Nearshore Wave Energy Resource Assessment for Off-Grid Islands: A Case Study in Cuyo Island, Palawan, Philippines

Abstract

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development, and one of the solutions seen to ensure energy security, energy access and promote a low-carbon future is the use of renewable energy sources. This study determines the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 8-year on-site wind speed data as inputs to develop a high-resolution wave energy model using SWAN and assesses its annual energy production through matching with wave energy devices. The results show that the average significant wave height (H_s), peak period (T_p) and wave power density (P_d) during a northeast monsoon are H_s = 1.35 m, T_p = 4.79 s and P_d = 4.05 kW/m, respectively, while a southwest monsoon, which is sheltered by the mainland, results in H_s = 0.52 m, T_p = 3.37 s and P_d = 0.34 kW/m. While the simulated model was observed to overestimate the significant wave height (bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the “observed values” (average r = 0.9). The annual energy production for Wave Dragon, Archimedes Wave Swing and Seawave Slot-Cone Generator are highest at 1970.6 MWh, 2462.04 MWh, 62.424 MWh and 4099.23 MWh, respectively.

1. Introduction

Resource assessment is an important tool for verifying and quantifying energy resources, and it serves as an initial step in the development of power supply operation. It is also essential in the characterization of an energy resource to support its development. In the Philippines, most off-grid island communities rely heavily on imported oils for their power generation needs [1], and off-grid communities are normally isolated island communities where it is impossible to be connected to the main grid. In 2018, 55.16% of installed capacity was coal and oil based, where coal alone shared 37.14% of these energy needs [2]. Although the Philippines is an archipelagic country, it is up for the task of 100% electrification by 2040 for off-grid areas [3], most of these being isolated small island communities.

Wave energy development can be considered as one of the options in electrifying unviable island communities which cannot easily be reached by government programs because of their geographical constraints [4,5,6,7]. Quantifying wave energy resources in these areas will be the basis of further developing and promoting renewable energy use and will also be the answer to the first three strategic directions of the energy sector in the country, which are to ensure energy security, expand energy access and promote a low-carbon future [3].

Several studies have already been conducted in assessing the Philippines’ wave energy resource. A recent study was conducted by Aminudin, Teh and Pacaldo (2021) [8] in Dumaran Island, Palawan, which assessed the offshore wave energy resources of the island using 40-year hindcast data from MetOceanView (MOV). Quitoras, Abundo and Danao (2018) [9] assessed the energy flux of forty-seven (47) coastal areas in the country, and the results show an energy flux of approximately 10–20 kW/m; this result is within the estimated global wave energy resource assessment as reported in [10,11,12]. Although the study covered a very large area, it does not include Palawan or any part of it or, in particular, the Island of Cuyo. Another study conducted by the Mindanao State University showed that ocean energy in the country can provide an estimated 17,000 megawatts of electricity, and if we can tap this energy, it would be of great help to mitigate the country’s dependency in coal and imported oils as sources of energy [13]. Moreover, a research group from the Marine Science Institute and the College of Engineering of the University of the Philippines started working together for the uptake of ocean renewable energy in the country by identifying potential sites for wave energy resources. Several hot spots had been identified in the northern part of Palawan (the Calamian group of Islands, Dumaran Island, Cuyo Island and El Nido) and in the southern part (Balabac Island and the Cagayan Islands in the Municipality of Cagayancillo) (Figure 1) [14].

However, there are significant knowledge gaps pertaining to quantification of the nearshore wave energy climate and high-resolution wave energy resource model on small islands in semi-enclosed areas that can be used to develop a wave energy project. Having sufficient information regarding wave energy resource potential for this specific type of islands in the Philippines will paved the way for an in-depth development or device solution for small-scale wave energy production to support the island’s power requirements.

Here, a high-resolution nearshore wave model was developed through SWAN (simulating wave nearshore), a third-generation numerical wave model. SWAN was used in different wave resource assessment projects: it was coupled with WAVEWATCH III to determine the wave energy resource along the northern Spanish coast, the model was validated with buoy data to evaluate its accuracy and presented statistical analysis of wave parameters and wave power results [15], the same method was used to determine the nearshore wave energy resource in Canary islands [6] and in Sicily, Italy [16], in Puerto Rico and the United States of Virgin Islands, SWAN was also used to simulate the nearshore wave energy resource for a possible wave power generation in the US Caribbean [17]. Some studies using SWAN for island nearshore wave energy assessment are in the Madiera Islands in Portugal [18], Long Island in New York [19], Hawaiian Islands [20], Azores Islands [21], Cape Verde Islands [22], Sardinia Island [23], Tenerife Island in Spain [4] and in the Baltic Sea [24].

