1 INTRODUCTION 1.1 Background of the Study
The Philippines is the center of marine biodiversity due to th e large number of species of corals and marine biota and vast ness of coastal resources (Carpenter, 2005). According to Th e World Factbook of the Central Intelligence Agency, the Phi lippines has a coastal length of 36,289 km, ranking 5th globa lly. Because of this huge amount of resource, studies on ener gy resources development were conducted as early as in 198 6 by Uehara et al and in 1996 by Fugro OCEANOR.
In this study, wave energy resource was assessed using local modeling of the wave climate using the Simulating Waves Nearshore (SWAN) Model. Several wave energy conversion (WEC) technologies were evaluated for suitability and economic viability.
The study area considered in this project is the coastal waters of the Zambales region stretching from Subic Bay to Iba, Zambales. The computational domain is about 6,000 square kilometers. The study is a quantification of the wave energy capacity of the Zambales Region using the Simulating Waves Nearshore (SWAN) Model of wind-generated waves in the study area and performance evaluation of various wave energy conversion systems.
Figure 1. Location of the Study Area
In the wave climate modeling of the study area, only wind- generated waves were considered. Also, wave propagation and dissipation process are limited by those which can be represented by SWAN cycle III. The wave model was calibrated using non- storm conditions.
The siting of the wave energy conversion devices was based on thresholds for wave heights and operating depth and on available information on land and waterway use in the case of Subic Bay.
For the production models and determination of availability factors of WEC devices, weather extremes and operation and maintenance schedules were not included in the computation. WEC availability was based solely from the device operating conditions.
Ocean Wave Power Modeling of the Iba Coast and Subic Bay, Zambales
Dean Ashton D. Plamenco
Construction Engineering and Management Group, Institute of Civil Engineering, University of the Philippines Diliman, Quezon City 1101
Email: [email protected]
Abstract
: This work has three main objectives. First is to quantify the available energy resource using the Simulating Waves Nearshore (SWAN) model. Data for model wind inputs were taken from the PAGASA Data Center and GEBCO 30-arc-second gridded bathymetry data were requested from the Buoy Oceanographic Data Center. The second objective is to model the production of wave energy conversion (WEC) devices in the study area. Monthly wave scatter diagrams were generated and used with the available WEC power matrices (Silva, 2012) to determine monthly power generation at the WEC sites.Performance factors were also studied to provide comparison of these technologies. Lastly, is the application of engineering economic analysis to determine the most suitable, economically viable and cost-competitive WEC technology for an ocean wave farm in the region. The SWAN wave model was able to estimate the wave conditions in the region with a root mean square error of 8.087%. Significant wave heights do not exceed 2 meters and dominant wave directions were observed to be influenced by the combination of monsoons and trade winds. The peak power production is reported in August at the Subic Bay - Silanguin Coast site. The energy resource is classified as low-band resource. Production modeling showed that the most suitable technology is the Pelamis WEC with average AF = 1.00 and CF = 9.25%. A 50-MW Pelamis wave farm model yielded a levelized cost of electricy of Php 17.29/ kWh.
Key words
: SWAN Modeling; Wave energy resource; Wave farm modelingFigure 2. Conceptual Framework 1.2 Objectives
This study focuses primarily to quantify the wave energy resources in the study area using existing and site-specific data. In this study, performance of wave energy conversion (WEC) technologies will also be evaluated and the most suitable technology will be identified based on power production models and economic analyses.
This study aims to extend the previous studies on the available wave resource using global models in the attempt to provide more accuracy via local nearshore modeling using actual site data. Also, validation of local wave climate modeling is challenging due to the lack of historical wave data. In this study, root mean square error (RMSE) model validation was employed with the use of actual site data gathered during the duration of the study. Thus, this study may be used as first- degree reference for more in-depth modeling and further site analyses for wave power development.
1.3 Significance of the Study
A climate adaptation strategy formulated by the Department of Energy in its 2012 Philippine Energy Plan aims to triple the renewables capacity by 2030. Several indicative projects in renewable power resources development were planned and awarded by the department in its thrust towards energy security. One of these projects is the pioneering ocean thermal plant in Cabangan, Zambales. In the field of PH ocean wave power, only resource assessments using global wave models were conducted.
2 WAVE CLIMATE SIMULATION USING SIMULATING WAVES NEARSHORE (SWAN) MODEL
The output files of the SWAN runs for Zambales Coast were post-processed using BlueKenueTM software. For Subic Bay – Silanguin Coast wave climate, computed
model wave heights were prepared into wave scatter diagrams using pivot tables.
2.1 Zambales Coast Climate Maps
Wave height classes (in meters) are represented using color scales, red arrowhead indicates North. The monthly maximum significant wave heights, Hs,max are indicated per monthly climate maps (see figures 4- 6). A positive correlation is computed between the resulting Hs,max and the model wind input (MWI). Figure 3 illustrates the trends of the MWI and Hs,max.
Figure 3. Correlation of MWI and Hs,max
A clockwise circulation in the dominant wave direction was observed in the monthly wave maps. Waves propagate to the North-Northeast starting from May and peaks in August during the influence of Habagat (Southwest Monsoon) then it reverses to South- Southwest in December during the Amihan season (Northeast Monsoon).
