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Renewable and Sustainable Energy Reviews 67 (2017) 36–50

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Practical battery size optimization of a PV system by consideringindividual customer damage function

Mahmoud Mehrabankhomartash n, Mohammad Rayati, Aras Sheikhi,Ali Mohammad RanjbarDepartment of Electrical Engineering, Sharif University of Technology, Tehran, Iran

a r t i c l e i n f o

Article history:Received 1 September 2015Received in revised form29 May 2016Accepted 24 August 2016

Keywords:Photovoltaic (PV) systemIndividual customer damage function (ICDF)Battery sizingFinancial evaluation

x.doi.org/10.1016/j.rser.2016.08.05021/& 2016 Elsevier Ltd. All rights reserved.

esponding author.ail addresses: [email protected]@gmail.com (M. [email protected] (M. Rayati), [email protected]@sharif.edu (A.M. Ranjbar).

a b s t r a c t

Today, energy crises attracted many researchers’ attention to renewable energy technologies especiallyphotovoltaic (PV) systems. The main challenge of PV systems is unpredictable nature of solar powergeneration. To overcome this challenge, a storage system is integrated which reduces demand reliance onelectricity grid and uses excess energy that solar panels produce. As investment cost of the storagesystem is considerable, finding an optimal technology, size, and configuration are crucial. In this paper,the optimal battery system is excluded from existing PV plant installing in a commercial building locatedin Mashhad/Iran. Here, the sizing procedure is based on a financial evaluation which considers the da-mage costs due to outages that the commercial building is experiencing during the PV system life spam.To compare results, the battery size is also determined by stochastic methods, e.g. Monte-Carlo simu-lation method. The simulation results confirm the advantages of the proposed approach compare withclassic ones.

& 2016 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361.1. Photovoltaic technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371.2. Energy storage systems (EES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381.3. A review on different methods of storage system sizing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391.4. An overview on the objective of this research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401.5. The structure of this paper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2. The weather data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403. The load demand surveying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.1. Obtaining hourly load profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.2. Obtaining load reliability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413.3. ICDF for the commercial building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4. Solar PV modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445. Battery modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446. The proposed method for sizing the battery bank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 457. Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7.1. A comparison with probabilistic methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487.2. Discussion on the effect of ICDF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

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Fig. 1. Global installed PV capacity.

Fig. 2. Different solar cell technologies market share in different years [7].

Fig. 3. Typical solar cell technologies [7].

M. Mehrabankhomartash et al. / Renewable and Sustainable Energy Reviews 67 (2017) 36–50 37


1. Introduction

Nowadays, the advantages of renewable energies and concernswith fossil fuels have caused a growing trend towards integrationof renewable energy resources in power system. Totally, the ad-vantages can be categorized as economic and environmental. Inthe realm of economic merits, renewable energy resources obviatethe need for establishing transmission lines for supplying remoteareas. Moreover, as distributed generation resources which areusually located near to consumptions, they avoid occupying thetransmission capacity for transmitting power to the remote areasand reduce the future investment cost for transmission expansion.In addition, transmission loss reduction is another importantconsequence.

Among renewable energy resources, photovoltaic (PV) systemsare more attractive in comparison with other resources such aswind for applications in urban areas, mainly because they arenoise free. PV systems have been evolved from a small scalemarket towards a mainstream electricity source [1]. Regarding thereport provided in [2], PV systems supply one percent of the worldtotal electricity consumptions. One of the major reasons of rapidspread of PV systems is obligations and tariffs imposed by gov-ernmental and regulatory bodies, to provide energy for remoteareas [3]. Regarding statistical data, Iran is among top 20 countriesin terms of greenhouse gas emission [4]. Moreover, Iran is locatedbetween 25 and 40 north latitude where is a favorable area toinstall PV systems [4]. Hence, these conditions provide a greatopportunity to exploit solar energy for electricity production inIran. This paper discusses battery sizing in installing a PV system inMashhad/Iran at 36°20′40″ N, 59°27′08″ E. Since this project refersto sizing a battery storage system in a PV system, a review onvarious PV and storage technologies is provided in Sections 1.1 and1.2, respectively. Afterwards, different methods of optimal sizingare reviewed in 1.3. Finally, the objective of this paper is presentedin 1.4.

1.1. Photovoltaic technologies

Solar cell, as the building block of the PV technology, was in-vented by Russell Ohl in 1940 [5]. In the history of PV technology,like other technologies, some decisions and economic issues wereas turning points. In the early 1970, due to politic issues and in-creasing need for energy resources, cost of fossil fuels drasticallyincreased. This issue led to an abrupt rise in research investmentsin the PV technology which resulted in decrease in the cost of PVpower from about 70$/W to about 10$/W [6]. Moreover, decisionson supporting renewable energies to avoid increasing rate ofgreenhouse gas production fostered the idea of using PV tech-nology in different communities [6]. This allegation has beensubstantiated by Fig. 1, which demonstrates the total installed PVcapacity.

Apart from political and economic issues, advancement of solarcell technology is another important factor. In other words, re-search on different solar cell technologies resulted in producingsolar cells with higher efficiency and longer life time which playsan important role in reduction of PV energy costs. The mostcommon PV cell technologies used by the manufactures are cate-gorized as Mono-Crystalline (Mono-Si), Polycrystalline (Poly-Si),and thin-film Silicon which has major share of market [7–9]. Thehybrid structure which uses nanotechnology is another technologyand is still used in research applications [10]. Fig. 2 shows share ofcommercial solar cell technologies in market. Moreover, Fig. 3provides a comparison between efficiency of these technologies.

As shown in Fig. 2, from 1990 to 2000, Mono-Si technology hada major market share. It is more efficient in comparison with othertechnologies. However, efforts to reduce costs and increase

production costs resulted in producing Poly-Si technology. Ac-cordingly, although Poly-Si is less efficient in comparison withMono-Si, it replaced Mono-Si technology as the leader of PVtechnology after 2000 [10]. Moreover, Thin-film technology hasgained attraction, mainly because it may reduce the production

Table 1Energy storage technologies [15–21].

Category Capital cost($/kW)

Life time(years)

Efficiency (%) Energy density(Wh/l)

Power rating(MW)

Response time maturity

Compressed air energy storage(CAES)

50–110 425 60�79 2�6 5�300 Seconds-minutes

Used

Flywheel energy storage (FES) 300–5000 15�20 490 20�80 0�0.25 o1 cycle DevelopedPumped hydro energy storage(PHES)

80�200 450 65�80 0.2�2 100�5000 minutes Used

Fuel cell 410000 10�30 34�44 600 0�50 o1/4 cycle DevelopingSuper capacitor 100�300 4�12 85�98 1�20 0�0.3 o1/4 cycle DevelopingSMES 200�300 420 75�80 6 0.1�10 o1/4 cycle DevelopingThermal energy storage system 200�300 5�40 30�60 80�500 0�300 Hours DevelopedLead-acid battery 50�400 5�20 63�90 50�80 0�40 o1/4 cycle MatureNickel-cadmium battery 400�2400 10�20 60�83 15�80 0�40 o1/4 cycle UsedSodium-sulfur battery 245�500 5�15 75�92 15�300 0�34 o1/4 cycle CommercializingLithium ion battery 600�2500 5�15 85�100 200�400 0�0.1 o1/4 cycle CommercializingVanadium redox battery 150�1000 5�30 75�85 20�70 0.03�3 o1/4 cycle DevelopedZinc bromine battery 150–1000 5�10 66�80 65 0�2 o1/4 cycle Developed

M. Mehrabankhomartash et al. / Renewable and Sustainable Energy Reviews 67 (2017) 36–5038


costs by using lower materials and energy for manufacturingprocess. However, its low efficiency needs to be improved in orderfor gaining a considerable market share [10].

