Implementation of Offshore Wind Turbines to Reduce Air Pollution In

Journal of Marine Science and Engineering

Article Implementation of Oﬀshore Wind Turbines to Reduce Air Pollution in Coastal Areas—Case Study Constanta Harbour in the Black Sea

Alina Beatrice Raileanu 1,2 , Florin Onea 1 and Eugen Rusu 1,* 1 Department of Mechanical Engineering, Faculty of Engineering, Dunarea de Jos University of Galati, 47 Domneasca Street, 800008 Galati, Romania; [email protected] (A.B.R.); ﬂ[email protected] (F.O.) 2 Danubius International Business School, 3 Galati Street, Danubius University, 800654 Galati, Romania * Correspondence: [email protected]

Received: 1 July 2020; Accepted: 22 July 2020; Published: 23 July 2020

Abstract: Considering the current concerns regarding the level of air pollution from the Black Sea area, the aim of the present work is to establish whether a cold ironing project that involves the use of the wind resources from the port of Constanta (Romania) could become a reality. The regional and local wind resources measured at a height of 100 m above sea level were assessed by taking into account 20 years (2000–2019) of ERA5 wind data. The wind speed significantly increases as we move towards the offshore areas, with the wind Class C7 reporting a maximum of 41%. By combining the annual electricity production with the emissions associated with the port activities, it was possible to show that at least 385 turbines (each rated at eight MW) will be required to cover the electricity demand for this port. The present study has found it difficult to implement such a project based only on the available wind resources and has identified that more likely a mixed project that involves some other resources will be more appropriate. Finally, it is worth mentioning that the future of the ship industry is becoming greener and definitely, a wind project located near Constanta harbour will represent a viable solution in this direction.

Keywords: Constanta port; air pollution; ERA5; wind turbines; coastal area

1. Introduction By analysing the global trade market one can easily notice that the maritime sector plays an important role in economic development. It is estimated that almost 80% of the global trades are transported by means of sea transportation. The sector is indeed very dynamic and has reported an annual increase of 3.2% between 2017 and 2020 [1]. On the other hand, industry is a major pollution source, and as a result, various legislative provisions have been introduced in order to protect the ocean environment and maritime resources. On a large scale, the CO2 emissions associated with global warming are generated by navigation between ports, while on a local level, the port activities (also known as hoteling) are the main factors that have a negative impact on the human health around the port cities. If no signiﬁcant change occurs, the air quality near the coastal cities will be signiﬁcantly reduced due to the large nitrogen oxides (NOx) emissions, Particulate Matter (PM) and Volatile Organic Compounds (VOCs), respectively [2,3]. This problem is well known and most of the major harbour areas are under constant surveillance. This is the case for the Shanghai Port [4], Naples [5], Los Angeles [6] or Sydney [7], respectively. Cold ironing or Onshore Power Supply (OPS) is one of the methods which could be used to help reduce local air pollution and improve the air quality near the coastal areas. OPS is used to describe the process of connecting a ship to a shore-side electrical supply in order to maintain the main

J. Mar. Sci. Eng. 2020, 8, 550; doi:10.3390/jmse8080550 www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2020, 8, 550 2 of 18 function of a ship (ex: heating, refrigeration, emergency systems, etc.) at berth, during which time the engines are turned off. While the concept looks promising, with almost 6% of fuel consumption of a ship being allocated to the hoteling period, at the moment, only 30 ports are using this technology. The main challenges to be tackled for the use of such technology are related to the additional port installations and are caused by the fact that most of the operating ships are not equipped with systems compatible with the use of electricity from the shore [8]. Renewable energy sources (RES) represent an important part of the cold ironing process and their use has already been implemented at the port of Gothenburg (Sweden), which uses a shore-power source based on a local wind farm. By using this source, it is estimated that for the six weekly ships (Roll-on/Roll-off) connected to this project, an emission reduction of 80 metric tonnes for NOx, 60 metric tonnes—SOx and 2 metric tonnes for PM is expected [9]. The port of Hamburg (Germany) is considered another important project, where a significant part of the electricity demand is covered from wind and solar sources. For an initial investment of €4.8 million, it has been estimated that for the year 2014 almost two-thirds of the power consumption has been covered from natural sources or from additional combined heat and power plant (CHP) [10,11]. In Kotrikla et al. [12], the possibility of reducing the overall emissions for the port of Mytilene (Greece) was discussed. The study estimated that the total energy requirements of the operating ships could be covered by a hybrid project that included four wind turbines (rated at 1.5 MW) and a 5 MW photovoltaic system. In general, it seems that only a hybrid renewable energy system can support the activities of a port, this aspect being highlighted in Wang et al. [13]. The onshore and offshore wind resources from the vicinity of the Cartagena Port (Spain) [8] were also taken into account, with the main conclusions of the study being that energy consumption can be covered from a renewable project even during the winter time when the demand is much higher. The Black Sea is one of the most polluted seas in the world and this is largely due to the emissions produced by ships, which are a significant contributor to the present situation. On a larger scale, three major air pollutant routes seem to emerge: (a) the south-western part of the Black Sea and Bosporus toward the Aegean Sea; (b) the south-eastern part of the Black Sea towards the eastern part of Turkey; and (c) the west to east area over the north of the Caucasus Peninsula. In general, the movement of the air masses over the Black Sea area is quite complicated; however, it is estimated that the eastern coast of the basin will be more affected by these emissions, taking into account the strong winds that are coming from the northern part of the sea [14]. Coincidence or not, the north-western part of the Black Sea is transited by over 300 ships that connect the Romanian ports with the ports of Ukraine, Bulgaria and the Bosporus strait. The traffic related to this region represents almost 40% of the total traffic in the Black Sea. In addition, a significant percentage of the ships used for maritime traffic in this region (36% of ships) were built before 1990 when no emission regulations were imposed. Among Romanian ports, Constanta can be definitely mentioned as being the most significant and is on the 5th position on a European level in terms of the volume of cargo processed by the port services. In the near future, a further increase in activity can be expected for this port [15]. The Black Sea is also known for the wind conditions that can be used to power a wind farm, especially in the western part of this basin where the resources are more significant [16,17]. Onea and Rusu [16] provide a comprehensive assessment of the Romanian wind energy potential related to this coastal environment. The results of the assessment are reported at 80 m height (above sea level) and, according to their findings, the wind speed significantly increases as we go from onshore to offshore (ex: Saint George from 6 to 7.2 m/s). The best sites to develop a wind farm are located in the north (Danube Delta) and south (Vama Veche), but these areas are quite isolated and the grid-connection will be challenging. If we look at the Constanta site, we notice that a wind project may become profitable if located 20 km from shore (at least). Some aspects related to the novelty of the present work are highlighted next. This work represents one of the first studies considering the use of the ERA5 wind data for a renewable analysis. Estimation of the wind turbines number required to support the Port of Constant’s activity represents another element of novelty, as well as the expected Levelized Cost of Energy (LCOE) values linked to a wind J. Mar. Sci. Eng. 2020, 8, 550 3 of 18 projectJ. Mar. Sci. that Eng. may 2020,operate 8, x FOR PEER in this REVIEW coastal environment. Besides these particular aspects, the 3 present of 18 work can also represent a general framework for the evaluation of how the offshore wind turbines can present work can also represent a general framework for the evaluation of how the offshore wind reduce air pollution in coastal areas, in general, and in the vicinity of the ports, in particular. turbines can reduce air pollution in coastal areas, in general, and in the vicinity of the ports, in In this context, the following research questions will be addressed as part of the present work: particular. (a) WhatIn this is context, the best the site following to develop research a wind questions farm near will the beConstanta addressed Port?as part of the present work: (b)(a) HowWhat many is the windbest site turbines to develop will bea wind required farm tonear support the Constanta a cold ironing Port? project? (c)(b) WhatHow ismany the expectedwind turbines cost ofwill electricity be required generated to support from a cold an o ironingffshore project? wind project? (c) What is the expected cost of electricity generated from an offshore wind project? 2. Materials and Methods 2. Materials and Methods 2.1. Constanta Harbour 2.1. Constanta Harbour Constanta harbour can be found on the southern part of the Romanian coastline, being located betweenConstanta the port harbour of Midia can (north)be found and on thethe portsouthern of Mangalia part of the (south), Romanian respectively, coastline, being as presented located in Figurebetween1. Thethe redport points of Midia in the(north) figure and indicate the port the of roadsteadMangalia (south), sector. respectively, By using this as sector presented as a basisin forFigure the analysis, 1. The red several points referencein the Figure points indicate were the defined roadstead at di sector.fferent By distances using this from sector the as shore, a basis namely: for 10the km—N1 analysis, and several S1; 30 reference km—N2, points C1 and were S2; defined 60 km—N3, at different C2 and distances S3. A sitefrom located the shore, at 60 namely: km from 10 the km—N1 and S1; 30 km—N2, C1 and S2; 60 km—N3, C2 and S3. A site located at 60 km from the shore shore can be still considered to be suitable for a wind project, taking into account that, at present, can be still considered to be suitable for a wind project, taking into account that, at present, the the average distance reported for the existing European wind projects is close to 59 km [18]. Table1 average distance reported for the existing European wind projects is close to 59 km [18]. Table 1 presents some additional information related to the points considered for evaluation, from which we presents some additional information related to the points considered for evaluation, from which we can notice that the water depth is in the range 30–56 m. can notice that the water depth is in the range 30–56 m.

