# Techno-Economic Assessment of Offshore Wind Energy in the Philippines

^{1}

^{2}

^{3}

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## Abstract

**:**

^{2}offshore area ranges from USD 157.66/MWh and USD 154.1/MWh. The breakeven electricity price for an offshore wind farm in the Philippines ranges from PHP 8.028/kWh to PHP 8.306/kWh.

## 1. Introduction

#### 1.1. Statement of the Problem

- Methodologies on techno-economic assessment of offshore wind energy have not been applied to the Philippine setting.
- A notion exists that it is a risky investment with high cost and uncertainty in return.
- There is no readily available and reliable information for investments regarding the viability of offshore wind farms in the Philippines.
- There has been no formulation for the recommendation of the viability of offshore wind energy in the Philippines.

#### 1.2. Objectives of the Study

- To develop a methodology for the techno-economic assessment of offshore wind farms in the Philippines.
- To assess the wind resource in the Philippine oceans for the potential of putting up an offshore wind farm.
- To investigate the economic viability of constructing an offshore wind farm in the Philippines through LCOE.
- To formulate a recommendation for the viability of OWF in the Philippines.

#### 1.3. Research Significance

#### 1.4. Limitations of the Study

## 2. Review of Related Literature

#### 2.1. Offshore Wind Farms

#### 2.2. Offshore Wind: Current Status

#### 2.2.1. Installed Capacity

#### 2.2.2. Number of Turbines and Project Area

#### 2.2.3. Distance to Shore and Water Depths

#### 2.2.4. Cost

#### 2.2.5. Wind Turbines

#### 2.3. Foundation Technologies

#### 2.4. Renewable Energy Law in the Philippines

#### 2.5. Related Techno-Economic Studies

^{2}with the capacity factor of 70% and LCOE between 72 and 100 USD/MWh for locations between 45° and 56° S. Rated at 13 m/s, the best location for the selected wind turbine was shown to be between 30° and 32° S. Power density at that location was between 700 W/m

^{2}and 900 W/m

^{2}with capacity factor ranging from 40 to 60%. The LCOE values were seen to range from 100 to 114 USD/MWh. It was determined that an increase in discount rate by only 2% can increase the LCOE threefold.

^{2}while the eastern coastline gets power densities of 63 to 393 W/m

^{2}. Theoretically, these translate to annual energy potential ranges of 54 to 823 MWh/km

^{2}and 107 to 1117 MWh/km

^{2}for the western and eastern coast, respectively. Compared to existing renewable energy resources in India within the 200 Euro/MWh FIT, a good 40% of the potential is available that is economically competitive. Bottom fixed wind farms in the western coastline will produce energy at costs of 231 and 262 Euro/MWh for 0 to 30 m and 30 to 50 m depths, respectively. The eastern coastline will see higher costs for similar installations at 308 and 334 Euro/MWh for 0 to 30 m and 30 to 50 m depths, respectively. Interest rate was seen as a very influential parameter for the calculation of the LPC.

#### 2.6. Exclusion Criteria

^{2}[27].

#### 2.7. Wind Curtailment

#### 2.8. Data Sources

#### 2.9. Technical Analysis

#### 2.9.1. Power Law

_{1}(m/s) is the wind speed at reference height z

_{1}(m); v

_{2}(m/s) is the wind speed at height z

_{2}; and z

_{o}is the roughness length factor. An established value of 0.0002 m is used, which corresponds to sea surface [16,17].

#### 2.9.2. Weibull Model

_{2}) as the Weibull probability density; k as the shape parameter (dimensionless); c as the scale parameter (m/s); and v

_{2}as the wind speed (m/s) [44]. The assumed value of the shape parameter based on another study is 2 [20].

#### 2.9.3. Wind Turbine Power Curve

#### 2.9.4. Wind Power and Wind Power Density

^{2}); P as the wind power (W); A as the swept area (m

^{2}); ρ as the air density (1.225 kg/m

^{3}); p(V

_{2}) as the wind probability density; and V

_{z}as the magnitude of the wind speed (m/s) [16].

#### 2.9.5. Annual Wind Energy Production

_{2}) as the Weibull probability density; and p(V

_{2}) as the wind turbine power output corresponding from the wind turbine curve [16].

#### 2.9.6. Capacity Factor

#### 2.9.7. Performance

_{d}as the energy possessed by the wind. The considered technically viable performance for the wind turbines in this study is greater than 10% [16].