1.2. Energy storage systems (EES)

There are some factors which have caused energy storagesystem (EES) as a critical component of power system. First of all,integration of renewable energy resources such as grid connectedPV systems exposes the power system to high generation fluc-tuation due to intermittent nature of weather condition [11,12].Accordingly, integration of EES into renewable energy resourcescan compensate for fluctuations of renewable energies generation.Moreover, during power outages, EES is the most important part ofuninterruptible power supply (UPS) system for enhancing powersystem reliability. Another advantage of EES refers to ancillaryservices such as frequency regulation [13]. In recent years, pumpedhydroelectric storage (PHES), as an example of EES, has beenemployed for balancing demand and supply and also frequencyregulation [14,15].

EES can be categorized as demonstrated in Table 1. In this tablebasic information on EES technologies have been provided. After-wards, complementary information on each EES technology hasbeen introduced.

Pumped hydroelectric storages (PHES) uses height differencebetween two water reservoirs for producing electricity. PHES as acase of application of EES in bulk power systems, provides ancil-lary services such as frequency control, mainly because it is fasterthan other plants to regulate frequency. Low capital cost and itslarge capacity in comparison with other EESs have made it as thepreferable case for frequency control in power system. However,the main drawback of this technology refers to the fact that itneeds a special geographical area. This is mainly due to the factthat PHES requires a water reservoir at a high elevation to produceelectricity [15].

Another EES technology is compressed air energy storage(CAES). A CAES system uses an underground site, e.g. a rock ca-vern, to store gas at a relatively high pressure. This stored potentialenergy can be used for electricity production by CAES technology[20]. Since CAES has fast conversion and storage process, it iscandidate for responding to rapid load changes. On the other hand,CAES uses fossil fuels which may raise environmental concerns.Moreover, due to high pressure vessels it needs to be used care-fully, specifically in residential areas [16].

Flywheel technology is another storage technology which useskinetic energy for storage of electricity. In this technology, elec-tricity is stored by spinning a flywheel using a motor and it is

released by reducing the angular velocity of flywheel. The mainadvantage of the flywheel technology is its long life time and alsohigh efficiency. However, its main drawbacks are low energydensity, frictional losses which cases self-discharge and finallyhigh capital cost [18,22].

Fuel cell is another EES technology. This technology uses thetransactions of energy during chemical reactions. The most well-known type of this technology is hydrogen fuel cell which workson the base of converting hydrogen and oxygen to water and re-leasing energy and vice versa [21]. High capital cost and also lowefficiency have caused it to be used in research applications.However, research on this technology is still attractive, mainlybecause with respect to Table 1 its energy density is too high. Thisfeature has made fuel cells as an attractive technology to be usedin vehicles [16].

Another technology which should be taken into considerationis Thermal Energy Storage (TES) system. This type of technologyhas two major types. The first type is Latent-fusion-heat whichuses liquid-solid transition of a material such as sodium hydroxideat constant temperature for releasing and storing energy. The nexttype of TES is Sensible Heat TES which is achieved by heating abulk material such as molten salt whose phase does not changeand its stored thermal energy can be used for producing watervapor to be used in a turbine [19]. The main advantages of thistechnology are its relatively high energy density and also its cap-ability to be used for large scale application which can be noticedfrom Table 1.

Another technology is Superconducting Magnetic Energy Sto-rage (SMES) which uses electromagnetic field as the intermediateof electricity storage. Simply, the functionality of SMES refers to asuperconducting coil which stores the energy in the magnetic fieldwhich is created by the current flowing through the coil. Regard-ing Table 1, its fast response and also high efficiency can make it adominant choice in the future. Due to its fast response it can beused for power quality and dynamic stability improvement, [23–25]. However, it is still used in research applications and withrespect to Table 1 just its small scale applications have beenintroduced.

Super capacitor is also another technology which is attractivefrom some points of view. First of all, its energy density is muchmore than conventional capacitors. Moreover, same as SMEStechnology, its fast response makes it a preferable choice forpower quality enhancement applications. Specifically, the majoradvantage of super capacitors over batteries is that they can becontinuously charged and discharged without any degrading,while the battery system life time is strongly dependent onnumber of charging and discharging cycles [16].



Battery storage systems have some merits which make it thebest choice for some applications. First of all, they can be used inresidential areas, mainly because they are safer than the afore-mentioned technologies. BESS can be exploited for various casesranging from kWh to MWh. Moreover, it can be simultaneouslyused for demand side management and uninterruptible powersupply applications. In this research, the storage system, which isused for the case study, is a battery storage system. Hence, a moredetailed study of various technologies of batter storage systemsseems to be necessary.

A battery storage system is the collection of battery blockswhich are connected to each other in series and parallel. Thecommercial types of batteries which are worth mentioning areLead acid battery, Nickel cadmium battery, Lithium ion battery,and finally Sodium Sulfur battery [12,17,18].

Lead acid battery is the most developed type of battery tech-nology which has been mature in industrial application. The mainadvantages of this technology over other battery technologies arelow capital cost, simple manufacturing, and good life time [26].However, using heavy metals, such as lead, makes it a treat to theenvironment [27].

Nickel cadmium battery is another technology which has beenintroduced about 100 years ago. Therefore, it has passed its de-veloping stage in industry. This technology needs low main-tenance. However, regarding Table 1, its capital cost is a little morein comparison with lead acid batteries. This is the main drawbackof nickel cadmium technology. That is because nickel is a toxicmetal and requires a careful method for disposal. On the otherhand, this technology has a robust reliability which has made it asa promising technology to be used in UPS, emergency lighting andgenerator starting [20].

Sodium Sulfur technology uses the molten form of sodium andsulfur at high temperature. It is well-known due to its applicationin load leveling and peak shaving in large scale energy storagesystems with about 65% of market share [17]. This technology hasgreat merits such as high energy density, high efficiency, zeromaintenance and long life time. However, high temperature con-dition which is required for maintaining molten form of sulfur is atreat to the environment [16].

Lithium ion battery is another battery technology which wasintroduced by Bell labs in the 1960s and came into the market bySony in 1990 [20]. One of the main advantages of Lithium ionbatteries is that it is lightweight. In this regard, it is a great choicefor mobile applications [16]. In addition, with regard to Table 1 thistechnology has great energy density and excellent efficiency. Thesetwo factors have led to commercializing lithium ion technology.