FigureFigure 1. 1.Constanta Constanta nearshore nearshore and and the the reference reference sites sites considered considered for for evaluation. evaluation. The The red red points points are are used toused highlight to highlight the roadstead the roadstead sector ofsector Constanta of Consta harbour.nta harbour. Figure Figure processed processed from Googlefrom Google Earth Earth (2020). (2020). Table 1. Main characteristics of the Constanta sites considered. Table 1. Main characteristics of the Constanta sites considered. No. Long (o) Lat (o) Distance to Shore (km) Water Depth (m) [19] No. Long (o) Lat (o) Distance to Shore (km) Water Depth (m) [19] N1 28.786 44.284 10 30 N2N1 29.00128.786 44.284 44.284 10 30 30 46 N3N2 29.36829.001 44.284 44.276 30 60 46 50 C1N3 29.04529.368 44.276 44.145 60 30 50 48 C2C1 29.42229.045 44.145 44.143 30 60 48 56 S1C2 28.78829.422 44.143 44.011 60 10 56 52 S2 29.033 44.012 30 38 S1 28.788 44.011 10 52 S3 29.409 44.011 60 37 S2 29.033 44.012 30 38 S3 29.409 44.011 60 37 In Figure2 is presented a map of the ship tra ﬃc reported near the port of Constanta with the mentionIn Figure that the 2 entranceis presented in the a map berth of area the ship is made traffic throughout reported near the southern the port partof Constanta of the port. with Point the S1 seemsmention to blockthat the the entrance entrance in fromthe berth the port,area sois made in this throughout case, a wind the farmsouthern located part onshoreof the port. seems Point to be S1 seems to block the entrance from the port, so in this case, a wind farm located onshore seems to be

J. Mar. Sci. Eng. 2020, 8, 550 4 of 18 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 4 of 18 more indicated.indicated. TheThe remainingremaining points points are are located located outside outside the the roadstead roadstead sector, sector, so various so various spatial spatial wind windfarm conﬁgurationsfarm configurations can be can designed be designed in order in order to avoid to avoid the main the shipmain routes. ship routes.

Figure 2. LocationLocation of of the the reference points and the port of Constanta activities reported for the time frameframe 17/07/2020 17/07/2020 16:30 h. Results processe processedd from Black Sea ship tratrafficffic [[20].20]. A first glimpse of the Romanian port activities is provided in Figure3, from which it can be A first glimpse of the Romanian port activities is provided in Figure 3, from which it can be noticed that the port of Constanta is by far one of the busiest ports in the region. Although the port of noticed that the port of Constanta is by far one of the busiest ports in the region. Although the port Midia also reported higher numbers for the ships defined by capacities located below 5000 tonnes, in of Midia also reported higher numbers for the ships defined by capacities located below 5000 tonnes, general, the value does not exceed 150 tonnes. The port of Mangalia reports lower activities that can in general, the value does not exceed 150 tonnes. The port of Mangalia reports lower activities that go to a maximum of 89 events (ships < 5000 tonnes), while for example a maximum of 6 activities was can go to a maximum of 89 events (ships < 5000 tonnes), while for example a maximum of 6 activities reported for larger ships (90,000–18,000 tonnes). Going back to the port of Constanta, we can notice was reported for larger ships (90,000–18,000 tonnes). Going back to the port of Constanta, we can that this is a dynamic environment, which is frequently used by ships that have capacities in the range notice that this is a dynamic environment, which is frequently used by ships that have capacities in of 5000 and 20,000 tonnes. In addition, the events in the port of Constanta also involve larger ships the range of 5000 and 20,000 tonnes. In addition, the events in the port of Constanta also involve (>90,000 tonnes), with a significant increase from 76 (in 2011) to 124 events being reported at the end of larger ships (>90,000 tonnes), with a significant increase from 76 (in 2011) to 124 events being reported 2019. On the contrary, the number of events involving smaller ships (<5000 tonnes) is significantly at the end of 2019. On the contrary, the number of events involving smaller ships (<5000 tonnes) is decreasing from 2687 (year 2011) to 1515 stops reported at the end of 2019. significantly decreasing from 2687 (year 2011) to 1515 stops reported at the end of 2019.

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Figure 3. Number of maritime ships reported for the main Romanian ports. The results include Figure 3. Number of maritime ships reported for the main Romanian ports. The results include differentFigure 3. ships Number capacities, of maritime being reportedships reported for: (a ) forPort the of mainConstanta; Romanian (b) Port ports. of Mangalia; The results (c) includePort of different ships capacities, being reported for: (a) Port of Constanta; (b) Port of Mangalia; (c) Port of Midia.different Data ships processed capacities, from being [21]. reported for: (a) Port of Constanta; (b) Port of Mangalia; (c) Port of Midia. Data processed from [21]. Midia. Data processed from [21]. FigureFigure4 4presents presents a shorta short description description of theof the Constanta Constanta harbour harbour activity, activity, including including the ship the ship tra ffi trafficc [21] Figure 4 presents a short description of the Constanta harbour activity, including the ship traffic and[21] theand associated the associated air pollution air pollution resulting resulting only fromonly from the port the activitiesport activities [22]. By[22]. looking By looking at this at data, this [21] and the associated air pollution resulting only from the port activities [22]. By looking at this wedata, can we notice can notice that the that tra ffithec associatedtraffic associated with this with port this is growingport is growing from one from year one to another,year to startinganother, data, we can notice that the traffic associated with this port is growing from one year to another, fromstarting 4763 from kilotons 4763 kilotons (in 2010) (in and 2010) reaching and reaching a maximum a maximum of 66,603 of kilotons66,603 kilotons (+40%) (+40%) at the at end the of end 2019. of starting from 4763 kilotons (in 2010) and reaching a maximum of 66,603 kilotons (+40%) at the end of This2019. trend This istrend also is reflected also reflected in the in level the of level air emissionsof air emissions where where at the at end the of end 2010 of the 2010 values the values were closewere 2019. This trend is also reflected in the level of air emissions where at the end of 2010 the values were toclose 5.52 to kilotons, 5.52 kilotons, gradually gradually increasing increasing to 6.89 to kilotons 6.89 kilotons (year 2016:(year +2016:25%) +25%) and 7.71 and kilotons 7.71 kilotons (year 2019:(year close to 5.52 kilotons, gradually increasing to 6.89 kilotons (year 2016: +25%) and 7.71 kilotons (year +2019:40%). +40%). It is important It is important to mention to mention that a that significant a significant share isshare covered is covered by NOx by emissionsNOx emissions (almost (almost 80%). 2019: +40%). It is important to mention that a significant share is covered by NOx emissions (almost From80%). theFrom literature the literature review, onlyreview, the only air emissions the air em fromissions the intervalfrom the of interval 2010 to 2016of 2010 were to mentioned, 2016 were 80%). From the literature review, only the air emissions from the interval of 2010 to 2016 were whilementioned, the expected while the values expected for the values years for 2017, the 2018 years and 2017, 2019 2018 were and estimated 2019 were in orderestimated to highlight in order the to mentioned, while the expected values for the years 2017, 2018 and 2019 were estimated in order to ascendanthighlight the trend. ascendant trend. highlight the ascendant trend.

