IET Renewable Power Generation

Case Study

Reliable integration of a concentrating solarpower plant in a small isolated systemthrough an appropriately sized batteryenergy storage system

IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

ISSN 1752-1416Received on 22nd July 2015Revised on 11th November 2015Accepted on 29th November 2015doi: 10.1049/iet-rpg.2015.0337www.ietdl.org

Alexandros I. Nikolaidis, Yiannis Koumparou, Georgios Makrides, Venizelos Efthymiou,George Elia Georghiou, Charalambos A. Charalambous ✉

FOSS Research Centre for Sustainable Energy, University of Cyprus, P.O. Box 20537, 1678, Nicosia, Cyprus✉ E-mail: cchara@ucy.ac.cy

Abstract: This study details a comprehensive storage-sizing methodology to allow a concentrating solar power plant toreliably integrate in the Cyprus power system which is classified as small and isolated. The need for providing a storagesolution arises from the fact that large quantities of renewable energy sources may impose certain challenges on howisolated systems operate. Developing solutions to these challenges will enable higher penetrations of renewablegeneration sources on such systems and the future growth of renewable energy. To this extent, the methodologydescribed in this study pertains to clear and explicit steps required to provide sufficient evidence toward the reliableintegration of a large-scale solar-dependent plant through an appropriately sized battery energy storage system.

1 Introduction

The primary objective of this paper is to scrutinise a technicalsolution that will allow a large-scale concentrating solar power(CSP) plant to reliably integrate in the small power system ofCyprus. The power system of Cyprus is characterised by specificconditions arising from its electrically isolated nature, thegeographic concentration of its conventional power plants and theuse of underground cables, in high proportion. These specificconditions enforce certain operational and control strategychallenges with regard to voltage and frequency stability of thesystem. In this aspect, the stability of such isolated power systemshas been and continues to be of major concern for system operators.

In general, reliably integrating variable generation sources [1] suchas wind and solar technologies require a series of flexibility optionsthat facilitate their secure accommodation in the electricitygeneration mix. Though, solar-dependent technologies are ratherpredictable at a macroscopic level (due to their direct associationwith the daily path of the Sun) sudden, unpredictable events (e.g.shading effect due to passing clouds) may induce significantvariability, especially at sub-hourly levels. This could potentiallylead to undesirable system-wide contingency events. To avoid theseconditions, energy storage solutions are becoming increasinglyattractive [2] due to their ability to provide a series of ancillary andreserve services [3, 4]. In particular, the services provided bybattery energy storage systems (BESSs) are considered appropriateto regulate the impact of intermittent power sources (e.g. CSPplants) on the overall system performance as well as providing otherwide-scale benefits [5]. To this end, the benefits from BESSs can befundamentally quantified in: (a) certain avoided costs to the overallsystem operation, (b) additional revenue streams to the storageowner/operator and (c) providing ancillary services to the benefit ofall end-users of electricity [6]. To make the above argument moreexplicit, we note that the avoided cost owed to storage use lies inavoiding the costs that would have been incurred when providing asimilar (to that of storage) service under a conventional powersolution. In fact, the costs that would have been avoided, through astorage solution, may embrace higher installation and operationcosts, volatile fuels and greenhouse gas (GHG) emissions costs aswell as higher removal/disposal costs [7]. In addition, the additionalrevenue streams to the storage owner would pertain to payments

received for: (i) maintaining CSP plant energy sales at itsanticipated/forecasted level, (ii) enhancing the supply/securitycapacity of the system by providing a series of ancillary services. Inparticular, BESSs are able to provide ancillary services bycontrolling their charge/discharge cycles. Some ancillary servicesinclude: fast frequency response, regulation, load following,ramping control and reserve capacity. The prevailing benefit of theprovision of such services by BESSs is the reduced generationoutput variability, which under certain conditions leads to reducedfuel usage and GHG emissions from conventional units [7].Consequently, the conventional generation equipment wear will bereduced and the equipment generation lifetime can be extended. Forexample, storage services can be supportive in terms of frequencyregulation endeavours, as BESSs can provide regulation up andregulation down by charging and discharging cycles [2]. The latterhighlights the inherent capability of appropriately sized storage unitsto contribute to the spinning reserve requirements of the system.Recent research literature shows a number of studies of energystorage systems suitable for renewable energy applications (e.g. [1,8]). More specifically, the energy storage-related literature is mainlyreported into two categories: (i) identifying system-wide benefits(e.g. [9–11]) and (ii) introducing sizing methodologies in order toexplore the optimum power and energy rating of various energystorage applications (e.g. [12–15]).