The next two technologies which are still in developing stageare vanadium redox battery and zinc bromine battery. These twotechnologies are newer than the aforementioned battery tech-nologies. Both of these technologies are categorized as flow bat-teries. Similar to the aforementioned conventional batteries, thistechnology directly converts chemical energy to electricity. How-ever, instead of using a single tank, it uses two separate tanksconsisting of the electrolytes. The main advantages of flow bat-teries is acquiring optimal power delivery characteristics withoutany need for maximizing energy density. In addition, it has morestable performance due to its separate tanks which avoids directinteraction of electrodes [28].

1.3. A review on different methods of storage system sizing

There are different methods of sizing which can be classified asfollows:

1. Probabilistic methods: Due to intermittent environment of PVgeneration, stochastic methods are of paramount importance.

Chee et al. [3], employed Monte Carlo simulation for sizing abattery capacity in a PV-battery system. For implementation ofMonte Carlo, a set of day samples are generated. Each daysample consists of a load, irradiance, and temperature profilesfor modeling PV generation and load consumption. Moreover, ineach day sample, a utility grid outages is generated in the mostprobable times of the day. By using Monte Carlo, for eachbattery capacity, the probability that the PV-backup batterysystem could prevent the load from experiencing outages iscalculated. Finally, the battery backup size which satisfies apredefined probability threshold is considered as the preferablechoice. Kaplani et al. [29] employed a similar method tocalculate the minimum battery size and PV size to be energyindependent for a predefined number of autonomous days. Inthis research, the number of autonomous days, i.e. outageduration, is considered as the key factor in sizing. Hence, fordifferent number of autonomous days, the minimum PV-backupbattery size and the corresponding success rate in supplying theload during utility grid outages has been calculated. Otherresearches such as hybrid PV-wind-diesel-battery system[30,31] involve optimal sizing with multi variables, i.e. PV plantsize, wind farm size, etc. In this researches, uncertainties ofsolar radiation, wind speed, fuel prices are considered andexpected energy not supplied (EENS) and net present cost fordifferent scenarios have been considered.

2. Iterative methods: In this approach, performance assessment ofthe system is made in each iteration until the optimum design isattained. In this approach, there exist various tools which can beused in each iteration such as Genetic Algorithm (GA), ParticleSwarm Optimization (PSO) [32]. These methods are discussed inmore details in [33] which reviews the works which adoptedthese approaches. Totally, each method which uses a tool forcalculation in a set of iterations can be considered as an iterativemethod. For example, in [34], mixed integer linear program-ming is used in optimization of a system with iterativeapproach. Moreover, in [35] by using iterative method andconsidering LPSP as the threshold of reliability, the mosteconomical configuration for the hybrid structure of solar, wind,and battery storage system is calculated. Therefore, in thisresearch, there are two important purposes which should beserved in designing a system. First, managing to design thesystem for a predefined level of reliability. Secondly, differentsystem designs which satisfy this level of reliability should beoptimized from financial perspective.

3. Analytical methods: In this approach, computational models areused for describing components which affect the optimization.This approach obviates the need for long time series that helpthe designer with simulating different configurations of casestudy. The computational models can be used for weather in-formation or components of the system such as battery storagesystem. In [36], Beta and Weibull distributions are used formodeling the intermittent nature of solar radiation and windspeed, respectively. Recently, computer tools which are categor-ized by [32] are also developed to evaluate integration ofrenewable energy resources. In [37], an extensive review ondifferent computer simulation tools is provided.

4. Multi-objective methods: Optimization of a system is usually amulti objective optimization problem. Accordingly, in this ap-proach, all of the individual objectives are merged to form asingle objective and Pareto solution is calculated. Pareto in-troduces a set of optimal solutions and any deviation fromPareto solutions is only possible when at least one of the ob-jectives is deteriorated [38]. Therefore, the designer chooses thebest solution by considering selection criteria [32]. Thesecriteria can be for instance, a reliability index, cost threshold,practical restrictions and etc. In [39], the cost constraint and

Fig. 4. The installed solar PV panels which are used for the consumption of KRECcommercial building.



reliability constraint are considered as the selection criteria inoptimal sizing a PV-Wind-Diesel generator system. In most ofthe researches, the optimization objective is optimizing size ofthe generation resources in the system. However, they usuallyconsider other objectives such as minimizing total generationcost [40] , pollutant emission [40–42], peak shaving [43].

5. Artificial intelligence (AI) methods: The main advantage of thesemethods concerns its ability to handle non-linear or stochasticenvironment of problems [33]. Moreover, AI can lead to satis-factory results without any need for weather information [44].This is too important for sizing a system in remote area or a casestudy in which there is no adequate information. The most well-known AI approaches are reported in literature includingartificial neural network (ANN), genetic algorithm (GA), particleswarm optimization (PSO), fuzzy logic, and colony optimization.Apart from the mentioned advantages of AI, GA has the abilityto find the global minimum instead of finding local minimums.This advantage has persuaded many researchers to use it insolving an optimization problem Bilal et al [45] used thistechnique for sizing a hybrid PV-wind system or in [46], asystem consisting of PV, wind, fuel cell, micro turbine andbattery storage system was optimally sized. On the other hand,although PSO technique cannot necessarily find the globaloptimum, its advantage over GA is that it just requires a fewequations which make it easier for implementation in softwareenvironment [33]. ANN is also another powerful technique forsizing. It is efficient in many, or most applications. However, itneeds a training procedure to be used efficiently. Mellit in [47]has provided applications of ANN in PV systems. About theapplication of fuzzy logic, in [36] this technique is used forfinding the optimal size of a PV-Wind-FC-Battery and dieselgenerator system.

6. Intuitive method: this method is used when there is no exactdata and information on the system. Therefore, the calculationis just conducted by average information on load, generation,weather, and etc. [48]. Since this method ignores detailedinformation and stochastic nature of weather, it may result inover/under sizing the system [49]. In recent years, this methodis less used rather than last decades, mainly because computertools enabled researchers to use exact information on thesystem for numerical simulation.

1.4. An overview on the objective of this research

With respect to the methods introduced in the previous sub-section, each of them has their own advantages and disadvantages.In this paper, the objective is sizing a backup battery system for agrid connected PV system. In other words, a backup battery systemis aimed to be optimally sized for integration into an existing gridconnected PV system which has no storage system. The aimed PV-backup battery system should be able to supply a medium sizecommercial building during power outages. Moreover, the backupbattery system is expected to be fully charged and in standbymode when the utility grid is available. The main contributions ofthis paper are as follows:

1. In this paper, the optimal sizing of the backup battery system iscalculated by considering damage costs imposed to the com-mercial building due to power outages. For clarification, in manyapplications, a consumer decides to add a backup system forimproving the reliability of system. Therefore, all of the costssuch as investment cost, maintenance cost, and also costs due todamages to the load are imposed to the consumer as the ownerof the system. Therefore, it is necessary to consider the damagecosts due to power outages in financial evaluations. This is ac-complished through defining Individual Customer Damage

Function (ICDF) for the commercial building. ICDF is reported inpapers concerning reliability concepts, [50–52]. This functiondefines a relationship between the outage time which the loadexperiences and corresponding damage costs imposed to theconsumer. Therefore, this function converts the outage durationto the cost which should be taken into account in financialevaluation.