FigureFigure 4.4. PortPort ofof ConstantaConstanta statisticsstatistics correspondingcorresponding toto thethe 20-year20-year timetime intervalinterval ofof 2010–2019,2010–2019, where:where: ((Figureaa)) overalloverall 4. Port shipship of tratraffic—in Constantaﬃc—in tonnes;tonnes; statistics ((bb)) airaircorresponding pollutionpollution (nitrogen(nitrogen to the 20-year oxidesoxides (NOx),(NOx),time interval ParticularParticular of 2010–2019, MatterMatter (PM)(PM) where: andand VolatileVolatile(a) overall Organic Organic ship traffic—in Compounds Compounds tonnes; (VOCs) (VOCs) (b) inair in kilotons kilotonspollution considering cons (nitrogenidering oxides only only the the (NOx), port portactivities. Particularactivities. The TheMatter airair pollutionpollution(PM) and associatedassociatedVolatile Organic withwith thethe Compounds yearsyears 2017,2017, (VOCs) 20182018 andand in 2019kilotons2019 werewere consestimated estimatedidering only from from the the the port existing existing activities. data data [ The21[21,22].,22 air]. pollution associated with the years 2017, 2018 and 2019 were estimated from the existing data [21,22]. 2.2. Wind Data 2.2. Wind Data 2.2. WindThe ERA5 Data is one of the newest projects maintained by the European Centre for Medium-Range The ERA5 is one of the newest projects maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF) being designed to replace the old ERA-Interim project. Some new WeatherThe ERA5Forecasts is one (ECMWF) of the newest being projects designed mainta to replaceined by the the oldEuropean ERA-Interim Centre project.for Medium-Range Some new improvements were made, as: the covered period was extended to 1950–present; the spatial resolution improvementsWeather Forecasts were (ECMWF) made, as: being the designedcovered periodto replace was the extended old ERA-Interim to 1950–present; project. theSome spatial new was increased; temporal resolution is set to hourly values; various newly reprocessed datasets and resolutionimprovements was increased;were made, temporal as: the resolutioncovered periodis set towas hourly extended values; to various 1950–present; newly reprocessedthe spatial recent instruments were considered; a new assimilation system is used—IFS Cycle 41r2 4D-Var [23,24]. datasetsresolution and was recent increased; instruments temporal were resolution considered; is aset new to assimilationhourly values; system various is used—IFS newly reprocessed Cycle 41r2 4D-Vardatasets [23,24]. and recent For instrumentsthis work, only were the considered; 20-year time a new interval assimilation from January system 2000is used—IFS to December Cycle 201941r2 4D-Var [23,24]. For this work, only the 20-year time interval from January 2000 to December 2019

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were processed for the Constanta sector, since this is the expected design life of a wind farm [25,26]. J.The Mar. wind Sci. Eng. speed2020 ,is8, processed 550 at 10 m height (above the sea), being defined by a spatial resolution6 of of 18 0.25° × 0.25° and a temporal resolution of 6 h (four values per day, 00-06-12-18 UTC—Coordinated Universal Time, respectively). Although a higher output resolution is available, according to the Fordocumentation this work, only of this the 20-yearERA5 project time interval the results from Januarywill be 2000the same to December and will 2019 not werecontain processed any extra for theinformation, Constanta just sector, that sincethe interpolation this is the expected process designwill be lifecarried of a out wind on farm a higher [25,26 resolution]. The wind [27]. speed is processed at 10 m height (above the sea), being defined by a spatial resolution of 0.25 0.25 and The wind industry is changing and this effect is more visible on the offshore◦ areas,× ◦being aexpected temporal larger resolution turbines of 6and h (fourmuch values higher per hub day, height 00-06-12-18s. In previous UTC—Coordinated studies, a hub Universal height of Time,80 m respectively).(U80) was considered Although as standard a higher output[28,29] while resolution at this is moment available, most according of the studies to the documentationare focused on the of this100 ERA5m height project (U100) the results [30–32]. will In be order the same to make and will this not adjustment, contain any the extra following information, equation just thatcan thebe interpolationconsidered [33]: process will be carried out on a higher resolution [27]. The wind industry is changing and this effect is more visible on the offshore areas, being expected ln𝑧 −ln𝑧 larger turbines and much higher hub𝑈100 heights. = 𝑈10 In previous studies,, a hub height of 80 m (U80) was(1) considered as standard [28,29] while at this momentln𝑧 most −ln of𝑧 the studies are focused on the 100 m heightwhere (UU100100) is [ 30the–32 wind]. In speed order toat make100 m this height adjustment, of (z100); theU10 following represents equation the initial can wind be considered conditions [33 at]: 10 m (z10), while z0—is the roughness factor (calm sea surface—0.0002 m). ln(z ) ln(z ) U100 = U10 100 − 10 , (1) ln(z ) ln(z ) 2.3. Wind Turbines 10 − 0 whereForU100 the iscurrent the wind evaluation, speed at 100five m different height of wind (z100 );tuUrbines10 represents (denoted the from initial A windto E) conditionswill be used at 10to midentify (z10), whileelectricityz0—is production, the roughness their factor characteristics (calm sea being surface—0.0002 indicated in m). Table 2 and Figure 5. The rated capacity of the turbines covers the interval of 2.5–8 MW, being possible to assess in this way the 2.3.performances Wind Turbines of some operational wind farms that include low capacity turbines, and also of some futureFor projects the current that are evaluation, based on fivehigh dicapacityfferent windgenerators. turbines Although (denoted each from turbine A to is E) characterised will be used by to identifyparticular electricity hub heights, production, for consistency their characteristics the performances being of indicated all the systems in Table will2 and be Figure evaluated5. The at rated100 m capacityheight. of the turbines covers the interval of 2.5–8 MW, being possible to assess in this way the performancesThe turbines of some are related operational to: turbine wind farmsA—General that include Electric low GE capacity 2.5 (General turbines, Electric, and alsoBoston, of some MA, futureUSA); turbine projects B—Siemens that are based SWT on 3.6 high (Siemens, capacity Münc generators.hen, Germany); Although turbine each C—Areva turbine is M5000 characterised (Areva, byParis, particular France); hub turbine heights, D—Siemens for consistency SWT 6.0 the (Sieme performancesns, München, of all Germany); the systems turbine will E—Vestas be evaluated V164 at 100(Vestas, m height. Aarhus N, Denmark).