#### 2.9.8. Array Spacing and Number of Turbines

#### 2.10. Economic Analysis

#### 2.10.1. Investment Cost

_{Total}can be seen in Equation (9) with C

_{WT}as the wind turbine cost; C

_{F}as the foundation cost; C

_{E}as the electrical cost; and other costs such as operation and maintenance [17].

#### 2.10.2. Multiple Linear Regression

_{j,j}= 0, 1, 2, …, k, are the regression coefficients; x

_{j,j}= 0, 1, 2, …, k, are the predictor variables; and ε is a random error term [54]. The predictor variables considered were date commissioned, type of foundation, hub height, sea depth, minimum distance from shore, useful life, offshore cable length, onshore cable length, inter-array cable length, distance from port, distance from grid, turbine size, number of turbines, and capacity. The estimated investment cost of existing offshore wind farms around the world for the last ten years were considered.

^{2}) was investigated using the statistical software to acquire the best multiple linear regression model. The coefficient of determination or the R

^{2}is a measure of the goodness-of-fit of the regression model. The highest and greater than 80% value will be applied to the economic analysis of the study.

#### 2.10.3. Multiple Regression Assumptions

^{2}significantly. In the VIF method, independent variables with greater than 5 values indicate that there is multicollinearity with each other and should be omitted carefully.

#### 2.10.4. Net Present Value

#### 2.10.5. Levelized Cost of Electricity

_{T}is the investment at time t; M

_{T}is the operating and maintenance cost at time t; E

_{T}is the energy produced (MWh) at time t; r is the discount rate (%); and t is the time from base year up to the total number of years of operation [16].

## 3. Methodology

#### 3.1. Framework of Methodology

#### 3.1.1. Exclusion Criteria

^{2}for a 126 m 6.2M126 turbine.

^{2}, and is headed by the Philippine National Oil Company (PNOC). Some of the other companies exploring oil and gas in the country are China International Mining Petroleum Corporation (197,000 km

^{2}), Mindoro-Palawan Oil and Gas Inc. (724,000 km

^{2}), Nido Petroleum Philipines Pty. Ltd. (1,344,000 km

^{2}), PNOC (36,000 km

^{2}), Gas2Grid Ltd. (75,000 km

^{2}), Polyard Petroleum International Company (684,000 km

^{2}), Nido Petroleum Philippines Pty. Ltd. (314,000 km

^{2}), and Otto Energy Investments Ltd. (988,000 km

^{2}). These oil and gas exploration areas were consolidated with other exclusion criteria to narrow down the potential location of the viability of offshore wind farms. The buffer spacing considered for this criterion was 5 km, as shown in the map, so that the offshore wind farm will not affect the oil and gas extraction areas.

#### 3.1.2. Technical Analysis

#### 3.1.3. Economic Analysis

#### 3.1.4. Sensitivity Analysis

#### 3.2. R Statistical Software

#### 3.3. GIS Software

## 4. Results and Discussions

#### 4.1. Technical Analysis

#### 4.1.1. Wind Speed

#### 4.1.2. Wind Power Density

^{2}and the highest was at 1777.830 W/m

^{2}, as seen in Figure 17a. The mean of the wind power density was at 700.918 W/m

^{2}. Corresponding to the areas of greatest wind speeds were those of the greatest wind power densities with the northern region of Ilocos Norte ranging from 1238.358 W/m

^{2}to 1771.519 W/m

^{2}, the northern region of Occidental Mindoro seeing 882.755 W/m

^{2}to 1604.634 W/m

^{2}, and finally, the southeastern region of Oriental Mindoro ranging from 873.324 W/m

^{2}to 1499.410 W/m

^{2}. For the wind power density of 6.2M126, the lowest was at 11.683 W/m

^{2}and the highest was at 2257.367 W/m

^{2}, as seen in Figure 17b. The mean of the wind power density was at 753.835 W/m

^{2}. Compared with SWT-3.6-120, there were increases of 1.25%, 26.97%, and 7.55% in the lowest, highest, and mean wind power densities, respectively. There was a variation in the increase of these measures due to the nature of the equation, which is dependent on the cube of the wind speed and the different Weibull probability distribution model for each turbine. The wind Weibull probability distribution model for SWT-3.6-120 takes into account 26 integers from 0 to 25, which represents the wind speed it operates in, while the 6.2M126 model takes into account 31 integers from 0 to 30. Similar to the results of SWT-3.6-120, the locations with the greatest wind power densities (as shown in the red contours) were found in the northern parts of Ilocos Norte, which had values ranging from 1405.655 W/m

^{2}to 2241.579 W/m

^{2}while the northern parts of Occidental Mindoro and the southeastern parts of Oriental Mindoro experience densities ranging from 937.934 W/m

^{2}to 1999.059 W/m

^{2}and 955.461 W/m

^{2}to 1757.770 W/m

^{2}, respectively.