2 Methodology

This paper in particular aims at investigating the BESS requirementsfor securing the reliable integration of a CSP plant by managing itsinherent short-term generation fluctuations resulting from solarirradiance variability. This is expected to ensure an enhancedutilisation of the CSP plant due to the capabilities of BESSs toprovide regulation reserve services [16, 17]. In developing ourmethodology for defining an appropriately sized BESS suitable fora large-scale CSP plant, a top-down meticulous approach isadopted based on: (a) historical direct normal irradiance (DNI)field measurements at the CSP plant’s installation location and (b)on realistic technical characteristics of BESSs. Hence, themethodology devised is practical and pragmatic and decouplesfrom complex simulation and theoretical optimisation models.

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Fig. 1 Steps of the methodology followed

An overview of the steps followed is given in Fig. 1. It ishighlighted that the proposed methodology renders thestorage-sizing process relatively simple and sequential. First, astatistical analysis of historical meteorological data is performed tocharacterise and classify the solar potential of the specific area thatthe CSP plant will be installed based on the K-POP method [18].Second, a series of daily generation patterns that include allexpected (per-minute) solar irradiance variability patterns arederived. The generation patterns are subsequently used in atwo-step process to determine an appropriate storage size thatcould smooth out any likely short-term CSP generation deviationsfrom anticipated generation values which are labelled according tohistorical DNI data. Finally, the robustness of the BESS sizingprocess is benchmarked against the specific set of operational rulesof the Cyprus transmission system operator (CYTSO) [19] underthe examination of real, commercially available storage optionswith specified technical characteristics.

2.1 Statistical analysis of solar irradiance patterns

Concentrating solar technologies track the Sun’s daily path through atwo-axis tracking system in order to utilise the DNI. DNI is the directsolar irradiance component received by a surface which is alwaysheld perpendicular (or normal) to the rays that come in a straightline from the direction of the Sun at its current position in the sky.Thus, CSP plants directly depend on DNI patterns due to the factthat DNI can be redirected (and, therefore, concentrated) toward a

Fig. 2 Daily kd and POPd values of the real-measured solar data at the CSP pla

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focal point. Solar measurements were obtained over a completetest year, on a per-minute basis, at the location where the CSPplant will be installed in order to capture the behaviour of thesolar resource. In particular, the solar irradiance as well as othermeteorological measurements were obtained from a meteorologicalstation (i.e. a climatic measuring site) installed near the CSP’sinstallation area. All solar irradiance and meteorologicalmeasurements, assessment and evaluation were performed asspecified by the World Meteorological Organisation standard [20].More specifically, the solar irradiance measurements were obtainedfrom a high accuracy two-axis tracker for stand-alone solartracking. The pointing accuracy of the selected tracker was

Table 1 Number of days and percentage of each class during the analysis year

Class

C1 C2 C3 C4 C5 C6 C7 C8 C9

number of days 162 16 5 64 77 10 4 26 1percentage of year, % 44.4 4.4 1.4 17.5 21.0 2.7 1.1 7.1 0.3

each day in a year as a function of the quantity and quality of thesolar irradiance. The visual representation of the daily solarirradiance can be plotted on a two-dimensional grid, where the xand y axes represent the daily values of kd and POPd, respectively(see Fig. 2). With reference to Fig. 2, the x-axis is divided into threesections based on the quantity of solar irradiance. Particularly theyare divided into high quantity (classes 1, 4 and 7, i.e. 0.6 < kd),medium quantity (classes 2, 5 and 8, i.e. 0.3 < kd < 0.6) and lowquantity (classes 3, 6 and 9, i.e. kd < 0.3). The y-axis depicts thequality of solar irradiance and is similarly divided into three sectionsbased on the quality of the daily sky conditions. Those are: clear ortotally overcast sky with no or few fluctuations (classes 1, 2 and 3,i.e. 0.9 < POPd), relatively small and infrequent fluctuations(classes 4, 5 and 6, i.e. 0.7 < POPd < 0.9) and large and frequentfluctuations (classes 7, 8 and 9, i.e. 0.5 < POPd < 0.7).