Figure 5. Power curves of the turbines considered for evaluation. The turbines are related to: turbine A—General Electric GE 2.5 (General Electric, Boston, MA, The Annual Electricity Production (AEP) of a wind generator can be estimated as [34]: USA); turbine B—Siemens SWT 3.6 (Siemens, München, Germany); turbine C—Areva M5000 (Areva, Paris, France); turbine D—Siemens SWT 6.0 (Siemens, München, Germany); turbine E—Vestas V164 AEP = 𝑇 × 𝑓𝑢𝑃𝑢𝑑𝑢, (2) (Vestas, Aarhus N, Denmark). The Annual Electricity Production (AEP) of a wind generator can be estimated as [34]:

Z cut out − AEP = T f (u)P(u)du, (2) × cut in − J. Mar. Sci. Eng. 2020, 8, 550 7 of 18 where AEP is deﬁned in MWh; T—average number of hours per year (8760 h/y); f(u)—Weibull probability density function; P(u)—power curve of a turbine; cut-in and cut-out—the turbine characteristics.

Table 2. Technical speciﬁcations of the selected wind turbines [16,35].

Rated Power Cut-In Rated Cut-Out Hub Height Turbine ID (MW) Speed (m/s) Speed (m/s) Speed (m/s) (m) General Electric GE 2.5 A 2.5 3 13 25 100 Siemens SWT 3.6 B 3.6 3.5 14 25 100 Areva M5000 C 5.0 4 12.5 25 100 Siemens SWT 6.0 D 6.0 4 13 25 100 Vestas V164 E 8.0 4 13 25 100

The Weibull PDF (probability density function), is deﬁned as [36]: ! " # k uk 1 uk f (u) = − exp , (3) c c − c where u—wind speed; k—the shape parameter; c—the scale parameter (in m/s). Another objective of the present work is to identify the economic viability of an oﬀshore wind project by using the Levelized Cost of Energy (LCOE). This includes the CAPEX (Capital Expenditure) and all OPEX (Operational expenditures) values, divided by the electricity produced by a project during a particular time interval. This can be expressed as [37]:

Pη CAPEX + OPEXt t=1 (1+r)t LCOE = , (4) Pη AEPt t=1 (1+r)t where: OPEXt—Operational expenditures reported for year t; AEPt—Annual Energy Production corresponding to year t; r—the discount rate; η—lifetime of the project; t—year from the start of the project. Taking into account that the cost of an oﬀshore project is inﬂuenced by the water depth and distance from the shore, a cost-scaling factor [38] was applied to each point, namely: 1.08—N1; 1.252—S1; 1.29—rest of the points. In order to establish the equivalence between the CO2 emissions associated with the Constanta air pollution and the AEP production, the following equation was considered [39]:

CO = AEP x y, (5) 2emissions × × where: CO2emissions—emission rate (in tonnes CO2/MWh); AEP—Annual Energy Production (in MWh); x —reductions of kilowatt-hours into avoided units of carbon dioxide emissions (x = 6.62 10 4); × − y—convert metric tonnes to tonnes (y = 1.102311).

3. Results

3.1. Assessment of the Wind Conditions Figure6 presents the spatial distribution of the U100 parameter over the entire Black Sea area, being also included in the reference points from the Constanta sector. According to these values, the areas located in the central part of this basin reveal the best conditions for the development of a wind project (U100 > 8 m/s) at which we can add the Azov Sea. By looking at the Romanian coastal area, we may notice that the wind conditions reveal much higher values than the Bulgarian area, but are below the values reported near the Crimea Peninsula that frequently report values of 7 m/s. J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 8 of 18 J. Mar. Sci. Eng. 2020, 8, 550 8 of 18 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 8 of 18

FigureFigure 6.6. TheThe 6. spatial Thespatial spatial distribution distribution distribution of theof of Uthe 100the U (mU100100/s) (m/s) parameter(m/s) parameter over theover over Black the the SeaBlack Black area. Sea Sea Resultsarea. area. Results computed Results computedfor thecomputed 20-year for the for time 20-yearthe interval 20-year time oftime interval 2000–2019. interval of of 2000–2019. 2000–2019.

The seasonalseasonalThe seasonal didifferencesﬀerences differences (in (in %)(in %) reported%) reported reported by by theby the western western region region region were were were computed computed computed in in Figure Figure in Figure7, where7, 7, wherethe Romanianwhere the Romanian the sectorRomanian wassector sector highlighted. was was highlighted. highlighted. During During During the spring the springspring time time theretime there isthere an is attenuationanis anattenuation attenuation of of the the of wind the wind conditions that can go up to 10 or 15% compared to the total time, while the central part of the windconditions conditions that can that go can up go to 10up orto 15%10 or compared 15% compared to the to total the time, total whiletime, while the central the central part of part the of Black the Black Sea seems to reveal no variation. A similar trend is reported for the summer time, where the BlackSea seems Sea seems to reveal to noreveal variation. no variation. A similar A trendsimilar is reportedtrend is reported for the summer for thetime, summer where time, the where Romanian the Romanian sector is associated with a decrease that goes up to 15%, compared to the southern part sector is associated with a decrease that goes up to 15%, compared to the southern part where a slight Romanianwhere sectora slight is increase associated of the with values a decreasecan be notice thatd (+5%).goes up As tofor 15%, the autumn compared time, to the the entire southern region part whereincreaseis adefined slight of the increaseby values positive can of valuesthe be noticedvalues that can (+5%). gobe upnotice As to for 10%d the(+5%). near autumn theAs coastalfor time, the areas,autumn the entire while time, regionfor thethe isentiresouthern deﬁned region by positiveis definedregion values by a 5% positive that can canbe valuesconsidered go up tothat 10% more can near representative.go theup coastalto 10% Much areas,near higherthe while coastal variations for the areas, southern (positive) while region forare theexpected a 5%southern can be regionconsideredduring a 5% more thecan winter be representative. considered season, when more Much the representative. Romanian higher variations sector Much reports (positive) higher values variations areclose expected to 15%, (positive) while during asare thewe expected go winter duringseason,north the when a winter maximum the Romanian season, of 20% when may sector bethe reportsno Romanianticed near values the sector closeUkrainian reports to 15%, waters. values while asclose we goto 15%, north while a maximum as we go of north20% may a maximum be noticed of near20% themay Ukrainian be noticed waters. near the Ukrainian waters.

Figure 7. The spatial differences (in %) between the total time data and the main seasons. Results for the U100 parameter (average values) considering the 20-year time interval of ERA5 data, where: (a) spring; (b) summer; (c) autumn; (d) winter.

Figure 7. TheThe spatial spatial differences diﬀerences (in (in %) %) between between the the total total time time data data and and the the main main seasons. seasons. Results Results for thefor theU100U 100parameter parameter (average (average values) values) considering considering the the20-year 20-year time time inte intervalrval of ERA5 of ERA5 data, data, where: where: (a) (spring;a) spring; (b) (summer;b) summer; (c) (autumn;c) autumn; (d) (winter.d) winter.

J. Mar. Sci. Eng. 2020, 8, 550 9 of 18

J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 9 of 18 Figure8 presents the Weibull distribution of the considered points, including the specific parametersFigure (c and8 presentsk). Although the Weibull the pointsdistribution are located of the considered in a relatively points, small including area that the definesspecific the Constantaparameters sector, (c and we cank). Although notice that the there points are are significant located in variations a relatively especially small area in the that case defines of the the scale parameter.Constanta For sector, the N-pointswe can notice close that to thethere shore, are signific a valueant of variations 6.97 m/s especially (N1) is noticed, in the case which of the gradually scale parameter. For the N-points close to the shore, a value of 6.97 m/s (N1) is noticed, which gradually increases to 7.99 and 8.35 m/s in the case of N2 and N3, respectively. Compared to these values increases to 7.99 and 8.35 m/s in the case of N2 and N3, respectively. Compared to these values the the S-points reveal slightly higher values (+1.62%), that go up to: S1 = 7.05 m/s, S2 = 8.12 m/s and S-points reveal slightly higher values (+1.62%), that go up to: S1 = 7.05 m/s, S2 = 8.12 m/s and S3 = 8.46 S3 = 8.46 m/s, respectively. m/s, respectively.

