Techno-economic Assessment of Floating Offshore Wind Energy in the Philippines
Article information
Abstract
Offshore wind development in the Philippines remains at a standstill owing to the relatively high cost of the technology despite the government’s ambitious goal to produce 19–50 GW by 2050. Previous studies have shown that bottom-fixed turbines can be utilized to reach the lower end of the production target. Still, most potential can be achieved in greater depths where floating turbines are more appropriate. Therefore, this study performs a techno-economic assessment for floating offshore wind energy using global information system (GIS) software to analyze the technical and economic potential of floating offshore wind energy in the Philippines. Data from the Global Wind Atlas was used to obtain the technical potential of sites that may be used for floating offshore wind turbines. Exclusion zones are set up according to distance from shore, marine protected areas, oil and petroleum sites, shipping lanes, and submarine cables. Factors on distance to shore, port, and substation, as well as natural hazards and bathymetry, are considered according to a weighted analytic hierarchical process. The net present value (NPV) of each potential site is then calculated by considering the capital, operational, and decommissioning cost of a site at a 6% interest rate and 25 years of operational life. Results show that there is a total potential of 813 GW that can be produced from a total of 20 non-contiguous sites. However, the current feed-in tariff (FIT) rate of 8.53 Php/kW is insufficient to make any sites profitable as all NPVs are negative. The breakeven prices can guide the Philippines government to set an appropriate rate at a minimum of 10.14 Php/kW.
1. Introduction
The Philippines set an ambitious goal of building 19–50 GW of offshore wind power by 2050, considering that the capacity of offshore wind power in the country is currently at 0 GW. This target aims to rectify the problematic energy landscape within the country, which has remained reliant on fossil fuels. Currently, fossil fuels in the country have a market share of 62% over the past decades (Department of Energy, 2023). While policy developments through Executive Order No. 21, s. 2023 (Office of the President, 2023) have been made to accelerate the development of offshore wind power in the country. Still, much remains uncertain about how technology can be adopted in the country. This is driven by the cost uncertainties in an already expensive technology.
The country is not new to wind energy, as several onshore wind energy sites have been developed previously (Department of Energy, 2023). However, offshore wind energy has recently been tackled within the country with initial estimates of 178 GW of offshore wind potential (Behr, 2022). Maandal et al. (2018) conducted a techno-economic assessment of offshore wind in the Philippines. However, their study was limited to bottom-fixed offshore turbines that can only operate to a depth of 50 m. This resulted in a calculated potential of 25 GW at a price ranging from 8.026 to 8.304 Php/kWh. This production is much lower than the initial WorldBank (Behr, 2022) estimate but sufficient to reach the lower end of the target by 2050. Additionally, this price is within the feed-in tariff range for wind, which is currently set at 8.53 Php/kWh (Balita, 2024).
Additional capacity may be realized in water depths greater than 50 m, where bottom-fixed turbines are unsuitable. Sotto et al. (2023) conducted a study assessing the technical feasibility of floating offshore wind turbines in Philippine waters at depths greater than 50 m, but not more than 120 m. The calculated potential is 710 GW, which is approximately 4× greater than that calculated by the World Bank. However, much remains uncertain regarding the development since the study lacked an economic analysis to evaluate whether developing these offshore wind energy sites can attract developers, given the current market rate in the Philippines.
This study aims to conduct a complete techno-economic assessment to evaluate the technical and economic viability of floating offshore wind turbines in the Philippines. The results will assist in building additional policy recommendations that the country can adopt to ensure that the target of 19–50 GW by 2050 is reached or at least approached. In addition, this study fills a gap in the techno-economic assessment of offshore wind energy in the Philippines, leading to differing values between the initial World Bank estimate and recent studies completed by Maandal et al. (2018) and Sotto et al. (2023).
2. Methodology
2.1 Overall Framework
The overall framework closely follows the assessment methodology of Maandal et al. (2018) and Sotto et al. (2023). It consists of four phases: (1) data gathering and setup, (2) geographical information system (GIS) modeling, (3) technical analysis, and (4) economic analysis. The GIS model was built using Quantum GIS (QGIS) software with data from readily available sources, as cited in the following sections.
The technical assessment of floating offshore wind in this study considers wind energy resources, wind turbine specifications, and the theoretical array layout. This results in typical wind-farm-producing power with a specific capacity factor. The economic assessment in this study is evaluated using the net present value and levelized cost of energy approaches. The MHI Vestas V164/9500 was used as the representative turbine for all the analyses.