Thus, based on the above-described GHI pattern classification, thenumber of days in a year, allocated in each class is summarised inTable 1.

The results show that 184 days (50% of the analysis year) exhibitno significant irradiance fluctuations (i.e. classes C1, C2 and C3),151 days (41% of the analysis year) exhibit varying sky conditionsin parts of a day (i.e. classes C4, C5 and C6) whilst 31 days (9%of the analysis year) exhibit high fluctuations during the entire day(i.e. classes C7, C8 and C9). Some typical recordings that fallunder each of the nine classes’ datasets (Fig. 2) are shown in Fig. 3.

Furthermore, a series of per-minute empirical distributions of DNIramp rates were extracted in order to capture the effective order ofthe DNI variability magnitude (see Fig. 4). The empirical

Fig. 3 Examples of the daily solar irradiance for the nine identified classes

IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

distributions are non-parametric distributions that result directlyfrom the particular sample of the variable under examination.Thus, based on the frequency (n) at which a value of the variableappears in the sample (N total observations), a correspondingprobability is inferred that equals to n/N. To this extent, in Fig. 4,the x-axis signifies the magnitude of the DNI ramp [in watt persquare metre (W/m2)], whereas the y-axis shows the cumulativefrequency (in percentage) of the empirical cumulative distributionfunction (ECDF). The ECDF of a sampled variable is created byarranging all observed values of the variable in an ascending orderand, subsequently, the corresponding frequency (i.e. the inferredprobability) of each point is added – starting from the smallestleading to the largest value of the examined variable. TheseECDFs are used to illustrate both the range of values that a givenrandom variable can take and the likelihood of a sampled randomvariable falling in a specific range. For example, the 95thpercentile of the per-minute DNI ramp ECDF is 90 W/m2. Thismeans that for 95% of the time, the per-minute DNI ramp will beless or equal to 90 W/m2. Conversely, the 95th percentile of the60-minute DNI ramp ECDF is 472 W/m2. Similar approaches torenewable energy source (RES) variability investigation can befound in [24, 25].

2.2 CSP plant generation pattern derivation

The particular characteristics of the CSP plant analysed in this paperpertain to a real project currently undertaken in the island of Cyprus

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Fig. 4 ECDF of DNI ramps for various timescales

Fig. 5 Power curve based on DNI level of the Stirling-based concentratingsolar dish

[26]. The candidate CSP plant is realised through Stirling-basedconcentrating solar dishes where each generating unit consists of atwo-axis tracking solar concentrator and a Stirling generator powerconversion unit [27]. More specifically, the CSP plant willcomprise 16.920 solar power dishes with a 3 kWe rated capacityper dish. Thus, the aggregate capacity of the plant will be 50,760kW. The annual capacity factor of the park is calculated to beequal to 26.27% based on the DNI measurements obtained and onthe associated power curve of the technology used (see Fig. 5).The degradation rate is 0.25% per year. The transformer, cablingand other auxiliary losses sum up to ∼5% of the available

Fig. 6 Transformation of DNI data to CSP plant generation outputa DNI per-minute patternb Respective per-minute generation output of generating unit

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generation. The plant’s site will cover a total area of ∼2 millionm2 and is characterised as mostly flat with smooth slopes. On thebasis of the technology specifics of the solar dish [26, 28], eachgenerating unit produces at its rated capacity level when exposedto 850 W/m2 solar direct irradiance and 20°C ambient temperature.Depending on actual ambient conditions, when the DNI is >850W/m2, the unit will continue to operate and will periodically gointo ‘energy spill’ mode to avoid taking in more energy than it canconvert. At DNI levels lower than 850 W/m2, the unit will simplyoperate at part-power. The specified output at various DNI levelsof the CSP technology considered is provided by the manufacturerand is reproduced in Fig. 5 [28].