FigureFigure 8. Weibull 8. Weibull PDF PDF (probability (probability density density function)function) distributions indicated indicated for for all allthe the reference reference sites, sites, consideringconsidering the the ERA5 ERA5 wind wind data data for for the the 20-year 20-year timetime interval of of 2000–2019. 2000–2019.

TheThe wind wind energy energy potential potential of of a a particular particular sitesite can be be evaluated evaluated througho throughoutut wind wind classes classes [35], [ 35], thatthat can can go fromgo from C1 C1 to to C7, C7, a highera higher class class being being consideredconsidered a a good good indicator. indicator. Table Table 3 3presents presents such such an an analysis,analysis, where where the the wind wind conditions conditions were were reported reported toto UU100 by using using Equation Equation (1). (1).

Table 3. Percentage of wind classes for the U100 parameter, corresponding to the 20-year time interval Table 3. Percentage of wind classes for the U100 parameter, corresponding to the 20-year time interval of 2000–2019. of 2000–2019. Wind Class (%) Site Wind Class (%) Site C1 C2 C3 C4 C5 C6 C7 N1C1 38.7 C211.4 C37.79 5.8 C4 5.21 C5 7.17 C623.9 C7 N1N2 38.7 30.1 11.4 9.68 7.796.56 5.5 5.8 5.16 5.21 7.41 7.1735.6 23.9 N2N3 30.1 28.2 9.68 8.17 6.566.12 4.93 5.5 4.97 5.16 7.22 7.4140.3 35.6 N3C1 28.2 29.5 8.17 9.31 6.126.52 5.27 4.93 5.12 4.97 7.28 7.22 37 40.3 C1C2 29.5 28.3 9.31 8.23 6.526.11 5.02 5.27 4.86 5.12 7.1 7.28 40.4 37 C2S1 28.3 38.5 8.23 11 6.117.45 5.86 5.02 5.28 4.86 7.21 7.124.8 40.4 S1S2 38.5 29.7 119.25 7.456.59 5.01 5.86 5.23 5.28 7.33 7.2136.9 24.8 S2 29.7 9.25 6.59 5.01 5.23 7.33 36.9 S3 27.7 8.3 6.11 4.81 4.87 7.23 41 S3 27.7 8.3 6.11 4.81 4.87 7.23 41 Wind class (U100): C1 ≤ 5.05 m/s; C2 = 5.05–5.86 m/s; C3 = 5.86–6.43 m/s; C4 = 6.43–6.89 m/s; C5 = 6.89– Wind class (U100): C1 5.05 m/s; C2 = 5.05–5.86 m/s; C3 = 5.86–6.43 m/s; C4 = 6.43–6.89 m/s; C5 = 6.89–7.35 m/s; 7.35 m/s; C6 = 7.35–8.04 m/s; C7 ≥ 8.04 m/s. C6 = 7.35–8.04 m/s; C7 ≤ 8.04 m/s. ≥ Generally, the distribution is divided between two dominant classes, namely C1 and C7, being expectedGenerally, values the of distribution 38.5% close is to divided the nearshore between points two (N1 dominant and S1) classes,and maximum namely of C1 41% and offshore C7, being expected(S3). The values nearshore of 38.5% points close (N1 to and the nearshoreS1) present points much (N1higher and values S1) and in Class maximum C1, but of as 41% we oexceedﬀshore 30 (S3). Thekm nearshore from shore, points Class (N1 C7 and starts S1) to present become much dominant higher gradually values increasing in Class C1, to but35.6% as (N2) we exceedand 40.3% 30 km from(N3). shore, By Classlooking C7 at starts the central to become points, dominant we may notice gradually that Point increasing C1 that to is 35.6%located (N2) on the and same 40.3% distance (N3). By lookingto shore at the as centralN2 and points,S2, presents we may slightly notice better that values Point for C1 Class that isC7 located (37%). on the same distance to shore as N2 andConsidering S2, presents that slightly in the introduction better values part for was Class brie C7fly mentioned (37%). that the pollutants from the port ofConsidering Constanta could that be in thetransported introduction by the part air masses was brieﬂy in the mentioned other regions that of the the pollutantsBlack Sea, in from Figure the 9 port of Constantais presented could the monthly be transported distribution by the of airthemasses wind roses in the reported other regionsby Site C1. of theThe Black results Sea, are inindicated Figure9 is in terms of the wind classes (U100) mentioned in Table 3. From the analysis of the wind classes, we presented the monthly distribution of the wind roses reported by Site C1. The results are indicated in terms of the wind classes (U100) mentioned in Table3. From the analysis of the wind classes, we can

J. Mar. Sci. Eng. 2020, 8, 550 10 of 18 J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 10 of 18 can notice that the values are divided between the C1 and C7 classes, as expected during the noticeJ. Mar. that Sci. theEng. values2020, 8, x areFOR dividedPEER REVIEW between the C1 and C7 classes, as expected during the wintertime 10 of 18 beingwintertime expected being conditions expected that conditions are more that energetic. are more In en termsergetic. of the In terms direction, of the it seemsdirection, that, it in seems fact, therethat, arein fact,can periods therenoticewhen are that periods the windvalues when isare the clearly divided wind blowing is betweenclearly from blowing the o ﬀC1shore fromand toC7 offshore onshore classes, to (ex:as onshore expected July, (ex: August, during July, October,August, the December).October,wintertime December). Another being expected dominantAnother conditions dominant pattern isthat pattern for are the more windis fo enr thattheergetic. wind blows In that terms from blows theof the south-west from direction, the south-west sector,it seems this that, sector, being thethisin casebeing fact, of there the May, case are April periodsof May, or November.when April the or wind November. According is clearly Acco blowing to theserding from results, to these offshore there results, to is onshore no there clear (ex:is evidenceno July, clear August, evidence that the emissionsthatOctober, the emissions from December). this from area Another this will area aﬀ dominantect will the affect eastern pattern the coast easternis for ofthe thecoast wind Black of that the Sea. blows Black from Sea. the south-west sector, this being the case of May, April or November. According to these results, there is no clear evidence that the emissions from this area will affect the eastern coast of the Black Sea.

Figure 9. TheThe monthly monthly wind wind roses roses associated associated with with Site Site C1, C1, expressed expressed in terms in terms of the of wind the wind classes classes (C1– Figure 9. The monthly wind roses associated with Site C1, expressed in terms of the wind classes (C1– (C1–C7).C7). Results Results reported reported for the for 20-year the 20-year time timeinterval interval of 2000–2019. of 2000–2019. C7). Results reported for the 20-year time interval of 2000–2019. 3.2. Wind Turbine Performances 3.2.3.2. Wind Wind Turbine Turbine Performances Performances Figure 10 reveals the distribution of the AEP for all the turbines where, as expected, the performance FigureFigure 10 10reveals reveals the the distribution distribution ofof the AEPAEP forfor allall the the turbines turbines where, where, as asexpected, expected, the the isperformance directlyperformance related is isdirectly todirectly the expectedrelated related to to rated thethe powerexpectedexpected of eachraratedted system. powerpower of Theof each each reported system. system. values The The reported oscillate reported values between: values N1oscillateoscillate= 5082–14,396 between: between: N1 MW; N1 = =5082–14,396 5082–14,396 N2 = 6805–20,030 MW;MW; N2 MW; = 6805–20,030 N3 = 7511–22,386 MW; MW; N3 N3 = 7511–22,386= MW; 7511–22,386 C1 = MW;7016–20,730 MW; C1 C1= 7016– = 7016– MW; C220,73020,730= 7521–22,423 MW; MW; C2 C2 = MW7521–22,423= 7521–22,423; S1 = 5208–14,810 MW; MW; S1S1 == MW; 5208–14,8105208–14,810 S2 = 7028–20,768 MW;MW; S2 S2 = MW; =7028–20,768 7028–20,768 S3 = 7673–22,917 MW; MW; S3 S3= MW.7673–22,917 = 7673–22,917 According toMW. theseMW. According data,According much to tothese better these data, performancesdata, much much better better are pe beingrformances related are are to being Sitebeing S3. related related to toSite Site S3. S3.