2.2 Technical Assessment and Analysis for Floating Offshore Wind
2.2.1 Wind energy resource (Power law, Rayleigh model, and Wind power density)
Wind speed data at a hub height zh, of 150 m were obtained from the Global Wind Atlas (2024). A power law, Eq. (1), was applied to the obtained average wind speed V1, to obtain the velocity profile V (z) over the entire swept area of the turbine. A roughness length factor, z0, of 6.09 × 10−3 m (Golbazi and Archer, 2019), typical of rough seas, was used. The average wind speed applied in this study was obtained from the Global Wind Atlas (2024). The velocities obtained were daily averages over 10 years from 2008 to 2017. However, the database only provides a single average for specific points; thus, the Rayleigh distribution model (Pishgar-Komleh et al., 2015) in Eq. (2). This simplified Weibull distribution model shows the same accuracy as the commonly observed shaped parameter 2 (Lizica-Simona Paraschiv et al., 2019). The Rayleigh probability density f(v) was obtained as a function of the measured wind speed v (m/s) and scale parameter c (m/s) obtained in Eq. (3), and the mean wind speed vm (m/s). The mean wind speed is obtained using Eq. (1) for all the height values.
The total power per unit area P/A (W/m2), which can be extracted from a specific site given a wind power potential, was further calculated according to the wind power density function in Eq. (4) from Mattar and Guzman-Ibarra (2017). The power per unit area is taken as a function of a simplified constant air density ρ = 1.225 kg/m3, wind speed Vz (m/s) at the given hub height, where z = 150 m, and the wind power probability distribution f(Vz), where v from Eq. (2), and v =Vz.
2.2.2 Wind turbine and array power calculations (Power curve, AEP)
The International Electrotechnical Commission standard 61400-12-1 (International Electrotechnical Commission, 2022) provides power measurement methods for wind turbines. Furthermore, it provides a standard calculation method for the annual energy production (AEP) of a typical wind turbine. Combined with the general model of Mattar and Guzman-Ibarra (2017), the AEP is given as a function of the total hours of operation T (h) in a year, calculated wind power probability distribution, and the turbine power curve.
The total power produced by a wind turbine array or farm is then obtained by multiplying the number of turbines by the AEP of a typical turbine unit. The number of turbines NT, is determined using the model (Eq. (6)) of Nagababu et al. (2017), which calculates the array spacing S(m2) using the rotor diameter D(m), downwind spacing factor (Fd), and crosswind spacing factor (Fc). Dividing the total available area A (m2) by the calculated array spacing yields the number of turbines (Eq. (7)).
2.2.3 Performance parameters
The technical viability of floating offshore wind turbines is evaluated against the total AEP of a wind farm and the capacity factor.
The capacity factor (CF) of a wind farm is given by Eq. (8), which is the ratio of the power generated by the wind farm Pfarm (MW) to the total power produced at full capacity Pfull.
2.3 Economic Assessment and Analysis for Floating Offshore Wind
2.3.1 Capital and operational expenditure
Capital expenditure (CapEx) covers investment costs for development, procurement, and installation until the project is operational. This includes the costs of surveys, permits, turbine manufacturing, balance of systems, commissioning, and contingencies. Operational expenditure (OpEx) addresses the annual expense of the operation before decommissioning. This includes maintenance, labor, ports, and/or shipyard rent.
2.3.2 Decommissioning expenditure
Decommissioning expenditure (DecEx) is the cost associated with removing or recovering a structure after its lifespan. The turbine can either be renewed after replacing its components or disposed of. Table 1 lists the inputs to the cost model, adapted from Diaz and Guedes Soares (2023).

Cost inputs for NPV calculation. Adapted from Diaz and Guedes Soares (2023)
2.3.3 Net present value
The economic benefits of energy technology can be assessed through two measures: (a) the net present value (NPV), as used by (Zore et al., 2018; Maandal et al., 2018), and (b) the levelized cost of energy (LCOE), as used by (NREL, 2022; Martinez and Iglesias, 2022a; Maandal et al., 2018). The NPV measures the incoming and outgoing cash flows of investment over a lifetime evaluated at present. In contrast, the LCOE measures the cost of producing energy as a function of the total cost over the total life cycle of the technology. This study uses the NPV approach to ensure cash flow for investors interested in offshore wind projects in the Philippines. Therefore, the NPV is evaluated at a 6% interest rate over 25 years of operation.