From the power output characteristic curve, shown in Fig. 5, theoutput (Pgen in percentage of rated capacity) of each generatingunit in relation to DNI (in W/m2) can be formulated as given in (1)

Pgen =0,

(DNI− 200)× 100%650

,

100%

DNI ≤ 200200 , DNI ≤ 850

DNI . 850

⎧⎪⎨⎪⎩

⎫⎪⎬⎪⎭

(1)

It is therefore realistic to assume that the dependence of the unit’soutput (Pgen) is mainly characterised by a linear relationship withDNI levels. Fig. 6 illustrates the transformation of DNI data toCSP plant generation output when using the relationship illustratedin Fig. 5 and the formulation shown in (1). It must be noted thatthe generation profile of the CSP plant is derived withoutconsidering the ramp and output smoothing that can be attributedto the geographic diversity of the large area that the CSP plantoccupies. Thus, the assumptions of calculating the generationoutput erred on the side of providing a conservative scenario, inthe sense that the CSP plant was modelled as a single-point–single-engine system. This approach provides a safety marginwhen it comes to sizing the storage required to smooth out theinherent variability of the CSP plant generation.

2.3 BESS sizing

The methodology followed aims at identifying the flexibilityrequirements that will facilitate the quantification of the BESSpower [megawatt (MW)] and energy [MW hour (MWh)] ratings.Depending on the specific services anticipated by the BESS, theplanners/investors may choose to obtain an initial estimation of therequired BESS size range, before selecting the most suitablecommercially available BESS for their intended purpose. BESSsare mainly divided into two main categories: (a) power-orientedand (b) energy-oriented systems. For example, adopting anenergy-oriented BESS provides the opportunity of time-shifting

IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

Fig. 7 CSP planta Per-minute generation profile, Pmgenb Anticipated generation profile, Pmref

the energy produced by an RES plant. However, such type of BESSmay not be able to mitigate the short-term variability of thegenerating source. Conversely, a power-oriented BESS couldmitigate any short-term generation variability but would not beadequate to time-shift any significant amounts of energy.

An appropriate BESS size is, in the context of this paper, definedas that size of BESS that: (a) minimises the dependence of the CSPplant on the overall system’s reserve levels, (b) ensures a morepredictable behaviour of the CSP plant in real-time operation and(c) facilitates an enhanced CSP plant dispatch that will allow for asmoother operation of the conventional power units. Therefore, itis necessary to identify and link both the power and energyrequirements of the BESS to the generating resource.

Fig. 8 Process of flexibility requirements quantification

2.3.1 Step 1: initial estimation of the necessaryBESS characteristics through addressing flexibilityrequirements: In this paper, the quantification of the flexibilityrequirements pertains in estimating the storage needs of the CSPplant to closely match an anticipated generation profile. Theanticipated generation profile is defined as the emulation of thepotential day-ahead forecasted generation profile that would bedisplayed to the system operator to provide an insight for theday-ahead unit commitment process. Therefore, any deviation ofthe actual per-minute CSP generation from the anticipated/forecasted point is utilised to provide an indication of thecompensation required from the BESS. To this end, we consider acomplete year of the CSP generation profiles based on theavailable real-measured DNI data. This test year is considered arepresentative example of the DNI behaviour based on the premisethat solar conditions in Cyprus have remained relatively stable forthe past years. Thus, it is expected that future DNI patterns will besimilar to the ones concluded by the historical data.

In particular the procedure unfolds as follows: first, the availablehistorical per-minute DNI time series are used to model the CSPplant’s power output on a per-minute timescale (see Fig. 7). Theper-minute DNI datasets are transformed into per-minutegeneration datasets by means of (1) and are organised on a dailybasis. These generation datasets are considered the actualper-minute behaviour of the CSP plant (Pmgen). Subsequently, foreach day of the test year, an anticipated generation profile iscreated by averaging the per-minute generation values during eachoperating hour. Thus, a new series of datasets is created that isused as the anticipated generation profile (Pmref ) of the CSP plant.These datasets represent the hourly energy generation of the CSPplant to the system for each day of the analysis year. Forillustration purposes, Fig. 7a shows an example of the actual dailyper-minute generation profile, Pmgen, whereas Fig. 7b shows therespective anticipated generation profile (i.e. hourly energy sales),Pmref , for the same day.

IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

Consequently, the resulting daily CSP generation patterns can beutilised to facilitate the quantification of the flexibility requirementsand, subsequently, the derivation of an appropriate BESS size. Asnoted in Section 2.2, the CSP plant was modelled as asingle-point–single-engine system. This suggests that thegeneration profiles were derived without adjusting for the rampand output smoothing that can be attributed to the geographicdiversity of the large area that the CSP plant occupies. The latterentails a conservative scenario in terms of assessing the impact onthe levels of reserve required to accommodate the variability of theCSP plant. This is because the analysis ignores the spatialsmoothing exhibited by the fact that the CSP plant is large and itsgenerating units are geographically distributed enough.

The quantification of the reserve requirements, RRm, for eachpoint in time, m, can be achieved by calculating the deviation ofthe actual generation value, Pmgen, from the correspondinganticipated value, Pmref , as in (2). The overall process is graphicallyshown in Fig. 8

RRm = Pmgen − Pmref (2)

Therefore, for each day of the analysis test year, the maximum powerand energy requirements that would be needed from the BESS tomatch the actual generation profile with its anticipated values canbe extracted. Moreover, the MWh to MW ratio for each day of theanalysis is derived in order to provide valuable insights in terms ofthe required relationship between the BESS power and energyratings. The MWh to MW ratio is a valuable index that identifiesthe type of BESS needed for a particular renewable application(i.e. power-oriented or energy-oriented BESS). A low MWh toMW ratio would imply that a power-oriented BESS is more

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Fig. 9 Distribution of the BESS requirements for the analysis yeara Power requirementsb Energy requirements

suitable for the particular application. Conversely, a high MWh toMW ratio would suggest that an energy-oriented BESS is needed.The BESS power and energy requirements as well as the MWh toMW ratio results can then be organised in an ascending order,thus providing three respective cumulative distributions. Thesedistributions are shown in Figs. 9a, b and 10 for the power,energy and MWh to MW ratio requirements, respectively. In thesefigures, the 50th, 75th and 95th percentiles of the distribution arehighlighted by a dashed light grey, a dashed dark grey and a soliddark grey line, respectively. For example, the 50th percentile ofthe MWh to MW ratio requirement distribution shows that for50% of the analysis year the BESS MWh to MW requirement wasless or equal to 0.295. This clearly demonstrates that the particularCSP application leans toward a power-oriented BESS solution thatis able to mitigate its sub-hourly variability. Under this approach,the interested stakeholder can thus initially estimate the range ofthe BESS size required in order to subsequently fine-tune the finalselection of an appropriate BESS configuration.

2.3.2 Step 2: calibration of the aggregation of the CSP plantwith commercially available BESS options: The second andfinal step of the methodology followed involves the calibration of thecrude BESS size under commercially available BESS options. Thisis necessary to subsequently benchmark the combined CSP plantand BESS behaviour against the operational rules of the system.To this end, two elements are required: (a) technical data fromcommercially available BESS options and (b) a concisedescription of the rules that must be obeyed by the renewableapplications integrating in the system.

Initially, an examination of some commercially available BESSoptions is performed. Table 2 shows the power and energy ratingsof some representative options for grid-level BESSs. Specifically,BESS Type I and Type II are considered energy-oriented, whereas

Fig. 10 Distribution of the BESS MWh to MW ratio for the analysis year

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Type III and Type IV are considered power-oriented. Thisparticular list is by no means exhaustive. It merely serves as arepresentative example of commercially available BESS options.Taking as an example the BESS Type IV, it exhibits a ∼1:4 MWhto MW ratio, whereas BESS Type II exhibits a 2:1 MWh to MWratio. It should be noted that for these particular BESSs the chargeand discharge capabilities are not symmetrical (the BESS is notcharged at the same rate as it is discharged). These particularitiesare taken into account in order to investigate the effectiveness ofvarious BESS ratings on capturing the per-minute outputvariability of the CSP plant.

