# 100% Renewable Energy Grid for Rural Electrification of Remote Areas: A Case Study in Jordan

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## Abstract

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_{2}are avoided yearly. This study can be easily extended to other rural cities in Jordan, as they have higher renewable energy system (RES) potential. The presented findings are essential not only for Jordan’s planning and economy-boosting but also for contributing to the ongoing force against climate change.

## 1. Introduction

_{2}) emissions [5]. According to Timmerberg et al. [6], the estimated rate of CO

_{2}emissions in the Middle East region is 0.396–0.682 kg CO

_{2}kWh

^{−1}, which means a considerable amount of CO

_{2}is released into the atmosphere. However, as they indicated, if the target for 2030 of the renewable energy share is met, the electricity-production CO

_{2}emissions are expected to drop to 0.341–0.514 kg CO

_{2}kWh

^{−1}. Attaining solutions to such environmental problems that we face today, long-term planning and actions become vital for sustainable development [1]. Also, a large portion of the industry’s research is heavily focused on advancements that reduce operational [7,8] or mending energy [9,10]. Besides, more importantly, renewable energy sources play a significant role in mitigating environment-related problems as they are environmentally friendly with affordable and competitive costs relative to conventional energy systems [11,12,13].

## 2. System Description and Methodology

#### 2.1. RES Description

#### 2.1.1. Solar Energy System

_{p}, can be calculated, as shown in A.2. The hourly solar resources, as well as the hourly ambient temperature for Al-Tafilah, were obtained using Meteonorm software, which provides the data based on Typical Metrological Year (TMY).

#### 2.1.2. Wind Energy System

_{Z}) can be extrapolated using equation A.3. By assuming that the energy generated is constant during the hour and each turbine generates the same amount in the case of having multiple turbines, the total hourly electrical energy generated by a wind turbine(s), E

_{w}, can be estimated by A.4 in App. An under wind energy model section. The Weibull distribution shape parameter of the available wind speed, K can be calculated based on Justus theory using Equation (A5). A wind turbine with 2 MW of rated power from GAMESA company (G114-2.0) was used in this study. It should be noted the hourly TMY wind speeds at ground level were obtained from Meteonorm software.

#### 2.1.3. Hydropower System

_{h}) of 1 MW, as reported in [32]. In this study, a 1 MW hydropower system with capacity factor (CF

_{h}) of 80% [41] is designed to provide continuous energy generation as part of the baseload of Al-Tafilah, where the annual energy production from the hydropower system, E

_{h}, can be estimated as shown in A.6.

#### 2.2. System Modelling and Energy Flow: With and Without an Energy Storage System

_{H}), which represents the annual fraction of demand met by the RES, was used. F

_{H}can be calculated by A.7. The forecasted demand of 2030 was used in this study, where the average hourly demand of Al-Tafilah in 2010 obtained from [44] was used for estimating the 2030 demand. The population of 2030 was forecasted using the Piecewise Cubic Hermite Interpolating Polynomial method, where 1994, 2004, 2015, and 2019 populations were used for this forecast [35,36].

#### 2.3. Optimization Procedure

#### 2.4. Economic Assessment and System’s Feasibility

## 3. Results and Discussion

_{2}[3,54]. The fuel-saving and reduction in CO

_{2}calculations were based on a previously developed code for the simulation of the power plant presented in those latter references, where detailed analysis of fuel consumption and CO

_{2}emission calculations for the power plant can be found. Based on estimations by the United States Environmental Protection Agency, this CO

_{2}reduction is equivalent to the carbon sequestered annually by US forest spreading an area of 61,589 acres. Therefore, the presented system also progresses Jordan’s adherence to the greenhouse gas limit set by the Paris agreement. For a developing country like Jordan, a transition towards 100% is crucial, as a study by Mathiesen et al. [55] associated such transition with large earnings on export potential, creating jobs, and economic growth. So, this work, which demonstrated a step further towards a 100% renewable energy grid, will support a more robust economy, at the same time, a greener Jordan.

