A techno-economic assessment of offshore wind

Energy 133 (2017) 191e205

Contents lists available at ScienceDirect

Energy journal homepage: www.elsevier.com/locate/energy

A techno-economic assessment of offshore wind energy in Chile n-Ibarra Cristian Mattar*, María Cristina Guzma Laboratory for Analysis of the Biosphere (LAB), University of Chile, Chile

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 October 2016 Received in revised form 11 May 2017 Accepted 15 May 2017 Available online 19 May 2017

Offshore wind energy potential and its technical and economic feasibility were determined in Chile. Wind speed data from ERA-Interim reanalysis 10 m above the sea surface between 1979 and 2014 was used. The capacity factor and performance of the V164e8.0 MW wind generator was also determined. Using this information along with data from other studies, the following economic indicators were calculated: Levelized Cost Of Energy (LCOE), Net Present Value (NPV), Internal Rate of Return (IRR) and Pay-Back (PB). The results show that the area between 45 and 56 S has the highest values in terms of both power density (~3190 W/m2) and capacity factor (~70%), as well as the lowest LCOE values (72e100 USD $/MWh). The area between 30 and 32 S was estimated to be the most suitable area for implementing an offshore wind project because of its wind power density (between 700 W/m2 and 900 W/ m2), capacity factors between 40% and 60%, LCOE between 100 and 114 USD$/MWh. This work shows how important studying Chile's offshore wind power is for to be used and for removing barriers to current knowledge about this renewable energy and the benefits it would bring to Chile's power array. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Offshore wind power ERA interim LCOE Chile

1. Introduction In recent decades, wind energy has seen improvements in efficiency and reducing unit costs of wind turbines, thus making it a competitive alternative to other energy sources [40,44]. In fact, installed wind power capacity has been growing rapidly worldwide where it has been supplemented by policy-making that incentivizes its use, crucial to its development in different countries [22]. Based on the latest report from the Global Wind Energy Council (GWEC) [17], installed wind power capacity reached ~ 370 GW by the end of 2015, a cumulative market growth of 16%. In light of this, its largescale use is expected to increase in the decades to come [14]. One of the main characteristics of offshore wind farms is their high energy density [40], which might even be comparable to conventional power plants in terms of their production capacity [42]. This is because sea surface wind experiences fewer disturbances as there are rarely any obstacles producing turbulence, and therefore better use of the wind for power generation [21]. Locating wind farms offshore is also an alternative when facing constraints on land availability and issues arising due to the noise and visual pollution caused by onshore wind farms [54]. However, offshore wind farms end up being more expensive to install and maintain than onshore

* Corresponding author. Av. Santa Rosa N 11315, Santiago, Chile. E-mail address: [email protected] (C. Mattar). http://dx.doi.org/10.1016/j.energy.2017.05.099 0360-5442/© 2017 Elsevier Ltd. All rights reserved.

facilities, where the levelized cost is estimated to vary between 108 and 172 V/MWh, depending on the design of the farm [3,35]. According to the report on the state of Chile's wind energy by n y Fomento de las Energías the Centro nacional para la Innovacio Sustentables [10] (2016), the total installed capacity by the end of February 2016 was ~960 MW, generating a total contribution of 24.03% within renewable energies. However, the total installed capacity is 4.6% of the system's current power, though all this power is attributable solely to onshore wind energy, where the highest capacity onshore wind farms in Chile are: “El Arrayan” with lica Taltal” with 115 MW of power; “Los Cururos” with 110 MW; “Eo 99 MW; “Talinay oriente” with 90 MW and “Valle de los vientos” with 90 MW, which were implemented between 2013 and 2015. Several studies have analyzed onshore wind energy in Chile, such as Watts & Jara's statistical analysis [60]; wind power potential in the region of Maule [37], wind power potential in Magallanes [63] and the “Wind explorer” developed by the Department of Geophysics at the University of Chile.1 However, knowledge about offshore wind power is still being developed. Some work has been done to enhance information regarding the status of offshore wind energy potential in Chile, from studies on synoptic climatology of

1 http://walker.dgf.uchile.cl/Explorador/Eolico2/Description and user's manual at: http://walker.dgf.uchile.cl/Explorador/Eolico2/info/Documentacion_Explorador_ Eolico_V2_Full.pdf.