#### 4.1.3. Wind Power

#### 4.1.4. Annual Energy Production

#### 4.1.5. Capacity Factor

#### 4.1.6. Performance

#### 4.2. Application of Exclusion Criteria

#### 4.3. Economic Analysis

#### 4.3.1. Multiple Linear Regression

#### 4.3.2. Regression Model Diagnostics

#### 4.3.3. Model Selection

^{2}and adjusted R

^{2}.

#### 4.3.4. Adjusted R^{2}

^{2}and adjusted R

^{2}with 97.40% and 96.75%, respectively. The model includes the independent variables minimum sea depth, area, offshore cable length, onshore cable length, port distance, capacity of the turbine, and plant capacity. This result implies that the model is a good fit for the observations considered.

#### 4.3.5. Investment Cost Regression Model 8

#### 4.3.6. Checking of Investment Cost Regression Model 8

#### 4.3.7. Selection of Investment Cost Regression Model

^{2}of model 27 showed that the intercept value was 0.50223 with coefficients of 0.21401, 0.21699, and 0.88934 for the minimum sea depth, capacity of turbine, and plant capacity, respectively. Among the independent variables, the plant capacity had the highest influence since it had the highest coefficient. The coefficient of determination of the adjusted R

^{2}was 90.5%, which indicates that the model was still a good fit for the observations considered. On the other hand, the P-value for the turbine capacity was 0.27987, which was not less than 0.05 and means that it was not statistically significant. Therefore, the independent variable capacity of the turbine was omitted.

^{2}of model 28 show that the intercept value was 0.61472 with coefficients of 0.25773 for the minimum sea depth and 0.87386 for the plant capacity. The plant capacity had the highest influence since it had the highest coefficient compared to the minimum sea depth. The coefficient of determination of the adjusted R

^{2}was 90.43%, which indicates that the model was still a good fit for the observations considered. This will be the regression model considered for acquiring the investment cost.

#### 4.3.8. Multiple Linear Regression Model Validation

#### 4.4. Levelized Cost of Electricity

^{2}. There were two cases considered for the said area, namely, construction of 34 SWT-3.6-120 turbines with a plant capacity of 122.4 MW, as shown in Figure 27, and the construction of 31 6.2M126 turbines with a plant capacity of 192.2 MW, as shown in Figure 28. The levelized cost of electricity for the 34 SWT-3.6-120 turbines range from USD 156.829/MWh to USD 158.498/MWh. For the case with 31 6.2M126 turbines, the resulting LCOE ranged from USD 153.207/MWh to USD 154.995/MWh. The results were validated against the study done by Mattar [16] wherein the computed LCOE in Chile was within 72 USD/MWh to 322 USD/MWh using a V164 8.0 MW turbine. Another validation was done by applying the 41.2% capacity factor and 4.14 MW turbine with LCOE of USD 181/MWh in the model done by [61]. The resulting LCOE after applying the 41.2% capacity factor and the 4.14 MW in this study was between USD 179.19/MWh and USD 183.63/MWh. The 6.2M126 turbine had lower LCOE because even though the investment cost was higher for this turbine, the plant capacity was greater in 6.2M126 than with SWT-3.6-120 given the same area. The plant capacity for 6.2M126 was 192.2 MW while SWT-3.6-120 had 122.4 MW.

^{2}offshore wind farm in the north of Cagayan. The SWT-3.6-120 turbine had a lower LCOE compared to the 6.2M126 in all plant capacities from 0 MW to 1000 MW. The LCOE was lower in the case of SWT-3.6-120 since the 6.2M126 turbines have high investment cost and lower capacity factor. The LCOE had a similar behavior with exponential curves because even though the total life cycle cost increased with the increase in the plant capacity, the magnitude of the plant capacity as a divisor greatly affects the LCOE.

^{2}offshore wind farm in the north of Cagayan. The 6.2M126 turbine had lower LCOE compared to the SWT-3.120 in all areas from 0 km

^{2}to 150 km

^{2}. Contrary to the results in the LCOE vs. plant capacity, the 6.2M126 turbine had less LCOE because even though its initial investment cost was higher compared with the SWT-3.6-120, the plant capacity of 6.2M126 was greater given the same area for both turbines.