To finalise the calibration of the sizing process, we take up on theparticular set of operational rules issued by the transmission systemoperator (CYTSO) with regard to RES plants entering thetransmission system [19] and use it as the effective operationalconstraint of the CSP plant performance. These rules pertain to thenecessary technical characteristics that RES plants should bear inorder to ensure a reliable integration in the Cyprus power system.The complete set of rules covers topics span from forecastingrequirements to frequency and voltage support during all theoperational stages of the system. However, bearing in mind theoverall scope of this paper, it is of particular interest to investigateto which extent the aggregated CSP plant and BESS systemperformance will cope with the anticipated generation variabilityand conform to the permissible ramp rate ranges.

Table 3 summarises the required ramp rate control that should berespected by each RES plant. It should be noted that positive ramprates (e.g. sudden generation increases) are fully manageable viaactive power curtailment and thus do not pose a major threat tothe system. Conversely, negative ramp rates (i.e. loss ofgeneration) can potentially lead to under-frequency events whichare a more of a critical issue, especially for isolated powersystems. Thus, the benchmarking procedure focuses on suchevents. On the basis of Table 3, the permissible per-minute ramprate of the CSP plant is 7% of its rated capacity (for plants’ size>20 MW) or, in absolute terms, ±3.5 MW/min.

Table 2 Power and energy ratings of grid-level BESSs

Characteristics BESSType I

BESSType II

BESSType III

BESSType IV

energy, kWh 620 1000 580 420continued dischargepower, kW

900 500 1100 1600

peak discharge power1 min, kW

1100 500 1100 1800

nominal charge power,kW

300 500 600 800

IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

Table 4 Scenario description

Scenario Description

A no BESS solution utilisationB utilisation of a power-oriented BESS consisting of 12 Type IV

modulesC utilisation of a power-oriented BESS consisting of 24 Type IV

modulesD utilisation of a power-oriented BESS consisting of 36 Type IV

modulesE utilisation of a power-oriented BESS consisting of 48 Type IV

modules

Table 3 CYTSO ramp rate control rules for RES plants in Cyprus

RES plantcapacity (P)

Permissible per-minute ramp rate (percentage of RESplant capacity), %

8 < P < 20 MW 15P > 20 MW 7

Fig. 11 Average daily negative ramping incidents of each class during theanalysis year for different BESS sizes as a percentage of daily operating time

On the basis of the relevant data shown above, the followingprocess takes place; for each minute when a negative ramp eventtakes place (i.e. a negative mismatch between actual andanticipated generation levels), an incident is marked. The numberof incidents is examined for five different scenarios that relate tovarious BESS sizes. The scenario description is provided in Table 4.

Subsequently, based on the pattern classification of Section 2.1,the average daily negative ramping incidents are calculated as apercentage of the daily operational time of the CSP plant. Theresults are given in Table 5 and can be interpreted as follows:taking as an example the case of operating the CSP plant withouta BESS (i.e. scenario A), under a C9 day the system would (onaverage) face negative ramping incidents for 46.67% of itsoperating hours. Conversely, when scenario E is considered, thenthe system would (on average) face negative ramping incidents for0.28% of its operating hours. Fig. 11 illustrates the results ofTable 5 diagrammatically. These results indicate that even arelatively small BESS can substantially contribute to theconfinement of short-term ramping events. Nevertheless, theresults of scenario E show that the particular BESS wouldeliminate almost any negative ramping events. This fact is in linewith the results of Step 1 and advocates the reliable integration ofthe CSP plant in the electrically isolated power system of Cyprus.