Figure 10. Annual electricity production (MWh) corresponding to the full time distribution. Figure 10. Annual electricity production (MWh) corresponding to the full time distribution.

Figure 10. Annual electricity production (MWh) corresponding to the full time distribution.

J. Mar. Sci. Eng. 2020, 8, 550 11 of 18

J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 11 of 18 The annual variation of the AEP indicator is presented in Figure 11, only for Turbine C (Areva M5000-116),The theannual rest variation of the turbines of the AEP are indicator supposed is presented to reveal in a Figure similar 11, trend, only for higher Turbine or C lower (Areva values M5000-116), the rest of the turbines are supposed to reveal a similar trend, higher or lower values associated with their rated power. The diﬀerences between the sites are more visible in this case, with associated with their rated power. The differences between the sites are more visible in this case, with the mentionthe mention that that Site Site N3 N3 shows shows a similara similar plot plot asas thethe one reported reported for for C2. C2. Although Although the thevalues values are in are in generalgeneral constant, constant, there there are are noticed noticed some some peaks, peaks, suchsuch in in the the case case of of the the year year 2001 2001 or for or the for year the year2011 2011 wherewhere a sharp a sharp decrease decrease may may occur. occur.

FigureFigure 11. Annual11. Annual distribution distribution of of the the Annual Annual ElectricityElectricity Production Production (A (AEP)EP) values values associated associated with with TurbineTurbine C (Areva C (Areva M5000-116). M5000-116).

The capacityThe capacity factor factor (Cf ()Cf is) is a frequentlya frequently usedused parameter that that can can identify identify the the reliability reliability of a wind of a wind turbine,turbine, being being deﬁned defined as [as16 [16]:]: 𝑃Pturbine C f = 100, (6) 𝐶 = Ratedpower × 100, (6) 𝑅𝑎𝑡𝑒𝑑 where: P —indicates the electric power produced by each system, and Rated —the rated power where:turbine Pturbine—indicates the electric power produced by each system, and Ratedpowerpower—the rated power of eachof each turbine. turbine. FigureFigure 12 presents12 presents the the monthly monthly distribution distribution of ofthis this parameter, parameter, considering considering only Turbine only C. Turbine The C. The resultsresults can be be divided divided into into two two distinct distinct periods, periods, namely: namely: October–March October–March (higher values) (higher and values) April– and April–SeptemberSeptember (lower (lower values). values). A maxi A maximummum of 49% of 49%may be may reported be reported during during the winter the wintermonths, months, but on but on averageaverage the the values values dodo notnot exceedexceed 30%. Better Better performa performancesnces are are being being expected expected near near the sites the sitesS3, C2 S3, C2 and N3,and whileN3, while on the on oppositethe opposite side, side, Points Points S1 S1 and and N1 N1 reveal reveal the the lowestlowest values (ex: (ex: 32% 32% in in February February or or 13.7% in June). 13.7%J. inMar. June). Sci. Eng. 2020, 8, x FOR PEER REVIEW 12 of 18

Figure 12. Capacity factor—monthly distribution associated with Turbine C (Areva M5000-116). Figure 12. Capacity factor—monthly distribution associated with Turbine C (Areva M5000-116).

Table 4 summarises the performances of the selected turbines, by indicating only the minimum and maximum values. Some new indicators were added, this being the case of operating and rated capacity that are used to identify the time period during which a turbine will be in operation and during which will operate at a wind speed higher than the rated speed of each system (see Table 2). The AEP values were already discussed, while the capacity factor is being divided between Turbines A and B. Maximum of 35% may be expected for Site S3, while a minimum of 19% is associated with Site N1.

Table 4. Turbines performances (U100) evaluated for the 20-year time interval of 2000–2019. Only the minimum and maximum values are indicated for each turbine (A, B, C, D or E).

Site AEP (MWh) Capacity Factor (%) Operating Capacity (%) Rated Capacity (%) N1 5082 (A)–14,396 (E) 19 (B)–23.2 (A) 75.7 (CDE)–87.3 (A) 2.33 (ADE)–3.04 (C) N2 6805 (A)–20,030(E) 26.2 (B)–31.1 (A) 81.3 (CDE)–89.9 (A) 5.57 (ADE)–6.78 (C) N3 7511 (A)–22,386 (E) 29.2 (B)–34.3 (A) 82 (CDE)–89.6 (A) 7.14 (ADE)–8.7 (C) C1 7016 (A)–20,730 (E) 27.1 (B)–32 (A) 81.5 (CDE)–89.9 (A) 6.09 (ADE)–7.41 (C) C2 7521 (A)–22,423 (E) 29.2 (B)–34.3 (A) 81.8 (CDE)–89.6 (A) 7.27 (ADE)–8.86 (C) S1 5208 (A)–14,810 (E) 19.5 (B)–23.8 (A) 75.8 (CDE)–87.6 (A) 2.53 (ADE)–3.34 (C) S2 7028 (A)–20,768 (E) 27.1 (B)–32.1 (A) 81.5 (CDE)–90.1 (A) 6.27 (ADE)–7.55 (C) S3 7673 (A)–22,917 (E) 29.9 (B)–35 (A) 82.1 (CDE)–90 (A) 7.82 (ADE)–9.53 (C)

As for the operating capacity, the values can go up to 90% for Turbine A and reveal similar values for Turbines C, D and E, respectively. Turbine C is defined by the highest rated capacity, this being expected since it has the lowest-rated speed (12.5 m/s) from all the considered turbines. In this case, the lowest values are accounted for by Turbines A, D and E (from 2.33 T to 7.87%) while a maximum of 9.53% is reported close to S3 (Turbine C).

J. Mar. Sci. Eng. 2020, 8, 550 12 of 18

Table4 summarises the performances of the selected turbines, by indicating only the minimum and maximum values. Some new indicators were added, this being the case of operating and rated capacity that are used to identify the time period during which a turbine will be in operation and during which will operate at a wind speed higher than the rated speed of each system (see Table2). The AEP values were already discussed, while the capacity factor is being divided between Turbines A and B. Maximum of 35% may be expected for Site S3, while a minimum of 19% is associated with Site N1.

Table 4. Turbines performances (U100) evaluated for the 20-year time interval of 2000–2019. Only the minimum and maximum values are indicated for each turbine (A, B, C, D or E).

As for the operating capacity, the values can go up to 90% for Turbine A and reveal similar values for Turbines C, D and E, respectively. Turbine C is deﬁned by the highest rated capacity, this being expected since it has the lowest-rated speed (12.5 m/s) from all the considered turbines. In this case, the lowest values are accounted for by Turbines A, D and E (from 2.33 T to 7.87%) while a maximum of 9.53% is reported close to S3 (Turbine C).