## 4. Conclusions

_{2}emissions could be avoided yearly, which demonstrates the environmental benefits of the proposed work. Therefore, these finding are essential not only for future renewable energy planning in the country and improving its economy but also for contributing to the ongoing force against climate change. When the constraint on the LCOE was removed, the RES fraction achieved was equal to 100%, with a slightly higher LCOE of 0.165 $/kWh. Since the prices of RES technologies are expected to drop dramatically in the next decades, the last scenario can also be adopted as the LCOE drops significantly with the RES price reduction. Finally, sensitivity analysis showed that the RES fraction of the hybrid PV/wind/hydro system without ESS is the most sensitive configuration to the variation in the resources and electrical demand. In contrast, the LCOE of the three configurations showed the largest sensitivity to the variation in the resources and demand compared to its sensitivity to the RES costs and annual discount rate variations.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Nomenclature

${E}_{h}$ | Annual energy production from the hydropower system, kWh | ${u}_{g}$ | Average wind speed at ground level, m/s |

${E}_{P}$ | Hourly energy generated from the PV power plant, kWh | ${\mathrm{u}}_{\mathrm{R}}$ | Rated wind speed, m/s |

${E}_{w}$ | generated by a wind turbine(s), kWh | ${u}_{Z}$ | Wind speed at hub height, m/s |

${E}_{st}^{t}$ | Energy stored at time t, kWh | $\overline{\mathrm{u}}$ | Mean wind speed at hub height, m/s |

${E}_{st}^{max}$ | Battery capacity, kWh | $\mathrm{Z}$ | Hub height, m |

${F}_{H}$ | RES Fraction, % | ${\mathrm{Z}}_{\mathrm{g}}$ | Height of the ground level, m |

${\mathrm{I}}_{\mathrm{R}}$ | Reference insolation at nominal conditions, Wh/m^{2} | Acronyms and Abbreviations | |

${I}_{T}$ | Global insolation on a tilted surface, Wh/m^{2} | ESS | Energy storage system |

$\mathrm{K}$ | Shape parameter of the Weibull distribution | GHGs | Greenhouse gases |

$\mathrm{L}$ | Lifetime of the system, years | GRG | Generalized reduced gradient |

$LCOE$ | Levelized Cost of Electricity, USD/kWh | LCOE | Levelized cost of electricity |

${\mathrm{M}}_{\mathrm{t}}$ | Yearly fixed maintenance cost of the RES, USD | NPV | Net present value |

${\mathrm{N}}_{\mathrm{m}}$ | Number of modules in the PV power plant | PBP | Payback period |

$\mathrm{NOCT}$ | Nominal operating cell temperature, ^{o}C | PV | Photovoltaic |

${\mathrm{P}}_{\mathrm{h}}$ | Hydropower capacity, kW | RES | Renewable energy system |

${\mathrm{P}}_{\mathrm{R}}$ | Rated electrical power of the wind turbine, kW | ZBB | Zinc-Bromine battery |

$r$ | Annual discount rate, % | Greek letters | |

${T}_{a}$ | Ambient temperature, ^{o}C | $\mathsf{\alpha}$ | Wind shear coefficient |

${\mathrm{T}}_{\mathrm{R},\mathrm{NOCT}}$ | Reference module temperature at nominal conditions, ^{o}C | ${\mathsf{\beta}}_{\mathrm{R}}$ | Temperature coefficient, 1/°C |

${\mathrm{T}}_{\mathrm{Ref},\mathrm{STC}}$ | Reference module temperature at standard conditions, ^{o}C | ${\mathsf{\eta}}_{\mathrm{l}}$ | System losses, % |

${\mathrm{u}}_{\mathrm{C}}$ | Cut-in wind speed of the wind turbine, m/s | ${\mathsf{\eta}}_{\mathrm{PV},\mathrm{R}}$ | Reference efficiency of the PV module, % |

${\mathrm{u}}_{\mathrm{F}}$ | Cut-out wind speed, m/s | ${\eta}_{PV}$ | PV module efficiency |