2.4 Analytic Hierarchy Process with Exclusion Criteria
2.4.1 Distance from shore, port, and electrical substation
Submarine cabling remains one of the most expensive components of offshore wind farms (NREL, 2022). This is driven by the distance from the shore to the substation. A minimum distance of 22 km from the shore is applied, with the constant applied by Martinez and Iglesias (2022b). This distance considers visual pollution and adopts the Netherlands’ policy, which restricts the construction of farms within the 22 km zone. In contrast, the distance to the port is considered to be the accessibility of the site for service. The three distances are scored separately for suitability. The maximum distance is set at 120 km to reduce the uncertainty associated with transmission costs (Sotto et al., 2023).
2.4.2 Bathymetry
Floating offshore wind can be used at depths greater than 50 m. However, mooring systems may differ according to depth, with sparse buoys not applicable at depths less than 120 m (The Wind Power, 2018). This variability is accounted for by suitability at higher depths, resulting in lower scores.
2.4.3 Natural hazards or geohazards
Natural hazards pose a threat to technology at all stages of development and operation. These include extreme typhoons with high wind speeds (Ma et al., 2017), earthquakes, and steep seabed gradients (Kadivar et al., 2024). The Philippines is naturally a hotspot for both typhoons and earthquakes, which can hamper, but not prevent development, as safeguards can be put in place. These natural hazards are included as factors instead of constraints at greater distances from known hazards (typhoons, fault lines, and earthquake epicenters), resulting in higher suitability.
2.4.4 Shipwrecks
Mascarenhas and Murali (2014) showed that shipwrecks can trigger distinct morphological changes around a site. This results in low seabed integrity, which threatens the reliability of the mooring system. This phenomenon is then included in the factors with a greater distance to shipwrecks, scoring higher in suitability.
2.4.5 Exclusion criteria
The following are set as exclusion zones where offshore wind development is prohibited.
(1) Oil and gas extraction sites – the Philippines has numerous oil and gas extraction sites that provide stability to the economy as of the time of writing. An exclusion zone of 5 km (Maandal et al., 2021) from the site boundaries was employed.
(2) Shipping routes – An exclusion zone of 4 km from all existing shipping routes was employed. This ensures that existing transport lines operate without impedance (Diaz and Guedez Soares, 2023).
(3) Submarine assets – these include cables for both power export and network accessibility. The Ministry of Natural Resources (2004) applies a minimum distance of 0.5 km from these cables. However, an exclusion zone of 3 km from all Philippine submarine cables was employed to ensure no disturbance.
(4) Marine Protected Areas – Republic Act No. 11038 (2018) established the National Integrated Protected Areas System (NIPAS), which designates protected land and areas. Weeks et al. (2010) found that most of the marine protected areas in the Philippines were less than 1 km2. A conservative exclusion zone, 3 km from the borders of these areas, was employed to prevent significant impacts on biodiversity.
2.4.6 Analytic hierarchy process
The analytic hierarchy process (AHP) systematically ranks alternatives according to weights assigned to factors multiplied by standardized scores, where the weights of each factor were adopted from Sotto et al. (2023). This study applied a nine-point scale of relative importance from Saaty and Ozdemir (2003) to analyze respondent-defined weights. The physical oceanography variable was dropped owing to the unavailability of data, and all other weights were normalized against a reduced total of 83.1% owing to an uncertain “physical oceanography” factor. A total of six factors still follow the recommendation of Saaty and Ozdemir to use fewer than seven factors. The normalized weights of these factors are listed in Table 2.

Normalized weights for each factor. Weights from Sotto et al. (2023) are adjusted according to a reduced total of 83.1%.
The suitability for offshore construction, including the constraints, was first evaluated and overlaid onto the GIS model. As discussed in previous sections, sites deemed moderately to highly suitable (Table 3) were selected for further technical and economic analyses.
3. Results and Discussion
3.1 Map of Constraints from Applied Exclusion Criteria
The constraint maps shown in Fig. 1 were generated to exclude areas within the Philippine exclusive economic zone (EEZ). Data for NIPAS marine protected areas and ferry routes were gathered from the Geoportal Philippines (2024), data for oil and gas extraction sites were obtained from the Department of Energy (2018), and submarine assets were obtained from the TeleGeography submarine cable map (TeleGeography 2020).
3.2 Standardized Factor Maps without Wind Speed Consideration
The standardized factor maps shown in Fig. 2 were generated using various data sources. Distance to shore is native to QGIS, whereas distance to ports and substations was calculated in QGIS with geographical points obtained from the Philippine Ports Authority (ASEAN Ports Association, 2019) and the National Grid Corporation of the Philippines (NGCP). Bathymetric data were obtained from the General Bathymetric Chart of the Oceans (GEBCO, 2023). Shipwreck data were obtained from Geoportal Philippines. Natural hazards were obtained from the Humanitarian Data Exchange (2024) for historical typhoon paths and from the Philippine Institute of Volcanology and Seismology (PHIVOLCS) for earthquakes of 6 or greater magnitude, active fault lines, and trenches. The geohazard map (Fig. 2(a)) is an overlay of the three identified geohazards.
