# Assessing the Potential of Wind Energy as Sustainable Energy Production in Ramallah, Palestine

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}at 100 m. In the conclusion, yearly energy outputs, capacity factors, and economic potential for fifteen wind turbines ranging in size from 0.5 to 5 MW had been evaluated. It was revealed that the greatest capacity factor is about 36% and has a high economic potential at a cost of less than 0.07 $/kWh for an appropriate selection of wind turbine models. This baseline research will be utilized as a decision-making basis for the best and most economical wind energy investment in Palestine.

## 1. Introduction

^{2}[15].

## 2. Materials and Methods

#### 2.1. Palestine’s Climate

#### 2.2. Data Collection and Site Description (31°53′ N 35°13′ E)

^{2}, it lies in the middle West Bank. It is the greatest city in Palestine with 44,587 inhabitants [8]. The Palestinian Meteorological Department provided wind speed data for the current study [23]. For six years, at intervals of three hours, it was recorded at an elevation of 10 m (2016–2021).

#### 2.3. Evaluation of Wind Potential

#### The Different Estimators for Computing the Weibull Parameters

#### WAsP Method

- (i)
- The fitted mean power density for the Weibull and the mean power density for the observed data must be the same;
- (ii)
- For the observed distribution, any frequency value more than the observed average speed must match the fitted Weibull.

#### Graphical Method (Least Squares)

#### Maximum Likelihood Estimator (MLE) Method

#### Moment Method (MOM)

#### Energy Pattern Factor Method

_{pf}) that is used in aerodynamics for designing blades is estimated from average (mean) wind speed as shown in Equations (14) and (15) [2,45]:

#### Empirical Method of Jesus

#### Empirical Method of Lysen

#### 2.4. Methodology

#### 2.4.1. Wind Speed Statistics

#### 2.4.2. Wind Direction

#### 2.4.3. Goodness-of-Fit Tests

^{2}, is the square of the correlation between Weibull and actual data. Equation (20) is utilized for estimating R

^{2}[1]:

#### 2.4.4. Wind Speed Fluctuation with Altitude

#### 2.5. Estimation and Investigation of Wind Power

_{mp}), and the wind speed carrying maximum energy (V

_{max}

_{,E}) are analyzed in this section.

#### 2.5.1. Wind Power Density

^{3}.

#### 2.5.2. The Most Probable Wind Speed (V_{mp})

_{mp}is significant in identifying the most probable wind speed for a particular wind probability distribution, V

_{mp}is estimated by [1]:

#### 2.5.3. Wind Speed Carrying Maximum Energy (V_{max,E})

_{max,E}, is likewise regarded as a relevant speed that must be assessed. It denotes the greatest possible energy at a given location and may be computed using the following equation [1]:

#### 2.6. Energy Output and Environmental, Technical, and Financial Feasibility Study for Different Commercial Wind Turbines by RETScreen

_{F}, is the ratio of its actual annual output, to its rated output calculated for the different wind turbines. The entire cost of investing in wind turbines (including installation, civil works, and other costs) is 1450 US$/kW while operation and maintenance costs (O&M) are 0.04 $/kWh in the last 5 years (2016–2020) with a lifetime of 20 years according to land-based wind market report (2021 edition) [52]. A detailed specification of the assumptions in the RETScrean can be found in Appendix A. Table 2 summarizes the technical details of the wind turbines employed in this investigation. The capacity factor, CF, net present value (NPV), gross annual energy production, simple payback period, gross annual GHG emission reduction (tCO

_{2}), and the energy production cost per KWh ($/KWh) are calculated for the selected wind turbines.

## 3. Results

_{mp}, V

_{max}

_{,E}, R

^{2}, RSME, MBE, and MAE calculated by the seven methods are shown in Table 3. It can be concluded that all the methods give relatively low errors, and that the maximum likelihood estimator has the lowest error and the highest R

^{2}. The maximum likelihood estimator is the best way to characterize the wind shape in Ramallah, as shown by goodness-of-fit test indicators. The actual wind data and Weibull curves for the seven methods are presented in Figure 4. The R

^{2}was found to be more than 0.99 demonstrating a very close agreement with the actual data.

_{mp}estimated by WAsP and the lowest by moment method are 2.13 and 2.00 m/s, respectively. The V

_{max}

_{,E}at 10 m was estimated using the seven estimation methods and is shown in Figure 6. The seven estimators had comparable outcomes. The highest V

_{max}

_{,E}estimated by the least-squares method and the lowest by WAsP method is 4.66 and 4.50 m/s, respectively.

