# Techno-Economic Assessment of a Grid-Independent Hybrid Power Plant for Co-Supplying a Remote Micro-Community with Electricity and Hydrogen

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## Abstract

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## 1. Introduction

## 2. Literature Review

_{2}emissions reduction. The results suggested that the system with vanadium redox flow storages would impose a lower NPC and LCOE. Ibrahim et al. [22] assessed two different combinations of off-grid hybrid systems for supplying a water treatment plant with electricity in Egypt. The first one comprised a PV system, wind turbine, diesel generator, and battery, while the other included a hydrokinetic turbine instead of a wind turbine. Aziz et al. [23] sought to optimize a hybrid power generation system in terms of economic, technical, and environmental views in order to provide a village in Iraq with electricity. The results of simulation by HOMER revealed that the hybrid solar/hydro/diesel/battery system was the most cost-effective one. Cordero et al. [9] studied the feasibility of the application of a hybrid hydrokinetic/wind/diesel system coupled with lead acid batteries to meet the load required by a university in the south of Ecuador. Four main parameters that were attempted to be optimized were NPC, LCOE, unmet electric demand, and penetration of the diesel generator. Kumar et al. [24] proposed and tested a bi-level approach in order to design rural micro-grids to be applied in a remote village in a hilly region. The design assessed the available resources in the proximity of the area, including solar, hydrokinetic, and hydro with the dam and diesel generator, batteries, and pump-hydro storage as backups.

## 3. Case Study Area

## 4. Technical Characteristics

#### 4.1. Modeling of PV System

- ${Y}_{PV}$: the rated capacity of the PV system under standard environment in kW,
- ${f}_{PV}$: derating or reduction factor which impacts the performance of the PV under real-world conditions in %,
- ${I}_{T}$: the solar radiation incident on the PV module in kW/m
^{2}, - ${I}_{s}$: the solar radiation incident under standard test conditions in kW/m
^{2}, - ${\phi}_{P}$: the temperature coefficient of power in $\xb0\mathrm{C}$,
- ${T}_{c}$: the PV cell temperature in the current time step in $\xb0\mathrm{C}$,
- ${T}_{s}$: the PV cell temperature under standard test conditions in $\xb0\mathrm{C}$.

^{2}, cell temperature of 25 $\xb0\mathrm{C}$, and without wind. Additionally, to simulate the real-world conditions, the derating factor is of high value as some circumstances, such as dust or snow cover, shading, and wire losses, may degrade the performance of the PV system.

- ${T}_{a}$: the ambient temperature in $\xb0\mathrm{C}$,
- ${T}_{c,NOCT}$: the PV nominal cell temperature, denoting the surface temperature that the PV may reach when exposed to a condition under which solar radiation, ambient temperature, and wind velocity are 0.8 kW/m
^{2}, 20 $\xb0\mathrm{C}$, 1 m/s, respectively, - ${\gamma}_{PV}$: the electrical conversion efficiency of the PV system in %.

- ${\overline{G}}_{b}$: the beam radiation in kW/m
^{2}, - ${\overline{G}}_{d}$: the diffuse radiation in kW/m
^{2}, - ${\overline{G}}_{o}$: the average extraterrestrial horizontal radiation in kW/m
^{2}, - $\theta $: the angle of incidence or the angle between the sun’s beam radiation and the PV surface in $\xb0$.
- ${\theta}_{z}$: the zenith angle in $\xb0$. It constitutes zero or 90$\xb0$ if the sun is directly overhead or at the horizon, respectively,
- $\beta $: the slope of the surface in $\xb0$.
- ${\rho}_{g}$: albedo in %, indicating the portion of solar radiation striking the ground and then reflecting. This value may change from 20% to 70% according to the surrounding area of the PV systems.

#### 4.2. Modeling of Wind Turbine

- ${\rho}_{air}$: the actual air density in kg/m
^{3}, - $A$: the area swept by the blades of the nominated turbine in m
^{2}, - $v$: the wind speed at the current time step in m/s,
- ${C}_{pw}$: the coefficient of the turbine performance in %,
- ${{\displaystyle \mathsf{\u0273}}}_{WT}$: the combined efficiency of the turbine and its generator,
- $t$: the whole time that the total produced energy is to be projected.