192

n-Ibarra / Energy 133 (2017) 191e205 C. Mattar, M.C. Guzma

near-surface wind [45] to preliminary studies on the wind power off Chile's coastline, estimated at no less than 150 km from the coast [31] and, specifically, off the coast of the Maule Region [30]. The above-mentioned work notwithstanding, there has yet to be an analysis on the technical and economic feasibility of using Chile's offshore wind power, as well as the potential costs of using it to generate electrical power. In addition, these new energy sources are in accordance with Law 20.698, which promotes expanding Chile's energy array by using renewable sources to reach a set target of 20% renewable energy by 2025 [28]. Therefore, the aim of this paper is to analyze the technical and economic feasibility of offshore wind power density in order to supplement existing information and encourage the implementation of such projects, with an eye to increasing the share of renewable energies supplying the country's energy array. The structure of this paper is as follows: Section 2 describes the study area and data used in this paper. Section 3 presents the methodology used, Section 4 the results and analysis, Section 5 the discussion of this work, and finally Section 6 presents the conclusions. 2. Study area, data and method

into consideration three possible rated capacities for a potential offshore installation: 80, 160 and 240 MW. The project's Life Cost Cycle Analysis (LCCA) is described below according to [35].): - Prior studies and planning; given by environmental studies on seabed, interface and engineering design, plus management services and project development. - Production and acquisition, which looked at the value of the turbines, the infrastructure to be used as determined by the particular seabed location and depth, the mooring system or anchoring, and finally the cost of connecting to the network (including the cost of necessary marine cable as a function of the distance to the coast). - Installation and commissioning, including costs of installing floating wind turbines, installing the mooring system and installing necessary electrical infrastructure. - Operation and maintenance costs given for staff and equipment and ships needed for maintenance. - End of project and decommissioning, corresponds to costs arising from the process of dismantling and removing parts, taking them back to land to be recycled later or sold as scrap.

2.1. Study area The study area consists of the region comprised of Chile's coastal border and 100 km to the west over the Pacific Ocean, and from 18 S to 56 S latitude (Fig. 1). It is important to point out that this area compliments the study carried out by Mattar and Villar-Poblete [31] in estimating wind energy power thanks to the better spatial resolution of the data used in this paper. This area of study is characterized by the presence of the southeastern Pacific's subtropical anticyclone, which directs wind toward the equator along most of Chile's coastline. At the same time, seasonal changes in wind speed in this area are produced largely by latitudinal changes in the wind component [45]. 2.2. Data 2.2.1. Reanalysis ERA-Interim ERA-Interim reanalysis is a sequential data assimilation system generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), where for each 12-h analysis cycle, the available observations are combined with information from a forecast model in order to estimate the evolving state of the global atmosphere and its underlying surface [13]. The data used in this study are time series of wind speed data. . . There were delivered in their components ( u , v ) via a global 0.125 0.125 grid [13]. These data include a time series from 1979 to 2014 at a temporal resolution of 3 h, delivered for a height of 10 m above sea level. Wind data provided by ERA-Interim have been widely used, tested and validated in numerous studies on atmospheric parameters, including analyses and simulations of wind fields in different areas of the world and for the estimation of offshore wind energy power [1,2,4,7,29,30,38,50e53,8,9,55]. This demonstrates that values delivered by ERA-Interim are a valid source of data for analyzing such general atmospheric circulation patterns as wind speed and direction, as well as offshore wind power. 2.2.2. Economic data The economic information used to estimate costs and profitability was compiled from prior work provided by Myhr et al. [35]. et al. [36]. In addition, based on these studies, it was and Mone divided into five project stages, which appear in Table 1 and take

2.3. Method 2.3.1. Estimation of the wind power density The magnitude of wind speed on each pixel was obtained using the following equation:

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi V¼

.2

.2

u þ v

(1)

where, V is the magnitude of wind speed at a height of 10 m (m/s); . . u is the wind component u (East-West) and v is the wind component v (North-South). This was done over the entire time series and all pixels of the study area. Then, the wind speed was extrapolated at a height of 140 m, matching the size of the V164e8.0 MW wind turbine (Supplementary 1), which was chosen for this evaluation because it was installed at the Østerild test center in Denmark, where it produced 192,000 kWh of electricity generated in a 24 h period,2 the most powerful commercial wind turbine currently available on the market. For extrapolation, equation (2) was applied, which requires a roughness length factor (Z0) that was estimated according to surface type. In this case, a Z0 was used, which corresponds to 0.0002 m, the value established for the sea surface [62].

h i ln ZZZ0 Vz ¼ Vi h i ln ZZ0i

(2)

where, Vz is the estimated wind speed at height ZZ (m/s); Vi is the wind speed at the height for which data are available (m/s); ZZ is the height at which the wind speed is being estimated (m); Z0 is the surface roughness length (m) and Zi is the height for which data are available (m). To determine the probability of wind events, the Weibull probability density function (3) is used and has been applied in other studies to determine wind power in other geographical areas [12,20,24,25,43,48,61]. To do this, the method of least squares was used, which ascertained the values A, which is the slope, and B,

2 http://www.vestas.com/%7E/media/vestas/investor/investor%20pdf/financial% 20reports/2014/ar/2014_ar_technology%20and%20service%20solutions.pdf.


193

Fig. 1. Study area for the calculation of wind energy potential.

which is the intercept of the linear regression, and with which the factors alpha (a) and beta (b) (4) are ascertained in order to be able to calculate the Weibull probability density.

b Vz pðVz Þ ¼ a a a ¼ eA ; b ¼ a B

b1

b

e

average wind power density for the desired period of time. The air density used was the wind industry's standard reference measurement, which at normal atmospheric pressure and at 15 [ C] is 1.225 [kg/m3].

Vz

a

(3)

P 1 ¼ r A 2

Z∞ V 3 pðVz ÞdV

(5)

0

(4)

where, Vz is the wind speed, a is the scale parameter and b is the shape parameter. From the probability density curve and wind speed data, wind energy potential was calculated using equation (5), which gives the

where, P=A is the wind power density; P is the wind power (W); A is the swept area (m2); r is the air density (Kg/m3); pðVz Þ is the wind

3

www.minenergia.cl.


194

Table 1 Costs considered for different project stages for different installed capacities. Stage

Description

Prior studies and planning

Production and acquisition

Installation and commissioning

Operation and maintenance End of project a

Environmental studies and station Studies of seabed Engineering design Project management and development Turbine Infrastructure Mooring or anchor Network connection Wind turbines Electrical Infrastructure Marine Cablea Operation Maintenance Decommissioning

80 MW

160 MW

240 MW

(USD)

(USD)

(USD)

4,780,127 860,423 111,855 59,283 132,800,000 57,509,248 7,081,018 28,932,995 12,086,168 13,413,047 395,883 3,200,000 6,000,000 10,220,990

8,100,713 1,458,128 189,557 100,465 265,600,000 115,018,496 14,162,037 57,865,990 24,172,336 17,096,770 395,883 6,400,000 12,000,000 20,402,391

11,028,811 1,985,186 258,074 136,779 398,400,000 172,527,744 21,243,055 86,798,985 36,258,504 20,780,493 395,883 9,600,000 18,000,000 30,583,793

Cost set for 1 km. This cost is not fixed in the analysis, because it is varied as a function of the distance from the coast of each pixel.

probability density and Vz is the magnitude of the wind speed (m/ s). So, to determine the energy, the following equation was used:

Z∞ E¼T

pðVz ÞPðVz ÞdVz

(6)

0

where, pðVz Þ is the Weibull probability density function for the time period T; PðVz Þ is the wind turbine's power output at a determined wind speed (wind turbine curve) and T is the time period under study (hours). 2.3.2. Capacity factor and performance The technical feasibility analysis is the estimated wind energy potential and its relation to energy production, based on the power curve for the V164e8.0 MW wind turbine. This was done on each pixel (spatial unit of 16 16 km in the study area). From this power curve model, the capacity factor (7) was calculated. This factor is defined as the ratio of the wind generation produced by the turbine and the total generation it outputs at full capacity over a period of time [25].