$\mathsf{\sigma}$ | Standard deviation of the wind speeds sample, m/s |

## Appendix A.

Quantity | Equation | Key Points | Equation Number |
---|---|---|---|

PV Energy model | |||

The PV module efficiency | ${\eta}_{PV}={\mathsf{\eta}}_{\mathrm{PV},\text{}\mathrm{R}}\times \left[1-{\mathsf{\beta}}_{\mathrm{R}}\times \left({T}_{a}+\left(\mathrm{NOCT}-{\mathrm{T}}_{\mathrm{R},\mathrm{NOCT}}\right)\times \frac{{I}_{T}}{{\mathrm{I}}_{\mathrm{R}}}-{\mathrm{T}}_{\mathrm{Ref},\mathrm{STC}}\right)\right]$ | Neglecting the effects of relative humidity and wind speeds, while considering the effect of the ambient temperature [56,57]. | (A1) |

The hourly energy generated from the PV power plant | ${\mathrm{E}}_{\mathrm{P}}={\eta}_{PV}\times {I}_{T}\times {\mathrm{A}}_{\mathrm{m}}\times {\mathrm{N}}_{\mathrm{m}}\times {\mathsf{\eta}}_{\mathrm{l}}$ | ${\mathsf{\eta}}_{\mathrm{l}}$ was taken 0.85 based on [58,59] | (A2) |

Wind Energy model | |||

The wind speed at hub height | ${u}_{Z}={u}_{g}\times {\left(\frac{\mathrm{Z}}{{\mathrm{Z}}_{\mathrm{g}}}\right)}^{\mathsf{\alpha}}$ | α can be taken as 1/7 [39], Z_{g} is the height of the ground level [m] at which speed is measured and it is equal 10 m. | (A3) |

total hourly electrical energy generated by a wind turbine(s) | ${\mathit{E}}_{\mathit{w}}=\{\begin{array}{cc}0& ,{u}_{Z}<{\mathrm{u}}_{\mathrm{C}}\mathit{or}{u}_{Z}{\mathrm{U}}_{\mathrm{F}}\\ \mathrm{N}\times {\mathrm{P}}_{\mathrm{R}\text{}}\times \frac{\text{}{\left({\mathrm{u}}_{\mathrm{C}}\right)}^{\mathrm{K}}-{\left({u}_{Z}\right)}^{\mathrm{K}}}{{\left({\mathrm{u}}_{\mathrm{C}}\right)}^{\mathrm{K}}-{\left({\mathrm{u}}_{\mathrm{R}}\right)}^{\mathrm{K}}}& ,{\mathrm{u}}_{\mathrm{c}}\le {u}_{Z}\le {\mathrm{u}}_{\mathrm{R}}\\ \mathrm{N}\text{}\times {\mathrm{P}}_{\mathrm{R}\text{}}& ,{\mathrm{u}}_{\mathrm{R}}{u}_{Z}\le {\mathrm{u}}_{\mathrm{F}}\end{array}$ | By [39] | (A4) |

Shape Parameter | $\mathrm{K}=\left\{{\left(\mathsf{\sigma}/\mathrm{u}\xaf\right)}^{-1.086}\right.$ | $1\le \mathrm{K}\le 10$ | (A5) |

Hydropower Model | |||

Annual energy production from the hydropower system | ${\mathrm{E}}_{\mathrm{h}}={\mathrm{P}}_{\mathrm{h}}\times {\mathrm{CF}}_{\mathrm{h}}\times 24\times 365$ | (${\mathrm{CF}}_{\mathrm{h}}$) of 80% [41] | (A6) |

Performance assessment of RES | |||

RES Fraction | ${F}_{H}=\frac{{D}_{RES}}{D}$ | $D$ is the hourly demand of Al-Tafilah [kWh] which was obtained from [44] | (A7) |

Economic Assessment of the RES | |||

The Levelized Cost of electricity | $LCOE=\frac{{\mathrm{C}}_{\mathrm{i}}+{{\displaystyle \sum}}_{t=1}^{L}\frac{{\mathrm{M}}_{\mathrm{t}}}{{\left(1+r\right)}^{t}}}{{{\displaystyle \sum}}_{t=1}^{L}\frac{{D}_{RES}}{{\left(1+r\right)}^{t}}}$ | (A8) |

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**Figure 1.**Energy flowchart of the photovoltaic (PV)/wind/hydro hybrid system: (

**a**) Without an energy storage system (ESS) and (

**b**) with ESS.

**Figure 3.**Demand met by the renewable energy system (RES) components without ESS (levelized cost 4 of electricity (LCOE) of 0.12 $/kWh).