^{2}to 5.93 m, 207.6 W/m

^{2}, respectively. Table 4 also shows that the annual k was increased from 1.90–2.55 from a height of 10 m to 100 m. Moreover, the annual c was improved from 3.1 to 6.78 m/s.

^{2}. At 75 m, ${P}_{d}$ is between 99.9 to 196.0 W/m

^{2}. At 100 m, ${P}_{d}\text{}$ is between 140.0 W/m

^{2}to 268.9 W/m

^{2}. It is obvious that the summer months have the highest power density. Figure 14 demonstrates that the largest wind ${P}_{d}\text{}$ with a high average, values occurring in June, July, and August while the lowest values occurred in October, November, and February.

_{f}), the yearly energy generated (E

_{out}), simple payback period, net present value (NPV), energy production cost, gross annual GHG emission reduction (tCO

_{2}), and GHG reduction equivalent to cars and trucks not used are presented in Table 5 for each model of the wind turbine. The findings have sufficiently shown that raising turbine hub heights enhanced the yearly capacity factor. The annual capacity factor varies from 15.5% for hub height 50 m to 35.7% for 50 m hub height 160 m. The results revealed that wind turbines with a hub height of fewer than 75 m have a capacity factor of less than 17%. Turbines with hub heights in the range of 75–100 m could reach a capacity factor of 24%. However, turbines with a hub height of more than 100 m could reach a 36% capacity factor.

_{f}should be more than 25% for a cost-effective wind power investment [2,53]. As a result of this, the hub height of the turbine should not be less than 75 m. Additionally, Table 5 displayed the yearly energy generated by wind turbines. Yearly energy generated changed depending on the turbine models and rated power and capacity factor. The lowest annual energy output was 0.765 GWh for power-wind 500–50 m with a rated power of 500 kW, a hub height of 50 m, and a capacity factor of 15.5%. However, the highest yearly energy output was 10.313 GWh for Fuhrlaender, FL3000–140m with a rated power of 3000 kW, a hub height of 140 m, and a capacity factor of 35%.

_{2}during its lifetime.

_{2}which is equivalent to a reduction of more than 700 cars and trucks not used.

## 4. Discussion

_{pf}, and empirical estimators to choose the more efficient estimator. The six-year wind profiles at a 10-m anemometer height were investigated to determine whether they were suitable for efficient energy production.

^{2}. At 75 m, ${P}_{d}\text{}$ is between 99.9 to 196.0 W/m

^{2}.

_{.}At 100 m,${P}_{d}$ is between 140.0 to 268.9 W/m

^{2}

_{.}The summer months have the highest power density.

## 5. Conclusions

- The mean monthly wind speed varies between 2.20–3.22 m/s at 10 m heights, 3.71–5.22 m/s at 50 m, 4.37–6.06 m/s at 75 m, and 4.95 and 6.79 m/s at 100 m;
- The variations of k and c were calculated to be in the range of 1.88–2.9 and 4.31–5.88 m/s respectively at 50 m, 2.00–3.22 m/s, and 5.09–6.82 m/s respectively at 75 m, and 2.1–3.25and 5.78–7.63 m/s, respectively, at 100 m;
- The main wind direction in Ramallah was from the west-northwest (WNW) with 29.5% of occurrence;
- The summer months have the highest power density and reach 129.9 at 50 m, 196.0 at 75 m, and 268.9 W/m
^{2}at 100 m; - C
_{f}for the fifteen selected wind turbines was found to vary from 16% to 36%; - Among the fifteen wind turbines studied, it was found that wind to energy W2E103/2500-160m has the highest capacity factors about 36%;
- For a cost-effective investment in wind energy, only five turbines could be suitable in Ramallah out of the 15 turbines that were studied;
- Wind energy has been found to have a high economic potential at a cost of less than 0.07 $/kWh for an appropriate selection of wind turbine models.