CF ¼

PE PN*T

2.3.3. LCOE, NPV and IRR To evaluate the economic profitability of an offshore wind farm in each pixel, three different scenarios were established for its installed capacity by using a generation unit equivalent to a wind farm of 80, 160 and 320 MW, respectively. The capacity for scenario 1 was determined with regard to what was used by the National Renewable Energy Laboratory (NREL) [26], while the capacity for scenarios 2 and 3 were determined in order to visualize the economic outcome at greater power output. These installed power outputs were evaluated economically for each 0.125 0.125 pixel across the study area. The evaluation applies the Levelized Cost Of Energy (LCOE) and cash flow in order to produce 3 economic indicators: the Net Present Value (NPV), Internal Rate of Return (IRR) and Pay-Back (PB). The initial assumptions established for the cost assessment with regard to the three scenarios of wind farms under evaluation are shown in Table 2. To estimate the LCOE, the life cycle costs of power generation technology per unit of electricity (MWh) are evaluated [57]. This approach has been widely used to compare energy production technologies in terms of cost-effectiveness [6]. However, this tool depends on many parameters that are partially known or entail some uncertainties [5]. From this, the LCOE was obtained through the following equation:

(7)

Pn

where, CF is the capacity factor; PE is the estimated output; PN is the rated output and T represents time. In addition, the wind turbine's annual seasonal performance was estimated, (8) which is given by the ratio between the energy produced and the energy possessed by the wind [59].

LCOE ¼ Pn

hEST ¼

E P ¼ Ed Pd

(8)

Where, hEST is the wind turbine's seasonal performance; E is the wind energy produced; Ed is the available wind energy; 〈P〉 is the functioning turbine's annual average useful potential, and hPd i is the average annual wind potential available. Generally, it is established that the range of values for an offshore wind farm's capacity for its effective approval may vary between 30% and 55% [56]. However, in this paper a capacity factor is considered feasible at 20% or higher, provided performance is greater than or equal to 10%. The reason for this is to show the relationship between the evaluated wind turbine and the wind energy power found. For this reason, economic indicators were applied only to pixels that fulfilled both requirements.

It þMt t¼0 ð1þrÞt

(9)

Et t¼0 ð1þrÞt

where, It represents the investments at time t; Mt is the maintenance and operating costs at time t; Et is the energy generated at time t; r is the discount rate of the evaluation; and t is the time between zero up to the total number of years for which the project is being evaluated. Incoming revenue is given by the evaluation of energy output with regard to capacity factor and the time value per MWh on the

Table 2 Characteristics under evaluation. Characteristic

Value

Source

Wind turbine Substructure Installed power Number of turbines Water depth Years of operation

Vestas V164e8.0 MW SPAR (Hywind-II) 80 MW/160 MW/240 MW 10/20/30 200 m 25

e Karimirad & Moan [23] e e Myhr et al. [35] Vestas [58]


international market. This value was set at 110 USD/MWh, based on the average price paid in countries like Germany, Portugal and Italy [16], bearing in mind they possess incentive schemes for renewable energy generation. In addition, the cash flow for each pixel considered the direct profit between investment and the price per MWh for offshore wind energy on the international market, which provided three economic indicators for the project's feasibility, as detailed below. The NPV is the present value of the incoming and outgoing cash flow from the investment, and thus shows the total benefit to be derived from the investment in a project, which is obtained by the following equation [27]:

NPV ¼

N X

FCt

t¼0

ð1 þ rÞt

(10)

where, FCt is the cash flow for a specific time period; r is the discount rate and t is the time from zero up to the number of years the project lasts. The result served as a criterion as to whether to carry out the project: if NPV> 0, this indicates the project should be carried out, if NPV ¼ 0, nothing is gained or lost by carrying out the project, and if NPV