This study used a 40-year wave hindcast from MOV (1978–2018) [25] and 8-year (2010–2018) on-site measurement of wind speed and wind direction at Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA)—Cuyo Station [26]. SWAN was used to model the wave climate during monsoon seasons and simulate a one (1) year wave hindcast (2018) at six locations (stations A–F, ~12–35 km from the island); for this study, the one year hindcast will be referred to as “hindcast parameters”. The wind data from PAGASA were used as wind input to SWAN to simulate a one (1) year wave hindcast (2018) at the same locations (stations A–F) and were used for validation; for this study, they will be referred to as “observed values”. To determine the annual energy production, a calculation was made by matching a suitable type of wave energy converter (WEC) that will optimize the wave energy resource at selected stations.

2. Materials and Methods

2.1. Study Area

Cuyo Island is the largest island among the 45 islands under Cuyo Archipelago, about 278.37 km northeast of the city of Puerto Princesa, Palawan, and has a land area of 57 km² (22 mi.²) (Figure 2). It has an estimated population of 34,556 (2015 CENSUS), which is about 4.04% of the total population of Palawan province [27,28]. The island was identified to have a good to excellent wind resource, and the average wind speed measured for 30 years was 5 m/s at 4 m of elevation [29]; given this, a potential wave energy resource as suggested in [14] is highly probable. There are three cases in which waves are propagated: (1), a large storm generates deep water waves that propagate across shallower water while the waves continue to grow due to wind, (2) in the same scenario, a storm generated winds in an area remote from the site of interest and as waves cross shallower water with negligible wind, they propagate to the site as swell, and lastly, (3) wind blows over an area of shallow water, generating waves that grow so large as to interact with the bottom [30]. This indicates that the island’s characteristic satisfies case number three (3), where a good wind resource plays a significant role in wave transformation.

2.2. 40-Year Wave Hindcast Dataset (1978–2018)

To describe the wave climate in Cuyo Island, a wave model was developed using SWAN and will be using MOV’s 40-year, 3-hourly interval wave hindcast dataset (1978–2018, with spatial resolution of 0.5° both for wave and wind) as initial condition and to describe the wave climate surrounding the island. MOV is high-resolution web-based weather forecasting developed by MetOcean Solutions in New Zealand using Ltd. WW3 Tolman Chalicov (MSL WW3 TC) wave model and NOAA Climate Forecast System Reanalysis (CFSR) for the wind model [9]. Several studies using MOV have been published in different fields of study, such as techno-economic assessment of wave energy [9], wave energy resource assessment [30], optimizing hybrid diesel–wave electrical system for an off-grid island [31], weather forecasting for marine operations [32] and marine weather monitoring [33]. For this study, nine (9) stations surrounding Cuyo Island are selected (Figure 3) to describe the offshore wave climate at ~65.7 km from the island by computing its average annual power density (P_d) and significant wave height (H_s), and among these stations, four (4) are selected (stations 4, 8, 12 and 14) that will serve as an initial condition to simulate the numerical wave model during northeast and southwest monsoons and further determine the wave energy resource of stations A–F, which are closer to the island (distance is ~23 km from the island) (Figure 4). The wave climate in these areas (stations A–F) will be used to determine the potential wave energy resource that can be harvested annually by matching it with a suitable nearshore wave energy converter.

2.3. Determination of H_s and P_d

The H_s introduces a well-defined and standardized statistic to denote the characteristic height of the random waves in a sea state [34]. In a time-domain analysis, H_s is defined as the mean height of the highest one-third of all waves, and H_1/3 is denoted in

$$H_{\frac{1}{3}}=\frac{1}{\raisebox{1ex}{$N$}\!\left/ \!\raisebox{-1ex}{$3$}\right.} {{\displaystyle \sum }}_{i=1}^{\raisebox{1ex}{$N$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}{H}_i$$

(1)

where N is the number of individual wave heights, and H_i is a series of wave heights ranked from highest to lowest [35].