## Author Contributions

## Funding

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

WB | West Bank |

GS | Gaza Strip |

PNA | Palestinian National Authority |

Probability density function | |

c | Weibull scale parameter |

k | Weibull shape parameter |

CDF | Accumulative distribution function |

LRRM | Least-squares regression method |

MLE | Maximum likelihood estimator |

MOM | Moment method |

Epf | Energy pattern factor |

$\mathsf{\u0490}$ | The upper incomplete gamma function |

$\overline{v}$ | Mean wind speed |

σ | Wind speed standard deviation |

R^{2} | Coefficient of determination |

RMSE | Root mean square error |

MBE | Mean bias error |

MAE | Mean bias absolute error |

α | Surface roughness coefficient |

P_{d} | Wind power density |

V_{mp} | Most probable wind speed |

V_{max,E} | Wind speed carrying maximum energy |

O&M | Operation and maintenance costs |

CF | Capacity factor |

NPV | Net present value |

GHG | Greenhouse gases |

Eout | The yearly energy generated |

(NREAP) | New renewable energy action plan |

GWh | Gigawatt hours |

$/KWh | United States dollar per kilowatt hours |

tCO_{2} | Tons of Co_{2} |

$ | United States dollar |

kW | Kilowatt |

## Appendix A

Characteristic | Value |
---|---|

Array losses | 2% |

Airfoil losses | 2% |

Miscellaneous losses | 6% |

Availability | 98% |

Initial costs | 1450 $/KW |

O&M costs (savings) | 40 $/KWh |

Electricity export rate $/KWh | 0.17 |

Pressure Coefficient | 0.971 |

Temperature Coefficient | 0.996 |

Losses Coefficient | 0.88 |

Transmission and distribution (T&D) losses | 7% |

GHG emission factor (excl. T&D) | 0.493 |

Fuel cost escalation rate | 2% |

Inflation rate | 2% |

Discount rate | 9% |

Reinvestment rate | 9% |

Project life | 20 year |

Incentives and grants | 0 |

Debt ratio | 0 |

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**Figure 2.**Palestine (WB and GS) [8].

Year | Mean Speed (m/s) | Standard Deviation (m/s) | Variation Coefficient | Monthly Minimum (m/s) | Monthly Maximum (m/s) | Median (m/s) |
---|---|---|---|---|---|---|

2016 | 2.82 | 1.688 | 59.8 | 2.37 | 3.40 | 2.86 |

2017 | 2.78 | 1.535 | 55.3 | 2.21 | 3.50 | 2.65 |

2018 | 2.77 | 1.653 | 59.7 | 1.95 | 3.60 | 2.75 |

2019 | 2.76 | 1.426 | 51.7 | 2.01 | 3.40 | 2.75 |

2020 | 2.46 | 1.530 | 62.2 | 1.49 | 3.60 | 2.37 |

2021 | 2.80 | 1.379 | 49.3 | 2.26 | 3.34 | 2.81 |

2016–2021 | 2.73 | 1.544 | 56.6 | 2.19 | 3.22 | 2.71 |

Wind Turbine Model | Rated Power (kW) | Rated Speed (m/s) | Cut-In Speed (m/s) | Cut-Out Speed (m/s) | Rotor Diameter (m) | Hub Height (m) |
---|---|---|---|---|---|---|

PowerWind 500–50m | 500 | 10 | 3 | 25 | 56 | 50 |

EWT DW 54–500KW–50m | 500 | 10 | 3 | 25 | 54 | 50 |

CSIC HZ Windpower H102–2000 | 2000 | 12 | 3 | 25 | 102 | 70 |

EWT DW 52–500KW–75m | 500 | 10 | 3 | 25 | 52 | 75 |

EWT DW 54–500KW–75m | 500 | 10 | 3 | 25 | 54 | 75 |

Guodian United Power UP77/1500–75m | 1500 | 11 | 3 | 25 | 75 | 77.36 |

AAER A–2000–100 | 2000 | 12 | 3 | 20 | 84 | 100 |

REpower MM92–100m | 2000 | 11 | 3 | 24 | 92.5 | 100 |

Sinovel SL1500/77–100m | 1500 | 12 | 3 | 20 | 77.4 | 100 |

Vensys77–100m | 1500 | 12 | 3 | 22 | 77 | 100 |

Wind To Energy W2E93/2000–100m | 2000 | 13 | 3 | 24 | 93 | 100 |

REpower 5M–117 | 5000 | 13 | 3 | 25 | 126 | 117 |

ENERCON–101–135m | 3000 | 13 | 3 | 25 | 101 | 135 |

Fuhrlaender FL3000–140m | 3000 | 13 | 3 | 25 | 120.6 | 140 |

Wind To Energy W2E103/2500–160m | 2500 | 12 | 3 | 25 | 103 | 160 |

Parameter Estimation Method | WAsP Method | Least-Squares Regression Method | Maximum Likelihood Method | Moment Method | Energy Pattern Factor Method | Empirical Method of Jestus | Empirical Method of Lysen |
---|---|---|---|---|---|---|---|