2. This research tries to provide information on various aspects ofsizing a battery backup system by conducting study on a case.The case study is located in Mashhad/Iran and consists of a110 kW grid connected PV plant which compensates for part ofa medium size commercial building demand. A backup batterysystem is aimed to be integrated into the system for supplyingthe load with the help of PV plant during outages of the utilitygrid.

1.5. The structure of this paper

The structure of this paper concerns a case study for discussingthe proposed method. Accordingly, the influential factors in theproposed method are discussed at first. Accordingly, weather in-formation in Section 2; load information in Section 3; PV modelingin Section 4, and battery modeling in Section 5 are provided.Thereafter, the proposed method is introduced and discussed inSection 6. To clarify the method, it is used for finding the optimalbattery size for the case study. Finally, conclusion of the paper isprovided in Section 8.

2. The weather data

Weather data is an inevitable factor in the battery sizingmethod which will be proposed in Section 6. Therefore, accessingto an integrated weather data base seems to be necessary. Here,five years hourly ambient temperature and solar irradiance his-torical data on the PV system location are employed. The solarirradiance is the power per unit area received from the Sun. ThePV panels can transform part of received solar power to electricity.In this case, the panels are capable of tracking the Sun all day longas shown in Fig. 4.

At now, the PV plant generation fluctuation is the main concernof this case study and it is the result of intermittent nature ofweather condition. For instance, in Fig. 5, solar irradiance profilefor January 1st and May 1st are provided which demonstrate the

Fig. 5. Solar irradiance of May 1st and Jan 1st, 2012 in Mashhad.

Fig. 6. KREC commercial building.



drastic variation of weather condition. A similar condition exists indifferent months of the year which is provided in Table 2.

Another important issue which can be inferred from Table 2refers to the fact that the average of solar irradiance in Mashhad isnot as much as solar irradiance in the south areas of Iran (whereare well known for their great solar irradiance). However, theaverage temperature is not too high which helps with improvingthe efficiency of PV generation. This is because the efficiency of PVmodule drastically drops by temperature rise [54].

Fig. 7. Load profile of two different days in 2012.

3. The load demand surveying

Load study plays a major role in finding the optimal batterysize. Load information affects the proposed method from twopoints of view. First, daily profile which determine the consump-tion in each time interval; secondly, studying the availability of theutility grid by conducting a survey on historical outages datawhich gives an insight into the network ratability.

3.1. Obtaining hourly load profile

Here, the PV plant is installed to supply the consumptions of amedium scale commercial building, i.e. KREC commercial building,which is shown in Fig. 6.

Here, five years hourly load profile of the building is gatheredfor this study. Fig. 7 shows the load profile of this building during asample days of winter and summer. As shown in Fig. 7, the peakload reaches more than 100 kW in winter and about 550 kW insummer. The difference between peak loads in winter and summerdays refers to the high energy consumption of air conditionerunits.

3.2. Obtaining load reliability

Fig. 8 shows the occurrence time of the utility grid outages andtheir duration in each year. Regarding this figure it can be inferred

Table 2Average solar irradiance and ambient temperature of Mashhad from 2009 to 2014 [53]

Jan Feb Mar Apr May J

Solar Irradiance ( )w/m2 148 187 252 316 375 3

Ambient temp. (°C) 2.8 4.8 9.1 15.5 20.6 2

that most of the power outages are usually occur at the eveningsand their duration are often less than 20 min. More information onthe statistics of the outages data is provided in Table 3. As shownin this table, the total outage time per year is about 2 h and themost severe utility outage lasts about 20 min, while some of theoutages duration are less 1 min.

The outage data are demonstrated in histograms and curves arefitted to estimate the probability that an outage may occur in atime interval and also the probability of its duration. The values ofestimated curves divided by the total number of outages areconsidered as probability density functions (PDF). In Fig. 9, thehistogram shows the number of outages in each hour of the day,while after dividing the fitted curve by the total number of outagesover recent 5 years the PDF of outage occurrence in each hour ofthe day is achieved. In addition, Fig. 10 consists of a histogramwhich demonstrates the number of outages and their duration.

.

une July Aug Sep Oct Nov Dec Year

92 397 357 304 212 160 153 271

5.8 27.9 26.7 22.1 16.1 8.6 4.7 15.4

Fig. 8. Historical outages data for five years (a) 2010, (b) 2011, (c) 2012, (d) 2013, (e) 2014.

Table 3Statistics of historical outage data.

Min outageduration (Min.)

Maximum outageduration (Min.)

Total outageduration (Min.)

Number ofoutages

2010 2.3 17.8 144.4 192011 0.01 17.7 129.2 172012 0.8 19.1 129.1 152013 2.4 21.2 116.9 132014 2.1 19.2 113.1 13



Moreover, a curve is used for estimation which is used as PDF ofoutage duration after dividing by the total number of outages tobe. Finally, Fig. 11 shows the total number of outage in each month

and its estimation curve. It is worth mentioning that the peak ofthe number of outages refers to summer which the demanddrastically increases and also April when ceremonies of new Ir-anian year are held.

3.3. ICDF for the commercial building

The proposed method is strongly dependent on the concept ofICDF. To explain the concept in more details, consider a microgridsuch as the case study i.e. a PV-backup battery system. Integrationof a backup battery system to the existing PV system requires aninvestment cost. If the scale of the battery system increases, theinvestment cost increases. This is shown by the blue dash-dot linein Fig. 12. On the other hand, integration of a battery system en-ables the new PV-backup battery system to supply the load during

Fig. 9. Total number of outages in each hour of the day throughout recent 5 years.

Fig. 10. Outage duration of historical outage data throughout recent 5 years.

Fig. 11. Total number of outages in each month of the year throughout recent5 years.

Fig. 12. PV system cost with considering the reliability worth.

Fig. 13. ICDF of the commercial building.



utility grid outages. Accordingly, the power system reliability im-proves and the damage costs imposed to the consumer decrease.Larger battery system results in enhancing the power system re-liability and reduction in the damage costs. This issue is demon-strated in the red dash line in Fig. 12. From the owner of the load'spoint of view, who has decided to install a PV-backup batterysystem, the total cost is the main issue. Therefore, as indicated in

the solid line of Fig. 12, finding the optimum point of the total costis the major concern. However, finding the damage costs doesneed a factor which can convert power outages to cost. To obstaclethis problem, the concept of individual customer damage functionwhich is a reliability worth index can be exploited.

This factor defines a relationship between the power outageduration and the amount of cost which is imposed to the load dueto the power outage [51,55]. In other words, ICDF of a load pro-vides the designer with the relationship between outage durationand damage costs.