#### 4.5. Price of Electricity

#### 4.6. Sensitivity Analysis

## 5. Conclusions and Recommendations

^{2}equal to 90.43%, which implies that it fits the model of the actual investment costs at the 95% confidence level. The regression model was also validated by applying it to 24 actual offshore wind farms, and the mean absolute percentage error of the regression model was calculated to be 11.33%, which means that there was 11.33% uncertainty when predicting the investment cost.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 25.**Multiple linear regression test for (

**a**) normality, (

**b**) linearity, (

**c**) homoscedasticity, and (

**d**) reliability of the 36 observations.

**Figure 26.**Multiple linear regression test for normality (

**a**), linearity (

**b**), homoscedasticity (

**c**), and reliability (

**d**) of model 27.

Name | Individual Variables | Variable Removed | VIF |
---|---|---|---|

Model 8 | MinSeaDepth + Area + OffshoreCableLength + OnshoreCableLength + PortDistance + CapTurbine + PlantCap | MaxSeaDepth, NumTurbine, lnterArrayCableLength | MinSeaDepth = 1.966903, Area = 3.014808, OffshoreCableLength = 2.154003, OnshoreCablelength = 1.248556, PortDistance = 1.377031, CapTurbine = 1.402520, PlantCap = 3.646495 |

Model 9 | MaxSeaDepth + Area + OffshoreCablelength + OnshoreCablelength + PortDistance + CapTurbine + PlantCap | MinSeaDepth, NumTurbine, lnterArrayCablelength | MaxSeaDepth = 2.474813, Area = 3.207744, OffshoreCablelength = 2.009005, OnshoreCableLength = 1.229875, PortDistance = 1.493217, CapTurbine = 1.647750, PlantCap = 3.860488 |

Model 16 | MinSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + CapTurbine + PlantCap | MaxSeaDepth, Area, lnterArrayCablelength, NumTurbine | MinSeaDepth = 1.966185, OffshoreCablelength = 2.113326, OnshoreCablelength = 1.229009, PortDistance = 1.363429, CapTurbine = 1.402447, PlantCap = 1.558588 |

Model 17 | MaxSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + Cap Turbine + PlantCap | MinSeaDepth, Area, lnterArrayCablelength, NumTurbine | MaxSea Depth = 2.325112, OffshoreCableLength = 2.008671, OnshoreCablelength = 1.210969, PortDistance = 1.440038, CapTurbine = 1.622965, PlantCap = 1.486171 |

Model 21 | MinSeaDepth + Area + OffshoreCableLength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MaxSeaDepth, lnterArrayCableLength | MinSeaDepth = 1.948631, Area = 2.866016, OffshoreCableLength = 1.920793, OnshoreCablelength = 1.247374, PortDistance = 1.370884, NumTurbine = 3.566499, CapTurbine = 1.615025 |

Model 22 | MaxSeaDepth +Area+ OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MinSeaDepth, lnterArrayCablelength | MaxSeaDepth = 2.422104, Area = 2.994082, OffshoreCablelength = 1.945860, OnshoreCablelength = 1.229641, PortDistance = 1.498961, NumTurbine = 3.730028, CapTurbine = 2.030133 |

Model 25 | MinSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MaxSeaDepth, Area, lnterArrayCablelength | MinSeaDepth = 1.943687, OffshoreCablelength = 1.920234, OnshoreCableLength = 1.224317, PortDistance = 1.344159, NumTurbine = 1.603537, Cap Turbine = 1.528544 |

Model 26 | MaxSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MinSeaDepth, Area, lnterArrayCablelength | MaxSeaDepth = 2.312621, OffshoreCablelength = 1.932499, OnshoreCablelength = 1.208195, PortDistance = 1.436073, NumTurbine = 1.538419, Cap Turbine = 1.841882 |

Name | Individual Variables | Variable Removed | Multiple R^{2} | Adj. R^{2} |
---|---|---|---|---|

Model 8 | MinSeaDepth + Area + OffshoreCablelength + OnshoreCablelength + PortDistance + CapTurbine + PlantCap | MaxSeaDepth, NumTurbine, lnterArrayCablelength | 97.40% | 96.75% |

