimised, that the
C.4 Artificial Neural Network (ANN)
battery SOC must never go below 40% [21] and the cost must
Artificial Neural Networks (ANNs) are methods of
be minimised. Only those @hromosomesOwhich then meet
optimisation based on the biological principle of the nervous
these conditions will be considered for the next generation of
system. The network is made up of neurons which h�ve an
solutions. [19]
input and output and a black box between them. A weIght.�ng
Sharhirinia et al. [15] use a GA technique to optimize a PV,
function related to the suitability of the neuron to the SolutIon
battery and wind DG system. A roulette wheel selecti�n
of the problem is attached to each neuron and at each level the
method is used, where the size of the roulette wheel slot IS
neuron outputs are summed with respect to their outputs �d
proportional to the chromosome fitness factor. This enables . IS
weighting functions to produce an output for the level. ThIS
the chromosomes most suited to the scenario to have a higher
a parallel processing approach. Different layers of the neurons
chance of advancing to the next generation and cross-over.
can be placed in series if needed for higher accuracy.
Different irradiation values and average wind speeds were
ANNs are adaptive by nature and therefore need to be
used to simulate different scenarios.
trained. Different topologies are used for different instances.
Ould Bilal et al. [12] shows a case study of an optimisation
As the main calculation occurs by the learning of the neural
of a wind, PV and battery DG system in Senegal. An
optimisation using Genetic Algorithms to minim�se the to�al
network' neural networks are good for instances where a lot of
data are not available. The available data can be used to train
cost of the system whilst maintaining a low LPSP IS used with
the neural network and then the neural network will use what
parameters of number of PV modules, power output of wind
it has learnt to calculate further values. Training occurs by
turbines, battery capacity and number of inverters and
initially giving the neuron different inputs and corresponding
regulators. The system is now functioning optimally.
output data so that it can calculate the process in between [21].
Another example of optimizing a PV-wind-battery system .IS
GA and neural networks can be used in conjunction with
reported by Koutroulis et al. [10]. A flow chart of the
each other to decrease the calculation time of optimisation.
optimization process is shown in Fig.1 [10].
This is done by training the neural network to test that the
outputs meet the initial conditions and then using the GA �o
A roulette method of selection based on the chromosome
fitness factor is used in this optimisation. The flow chart
create new solutions to feed to the neural network. ThIS
shown in Fig.l is repeated until a predefined number of
substantially decreases the time taken to calculate the optimal
generations have been completed and the system is then
solution, while keeping the accuracy of each of the methods
considered optimised. The constraints evaluation and
[16].
chromosome repair after each step consists of testing each
resulting chromosome to ensure that they still comply to the
VI. CONCLUSION
restrictions in terms of LPSP. If they do not, chromosome
repair occurs by replacing the chromosome with its parent. In
the case where the chromosome resulted from cross over, the Energy storage is deemed necessary and important within
chromosome is replaced by the parent with the highest fitness distributed generation to ensure the enhancement of power
factor. [10] quality and reliability as well as minimization of energy
Genetic algorithms are seen to have a lot of success in these imbalance of the system. While a variety of energy storage
optimisation problems and their use is on the increase in techniques have been discussed within this paper, batteries
related areas were found to be the most proven and easily used energy
storage type for the considered application. Lead acid batteries
are seen as the primary solution due to the fact that they are
 6

cost-effective and have the efficiency needed for the system. [10] E. Koutroulis, D. Kolokotsa, A. Potirakis, and others. "Methodology for
optimal sizing of stand-alone photovoltaic/wind-generator systems using
There are many possible ways of optimizing systems to ensure
genetic algorithms". Solar Energy. 80(9):1072-1088.
the lowest possible LPSP so as to ensure that the system is [II] A. Mellit, M. Benghanem, S.A. Kalogirou. "Modeling and simulation of
reliable and the load is always met by the supply as well as by a stand-alone photovoltaic system using an adaptive artificial neural network:
optimizing the system for the lowest possible cost. Various Proposition for a new sizing procedure". Renewable Energy. 32(2):285-313.
techniques of optimization have been outlined in this paper. [12] B. Ould Bilal, V. Sambou, P.A. Ndiaye, and others. "Optimal design of
a hybrid solarl)vind-battery system using the minimization of the annualized
These techniques vary in complexity and therefore the cost system and the minimization of the loss of power supply probability
technique chosen must match the application for which it is (LPSP)". Renewable Energy. 35(10):2388-2390.
needed. While optimization increases the reliability of the [13] J. Paska, P. Bicze1, M. Kios, "Technical and economic aspects of
electricity storage systems co-operating with renewable energy sources".
system, energy storage is still the main limiting factor in terms
Electrical Power Quality and Utilisation, 2009. EPQU 2009. 10th
of distributed generation systems, where the main electrical International Coriference on. 1.
grid is not available as a backup should the load not be able to [14] A. Roy, S.B. Kedare, S. Bandyopadhyay. "Optimum sizing of wind­
be met. Therefore, whilst lead acid batteries have been battery systems incorporating resource uncertainty". Applied Energy.