The main approach to finding ICDF of a load is surveying theload with tools such providing questionnaire for customer [50]. Byconducting a survey on the commercial building the ICDF for theload is shown in Fig. 13. It is worth mentioning that the cost istraditionally introduced with the dimension of $/W [56]. This costis the damage cost divided by the peak power of the load and inthis case the peak power as mentioned is about 550 kW. For ex-ample, with respect to Fig. 13, the damage cost imposed to thecommercial building following a 20 min power outage is about 5$/W*550 kW.

Fig. 14. A simplified schematic diagram of PV system.

Fig. 15. The aging effect on efficiency of PV module output power [57].

Table 4Basic characteristics of PVmodule and inverter[57,59].

ηPI 0. 98

KT −0. 0044 1/ Co

NOCT 47.5 °Cη −m STC 0. 152

TSTC 25 °C

Table 5The battery bidirectional converter speci-fications [59].

ηBI 0. 96Rated input voltage V48Maximum AC input power 11. 5kWMaximum AC output power 5. 43kW



4. Solar PV modeling

The PV system schematic diagram is shown in Fig. 14. The solarenergy is received by the PV plant which generates ( )P tPV as (1)[54].

( ) ( ) ( )= η ( )P t G t .A. t , 1PV m

here variable ( )G t (in W/m2) is the solar irradiance on PV systemsurface at time t. This variable was shown for a sample day inFig. 5. Parameter A is the total PV surface area in m2 which is810 m2 in this study. Finally, variable η ( )tm is the PV module effi-ciency at time t. The PV module output power degrades as systemage increases (Fig. 15).

Moreover, the PV module efficiency linearly decreases withtemperature as defined in (2).

( )( )η ( )=η + − ( )−⎡⎣ ⎤⎦t 1 K T t T 2m m STC T c STC

here parameter η −m STC is the module efficiency in the standard testcondition, KT is the power coefficient temperature of PV module,TSTC is the standard test condition temperature, and ( )T tc is the celltemperature which can be calculated as (3) [58].

( )= ( ) + ( ) −( )

⎛⎝⎜

⎞⎠⎟T t T t G t .

NOCT 20800 3c a

in which, variable ( )T ta is the outdoor ambient temperature at timet, NOCT is the normal operating cell temperature which is com-monly mentioned in the PV module datasheets.

The PV system also contains a PV inverter in addition to the PVmodules as shown in Fig. 9. The overall PV system output powerdepends on the inverter efficiency (ηPI) as (4).

( )= ( ) η ( )P t P t . 4PVO PV PI

The implemented PV module is ND-R240A5 made by SHARPCompany which has poly-Si technology [57]. In addition, the im-plemented inverter is SMC 7000TL [59]. As the nominal outputand surface of the implemented PV module is 124. 2 W/m2 and1. 64 m2, respectively, the nominal output of PV system containing540 modules is 110 kW. Table 4 indicates the other basic char-acteristics of PV module and inverter.

5. Battery modeling

The proper size selection of battery bank requires a perfectmodeling of battery blocks and bidirectional converter. Here, the

Fig. 16. Parallel and series battery blocks in battery bank.

Table 6The battery block char-acteristics [60].

CAPbcnom 820 AH

Ibcnom 41 A

Vbcnom 6 V

DODmax 0.9

Fig. 17. Effect of outage with respect to the load and generation profile.



SUNNY-ISLAND 8.0H is used as the bidirectional converter of thebattery system whose specifications are listed in Table 5 [59].

here ηBI is the bidirectional converter efficiency, the rated inputvoltage is the voltage which the converter is designed to workproperly, the maximum AC input power is the nominal chargingpower of battery, and the maximum AC output power is thenominal discharging power of battery. The converter is connectedto the battery bank containing several battery blocks as shown inFig. 16.

The number of series battery blocks (n) is calculated based onthe fact that the battery bank voltage should meet the rated vol-tage of bidirectional converter. In our work, 6CS 25PS- Rolls bat-tery is used as battery block. The nominal voltage (Vbc

nom), current( Ibc

nom), capacity ( CAPbcnom), and the maximum discharge depth

(DODmax) of battery block are indicated in Table 6. As the nominalvoltage of the bidirectional converter and battery block are 48 Vand 6 V respectively, 8 battery blocks have to be used in series.

The battery bank has three states during the simulation:charging, discharging, and standby. The battery state of charge(SOC) is computed at each time and each state as follows.

( )

( )

( )

( )

+ =

+η η

−∆ η η

( )

+

−

⎧

⎨

⎪⎪⎪

⎩

⎪⎪⎪

SOC t 1

SOC tI . .

CAP .n. mcharging state

SOC tI . t. .

CAP .n. mdischarging state

SOC t standby state 5

bbmax

bb i

bcnom

bbmax

bb i

bcnom

here Ibbmax is the battery bank maximum current, CAPbc

nom is batteryblock capacity in ampere hours (AH), and ∆t is the time step(15 min). Note that, SOC must be within its limits as follows in asimulation.

≤ ( )≤ ( )SOC SOC t SOC 6min max

where SOCmin and SOCmax are the minimum and maximum chargestate of the battery bank, respectively. The maximum value of the

SOC is 1 and its minimum value is determined by the maximumdepth of discharge as follows:

= − ( )SOC 1 DOD 7min max

It should be mentioned that the battery system is aimed to beemployed as a backup battery system. Therefore, in most of thetimes it is in standby mode. However, charging and dischargingstates are important during outages of the utility grid. In otherwords, following an outage the battery charges when PV genera-tion is more than load consumption, while the battery dischargeswhen the load consumption is more than PV generation. More-over, after the utility grid is revived, the battery charges to itsmaximum state of charge (SOCmax) and remains in standby mode.

6. The proposed method for sizing the battery bank

Briefly, the proposed method uses financial evaluation for dif-ferent numbers of parallel battery blocks or m and the battery sizewhich results in the minimum Net Present Cost (NPC) is con-sidered as the optimal battery system. However, in this methodthe financial evaluation considers the utility outages which influ-ences the cash flow.

The financial evaluation over the life time of the battery systemis carried out. Moreover, a number of utility grid outages are dis-tributed along the financial evaluation period. These outages aregenerated by using the PDFs introduced in 3.2. To explain in moredetails, outage samples are generated based on the historical data.The occurrence hour and day of each outage is calculated based onthe PDF introduced in Figs. 9 and 11, respectively. In addition, theoutage duration of each sample is calculated with respect toFig. 10. It is worth mentioning that the samples are generated byusing inverse transform method [61] which considers the PDFs toprovide samples which simulates the outages of the utility grid inthe best way.

Until now a financial evaluation with a number of outagesamples are introduced. The historical data are used for simulationof the system throughout the life time of the battery system. Inother words, historical load profiles are used for the load and alsosolar irradiance and temperature data are used as the inputs forcalculation of PV generation profile. In this study the battery sys-tem life time is considered 10 years [60], while historical data areavailable for 5 years. To compensate for this lack of information, itis assumed that the load, solar irradiance and temperature profileare repeated for the time interval between the 5th year and the10th year. It should be emphasized that the outages are the

Fig. 18. Overview of financial evaluation method for a specific battery size.