Figure 4. Zambales Wave Climate Maps for the Months JFMA
Figure 5. Zambales Wave Climate Maps for the Months MJJA
Figure 6. Zambales Wave Climate Maps for the Months SOND
2.2 Subic Bay - Silanguin Coast (SBSC) Wave Climate Maps
The model computed significant wave heights, Hs, and peak periods, Tp, are arranged into wave classes using a wave scatter diagram. A wave scatter diagram is a method of presenting the joint probability distribution of the energy bins, (Hs, Tp) at a given location.
Figure 7. SBSC January Wave Scatter Diagram
Figure 8. SBSC August Wave Scatter Diagram 2.3 Wave Climate Model Validation using Field Data Site-specific nearshore and offshore data were obtained for 15 days during a field observation at a private resort in Iba, Zambales. Calibrated wave gauges were borrowed from the Marine Science Institute Physical Oceanography Group. The recorded data were processed through Fast-Fourier Transform Method using the wave gauge companion software, Ruskin. The computed offshore and nearshore significant wave heights are 1.644 m and 1.392 m, respectively and the root-mean-square error reported is 8.087%.
2.4 Potential Wave Farm Sites
Using the wave climate maps and the available information on the Subic Bay Master Plan, two candidate locations for the siting of a wave energy conversion farm are identified. The
locations were selected based from the point of highest energy bin (Hs, Tp) and based from the coastal management plan identifying waterways and other features where it is not suitable to site the wave farm.
First is San Felipe - San Narciso (SFSN) wave farm site, an ocean area located west of San Felipe and San Narciso, Zambales. Majority of the higher significant wave heights reach this point and they are known surfing hotspots. Highest energy bin is (1.61 m, 6.51 s).
Figure 8. SFSN Wave Farm Site (Google Earth) The second potential Silanguin Coast (SC) wave farm site, located south of Silanguin Zambales, west of the waterway to the Subic Bay area. High waves are observed at this point due to development of waves coming from the South-Southeast during Habagat season and West-Northwest in the Amihan season.
Figure 9. SC Wave Farm Site (Google Earth)
3 PERFORMANCE EVALUATION OF WAVE ENERGY CONVERSION (WEC) DEVICES 3.1 WEC Technology Selection
For the selection of WEC technologies to be evaluated in the WEC sites, the Technology Readiness Level (TRL) by SI Ocean in 2012 is mainly considered. Wave energy converters
with TRL 6 and 7 are chosen. The table below summarizes the specs of the candidate WECs for the wave farm models.
Table 1. TRL and Power Take-off Mechanism of the Candidate WECs
3.2 Production Modeling
Using the model wave scatter diagram in the WEC sites and the WEC power matrix from the results of Silva (2013), in computing the monthly power generation in the WEC site, the following equation was used:
(1) where MPG is the monthly power generation in kilowatt- hours, WSDi is the frequency distribution of the energy bin i, (Hs, Tp)i, in hours, and WPMi is the power matrix value of the WEC at energy bin i, (Hs, Tp)i, in kilowatts.
Table 2. SC Wave Farm Model Results
Figure 10. SC Wave Farm Monthly Generation 50-MW
Wave Farm
Max Generation (MWh), Month
Total Generation
(MWh)
Average Capacity Factor (%) Aquabuoy 4880.75, Aug 31913.96 7.33
Pelamis 5634.26, Aug 40619.47 9.25 Wave Dragon 4942.21, Aug 38592.45 7.86
Table 3. SFSN Wave Farm Model Results 50-MW
Wave Farm
Max Generation (MWh), Month
Total Generation
(MWh)
Average Capacity Factor (%) Aquabuoy 966.41, May 7684.08 2.70
Pelamis 2002.38, August 15122.93 3.53 Wave Dragon 2079.10, May 19962.54 4.40
Figure 11. SFSN Wave Farm Monthly Generation 3.3 Performance Factors
In selecting the most suitable WEC technology for a wave conversion facility in the study area, performance factors of production models were introduced. Capacity factor (CF) is a measure of efficiency of wave energy conversion. While the availability factor (AF) corresponds to the degree of utilization of the WEC technology in the site.
𝐶𝐹 = 𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡
𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑂𝑢𝑡𝑝𝑢𝑡 𝑎𝑡 𝑁𝑎𝑚𝑒𝑝𝑙𝑎𝑡𝑒 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (2) 𝐴𝐹 =𝐴𝑐𝑡𝑢𝑎𝑙 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑇𝑖𝑚𝑒
𝑇𝑖𝑚𝑒 𝑃𝑒𝑟𝑖𝑜𝑑 (3)
Based from the production model, a Pelamis wave farm is the most efficient based on the capacity factor and the most utilized conversion technology based on the availability factor. Bucher and Couch (2012) suggests a parameter which is the ratio of the capacity factor and the availability factor (CF/AF) as basis of the marginal efficiency of the wave conversion technology for a wave farm site. The maximum values of this parameter for the San Felipe-San Narciso Site is 16.57% with the AquaBuoy while for the Subic Bay- Silanguin Coast it is 15.15% with Pelamis WEC.