- ${v}_{anem}$: the recorded wind speed at the height of the anemometer in m/s,
- ${H}_{hub}$: the distance between the rotor of the nominated wind turbine and the ground in m,
- ${H}_{anem}$: the anemometer height in m,
- $z$: the power law coefficient (Equation (6)).$$\mathsf{\alpha}}=\frac{\left[0.37-0.088\times \mathrm{ln}\left({v}_{anem}\right)\right]}{\left[1-0.088\times \mathrm{ln}\left(\frac{{H}_{anem}}{10}\right)\right]$$

#### 4.3. Modeling of Hydrokinetic Turbine

- ${\rho}_{water}$: the density of water in kg/m
^{3}, - ${A}_{HKT}$: the area rotated by the blades of the hydrokinetic turbine in m
^{2}, - ${v}_{water}$: the speed of water flow in m/s,
- ${{\displaystyle \mathsf{\u0273}}}_{HKT}$: the combined efficiency of the hydrokinetic turbine and the generator,
- $h$: the whole time during which the hydrokinetic turbine operates and the produced energy is to be projected,
- ${C}_{pHKT}$: the performance coefficient of hydrokinetic turbine which can be obtained by Equation (8) [47].$${C}_{pHKT}=\frac{{P}_{rotor}}{{P}_{avaiable}}$$
- ${P}_{rotor}$: the amount of power that is produced by the rotor in kW,
- ${P}_{avaiable}$: the amount of power available in the free stream in kW.

#### 4.4. Modeling of Inverter

- ${P}_{l,s}\left(t\right)$: the power sent to the load side from output of the inverter in kW,
- ${P}_{i}\left(t\right)$: the input power of the inverter,
- ${{\displaystyle \mathsf{\u0273}}}_{inv}$: the efficiency of the inverter.

#### 4.5. Modeling of Battery

- ${V}_{bus}$: the voltage in bus,
- ${{\displaystyle \mathsf{\u0273}}}_{bat}$: the efficiency of the battery in %,
- ${P}_{b}\left(t\right)$: the load power of the battery in kW that can be determined by Equation (11) [48].$${P}_{b}\left(t\right)=\frac{k\times {Q}_{1}\left(t\right)\times \mathrm{exp}\left(-k\right)+Q\left(t\right)\times k\times c\times \left(1-\mathrm{exp}\left(-k\mathsf{\u2206}t\right)\right)}{1-\mathrm{exp}\left(-k\mathsf{\u2206}t\right)+c\times \left(k\mathsf{\u2206}t-1+\mathrm{exp}\left(-k\mathsf{\u2206}t\right)\right)}$$
- $k$: the constant energy storage rate,
- ${Q}_{1}\left(t\right)$: the amount of energy available at the start of the operating interval and above the minimum state of charge,
- $Q\left(t\right)$: the total energy at the start of the passage of time,
- $c$: the storage capacity ratio,
- $\mathsf{\u2206}t$: the time interval.

- $L{E}_{S}$: life expectancy of the system, which is the lifetime of the project,
- ${L}_{bat}^{last.year}$: the duration of the battery in the last year operation of the system,
- ${T}_{bat}^{L}$: the period of time from the beginning of the year to the last battery replaced.

#### 4.6. Modeling of Electrolyzer

_{2}and O

_{2}, using electrical energy has long been employed for making hydrogen. This process can occur via a water electrolysis system by which electricity is used to generate hydrogen. Equation (13) can be applied to evaluate the rate of obtained hydrogen [42].