Table 7The financial input data for cash flow.

The inflation rate 15%The electricity increasing rate 2%The battery block cost (costbc) [60] 1170$/cellThe annual operation and maintenance cost 0.01* battery capital costThe bidirectional converter cost [60] 0. 745$/WElectricity price 0.015 $/kWh

Fig. 19. Occurrence and duration time of generated outages for a sample year offinancial evaluation.

Fig. 20. Damage cost for = = =mm 1, m 2, 3.



exception of this issue and they are generated for 10 yearssimulation.

Totally, the cash flow is strongly affected by two importantfactors. The first factor refers to the capital cost of the batterysystem. The next one concerns the damage costs which are asso-ciated with the power outages that the PV-battery system couldnot supply the load. Other costs which are taken into account aremaintenance cost, electricity charge cost, bidirectional convertercost. Note that these costs cannot strongly affect the cash flow.This is because maintenance and electricity charge costs are notcomparable with investment costs or damage costs. Moreover,bidirectional converter cost is related to the required power of theload, while the batteries are related to the energy needed by theload. Therefore, the bidirectional converter is not related to thebattery configuration and is the same for all battery configurations.

Fig. 18 provides an overview of the proposed method. First, allof the system information including economic costs, probabilisticdata, load, and weather profiles are read as the inputs. The eco-nomic cost are mentioned in Table 7. The battery size is initializedwith the minimum number of battery blocks. For this battery size,the financial evaluation is performed. In the financial evaluation,when a power outage occurs, if the PV-backup battery systemcannot supply the load, the total time which the load experiencesthe outage is converted to the cost by using ICDF of the loadprovided in Fig. 13. This condition is shown in Fig. 17. At t1timeinterval, the PV-backup battery system supplies the load. However,when the backup battery system is deeply discharged and the PVgeneration cannot supply the load alone, the load experiences the

Fig. 21. Total damage cost variation over different iterations for= = =m 1, m 2, m 3.

Fig. 22. Success rate for different battery rows i.e. m.

Fig. 23. Damage cost for d

Fig. 24. Net present cost for different battery row.



power outage. Therefore, the equivalent damages cost of t2 timeinterval is calculated by using ICDF of the load. This damage cost isadded to the total net present cost of financial evaluation byconsidering inflation.

The financial evaluation is carried out for a predefined numberof iterations. In each iteration, a new set of outage samples aregenerated and distributed along the battery life time period. Theresult of the Nth iteration is stored in ( )cost Size, N . Finally, the costof the battery size is calculated as (8).

The battery size which has the minimum value of ( )COST Size isselected as the optimal battery size.

( )=∑ ( )

( )= N

COST Sizecost Size,

N 8N 1N

max

max

where Nmax is the total number of iteration for a size, ( )cost Size, Nis the net cost of battery bank for a size and Nth iteration of wholelife cash flow.

ifferent values of m.

Table 8The optimum battery size and threshold battery size features.

Parameter The battery chosen bythe proposed method

The battery chosen byprobabilistic method

LOLP 0 0.15Investment cost ($) 37,440 46,800Outages cost duringEvaluation ($)

7820 19,820

Maintenance cost($/year)

374 468

Net present cost($) 119,270 130,500m 5 4n 8 8Battery size (kWh) 196.8 kWh 157.44 kWh



7. Simulation results

Referring to the previous sections, the outages are generated byusing inverse transform method. Fig. 19 shows the generatedoutages over battery life time period i.e. 10 years of financialevaluation. As indicated in Fig. 19, about 16 outages per year aregenerated and most of them occur between 12:00 and 20:00.

With regard to the methodology discussed in the previoussection, financial evaluation for each battery size is conducted in50 iterations i.e. Nmax¼50. For a specific battery size, the batterycapital cost does not change. However, the damage costs vary overdifferent years and also in different iterations. As an example,Fig. 20 shows the damage cost variation over different years for

=m 1, =m 2, and =m 3. Moreover, Fig. 21 shows the variation oftotal damage cost in different iterations for =m 1, =m 2, and

=m 3.Fig. 22 shows the percentage of total outages that the PV-bat-

tery system has managed to supply the load, i.e. success rate fordifferent battery configurations. Moreover, Fig. 23 shows these twomajor costs for each parallel number of battery rows, i.e. m. Asshown in this figure for =m 1, 2, 3, 4, the outage cost is con-siderable, while their capital cost is low. With regard to Fig. 22, thisis due to the fact that the corresponding battery systems are sosmall that cannot supply the load during all of the outages and amajor part of their cash flow consists of the damage costs. On theother hand, for ≥m 5, the battery-PV system has managed tosupply the load in response to the outages. Therefore, as shown inFig. 23, the damage cost reaches zero, while the battery capitalcost increases. It can be concluded there is a trade-off between thecapital cost and damage cost for each battery size.

Net present cost for each battery configuration including bi-directional converter cost, maintenance cost, and electricity chargecost is shown in Fig. 24. It can be concluded that battery systemwith 5 row i.e. =m 5 results in the minimum NPC. As mentionedin Section 5, the series number of batteries is calculated based onthe match between bi-directional converter voltage and batterysystem voltage and in this work 6 batteries should be connected inseries. Therefore, the optimal battery configuration has 8 batteriesin series and 5 rows. It should be mentioned that a noticeable risein NPC is due to capital cost of bi-directional converter which isthe same for each battery size.

7.1. A comparison with probabilistic methods

As mentioned in [3], the probabilistic method considers athreshold for a reliability index such as loss of load probability(LOLP) for sizing a battery system. LOLP is defined as the ratio ofthe number of outages which the system could not supply the loadto the total number of outages. Usually, with respect to the loadrequirements a threshold for a reliability index is considered and

the smallest battery size which fulfills the reliability requirementis considered as the best choice. In this case, when the threshold ofLOLP is 0.15, regarding Fig. 22, n¼8 and m¼4 are the best choices.The main difference between the proposed method and theprobabilistic method refers to the fact that the proposed methodconsiders the sizing problem from both reliability and economicpoints of view, while the probabilistic method just considers thereliability as the main aspect of the problem. Table 8 provides acomparison between the proposed method and the probabilisticmethod.

7.2. Discussion on the effect of ICDF

ICDF is a factor which defines a relationship between the poweroutage duration which is imposed to a consumer and the damagecosts as the consequences of the power outage. If a load is im-portant and requires a reliable generation, it is obvious that itsoutage involves high damage cost. For example in an industrialload which incorporates thousands of workers and its hourly in-come is thousands of dollars, it is strongly recommended to avoidany power outage. In this condition, a correct load survey willresult in an ICDF with an expensive damage cost. By using theproposed method, the system configuration will results in a sys-tem which has a large battery system with ≅LOLP 0. On the otherhand, if a load is not important and any outage has little damagecost, the proposed method will results in smaller battery systemsand larger amounts of LOLP. Hence, in the extreme case the pro-posed method will result in a system with no battery system and

≅LOLP 1. Therefore, there is a trade-off between the investmentcost and reliability which is quantified by using ICDF. In this casestudy for various damage costs considered by using ICDF, the op-timum battery size was shown. It can be inferred that ICDF canseverely affect the results. Hence, an exact and comprehensiveload surveying for calculation of ICDF is of key importance. In thiscase study, with respect to Fig. 13, the load is important and tooexpensive. Moreover, with regard to the results, the optimumbattery configuration resulted in =LOLP 0 which support thisclaim that this method considers the importance of a load in fi-nancial evaluation.