To estimate the wave power, the most commonly used equation regardless of water depth is given by

$$P=0.577H_s^{2}T$$

(2)

where H_s is the wave height, and T is the given period [9].

2.4. Validation

To validate the accuracy of the resulting wave model, statistical analysis between the model result and the observed values will be calculated using the following statistical metrics or error statistics [15]:

$$\mathrm{Mean}\mathrm{of}\mathrm{measured}\mathrm{parameters};\overline{X}=\frac{1}{n} {\displaystyle \sum }{x}_i,$$

(3)

$$\mathrm{Mean}\mathrm{of}\mathrm{hindcast}\mathrm{parameters};\overline{Y}=\frac{1}{n} {\displaystyle \sum }{y}_i,$$

(4)

$$\mathrm{Bias};b=\frac{1}{n} {\displaystyle \sum }\left(y_i–x_i\right),$$

(5)

$$\mathrm{Root}\mathrm{Mean}\mathrm{Square}\mathrm{Error};=\sqrt{\frac{1}{n}{\displaystyle \sum }{\left(x_i-y_i\right)}^2},$$

(6)

$$\mathrm{Scatter}\mathrm{Index};\mathrm{SI}=\frac{RMSE}{\overline{X}},$$

(7)

$$\mathrm{Pearson}’\mathrm{s}\mathrm{Correlation}\mathrm{Coefficient};r=\frac{{\displaystyle \sum }\left(x_i-\overline{X}\right)\left(y_i-\overline{Y}\right)}{\sqrt{{\displaystyle \sum }{\left(x_i-\overline{X}\right)}^2{\displaystyle \sum }{\left(y_i-\overline{Y}\right)}^2}},$$

(8)

Here, $x_{i }$ is the H_s of the “observed values”, $y_i$ is the H_s of the “hindcast parameters” and $n$ is total values for both parameters.

2.5. SWAN Wave Model

In this study, a nested model was used to provide the necessary boundary conditions for the Cuyo wave model. The coarse grid is a rectangular 110 × 120 grid with ~1.5 km resolution, rotated 45° to align the grid with the dominant wind and wave directions due to the northeast (NE, Amihan) and southwest (SW, Habagat) monsoons (Figure 5, white grid). On the other hand, the nested, high-resolution grid is a 123 × 93 rectangular grid with ~500 m resolution and focused on the east side of the Cuyo Archipelago (Figure 5, blue grid). Subsequently, downloaded bathymetric data from GEBCO2021 [36] with 450 m resolution were also interpolated onto the model grid (Figure 6).

Four simulations were performed to represent the NE and SW monsoon wave conditions. The boundary and wave conditions (H_s, and peak wave direction (D_p) and period (T_p) were assumed to be uniform along the specific boundary orientation) inputted into the model runs were based on the computed mean of 3-hourly MOV 2008–2018 data (

Table 1. Mean H_s, T_p and D_p from MOV’s 2008–2018 data for the northeast (December–January–February (DJF)/Amihan) and southwest (June–July–August (JJA)/Habagat) monsoons.

Station	DJF Mean (Amihan)			JJA Mean (Habagat)
	Hs	Tp	Dp	Hs	Tp	Dp
4	1.1131	5.2592	46.6263	0.5066	4.1881	−167.453
8	0.8601	4.8991	28.2988	0.5044	4.2172	−150.142
12	1.106	4.8461	38.0907	0.4972	4.3267	−153.027
14	1.1139	5.3135	62.773	0.4829	4.4801	−164.637

) taken from the following stations (Figure 7):

• Station 4 (southwest boundary orientation) at 10.5° N and 120.5° E.
• Station 8 (southeast boundary orientation) at 10.5° N and 121.5° E.
• Station 12 (northeast boundary orientation) at 11.5° N and 121.5° E.
• Station 14 (northwest boundary orientation) at 11.5° N and 120.5° E.

Additionally, the following parameter settings were applied in the wave model:

• Wave spectrum.

  ∘

  At the wave model boundary, a JONSWAP spectrum with a peak enhancement factor of 3.3 was assumed.

  ∘

  Similarly, a directional spreading of approximately 25° (power function, with power = 4) was assumed.

• Physical parameters.