3.3 Technoeconomics of Potential Sites
The weighted overlay map shown in Fig. 3(a) shows all maps overlaid with their corresponding weights, as discussed in Section 2.4.6. Most moderately to highly suitable sites are scattered, with concentrations in South Luzon outside the borders of Quezon, Camarines Sur, Camarines Norte, and Catanduanes. Additional suitable sites in northern Luzon were Pangasinan and Cagayan. Most sites are in the Visayas region along the major islands of Panay, Negros, Cebu, Leyte, and Samar. The sites within Mindanao included Zamboanga and Agusan. Fig. 3(b) enhances the visibility of these sites because portions that are not applicable for floating offshore wind farms are entirely excluded from the map. Finally, Fig. 4 shows 20 out of the 45 suitable sites when the 22 km buffer for mitigation of visual pollution and adoption of the Netherlands policy on offshore wind is applied. These 20 sites were ranked as the top sites according to the average wind speed data, wherein all 45 sites were considered based solely on a threshold suitability score of 4 (moderately suitable). Moreover, the total area for these sites is calculated to be 2094.83 km2 with a maximum distance of 49 km, and most were within 22–30 km from the shore.

Overlay maps without 22 km buffer: (a) Weighted overlay map without consideration for wind speed, and (b) with constraints applied
Five of these 20 noncontiguous sites were located near Magsaysay, Palawan, although these sites had wind speeds of less than 8 m/s, with a maximum of 7.95 m/s. These wind speeds are lower than sites outside the 22 km buffer zone, capping at 9.14 m/s located in areas just below Guimaras. Nonetheless, these 20 sites already have a huge resource potential of 812.94 GW, which is greater than 4× the initial estimated potential of 178 GW (World Bank, 2022) and 100 GW greater than that of Sotto et al. (2023).
Of these 20 sites, the largest areas were located northwest of Catanduanes (area = 342 km2), and two were located west of Masbate (area = 337 km2 and area = 234.99 km2). These sites produced the highest AEP owing to their vast size. However, the wind speed for all these sites was less than 7 m/s (6.64, 6.75, and 6.25 m/s, respectively), which makes them rank low when considering the capacity factor.
The NPV for all sites, considering the current feed-in tariff for wind in the Philippines of 8.53 Php/kWh, is negative. This indicates that these wind turbines lead to a general loss because the costs of development, operation, and decommissioning exceed the revenue cost of the balance of systems, particularly for submarine cabling. Nonetheless, market rates or feed-in tariff rates still need to increase for offshore wind power to be economically viable in the Philippines. It should be noted that the breakeven price is also driven by the high exchange rate between the Php and USD, which was 56.78 Php/USD at the time of writing. Table 4 summarizes the wind speed, area, AEP, NPV at the FIT rate, and break-even price for the top 20 sites.
4. Conclusions
The techno-economic assessment of floating offshore wind in the Philippines has shown huge potential for technology. The total potential for floating offshore is 813 GW, which is higher than the 50 GW by 2050 target set by the country. Moreover, the 813 GW are all within 49 km of the shore, most within the 22–30 km mark. This is advantageous from a technical perspective.
However, the economic analysis showed that developers will operate at a net loss if they develop at these sites. Thus, there is a need for the government to re-evaluate plans for offshore wind beyond the lower end of the 2050 target. The minimum feed-in tariff must be 10.14 Php/kWh to ensure profitability within the current state of the economy.
A detailed feasibility study is required for each site to ensure that the data are up-to-date, as banking institutions also need this to release funding. Additionally, the current assessment does not consider socioeconomic factors, such as community acceptance, and detailed environmental factors, such as potential fish nurseries. These are important considerations for ensuring the technology is viable for an archipelagic and biodiverse country like the Philippines. These additional factors require additional stakeholder interviews, which may then alter the weights of the factors because a new set of stakeholders/personnel is considered. Future studies may be done to tackle these considerations.
Notes
No potential conflict of interest relevant to this article was reported.
This research was disseminated by the Department of Science and Technology, Engineering Research for Development and Technology (DOST-ERDT). This research was partially funded by the Department of Science and Technology (DOST) Student Thesis Grant awarded to A.V. Abella and J.M. Pasaraba.