8. Conclusion

Intermittent nature of weather condition causes PV generationfluctuation which is one of the major concerns with applying PVsystems. To deal with this problem, integrating a battery systemwith PV seems essential. However, high capital cost of this packagemake it necessary to have a comprehensive study on finding theoptimal battery configuration and size. For the owner of a PVsystem, consisting PV plant, battery system and a load, there existsan indispensable cost stems from power outages. The damagecosts should be taken into account when the PV-battery systemcannot supply the load in response to a utility grid outage. Thispaper has proposed a method which considers damage costs infinancial evaluation by using Individual Customer Damage Func-tion (ICDF). ICDF is a reliability worth measurement index whichdefines a relationship between outage duration and damage cost.This research has used the proposed method for finding the op-timal battery size to be integrated into a 110 kW PV plant and acommercial load with the peak consumption of 550 kW. Theaimed PV-battery system is expected to supply the commercialload during power outages.

For calculating the optimal battery size, financial evaluation foreach battery size was calculated over its life spam and the optimalbattery system was selected. Moreover, a set of power outageswere generated and distributed throughout the financial



evaluation period by considering historical load and local weatherdatabase.

Simulation results demonstrated that 196.8 kW battery systemis optimal. Noteworthy, this PV-battery system could supply theload in all of the outages, i.e. LOLP equals zero. This is mainly dueto the fact that the load was too expensive; hence, it would bemore economical to utilize a larger battery system to avoid anyoutages.

The results of this method was compared with probabilisticmethods which consider only a threshold for LOLP. In the prob-abilistic methods, the minimum battery size which meet the ac-ceptable LOLP value is considered as the best choice. The simula-tion results confirm that although these kind of methods reach toa battery system with the capacity of 157.44 kWh, net present costis about 11,000$ more than the net present cost of our proposedmethod. It refers to the fact that the owner of the smaller batterysystem is at considerable risk to drastic damages costs.

Acknowledgment

The authors are thankful for kind supports from Khorasan Re-gional Electricity Company (KREC). We also thank Dr. MahdiOloomi and Mohammadmehdi Arabshahi for their great com-ments and collaboration.

References

[1] Masson Gaëtan, Latour Marie, Rekinger Manoël, Theologitis Ioannis-Thomas,Papoutsi Myrto. Global market outlook. 2013; [Online] Available: ⟨http://www.epa.org⟩.

[2] Masson G. Snapshot of Global PV Markets. 2014. [Online] Available: ⟨http://www.iea-pvps.org⟩.

[3] Tan Chee, Wei Tim C, Green, Hernandez-Aramburo Carlos A. A stochasticmethod for battery sizing with uninterruptible-power and demand shiftcapabilities in PV (photovoltaic) systems. Energy 2010;35(12):5082–92.

[4] Alamdari Pouria, Nematollahi Omid, Alemrajabi Ali Akbar. Solar energy po-tentials in Iran: a review. Renew. Sustain. Energy Rev. 2013;21:778–88.

[5] Michael Riordan, Hoddeson Lillian. The origins of the pn junction. Spectr. IEEE1997;34(6):46–51.

[6] Khalilpour Rajab, Vassallo Anthony. Planning and operation scheduling of PV-battery systems: a novel methodology. Renew. Sustain. Energy Rev.2016;53:198–204.

[7] The Fraunhofer Institute for Solar Energy. Fraunhofer ISE. March 2016. [Online]Available: ⟨https://www.ise.fraunhofer.de⟩ [accessed 2016].

[8] Bhubaneswari Parida, Iniyan S, Goic Ranko. A review of solar photovoltaictechnologies. Renew. Sustain. Energy Rev. 2011;15(3):1625–36.

[9] Chowdhury Sunetra, Crossley Peter. Microgrids and active distribution net-works. Inst. Eng. Technol. 2009.

[10] El Chaar L, Lamont LA, Elzein N. PV technology-industry update. In: Power andEnergy Society General Meeting; 2010.

[11] Eltawil Mohamed A, Zhao Zhengming. Grid-connected photovoltaic powersystems: technical and potential problems—a review. Renew. Sustain. EnergyRev. 2010;14(1):112–29.

[12] Hadjipaschalis Ioannis, Poullikkas Andreas, Efthimiou Venizelos. Overview ofcurrent and future energy storage technologies for electric power applications.Renew. Sustain. Energy Rev. 2009;13(6):1513–22.

[13] Ribeiro Paulo F, Johnson Brian K, Crow Mariesa L, Arsoy Aysen, Liu Yilu. Energystorage systems for advanced power applications. Proc. IEEE 2001;89(12):1744–56.

[14] Suberu Mohammed Yekini, Mustafa Mohd Wazir, Bashir Nouruddeen. Energystorage systems for renewable energy power sector integration and mitigationof intermittency. Renew. Sustain. Energy Rev. 2014;35:499–514.

[15] Rehman Shafiqur, Al-Hadhrami Luai M, Alam Md Mahbub. Pumped hydroenergy storage system: a technological review. Renew. Sustain. Energy Rev.2015;44:586–98.

[16] Mahlia TMI, et al. A review of available methods and development on energystorage; technology update. Renew. Sustain. Energy Rev. 2014;33:532–45.

[17] Cho Jaephil, Jeong Sookyung, Kim Youngsik. Commercial and research batterytechnologies for electrical energy storage applications. Prog. Energy Combust.Sci. 2015;48:84–101.

[18] Zhao H, et al. Review of energy storage system for wind power integrationsupport. Appl. Energy 2015;137:545–53.

[19] Ibrahim Hussein, Ilinca Adrian, Perron Jean. Energy storage systems—char-acteristics and comparisons. Renew. Sustain. Energy Rev. 2008;12(5):1221–50.

[20] Chen H, et al. Progress in electrical energy storage system: a critical review.

Prog. Nat. Sci. 2009;19(3):291–312.[21] Beaudin M, et al. Energy storage for mitigating the variability of renewable

electricity sources: an updated review. Energy Sustain. Dev. 2010;14(4):302–14.

[22] Bolund Björn, Bernhoff Hans, Leijon Mats. Flywheel energy and power storagesystems. Renew. Sustain. Energy Rev. 2007;11(2):235–58.

[23] Chen S-S, et al. Power flow control and damping enhancement of a large windfarm using a superconducting magnetic energy storage unit. Renew. PowerGener. IET 2009;3(1):23–38.

[24] Nomura S, et al. Wind farms linked by SMES systems. Appl. Supercond. IEEETrans. 2005;15(2):1951–4.

[25] Wang L, et al. Dynamic stability enhancement and power flow control of ahybrid wind and marine-current farm using SMES. Energy Convers. IEEETrans. 2009;24(3):626–39.