  ∘

  Third-generation mode for wind growth, quadruplet interactions and whitecapping (based on Komen, Hasselmann and Hasselmann, 1984 [37]) were considered.

  ∘

  Constant depth induced breaking (alpha = 1 (the coefficient for determining the rate of dissipation), and gamma = 0.73 (the value of the breaker parameter defined as H_m0/d)).

  ∘

  Constant JONSWAP bottom friction (friction coefficient = 0.067 m²/s³). (The bottom friction is computed based on the empirical model of JONSWAP (Hasselmann et al., 1973) [38]. The coefficient of the JONSWAP formulation is set at 0.067 m²/s³, which is a typical default value for wind sea conditions.)

  ∘

  Non-linear wave–wave interactions due to the triads were not considered.

  ∘

  No diffraction.

• Numerical parameters.

  ∘

  The amount of diffusion of the implicit scheme in the directional space (through directional discretization parameter) and frequency space (through the frequency discretization) was set to the default value 0.5.

  ∘

  Accuracy:

  ▪

  Relative change H_s–T_m01: 0.02.

  ▪

  Relative change with respect to the mean value: 0.02 for both H_s and T_m01.

  ▪

  Convergence percentage of wet grid points: 98%.

  ▪

  Maximum number of iterations: 15.

2.6. Annual Energy Production (AEP)

AEP was computed using the stations’ wave scatter diagram and wave energy devices power matrix using the formula given in Equation (9) [9,39]. Wave scatter diagram is the condition of sea state at a particular location in a year, which is generated from the historical data (see Appendix A, Figure A1), while the wave energy converters’ (WEC) power matrix is the actual amount of available energy the device can capture (see Appendix B, Figure A2). This study uses Wave Dragon 5900 kW (WD5.9), Wave Dragon 7000 kW (WD7), Archimedes Wave Swing (AWS) (2470 kW) and Seawave Slot-Cone Generator (SSG) (20,000 kW) to determine the potential annual energy that can be utilized surrounding Cuyo Island. Wave Dragon is classified as a floating overtopping wave energy device that can be considered both as a nearshore (working at depth higher than 6 m) or as an offshore device (depth higher than 25 m) [39]. AWS is a heaving point absorber classified as a nearshore device; however, unlike other devices, it does not have a fixed-rated power level, but the power output continues to rise along with both wave height and period [40]. SSG is an overtopping wave energy which works under a wide spectrum of different wave conditions, giving a high overall efficiency, and it is considered a shoreline type WEC [41]. This device is considered in this study because the shoreline on the southern part of the island probably has the same intensity as stations D and E and can be considered to a much smaller island community near the stations A–F. These devices are all considered applicable to remote islands [42,43], which makes them suitable in the study area for evaluating the annual energy that can potentially be harvested.

AEP in MWh = Wave Scatter Diagram (in hours) × Power matrix (MW)

(9)

3. Results

3.1. Dominant Wind Direction (PAGASA—Cuyo Station) vs. Dominant Wave Direction (Station 10)

This study focused on the nearshore wave energy resource of the island, and the nearest dataset that will help describe the wave behavior nearshore is the 8-year (2010–2017) PAGASA—Cuyo Station wind speed and wind direction dataset and the 40-year hindcast wave height and wave direction at station 10 (see Figure 3 for the location of station 10), which is 15 km away from the island and has an annual significant wave height (H_s) of 1.2 m and annual wave power density (P_d) of 3.13 kW/m [31]. The wind and wave directions are presented using wind rose and wave rose diagram; a rose diagram represents the two-dimensional orientation of the wind and wave climate that represents the relative frequencies of different wind and wave directions and the wind and wave heights over a period of time. It displays the distribution of data in a way that can be easily understood and evaluated [44]. Figure 8 shows the wave rose diagram of the 40-year hindcast at station 10, taken every five (5) years starting from 1978 to 2018. It can be observed that the dominant wave directions are consistent and are coming from the northeastern and southwestern sides of the island. This is mainly due to the northeast monsoon, which typically occurs from the months of December–February, and the southwest monsoon, which occurs from the months of June–August. The dominant wave direction is consistent with the eight (8)-year on-site wind measurement from 2010–2018 (Figure 9). It can be seen that the dominant wind directions are also coming from the northeastern and southwestern parst of the island. These observations strengthen the use of hindcast data as initial conditions to simulate the nearshore wave climate of the island. Additionally, a high correlation (r = 0.75) between the hindcast wave data in station 10 and PAGASA on-site wind measurements was computed and presented in

Table 2. Correlation between hindcast H_s data from MOV and PAGASA measured wind speed.