Figure 12. SC Wave Farm Capacity Factors
Figure 13. SFSN Wave Farm Capacity Factors
Figure 14. SC Wave Farm Availability Factors
Figure 15. SFSN Wave Farm Availability Factors
4 ECONOMIC ANALYSIS OF WAVE FARM MODELS AT SUBIC – SILANGUIN WEC SITE 4.1 Wave Farm Scenario
Using the available data on ocean power systems and current policies set by the Department of Energy & National Renewable Energy Board (NREB) with the Renewable Energy Act No. 9513 (REA 9513), a simple wave farm scenario was outlined for a 20-year study period. The electric generation of the wave farms are assumed to be constant over the study period. The feed-in-tariff (FiT) of Php 17.65 /kWh, the amount of pesos power producers are paid per kilowatt hour contracted to the grid, came from the NREB excel FiT Model. Cost estimates for a 50MW wave farm were obtained from Black & Veatch, a consulting firm on marine power systems. Their base case estimates for a 50-MW wave farm
are Php 3.4 billion for the capital expenses (CapEx) and Php 220 million for the annual operation and maintenance costs (O&M). A contingency of 15% for capital expenses was set in addition to the base case cost estimate, which is a good uncertainty estimate provided by the International Energy Agency. For the construction costs, it is assumed that it will be 70% bank-financed at 5% interest per annum and is payable within 12 years. As set by the REA 9513, ocean power producers are tax-exempted for the first 7 years and then after the exemption period 10% corporate tax will be imposed. No value-added-tax will be imposed on sale of power and producers enjoy dutyfree import of machineries, equipment and materials. Depreciation is calculated using 25% accelerated 1.5 declining-balance method. Acceleration of depreciation is often used in power systems for capital recovery and to accelerate the return of investment. Lastly, in the computation of levelized cost of electricity, a discount rate of 4% is used which is the typical interest rate set by the Bangko Sentral ng Pilipinas, playing in between 3-4% in the past decade.
Figure 16. SC Wave Farm Annual Net Worth
Figure 17. Simple Cost of Energy in the SC Wave Farm It was computed that a Pelamis wave farm achieves the maximum annual worth for a 50-MW wave farm at the Subic
Silanguin Site. To compare the three alternatives, the net present worth (PW) of the wave farm at the BSP discount rate is computed. The table below shows the net present worth, EOY1 and EOY20 costs of electricity (COE) of AquaBuoy, Pelamis and Wave Dragon wave farms.
Table 4. Net Present Worth of 50 MW Wave Farm Models
4.2 Levelized Cost of Electricity
To be able to evaluate the competitiveness of the cost of renewable electricity, the International Renewable Agency reports levelized costs of electricity (LCOE) which is a normalization of cost of energy by source. In this study, LCOE was computed per WEC alternative per WEC site. This was done to report the most cost-competitive wave farm model.
(4)
where It is the investment costs in year t, Mt is the operation and maintenance costs in year t, Ft is the generation costs in year t (for the case of wave power, Ft = 0), Et is the total power generation in year t, r is the discount rate, and n is the duration of the study period.
Table 5. LCOE of SC Wave Farms
Comparing the computed LCOEs of the wave farm models with the generation costs of MERALCO, it can be said that ocean power systems are competitive when it comes to generation costs. Furthermore, a 50MW Pelamis wave farm can produce up to 40 GWh a year based on the production model which can accommodate the demand in low- consumption months.
5 CONCLUSIONS
The results of the wave modeling showed that the SWAN Model is able to estimate the wave state conditions in the study area. The significant wave heights do not exceed 2 meters year-round in the computational domain. Also, the generated wave maps showed circulation in dominant wave
directions across the months due to the governing monsoon winds, Habagat (Southwest monsoon) and Amihan (Northeast monsoon). The highest wave energy bin is observed in August where peak power generation is reported. Peak energy production is also observed in months with strong winds primarily due to high-period waves. The wave energy resource in the study area can be classified as low-band resource, ranging in between 10-15 kW per square meter. For the 50MW wave farm model, It is most likely to be sited at the Subic Bay-Silanguin Coast Site with a maximum monthly generation of 5634.26 MWh. The most efficient wave energy conversion technology for the farm is Pelamis with an average availability factor, AF = 1.00 and average capacity factor = 9.25%, peaking in August at 15.15%. The economic analysis suggests that the 50MW Pelamis wave farm is competitive at the LCOE of Php 17.29/ kWh.
ACKNOWLEDGEMENTS
The researcher would like to thank the Institute of Civil Engineering for the endorsement to the grant as well as the College of Engineering, UP Diliman. The Philippine Atmospheric, Geophysical, and Astronomical Services Administration, National Mapping and Resource Information Authority, and Subic Bay Metropolitan Authority for their help in preparing the preliminary data needed in the model.
The Marine Science Institute, especially the Physical Oceanography Group headed by Dr. Cesar Villanoy for his expertise and guidance. The UP Engineering Research Development Foundation, Inc. for the substantial financial support to this research work.
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