4. Discussion The negative environmental impact caused by ship navigation and port activities is a reality. At the same time, it is estimated that almost 50% of the entire shipping and port activities costs are associated with fuel. At this moment a 100% emission-free policy is impossible to implement, and more likely with the existing technology, a 20% to 30% reduction can be achieved [10,15]. Motivated by the fact that some important air pollutants are released only during the port activities (also known as hoteling), the idea was to investigate what it would take to develop a cold ironing project in the port of Constanta by using the local wind energy as the main supply source. In order to make such an estimation, a first step was to identify the level of air emissions from this port, the best sources of data being found in Nicolae et al. [22]. By using the traffic values and the fuel consumption reported for the port of Constanta, the authors developed a computational tool that can predict the air emissions for the two main activities: (a) ship navigation; and (b) port activities. These results were discussed in Figure4, with the main conclusion being that air emissions are expected to escalate in the near future. The port of Constanta is a major source of pollution, but there is evidence that, in fact, some other coastal areas of the Black Sea (ex: Turkey) will be more affected [14]. The reanalysis data represents one of the best sources of wind data for the marine environment and the ones coming from the ECMWF research institute are no exception. The ERA5 is a state-of-the-art database being frequently used to assess the wind conditions from a meteorological and renewable point of view [40–42]. From the knowledge of the authors, at this moment there are limited (or no studies) that involve the assessment of the ERA5 wind data for the Black Sea basin, which could be considered an element of novelty. In Davy et al. [43], the ERA-Interim wind dataset (that was replaced by ERA5) was evaluated for the entire Black Sea area for the time interval located between 1979 and 2004. According to the spatial map presented in this work (U10 parameter), the best regions to develop a wind project are located in the Azov Sea and on the north-western part of the basin. In the present J. Mar. Sci. Eng. 2020, 8, 550 13 of 18 work, the Azov Sea is also highlighted, but in terms of the Black Sea, the best wind resources are associated with the centre part of this basin (ex: Crimea and Sinop Peninsula). In Onea and Rusu [16], the wind conditions from the vicinity of the port of Constanta (U80; ERA-Interim) were evaluated by taking into account various distances from the shore, namely: 0, 20, 40, 60 and 80 km, respectively. For these points, the wind conditions go from 5.2 m/s (0 km) up to 6.9 m/s (60 km), values that translate to the U100 parameter are similar to 5.29 and 7.02 m/s. From the analysis of the average values presented in Figure6, we can notice that the Constanta site seems to reveal values that are in the same range. The Areva M5000 (Turbine C) was considered in the mentioned study, with a capacity factor that goes from 26.3% (20 km) to 29.9% (60 km), respectively. Compared to these values (that are reported at 90 m height), the present work reports values in the range of between 22.2% and 34.9%, respectively. The present paper is focused on the same target area being a continuation of Onea and Rusu [16]. Some elements of novelty and improvements that can be highlighted are: (a) ERA5 wind data was considered instead of ERA-Interim data; (b) additional wind turbines were used; (c) an estimation of the expected wind turbines required to power the port of Constanta was made by taking into account the air pollutions; (d) the expected LCOE values are indicated. The main purpose of a wind farm is to replace the pollutant emissions associated with fossil fuels consumption. By using Equation (5), it was possible to determine how many turbines will be required to cover the total emissions coming from the port activities, as it can be noticed in Table5. This is a theoretical case study, since in reality, it will be impossible to cover 100% of the energy needs through natural sources, and even if this were possible at the moment there is no infrastructure or interest to implement a cold ironing project at the port of Constanta.

Table 5. Average number of turbines estimated to cover the air pollution generated by Constanta Port activities. The results are indicated for the 20-year time interval of 2010–2019.

Site Turbine A Turbine B Turbine C Turbine D Turbine E N1 1741 1480 913 821 615 N2 1301 1074 660 590 442 N3 1176 962 591 527 395 C1 1260 1037 639 569 427 C2 1175 960 591 526 394 S1 1700 1442 888 799 599 S2 1258 1034 637 568 426 S3 1151 938 578 514 385

As expected, the number of the turbines signiﬁcantly drops as we go from Turbine A to Turbine C. By looking on the current European oﬀshore market [40] we can notice that for the marine environment the tendency is to use large scale wind turbines, so probably that Turbines A and B will no longer be considered an attractive solution. The estimated number of turbines is quite high and by looking on the largest projects (operational/under construction) we can make a comparison, namely: London Array—175 units; Hornsea 1—174 units; Gwynt y Môr—160 units; Greater Gabbard —140 units; Chenjiagang—134 units; Rampion—116 units; Anholt and Greater Changhua—111 units [44]. In the best case (Turbine E), it will take almost 2.2 farms similar to the London Array project to cover the electricity demand from Site S3. Another problem that needs to be taken into account is the number of ships that increase from year to year. In Table6 is presented this dynamic, by taking into account as a base a turbine that operated in 2010 and a similar one reported for 2019. The values (in %) indicate the increase in the number of turbines required to cover the air pollution from the port of Constanta. For this 10-year interval, the values increase on average by 50%, being reported percentages in the range of: Turbine A (43–53.8%); Turbine B (46.8–57.4%); Turbine C (42.3–54.8%); Turbine D (44.2–56.8%); Turbine E (43.6–57.4%). J. Mar. Sci. Eng. 2020, 8, x FOR PEER REVIEW 14 of 18 environment the tendency is to use large scale wind turbines, so probably that Turbines A and B will no longer be considered an attractive solution. The estimated number of turbines is quite high and by looking on the largest projects (operational/under construction) we can make a comparison, namely: London Array—175 units; Hornsea 1—174 units; Gwynt y Môr—160 units; Greater Gabbard —140 units; Chenjiagang—134 units; Rampion—116 units; Anholt and Greater Changhua—111 units [44]. In the best case (Turbine E), it will take almost 2.2 farms similar to the London Array project to cover the electricity demand from Site S3. Another problem that needs to be taken into account is the number of ships that increase from year to year. In Table 6 is presented this dynamic, by taking into account as a base a turbine that operated in 2010 and a similar one reported for 2019. The values (in %) indicate the increase in the number of turbines required to cover the air pollution from the port of Constanta. For this 10-year interval, the values increase on average by 50%, being reported percentages in the range of: Turbine A (43–53.8%); Turbine B (46.8–57.4%); Turbine C (42.3–54.8%); Turbine D (44.2–56.8%); Turbine E (43.6–57.4%).J. Mar. Sci. Eng. 2020, 8, 550 14 of 18

Table 6. The enhancement in the percentage of the number of wind turbines considering as reference theTable emissions 6. Theenhancement from the years in 2010 the percentage(with the lowe of thest values) number and of wind 2019 turbines(with the considering highest values). as reference the emissions from the years 2010 (with the lowest values) and 2019 (with the highest values). Site Turbine A Turbine B Turbine C Turbine D Turbine E N1 Site53.8 Turbine A 57.4 Turbine B Turbine 54.8 C Turbine D56.8 Turbine E 57.4 N2 N145.9 53.8 50.3 57.4 54.8 45.7 56.847.5 57.4 47.3 N3 N244.6 45.9 48.3 50.3 45.7 43.8 47.545.7 47.3 45.7 C1 N344.4 44.6 48.2 48.3 43.8 44.1 45.7 45.8 45.7 45.9 C1 44.4 48.2 44.1 45.8 45.9 C2 C245 45 47.6 47.6 43.8 43.8 45.9 45.9 45.5 45.5 S1 S152.1 52.1 55.5 55.5 52.5 52.5 54.1 54.1 54.6 54.6 S2 S243.5 43.5 47.1 47.1 42.8 42.8 44.5 44.5 44.5 44.5 S3 S343 43 46.8 46.8 42.3 42.3 44.2 44.2 43.6 43.6