k | 1.930 | 1.837 | 1.901 | 1.833 | 1.883 | 1.857 | 1.857 |

c (m/s) | 3.111 | 3.123 | 3.104 | 3.073 | 3.076 | 3.075 | 3.077 |

R^{2} | 0.99842 | 0.99854 | 0.99855 | 0.99848 | 0.99842 | 0.99845 | 0.99846 |

RMSE | 0.01100 | 0.01014 | 0.01010 | 0.01056 | 0.01093 | 0.01074 | 0.01072 |

MBE | −0.09653 | −0.09339 | −0.09312 | −0.09459 | −0.09607 | −0.09531 | −0.09526 |

MAE | 0.09654 | 0.09339 | 0.09312 | 0.09459 | 0.09607 | 0.09531 | 0.09526 |

${P}_{d}$ (w/m^{2}) | 25.5 | 27.4 | 25.8 | 26.1 | 25.4 | 25.8 | 25.8 |

V_{mp} | 2.13 | 2.04 | 2.10 | 2.00 | 2.06 | 2.03 | 2.03 |

V_{max,E} | 4.50 | 4.66 | 4.53 | 4.60 | 4.52 | 4.56 | 4.56 |

h (m) | V (m/s) | k | c (m/s) | P_{d} (W/m^{2}) |
---|---|---|---|---|

10 | 2.73 | 1.90 | 3.10 | 25.77 |

50 | 4.51 | 2.28 | 5.15 | 98.83 |

75 | 5.26 | 2.43 | 6.06 | 153.28 |

100 | 5.93 | 2.55 | 6.78 | 207.59 |

**Table 5.**Energy, emission, and financial parameters for selected wind turbines at hub heights (50–160 m).

Wind Turbine Model | C_{f} (%) | E_{out} (GWh) | Simple Payback (Year) | Net Present Value (NPV)($) | Energy Production Cost ($/KWh) | Gross Annual GHG Emission Reduction (tCO_{2}) | GHG Reduction Equivalent to Cars and Trucks Not Used |
---|---|---|---|---|---|---|---|

PowerWind 500–50m | 15.5 | 0.765 | 7.6 | 293,363 | 0.152 | 334 | 61.2 |

EWT DW 54–500KW–50m | 16.7 | 0.825 | 7 | 388,780 | 0.141 | 360 | 65.2 |

CSIC HZ WindpowerH102–2000 | 17 | 3.365 | 6.8 | 1,662,588 | 0.138 | 1469 | 269 |

EWT DW 52–500KW–75m | 21.8 | 1.08 | 5.1 | 799,630 | 0.108 | 471 | 86.3 |

EWT DW 54–500KW–75m | 23.8 | 1.178 | 4.6 | 958,613 | 0.099 | 514 | 94.2 |

Guodian United Power UP77/1500–75m | 18.3 | 2.718 | 6.2 | 1,560,615 | 0.128 | 1186 | 217 |

AAER A–2000–100 | 19.6 | 3.874 | 5.8 | 2,482,432 | 0.12 | 1691 | 310 |

REpower MM92–100m | 23.8 | 4.704 | 4.6 | 3,818,911 | 0.099 | 2053 | 376 |

Sinovel SL1500/77–100m | 21.1 | 3.138 | 5.3 | 2,235,957 | 0.11 | 1369 | 251 |

Vensys77–100m | 22.1 | 3.279 | 5 | 2,463,305 | 0.106 | 1431 | 262 |

Wind To Energy W2E93/2000–100m | 22.5 | 4.461 | 4.9 | 3,427,503 | 0.104 | 1947 | 357 |

REpower 5M–117 | 20.2 | 10.017 | 5.5 | 6,741,153 | 0.116 | 4372 | 801 |

ENERCON–101–135m | 27.8 | 8.251 | 3.9 | 7,653,764 | 0.085 | 3601 | 660 |

Fuhrlaender FL3000–140m | 34.7 | 10.313 | 3 | 10,973,802 | 0.068 | 4501 | 824 |

Wind To Energy W2E103/2500–160m | 35.7 | 8.751 | 3 | 9,398,214 | 0.066 | 3819 | 700 |

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**MDPI and ACS Style**

Abdallah, R.; Çamur, H.
Assessing the Potential of Wind Energy as Sustainable Energy Production in Ramallah, Palestine. *Sustainability* **2022**, *14*, 9352.
https://doi.org/10.3390/su14159352

**AMA Style**

Abdallah R, Çamur H.
Assessing the Potential of Wind Energy as Sustainable Energy Production in Ramallah, Palestine. *Sustainability*. 2022; 14(15):9352.
https://doi.org/10.3390/su14159352

**Chicago/Turabian Style**

Abdallah, Ramez, and Hüseyin Çamur.
2022. "Assessing the Potential of Wind Energy as Sustainable Energy Production in Ramallah, Palestine" *Sustainability* 14, no. 15: 9352.
https://doi.org/10.3390/su14159352