**Figure 9.**The sensitivity of the RES fraction to the variations in the: (

**a**) Solar and wind resources and (

**b**) demand.

**Figure 10.**The sensitivity of the LCOE to the variations in the: (

**a**) Solar and wind resources, (

**b**) demand, (

**c**) RES costs, and (

**d**) annual discount rate.

Decision Variables | Objective Functions | Constraints | |
---|---|---|---|

Without ESS | With ZBB | ||

PV and wind capacities | PV, wind and ZBB capacities | Maximizing F_{R} | LCOE ≤ 0.12 |

**Table 2.**The economic parameters of the PV, the wind, and the hydropower systems in addition to the electricity purchase tariff and the annual discount rate for Al-Tafilah, Jordan.

Parameter | Value | Reference |
---|---|---|

PV system capital cost ($/kW) | 1533 | [47] |

Wind system capital cost ($/kW) | 1516 | [47] |

Hydropower capital cost ($/kW) | 3000 | [41] |

Zinc-Bromine capital cost ($/kW) | 195 | [48] |

PV system maintenance cost ($/kW) | 24.68 | [49] |

Wind system maintenance cost ($/kW) | 39.53 | [50] |

Hydro power maintenance cost ($/kW) | 75 | [41] |

System expected lifetime (Years) | 25 | [41,48,51] |

Electricity purchase tariff ($/MWh) | 120 | [52] |

Annual discount rate (%) | 5 | [37] |

Parameter | Value |
---|---|

PV Capacity (MW) | 29.37 |

Wind Capacity (MW) | 56 |

Hydropower Capacity (MW) | 1 |

Capacity Factor (%) | 26 |

RES Fraction (%) | 71.5 |

LCOE ($/kWh) | 0.12 |

NPV (M$) | 167.53 |

PBP (Years) | 6.235 |

Parameter | Value | |
---|---|---|

Constrained | Unconstrained | |

PV Capacity (MW) | 75.4 | 116.39 |

Wind Capacity (MW) | 28 | 26 |

Hydropower Capacity (MW) | 1 | 1 |

ZBB (GWh) | 0.259 | 0.415 |

Capacity Factor (%) | 22.48 | 21.55 |

RES Fraction (%) | 98.79 | 99.93 |

LCOE ($/kWh) | 0.12 | 0.165 |

NPV (M$) | 119.55 | 140.45 |

PBP (Years) | 9 | 9.62 |

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## Share and Cite

**MDPI and ACS Style**

Al-Ghussain, L.; Abujubbeh, M.; Darwish Ahmad, A.; Abubaker, A.M.; Taylan, O.; Fahrioglu, M.; Akafuah, N.K. 100% Renewable Energy Grid for Rural Electrification of Remote Areas: A Case Study in Jordan. *Energies* **2020**, *13*, 4908.
https://doi.org/10.3390/en13184908

**AMA Style**

Al-Ghussain L, Abujubbeh M, Darwish Ahmad A, Abubaker AM, Taylan O, Fahrioglu M, Akafuah NK. 100% Renewable Energy Grid for Rural Electrification of Remote Areas: A Case Study in Jordan. *Energies*. 2020; 13(18):4908.
https://doi.org/10.3390/en13184908

**Chicago/Turabian Style**

Al-Ghussain, Loiy, Mohammad Abujubbeh, Adnan Darwish Ahmad, Ahmad M. Abubaker, Onur Taylan, Murat Fahrioglu, and Nelson K. Akafuah. 2020. "100% Renewable Energy Grid for Rural Electrification of Remote Areas: A Case Study in Jordan" *Energies* 13, no. 18: 4908.
https://doi.org/10.3390/en13184908