Station	Distance from PAGASA—Cuyo Station (km)	Correlation Factor (r)	Average Annual Hs (m)	Average Annual Pd (kW/m)
4	68	0.71	1.34	4.28
7	40	0.6	1.11	2.66
8	66	0.51	1.17	3.06
9	56	0.66	1.16	3.05
10	15	0.75	1.20	3.13
11	60	0.76	1.44	5.00
12	92	0.75	1.38	4.25
13	72	0.73	1.44	4.88
14	92	0.62	1.40	4.88

H_s and P_d were computed using the hindcast data from MOV.

[31], and the table also shows the distance of the nine (9) stations from PAGASA station, as well as the average H_s and P_d at these stations. Similarly, the coefficient of correlation^® presented in the analysis coincides with other studies, which validates the quality of the MOV hindcast data (

Table 3. Studies conducted that validate MOV’s data.

MOV’s Wave Hindcast Validation	Results	Year	Source
Hindcast significant wave height was validated against (1) altimeters in the entire Tanaki grid area and (2) wave rider buoy moored ~17 m deep near the port of Tanaki.	1. Model Hs does not reveal significant or systematic errors and shows satisfactory overall performance of the wave model.	2021	[45]
	2. Results of the model data validation against the buoy were also in good agreement, especially when considering the proximity of the buoy to shore and the mooring’s relatively shallow depth (Hs, r = 0.933, Tp, r = 0.624).
The mean monthly distribution of significant wave height from MOV was validated against the wave data from FugroGeos for the Central–Southern North Sea region.	1. The coefficient of correlation is satisfactory at r = 0.55.	2021	[46]
Assessment of the suitability of the hindcast directional wave spectra data in the northern and southern hemisphere was compared against buoy observations from National Oceanic and Atmospheric Observation’s National Data Buoy Center.	1. The Hs time series obtained from the hindcast and the observations are well correlated (rHs > 0.8 for all the selected buoys).	2019	[47]
	2. The RMSE values are within 10% of the maximum value attained by the model for all the selected buoys
	3. The monthly mean spectra from model and observations are well correlated, with r ~0.9.
A report on meteorological and oceanographic conditions at Astrolabe Reef prepared by MetOcean Solutions Limited has been reviewed by NIWA. These statistics are derived from numerical model simulations, which were first validated against available data for winds, waves and currents	1. The verification of significant wave heights against measurements is satisfactory. It gives some confidence in the accuracy of the hindcast winds, in the absence of direct verification at a nearby location in the Bay of Plenty.	2013	[48]

).

3.2. Wind and Wave Climatology from MOV and PAGASA—Cuyo Station Data

Using the MOV Station 10 data for 2008–2018, the monthly wave climatology in Cuyo was generated (Figure 10). The monthly variability of the significant wave heights (median) ranges from 0.1–1.1 m with extreme significant wave heights reaching almost 2.7 m (January) and outliers reaching as high as 4.1 m. The outliers are usually found all throughout the year, with the highest outliers in November and December. The seasonal signal of the H_s related to the monsoons is also observed, wherein higher wave heights are recorded during the monsoon peaks and lower wave heights during the monsoon transition period. The months with the highest extreme H_s and outliers coincide with the northeast monsoon months (December, January and February).

Likewise, the compass roses of H_s (Figure 11) and T_p (Figure 12) with propagation direction show the strong monsoonal influence with most waves clustered along the N–NNE and WSW–SW–SSW directions. It is also notable in Figure 11 that H_s is higher during the NE monsoon (~25% H_s is ≥1.5 m) compared to the SW monsoon (~5% H_s is ≥1.5 m). This observation is also consistent with T_p (Figure 12), wherein ~80% (majority of the waves) is ≥4 s during the NE monsoon, while only ~40% is ≥4 s during the SW monsoon.