BesidesBesides the environmental aspects,aspects, thethe economic economic aspects aspects are are also also important, important, this this being being revealed revealed by bythe the LCOE LCOE indicator. indicator. Usually Usually a 20-year a 20-year interval interval is required is required to calculate to calculate this parameter this parameter so the so entire the entire ERA5 ERA5wind datawind were data considered were considered (from 2000 (from to 2019), 2000 to including 2019), including the following the following values: CAPEX values:= CAPEX4500 USD = 4500/kW ; USD/kW;OPEX = 0.048 OPEX USD = 0.048/kWh; USD/kWh; inﬂation =inflation2% per = year; 2% per ageing year; of ageing the systems of the =systems1.6% per = 1.6% year. per In year. the end, In thea cost-scaling end, a cost-scaling factor was factor applied was in applied order to in make order a to di ﬀmakeerence a difference between each between site [38 each]. Figure site [38]. 13 presents Figure 13these presents results, these from results, which from it can which be noticed it can thatbe noticed Turbines that D andTurbines E reveal D and similar E reveal values. similar values.

Figure 13. Levelized Cost of Energy (LCOE) estimated in the reference sites for all the wind turbines Figure 13. Levelized Cost of Energy (LCOE) estimated in the reference sites for all the wind turbines considered (A, B, C, D and E). considered (A, B, C, D and E). According to these results, Site S1 should be avoided taking into account that it reports much According to these results, Site S1 should be avoided taking into account that it reports much higher LCOE values (range of 0.32 and 0.39 USD/kW). The most promising sites are N3, C2 and S3 higher LCOE values (range of 0.32 and 0.39 USD/kW). The most promising sites are N3, C2 and S3 where a minimum of 0.23 USD/kW may be reported in the case of Turbines A and C, respectively. From

this point of view, Turbine B (Siemens SWT 3.6) reveals much lower performances, being followed by Turbines D and E, which may report a minimum of 0.25 USD/kW and a maximum of 0.36 USD/kW near Site S1. By looking on the EU expected targets [35] for the year 2025 (0.11 USD/kW) and for 2030 (0.08 USD/kW), we can notice that the minimum values reported at the Constanta sites are not even close to these indicators. This provides a rough estimation over the economic viability of an offshore wind farm that may operate in the western part of the Black Sea, this being one of the first analyses of this kind from the knowledge of the authors. Finally, some considerations concerning the foundations will be provided at this point. This is because with the new generation of wind turbines (10 MW or greater) the offshore wind industry is already running into the problem that existing foundation types (piles) are reaching their limits in “workable size”. Obviously, the foundation type depends on the soil conditions and typically a foundation makes up to 30% of a wind turbine cost, even at 20 m depth. From this perspective, in order to implement wind farms in the target area, additional studies concerning the soil conditions J. Mar. Sci. Eng. 2020, 8, 550 15 of 18 and identification of the most appropriate solutions for foundation should be carried out. Additionally, an important issue in designing the foundation is represented by the current regime. This is because in the nearshore of the Black Sea, and especially in its western side, significant marine and nearshore currents are present [45,46].

5. Conclusions In the present work, a general assessment of the wind conditions from the vicinity of the port of Constanta (Black Sea west) was carried out, by taking into account the performance of some commercial wind turbines as well. The main idea was to see how a wind farm could support a cold ironing project that may be developed in this region, taking into account that the air pollution coming from the berth activities is a reality. First, based on the 20 years of ERA5 data, it was possible to provide a general picture of the wind conditions over the entire Black Sea area, gradually focusing on the western part and the Constanta sector. Although there are studies focused on different geographical areas, suggesting that there is no difference between onshore and nearshore wind conditions, according to these results it is clear that an offshore project will achieve better results. Apart from the performance of various wind turbines, some other important indicators were taken into account such as the CO2 emissions and LCOE. Going back to the original research questions, the following answers can be provided:

(a) From the annual distribution of the AEP, we can definitely say that there is a significant gap between the nearshore sites (10 km) and the offshore ones exceeding 30 km. An offshore farm will be more recommended for larger projects since these can be assembled without disturbing the port of Constanta activities; (b) The number of turbines required to cover the electricity demand from the port of Constanta (hoteling) was estimated to be between 385 and 1741 units, depending on the site and the turbine rated capacity. Even in an optimistic scenario, it will take three or four large wind projects to cover this demand, so probably a hybrid system will be more appropriate. This is only one part of the story, because, at present, it seems that every 10 years, the electricity demand of the port of Constanta increases by at least 40%; (c) The best LCOE value reported for the Constanta area is close to 0.23 USD/kW, which easily exceeds the EU expectations for the near future. A renewable energy project is not a cheap solution, this being one of the reasons why most of the countries are applying a tax deduction for this type of investment. From an economical point of view, it is expected that a larger offshore project will be a better alternative to a similar one located onshore.

Looking at the current results we can mention that, in fact, the wind conditions are relatively low, this aspect is reflected by the capacity factor of the turbines (<40%) comparable to the onshore systems. Although the wind conditions are reported at 100 m height, we notice that the average wind speed can go up to 8 m/s, but even so, these conditions are below the rated wind speed of the turbines (ex: Areva M5000—12.5 m/s). This means that most of the time the wind turbines selected will not operate at full capacity, and as a consequence, the LCOE index has higher values. In order to increase the efficiency of a wind project, it will be more indicated to consider turbines defined by lower-rated speed, such as the MHI Vestas’ 10 MW turbine (Vestas, Aarhus N, Denmark) that will be defined by a rated wind speed of 10 m/s. The cold ironing concept is emerging and slowly some major ports are taking the initiative to shift this sector to a more sustainable future. Definitely, there are a number of significant challenges that need to be overcome, including the old technological systems that need to be redesigned and the investments required to update a port infrastructure. Finally, it is difficult to predict what will happen in the future because the wind and shipping industry is evolving very fast, but certainly, a cold ironing project located on the port of Constanta will be a major gain for the entire Black Sea ecosystem. J. Mar. Sci. Eng. 2020, 8, 550 16 of 18

Author Contributions: A.B.R. performed the literature review and assembly of the manuscript. F.O. processed the dataset and carried out the statistical analysis. E.R. designed and supervised the present work. All authors have read and agreed to the published version of the manuscript. Funding: The work of the first author was supported by the project ANTREPRENORDOC, in the framework of Human Resources Development Operational Programme 2014–2020, financed from the European Social Fund under the contract number 36355/23.05.2019 HRD OP/380/6/13—SMIS Code: 123847. The work of the third author was supported by the project “Excellence, performance and competitiveness in the Research, Development and Innovation activities at “Dunarea de Jos” University of Galati”, acronym “EXPERT”, financed by the Romanian Ministry of Research and Innovation in the framework of Programme 1—Development of the national research and development system, Sub-programme 1.2—Institutional Performance—Projects for financing excellence in Research, Development and Innovation, Contract no. 14PFE/17.10.2018. Acknowledgments: The ERA5 wind data used in this study have been obtained from the ECMWF data server (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5). Conflicts of Interest: The authors declare no conflict of interest.

Nomenclature

AEP Annual Electricity Production CAPEX Capital Expenditure CHP combined heat and power plant ECMWF European Centre for Medium-Range Weather Forecasts LCOE Levelized Cost of Energy NOx Nitrogen oxides OPEX Operational expenditures OPS Onshore Power Supply PM Particulate Matter RES renewable energy sources U100 wind speed reported at 100 m height above sea level UTC Coordinated Universal Time VOCs Volatile Organic Compounds

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