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identified as a good solution for the hybrid PV and wind
[15] A.H. Shahirinia, and others. "Optimal sizing of hybrid power system
power generation system currently, more research needs to be using genetic algorithm". Future Power Systems, 2005 International
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[20] Yu Zhang, Zhenhua Jiang, Xunwei Yu "Control Strategies for
The authors would like to acknowledge the support BatterylSupercapacitor Hybrid Energy Storage Systems". Energy 2030
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IX. Biographies
VIII. REFERENCES
G.Coppez received her BSc in Engineering in 2006 from the University of

[1] B.R. Alarnri, A.R. Alarnri. "Technical review of energy storage Cape Town, South Africa. She worked as an Automation and Instrumentation
technologies when integrated with intermittent renewable energy". Engineer for a South African marine engineering company, Marine and
Sustainable Power Generation and Supply, 2009. SUPERGEN '09. Mineral Projects, from 2007 to 2009. She is currently registered as a MSc
International Conference on. 1. (Engineeering) Student in the Electrical Engineering Department of the
[2] A. Barin and others. "Multicriteria decision making for management of University of Cape Town. Emllil: gbrielle.coppeZ@uct.ac.za
storage energy technologies on renewable hybrid systems - the analytic
hierarchy process and the fuzzy logic". Energy Market, 2009. EEM 2009. 6th S.Chowdhury received her BEE and PhD in 1991 and 1998 respectively. She
International Conference on the European. 1. was connected to MIS M.N.Dastur & Co. Ltd as Electrical Engineer from
[3] R. Belfkira and others. "Optimal sizing of stand-alone hybrid windIPV 1991 to 1996. She served WomenG Polytechnic, Kolkata, India as Senior
system with battery storage". Power Electronics and Applications, 2007 Lecturer from 1998 to 2006. She is currently the Senior Research Officer in
European Conference on. 1. the Electrical Engineering Department of The University of Cape Town,
[4] J.L. Bernal-Agustin, R. Dufo-Lopez, "Simulation and optimization of South Africa. She became member of IEEE in 2003. She visited Brunei
stand-alone hybrid renewable energy systems". Renewable and Sustainable University, UK and The University of Manchester, UK several times on
Energy Reviews. 13(8):2111-2118. collaborative research progrannne. She has published two books and over 55
[5] K.C. Divya, 1. 0stergaard, "Battery energy storage technology for power papers mainly in power systems. She is a Member of the IET (UK) and IE(I)
systemsN An overview". Electric Power Systems Research. 79(4):511-520. and Member of IEEE(USA). Email: sunetra.CAowdlj.ur\i@U£t.ap;.za
[6] M. Diir,r A. Cmden, S. Gair, and others. "Dynamic model of a lead acid
S.P. Chowdhury received his BEE, MEE and PhD in 1987, 1989 and 1992
battery for use in a domestic fuel cell system". Journal of Power Sources.
161(2):1400-1411. respectively. In 1993, he joined E.E.Deptt. of Jadavpur University, Koikata,
[7] H. Ibrahim, A. Iiinca, J. Perron. "Energy storage systemsN Characteristics India as Lecturer and served till 2008 in the capacity of Professor. He is
and comparisons". Renewable and Sustainable Energy Reviews. 12(5):1221- currently Associate Professor in Electrical Engineering Department of the
1250. University of Cape Town, South Africa. He became IEEE member in 2003.
[8] D. Ipsakis, S. Voutetakis, P. Seferlis, and others. "Power management He visited Brunei University, UK and The University of Manchester, UK
strategies for a stand-alone power system using renewable energy sources and several times on collaborative research programme. He has published two
hydrogen storage". International Journal of Hydrogen Energy. 34(16):7081- books and over 110 papers mainly in power systems and renewable energy.
7095. He is a fellow of the IET (UK) with C.Eng. IE (I) and the IETE (I) and
[9] N. Jantharamin, L. Zhang. "A new dynamic model for lead-acid Member of IEEE (USA). He is a member of Knowledge management Board
batteries". Power Electronics, Machines and Drives, 2008. PEMD 2008. 4th and Council of the IET (UK).Email: sp.ehowdhurv@uet.ae.za
lET Conference on. 86.