In terms of wind velocities, the 2010–2018 data from MOV station 10 (3 hourly data) and PAGASA wind station at Cuyo (daily data) were used to determine the wind conditions in the area. It is to be noted that the MOV station is further offshore and located ~16 km north of the PAGASA—Cuyo Station (more sheltered since it is located on land and only at 4 m elevation), thus showing some differences in wind velocities (Figure 13). Noticeably, wind speeds from PAGASA station (majority below 8 m/s) are weaker compared to MOV data (~30% winds are ≥8 m/s) (Figure 13). In terms of direction, the northeasterly wind produces stronger winds (~25% winds are ≥8 m/s) compared to the southwesterly wind (~5% winds are ≥8 m/s) (Figure 13). Moreover, the prominent wind direction, especially from the PAGASA station (SSW and NE wind), agrees with the wave direction.

3.3. Monsoon Wave Model for Cuyo Island

MOV’s wave data and PAGASA’s wind data both agree that the observed H_s is related to the monsoons, wherein higher wave heights are recorded, and lower wave heights take place during monsoon transition periods. The wave model developed describes the wave climate surrounding the island and specifically determines the wave parameters in the six (6) points of interest (stations A–F), which are closer to the island (Figure 14). Stations A–F have an average distance of 24.3 km from the island: the closest is 12.35 km (station A), and the farthest is 35.35 km (station C). During the northeast monsoon season, H_s and T_p are the highest at station F (1.49 m and 4.87 s, respectively), followed by stations A and E, (1.43 m and 4.7 s, respectively); other stations are much lower due to the sheltering effects of nearby islets and the main island (

Table 4. Summary results of wave parameters for the NE and SW monsoons at the six (6) points of interest near the island (simulated using MOV data).

Sta	Lon	Lat	DJF-NE Monsoon				JJA-SW Monsoon
			Hs (m)	Tp (s)	Total Wave Energy (J/m2)	Pd (W/m)	Hs (m)	Tp (s)	Total Wave Energy (J/m2)	Pd (W/m)
A	121	11	1.428	4.7781	1249.04	3978.25	0.4258	2.5658	111.0763	193.8773
B	120.85	10.9	1.2547	4.361	964.3382	2789.88	0.5449	3.0525	181.8621	379.0649
C	121	10.67	1.1962	4.2348	876.5134	2361.11	0.5437	3.0582	181.0445	366.3609
D	121.18	10.78	1.3077	4.5399	1047.54	3250.23	0.5657	3.1477	195.9928	420.4111
E	121.27	11	1.427	4.746	1247.26	3910.13	0.5458	3.0908	182.4629	388.3967
F	120.82	10.8	1.4887	4.871	1357.53	4246.80	0.4909	2.834	147.609	280.2246

) (Figure 15a,b). Results of these points are expected to be a little lower than the MOV stations (

Table 5. Summary results of wave parameters for the NE and SW monsoons at the nine (9) MOV stations.

Sta	Lon	Lat	DJF-NE Monsoon				JJA-SW Monsoon
			Hs (m)	Tp (s)	Total Wave Energy (J/m2)	Pd (W/m)	Hs (m)	Tp (s)	Total Wave Energy (J/m2)	Pd (W/m)
14	120.5	11.5	1.4042	4.7092	1207.84	3968.37	0.5654	3.0135	195.8246	419.0981
13	121	11.5	1.4408	4.6642	1271.41	3891.82	0.5154	2.7885	162.6904	299.367
12	121.5	11.5	1.3	4.3625	1035.14	2968.29	0.5552	2.9305	188.8014	366.2484
11	120.5	11	1.4885	4.7751	1357.16	4280.96	0.5849	3.0785	209.5412	431.2502
10	121	11	1.5135	4.8353	1403.07	4513.25	0.5274	2.8493	170.3466	331.3767
9	121.5	11	1.4505	4.6481	1288.67	3923.06	0.5835	3.0309	208.5396	420.899
4	120.5	10.5	1.5487	4.8773	1469.17	4690.35	0.6025	3.1567	222.3052	484.0884
7	121	10.5	1.5058	4.8147	1388.73	4501.62	0.5889	3.0644	212.4356	436.9373
8	121.5	10.5	1.4317	4.6555	1255.57	3879.12	0.5759	3.0252	203.1603	411.3036

) because these points are shallower [49] and more sheltered from the northeast monsoon and southwest monsoon winds [50].