Model 9 | MaxSeaDepth + Area + OffshoreCablelength + OnshoreCablelength + PortDistance + CapTurbine + PlantCap | MinSeaDepth, NumTurbine, lnterArrayCablelength | 97.24% | 96.55% |

Model 16 | MinSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + CapTurbine+ PIantcap | MaxSeaDepth, Area, lnterArrayCablelength, NumTurbine | 96.42% | 95.68% |

Model 17 | MaxSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + CapTurbine + PlantCap | MinSeaDepth, Area, lnterArrayCablelength, NumTurbine | 96.34% | 95.59% |

Model 21 | MinSeaDepth + Area + OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MaxSeaDepth, lnterArrayCablelength | 91.80% | 89.75% |

Model 22 | MaxSeaDepth +Area+ OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine+ CapTurbine | Plant Cap, MinSeaDepth, lnterArrayCablelength | 91.97% | 89.96% |

Model 25 | MinSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MaxSeaDepth, Area, lnterArrayCablelength | 91.61% | 89.87% |

Model 26 | MaxSeaDepth + OffshoreCablelength + OnshoreCablelength + PortDistance + NumTurbine + CapTurbine | PlantCap, MinSeaDepth, Area, lnterArrayCablelength | 91.85% | 90.16% |

Actual Investment Cost (Million USD) | Predicted Investment Cost (Million USD) | Residuals | MAPE | |
---|---|---|---|---|

1 | 1155.0303 | 1272.5352 | −117.504902 | 0.10173318 |

2 | 1610.5574 | 1636.7401 | −26.182712 | 0.01625693 |

3 | 1155.0303 | 1272.5352 | −117.504902 | 0.10173318 |

4 | 1443.7878 | 1302.7241 | 141.063756 | 0.09770394 |

5 | 2079.0545 | 1988.9850 | 90.069423 | 0.04332230 |

6 | 2485.6456 | 2534.3208 | −48.675219 | 0.01958253 |

7 | 1389.2856 | 1287.8844 | 101.401244 | 0.07298805 |

8 | 463.2340 | 334.1906 | 129.043308 | 0.27857048 |

9 | 1501.5393 | 1315.9762 | 185.563161 | 0.12358195 |

10 | 1355.8067 | 1331.0228 | 24.783887 | 0.01827981 |

11 | 1039.5272 | 996.5151 | 43.012121 | 0.04137662 |

12 | 2146.6939 | 1997.6728 | 149.021163 | 0.06941891 |

13 | 784.7990 | 717.1558 | 67.643246 | 0.08619180 |

14 | 740.0445 | 826.3828 | −86.338284 | 0.11666634 |

15 | 1006.9377 | 922.6834 | 84.254318 | 0.08367381 |

16 | 578.7908 | 477.7106 | 101.080216 | 0.17464032 |

17 | 415.9957 | 424.3203 | −8.324543 | 0.02001113 |

18 | 297.5603 | 401.2918 | −103.731481 | 0.34860655 |

19 | 1030.7567 | 753.9011 | 276.855596 | 0.26859451 |

20 | 517.0838 | 606.1924 | −89.108637 | 0.17232921 |

21 | 3163.5490 | 2602.6829 | 560.866112 | 0.17729016 |

22 | 492.4285 | 577.0530 | −84.624516 | 0.17185138 |

23 | 458.1789 | 479.7112 | −21.532298 | 0.04699540 |

24 | 898.0267 | 836.7780 | 61.248712 | 0.06820367 |

25 | NA | NA | NA | 0.11331676 |

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## Share and Cite

**MDPI and ACS Style**

Maandal, G.L.D.; Tamayao-Kieke, M.-A.M.; Danao, L.A.M.
Techno-Economic Assessment of Offshore Wind Energy in the Philippines. *J. Mar. Sci. Eng.* **2021**, *9*, 758.
https://doi.org/10.3390/jmse9070758

**AMA Style**

Maandal GLD, Tamayao-Kieke M-AM, Danao LAM.
Techno-Economic Assessment of Offshore Wind Energy in the Philippines. *Journal of Marine Science and Engineering*. 2021; 9(7):758.
https://doi.org/10.3390/jmse9070758

**Chicago/Turabian Style**

Maandal, Gerard Lorenz D., Mili-Ann M. Tamayao-Kieke, and Louis Angelo M. Danao.
2021. "Techno-Economic Assessment of Offshore Wind Energy in the Philippines" *Journal of Marine Science and Engineering* 9, no. 7: 758.
https://doi.org/10.3390/jmse9070758