[26] Huggins RA. Energy Storage. New York: Springer; 2010.[27] Electropaedia. Battery and energy technologies: lead acid battery; 2015.

[Online] Available: http://www.mpoweruk.com/leadacid.htm.[28] Nguyen Trung, Robert F Savinell. Flow batteries. Electrochem. Soc. Interface

2011;19(3):54–6.[29] Kaplani E, Kaplanis S. A stochastic simulation model for reliable PV system

sizing providing for solar radiation fluctuations. Appl. Energy 2012;97:970–81.[30] Tina G, Gagliano S, Raiti S. Hybrid solar/wind power system probabilistic

modelling for long-term performance assessment. Sol. Energy 2006;80(5):578–88.

[31] Yang HX, Lu L, Burnett J. Weather data and probability analysis of hybridphotovoltaic–wind power generation systems in Hong Kong. Renew. Energy2003;28(11):1813–24.

[32] Luna-Rubio R, et al. Optimal sizing of renewable hybrids energy systems: areview of methodologies. Sol. Energy 2012;86(4):1077–88.

[33] Erdinc O, Uzunoglu M. Optimum design of hybrid renewable energy systems:overview of different approaches. Renew. Sustain. Energy Rev. 2012;16(3):1412–25.

[34] Gupta Ajai, Saini RP, Sharma MP. Steady-state modelling of hybrid energysystem for off grid electrification of cluster of villages. Renew. Energy 2010;35(2):520–35.

[35] Yang Hongxing, Lu Lin, Zhou Wei. A novel optimization sizing model for hy-brid solar-wind power generation system. Sol. Energy 2007;81(1):76–84.

[36] Khatod Dheeraj, Kumar Vinay, Pant, Sharma Jaydev. Analytical approach forwell-being assessment of small autonomous power systems with solar andwind energy sources. Energy Convers. IEEE Trans. 2010;25(2):535–45.

[37] Connolly D, et al. A review of computer tools for analysing the integration ofrenewable energy into various energy systems. Appl. Energy 2010;87(4):1059–82.

[38] Fadaee M, Radzi MAM. Multi-objective optimization of a stand-alone hybridrenewable energy system by using evolutionary algorithms: a review. Renew.Sustain. Energy Rev. 2012;16(5):3364–9.

[39] Maheri A. Multi-objective design optimisation of standalone hybrid wind-PV-diesel systems under uncertainties. Renew. Energy 2014;66:650–61.

[40] Ippolito MG e a. Multi-objective optimized management of electrical energystorage systems in an islanded network with renewable energy sources underdifferent design scenarios. Energy 2014;64:648–62.

[41] Sharafi Masoud, Tarek YELMekkawy. Multi-objective optimal design of hybridrenewable energy systems using PSO-simulation based approach. Renew.Energy 2014;68:67–79.

[42] Arnette Andrew, Christopher W Zobel. An optimization model for regionalrenewable energy development. Renew. Sustain. Energy Rev. 2012;17(7):4606–15.

[43] Tant J, et al. Multiobjective battery storage to improve PV integration in re-sidential distribution grids. Sustain. Energy IEEE Trans. 2013;4(1):182–91.

[44] Chauhan Anurag, Saini RP. A review on integrated renewable energy systembased power generation for stand-alone applications: configurations, storageoptions, sizing methodologies and control. Renew. Sustain. Energy Rev.2014;38:99–120.

[45] Bilal BO, et al. Optimal design of a hybrid solar–wind-battery system using theminimization of the annualized cost system and the minimization of the lossof power supply probability (LPSP). Renew. Energy 2010;35(10):2388–90.

[46] Kalantar M. Dynamic behavior of a stand-alone hybrid power generationsystem of wind turbine, microturbine, solar array and battery storage. Appl.Energy 2010;87(10):3051–64.

[47] Mellit A, et al. Artificial intelligence techniques for sizing photovoltaic sys-tems: a review. Renew. Sustain. Energy Rev. 2009;13(2):406–19.

[48] Kazem Hussein A, Khatib Tamer, Sopian Kamaruzzaman. Sizing of a standa-lone photovoltaic/battery system at minimum cost for remote housing elec-trification in Sohar, Oman. Energy Build. 2013;61:108–15.

[49] Shrestha GB, Goel L. A study on optimal sizing of stand-alone photovoltaicstations. Energy Convers. IEEE Trans. 1998;13(4):373–8.

[50] Kariuki KK, Allan Ronald N. Evaluation of reliability worth and value of lostload. Gener., Transm. Distrib. IEE Proc. 1996;143(2):171–80.

[51] Tollefson G, et al. A Canadian customer survey to assess power system re-liability worth. Power Syst. IEEE Trans. 1994;9(1):443–50.

[52] Wacker Garry, Billinton Roy. Customer cost of electric service interruptions.Proc. IEEE 1989;77(6):919–30.

[53] Meteo data sources-PV syst; July 2015. [Online] Available: http://www.pvsyst.com/en/publications/meteo-data-sources.

[54] Almaktar, Mohamed, Rahman Hasimah Abdul, Hassan Mohammad Yusri, Ef-fect of losses Resistances, Module Temperature variation, and Partial Shading

http://www.epa.org

http://www.epa.org

http://www.iea-pvps.org

http://www.iea-pvps.org

http://refhub.elsevier.com/S1364-0321(16)30483-X/sbref1














http://https://www.ise.fraunhofer.de





























































http://www.mpoweruk.com/leadacid.htm



























































































http://www.pvsyst.com/en/publications/meteo-data-sources

http://www.pvsyst.com/en/publications/meteo-data-sources



on PV Output Power. In: IEEE international conference on power and energy(PECon); 2012.

[55] Dzobo Oliver, Gaunt CT, R. Herman. Investigating the use of probability dis-tribution functions in reliability-worth analysis of electric power systems. Int.J. Electr. Power Energy Syst. 2012;37(1):110–6.

[56] Dialynas EN, Megalocnomos SM, Dali VC. Dialynas, Interruption cost analysisfor the electrical power customers in Greece. in: Electricity Distribution, 2001.Part 1: Contributions. CIRED. In: 16th international conference and exhibitionon (IEE conference publication No. 482). Vol. 2. IET, Amestrdam; 2001.

[57] Solar panels solar world, sharp, solon. Official distributer. July 2015. [Online]

Available: http://eng.sfe-solar.com.[58] Rahmani R, et al., A complete model of stand-alone photovoltaic array in

MATLAB-Simulink environment. In: IEEE student conference on research anddevelopment (SCOReD); 2011.

[59] SMA solar technology AG. July 2015. [Online] Available: http://www.sma.de.[60] Wholesale solar. July 2015. [Online] Available: http://www.wholesalesolar.

com.[61] Fishman G. Monte Carlo: Concepts, Algorithms, and Applications. Germany:

Springer Science & Business Media; 2013.





http://eng.sfe-solar.com

http://www.sma.de

http://www.wholesalesolar.com

http://www.wholesalesolar.com




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