Figure 15, Figure 16 and Figure 17 show the wave model results for H_s, T_p and energy transfer, respectively, during the northeast monsoon, and it can be observed that in the northeastern and southeastern sides of the island, specifically on stations A, E and F, that the results are the highest, having an average H_s, T_p and P_d of 1.35 m, 4.79 s and 4.05 kW/m, respectively, the highest being at station F with P_d = 4.25 kW/m and T_p = 4.87 s. These results can be confirmed in Figure 18, which shows that the peak wind directions converged on these areas due to less obstructions from other islets, resulting in a minimal sheltering effect and its exposure to the much stronger northeast monsoon winds. Moreover, the water depth at those stations is much deeper compared to other stations, as seen in the bathymetry data, giving more active wave behavior.

Figure 19, Figure 20 and Figure 21 show the wave model results for the H_s, T_p and P_d model for southwest monsoon season, respectively, and stations A–F have an average H_s = 0.52 m, T_p = 3.37 s and P_d = 0.34 kW/m. The model results from the southwest monsoon model have lower values compared to the northeast monsoon model due to the sheltering effect of the mainland of Palawan, which makes the wind passing from the southwest decrease its magnitude before reaching Cuyo Island.

3.4. Model Validation

Table 6. Summary of statistical metrics between “observed values” and “hindcast parameters”.

Station	$\overline{\mathit{x}}$	$\overline{\mathit{y}}$	Bias	RMSE	SI	r
A	0.40	0.80	0.40	0.55	1.38	0.92
B	0.37	0.79	0.41	0.54	1.45	0.88
C	0.35	0.77	0.37	0.54	1.58	0.85
D	0.41	0.82	0.41	0.54	1.32	0.90
E	0.45	0.86	0.42	0.56	1.24	0.91
F	0.47	0.86	0.38	0.51	1.08	0.94

shows the statistical metrics result between the “observed values” and “hindcast parameters”, and it is shown that the “hindcast parameters” were overestimated by the wave model compared to the “observed values”, having an average bias of 0.398 m, where the lowest is at station F at 0.38 m and the highest at station E at 0.42 m. A good model has an RMSE value close to zero, and the closer it is the better ability of the model to accurately predict the data. Here, the RMSE has an average value of 0.54, the lowest at 0.51 (station F) and the highest at 0.56 (station E), which in this case is an acceptable value considering the complex topography on land where the “observed values” were taken. In general, overestimation and the degree of variance on all stations may be explained by the following factors: the distance, location and topographical differences of the two parameters. PAGASA—Cuyo Station is located on land and has an average distance from stations A to F of about 24.3 km, the nearest at 12.35 km (station A) and the farthest at 35.35 km (station C), and the hub height of the equipment used to measure wind speed and wind direction in PAGASA—Cuyo Station is only at 4 m of elevation, where it is more prone to disturbances caused by nearby buildings and trees. Moreover, the locations of stations A, B and C are in between small islands, which may affect or distort the wind and wave flow on the area, whereas stations D, E and F are more exposed with less sheltering from nearby islands (see Figure 14).

Scatter index (SI) values also tell us a constant overestimation of the model, with all the results being higher than one, the lowest at station F being 1.08 and the highest being 1.58 (station C). These values may also be due to the three factors mentioned, which tend to overestimate the simulated wave height. Figure 22 shows the scatter plot graph of the two parameters; in general, the data are clustered linearly and tend to increase exponentially at higher values starting between 0.8 and 1.0 m of the observed values. Few outliers can be observed on all stations but may not affect the overall result of the wave parameters. Finally, the data show a strong positive relationship with an average r = 0.90, and the highest is at station F (r = 0.94) followed by station A (r = 0.92), and the lowest being station C (r = 0.85).

3.5. AEP

Table 7. AEP results of Wave Dragon, AWS and SSG, in MWh.