2.4 Critical evaluation

The methodology outlined in this paper offers a transparenttop-down approach for determining an appropriate BESS size forthe mitigation of the inherent real-time variability of RESapplications. The latter implies a disengagement of conventionalunits from continuously following the increased net loadvariability, thus reducing the need for frequent ramping andextending their expected useful lifetime. Even though sizingenergy storage applications is a complex issue due to the multipleparameters involved in the process, the fundamental logic of theiroperation is rather straight-forward. Thus, the reliance of the

Table 5 Average daily negative ramping incidents of each class during the ana

C1, % C2, % C3, % C4, %

scenarios A 8.30 12.24 12.50 14.38B 0.77 2.31 2.50 3.39C 0.00 0.00 0.00 0.00D 0.00 0.00 0.00 0.00E 0.00 0.00 0.00 0.00

IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

proposed method on representative historical data and storagetechnical characteristics renders the evaluation and analysis simpleand sequential. To this extent, planners/investors may use thismethod to assess and calibrate the order of magnitude of BESSrequired for their intended application without the need forcomplex simulation and theoretical optimisation models. This isachieved by simply following a two-step storage-sizing process.As shown in previous sections, the first step involves thecalculation of the BESS power and energy ratings as well asthe MWh to MW ratio required, whereas the second step links thetheoretical result to real, commercially available BESS options.This two-step process capitalises on the natural cyclical operationof BESSs in a bidirectional manner (i.e. offering or absorbingpower at different points in time based on the needs of the system)in order to facilitate the CSP plant to match its anticipated profileresulting from the historical DNI data treatment. This maypotentially suggest an enhanced market participation of theparticular renewable plant due to its increased predictability. Mostimportantly, however, the evaluation results clearly show that thedispatch ability of the CSP plant is greatly enhanced through acarefully selected power-oriented BESS setup. Finally, themethodology presented can be easily extended to include costconsiderations, albeit including relevant data and assumptionsregarding the balancing market mechanism of each power systemin order to quantify the implied costs of variability that would beborne by the system. To achieve a holistic cost and benefitanalysis, the cost of storage units in the market is also crucial. Itshould be noted the cost and benefit analysis of a BESSinvestment should entail a thorough understanding of thebalancing mechanisms and costs that apply in a specific system.Fundamentally, the role of the balancing mechanisms is: (a) toprovide the necessary reserve capacity in order for the system toeffectively respond to real-time power fluctuations (includingsudden, abrupt events such as the loss of a generating unit) and (b)to cover the energy imbalances that may take place in order tocontinuously and reliably satisfy the load. Therefore, the costsassociated with the balancing mechanisms usually entail a capacity(i.e. fixed) cost and a variable (i.e. energy) component. Inderegulated market environments, the balancing mechanisms’ costs

lysis year for different BESS sizes as a percentage of daily operating time

Classes

C5, % C6, % C7, % C8, % C9, %

20.31 25.13 26.63 31.16 46.676.79 11.10 11.98 13.93 20.560.67 2.31 2.60 4.03 7.080.00 0.03 0.28 0.84 1.940.00 0.00 0.00 0.00 0.28

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are mainly determined by the opportunity cost of each generator thatparticipates in the balancing process. (The opportunity cost is theprofit that the balancing generator forgoes from selling energy tothe market in order to create headroom to provide reserve capacity[3, 4, 29].) It should be noted that the CSP generator analysed inthis paper will be integrated in the small, electrically isolatedsystem of Cyprus, where the balancing services are currentlymonopolised by a vertically integrated utility (E.A.C.). However,the associated balancing costs are not explicitly specified and anopen/public consultation [30] is currently undertaken in Cyprus todefine the specific market rules that would apply in the near future.

3 Conclusion

BESSs are becoming a priority for modern power systems thatundergo a major transition toward a state that involves highpenetrations of variable generation sources. Their natural cyclicaloperation and fast response are the keystones on which RESapplications will rely in the future in order to manage theirvariability as traditional power systems are moving away fromconventional, carbon-based generation technologies. To this extent,this paper has focused on providing a way of appropriately sizinga BESS capable of facilitating the management of short-term,sub-hourly generation fluctuations, thereby improving their relativepredictability and potentially their market participation. Themethod presented is able to provide clear estimations of therequired BESS characteristics for RES applications in order toenable the interested stakeholders to explore such technicalsolutions in an effective manner.

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IET Renew. Power Gener., pp. 1–8& The Institution of Engineering and Technology 2016

1 Introduction2 Methodology3 Conclusion4 References