Wave Energy Converter	Annual Energy Production (AEP), in MWh						Capacity Factor (%)
	A	B	C	D	E	F
Wave Dragon, 5900 kW	1626.65	1399.19	1280.77	1705.38	1970.61	1937.01	3.1
Wave Dragon, 7000 kW	2046.15	1805.07	1657.62	2174.61	2462.04	2427.66	3.4
Archimedes Wave Swing, 2470 kW	50.754	23.118	18.834	44.445	62.424	61.584	0.2
Seawave Slot-Cone Generator, 20,000 kW	3322.45	2618.643	2268.44	3466.91	3170.892	4099.23	1.8

shows the AEP of the three WECs computed using Equation (9). Stations E and F draw the highest energy production for all the WECs tested. At station E, WD5.9, WD7 and AWS draw the highest energy production with 1970.61 MWh, 2462.04 MWh and 62.42 MWh, respectively, while at station F, SSG has the highest AEP with 4099.23 MWh. The capacity factors for three WECs are <3.5%, the highest is WD7 at 3.4%, and the lowest is AWS at 0.2%. This is because most of the data are at lower values and are not within the devices’ energy production capability. Cuyo Island has an average power demand of 0.984 MW and a peak power demand of 1.4 MW [31]. Although the highest capacity factor is only at 3.4%, the AEP of WD7 can supply ~28.53% of the islands average power demand and ~20.0% of the islands peak power demand. Given this scenario and the resulting wave model, when applied to a much smaller island community in the Cuyo Archipelago, a single WD7 can perhaps cover their energy needs. A sample computation of the AEP is presented in

Table 8. Sample computation of AEP for Wave Dragon (7000 kW) at Station F.

Annual Energy Production
Wave Dragon, 7000 kW			Tp, in seconds
			4	5	6	7	8	9
			5	6	7	8	9	10
Hs, in meters	0	1	21.12	1.5	1.08	1.08	1.08	1.08
	1	2	1094	396.9
	2	3		365.4	236.7
	3	4			170.4	67.62
	4	5				69.15
AEP = 2427.48 MWh

and the rest are in Appendix C (Figure A3, Figure A4, Figure A5 and Figure A6).

4. Conclusions

Wave energy resource assessment in the Philippines ranges from 10–20 kW/m as reported in Quitoras et al. [9], in the 47 sites under the coastal regions of Catanduanes, Samar, Siargao Island, Surigao del Sur and Western Luzon. In Wan et. al, [51] along Luzon strait, the exploitable wave energy resource is at 10–15 kW/m, which agrees with the findings in [9]. In Dumaran’s study [8], the wave energy resource surrounding the island within a 100 km radius is less than 4.5 kW/m, and this is in agreement with Mirzae et al. [52], where the semi-enclosed sea or sheltered areas have a lower probability of harnessing wave energy resource that will exceed 5 kW/m at any season. With the same topographical characteristic as Dumaran Island, the resulting nearshore P_d in Cuyo Island during the monsoon seasons is also less than 5 kW/m, the highest being 4.25 kW/m (station F) and the lowest being 2.36 kW/m (station C). During the northeast monsoon (Amihan Season) and during the southeast monsoon (Habagat Season), the highest and lowest P_d are at 0.42 kW/m and 0.19 kW/m, respectively, which is in agreement with [8] and [48]. Although the “hindcast parameters” overestimate the “observed values” with average bias, RSME and SI values of 0.398, 0.54 and 1.34, respectively, the “hindcast parameters” have a strong positive relationship with the “observed values” having an average correlation coefficient (r) of 0.90, the highest being 0.94 (station F) and the lowest being 0.85 (station C). This signifies that the measured wind data are in agreement with the simulated wave data and therefore can be used as a reference in analyzing the nearshore wave energy resource surrounding the island of Cuyo either for exploitation or testing of nearshore and shoreline WEC [53,54]. The highest AEP is at station F, SSG with 4099.23 MWh, followed by WD7 with 2462.04 MWh, but in terms of capacity factor, WD7 is the highest at 3.4%; low capacity factors happen because the majority of the data falls on lower values of H_s and T_p where the wave energy device is not capable of producing energy or the energy produced is low. Although the highest is only at a 3.4% capacity factor, it can supply 28.53% of the islands’ average power demand or 20.0% of their peak demand. If these scenarios can be realized, they will be able to lessen the country’s dependency on fossil fuels for electrifying off-grid and isolated islands. Results show that downscaling of WEC to increase the capacity factor of the device in milder resources can answer the challenges in bringing sustainable renewable energy resources to unviable islands in the Philippines or in any areas of the same climate characteristics.

To predict more accurately and enhance the high-resolution wave model of the wave energy resource in an isolated island of the same characteristics, it is recommended to have an on-site measurement of the wave parameters for a minimum of 1 year or maybe longer.