- ${I}_{ele}$: the electrolyzer current,
- ${N}_{c}$: the number of total cells which are in series in the electrolyzer,
- $F$: the coefficient of Faraday,
- ${{\displaystyle \mathsf{\u0273}}}_{F}$: the Faraday efficiency that can be determined by Equation (14) [48].$${{\displaystyle \mathsf{\u0273}}}_{F}=96\times \mathrm{exp}\left(\frac{0.09}{{I}_{ele}}-\frac{75.5}{{I}_{ele}{}^{2}}\right)$$

- ${B}_{E}$ and ${A}_{E}$: the coefficients of curve consumption in kW/kg/h,
- ${R}_{n,hydrogen}$: The nominal mass flow rate of hydrogen in kg/h.

## 5. Economic Features

- $n$: the lifetime of the project, same as $L{E}_{S}$ in Equation (12),
- $i$: the real amount of interest rate in % determined by Equation (17),
- $CRF$: the capital recovery factor obtained by Equation (18),
- ${C}_{ta}$: the total annualized cost of the system equating to the aggregation of capital cost, replacement cost and OM cost.$$i=\frac{{i}_{n}-f}{1+f}$$
- ${i}_{n}$: the nominal interest rate in %,
- $f$: the inflation rate.$$CRF\left(i,n\right)=\frac{i\times {(1+i)}^{n}}{{\left(1+i\right)}^{n}-1}$$

- ${V}_{elec}$: the value of electricity in $/kWh,
- ${M}_{hydrogen}$: the total amount of hydrogen gained at the output of the electrolyzer in kg.

## 6. Assumptions

## 7. Techno-Economic Assessment

## 8. Sensitivity Analysis and Discussion

## 9. Conclusions

## 10. Future Research Direct

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**The location of the micro-community under study in Khuzestan province in the vicinity of Karun River.

**Figure 4.**Schematic of the off-grid integrated system (WT: wind turbine, HKT: hydrokinetic turbine).

**Figure 16.**Total NPC ($) when water flow velocity, wind speed, solar radiation, and temperature fluctuate.

Component | Size/Number | Lifetime (yr) | Capital Cost | Replacement Cost | OM Cost | Other Specifications |
---|---|---|---|---|---|---|

Wind turbine | 10 (kW) | 20 | 2000 ($/kW) | 1200 ($/kW) | 100 ($/kW/yr) | Electrical bus: AC Hub height: 16 m Rotor diameter: 15.8 m Cut-in wind velocity: 2.75 m/s Cut-out wind velocity: 20 m/s |

PV system | 40 (kW) | 25 | 1300 ($/kW) | 1300 ($/kW) | 20 ($/kW/yr) | Electrical bus: AC Derating (reduction) factor: 96% Temperature coefficient: −0.41%$/\xb0\mathrm{C}$ operating temperature: 45 $\xb0\mathrm{C}$ Efficiency at standard test conditions: 17.3% Ground reflectance: 20% Tracking system: no tacking |

Hydrokinetic turbine | 20 (kW) | 20 | 35,000 ($/#) | 21,000 ($/#) | 1200 ($/#/yr) | Electrical bus: AC Size: 2.3 m × 3 m Weight: 750 kg Rotor diameter: 1.54 m Water depth required: 3 m |

Converter | 25 (kW) | 10 | 300 ($/kW) | 300 ($/kW) | 0 | Rectifier efficiency: 94% Inverter efficiency: 96% Rectifier relative capacity: 80% |

Battery | 1(#) | 20 | 12,000 ($/#) | 12,000 ($/#) | 20 ($/yr) | Type: vanadium redox flow battery Throughput: 876,000 kWh Nominal Voltage: 48 V Nominal Capacity: 100 kWh Roundtrip efficiency: 64% Maximum charge current: 200 (A) Maximum discharge current: 313 (A) Initial state of charge: 100% |

Electrolyzer | 20 (kW) | 15 | 2000 ($/kW) | 2000 ($/kW) | 50 ($/kW/yr) | Electrical bus: DC Efficiency: 85% |

Hydrogen Tank | 100 (kg) | 25 | 300 ($/kg) | 300 ($/kg) | 0 | Initial tank level: 0 |

Component | Capital ($) | Replacement ($) | OM ($) | Salvage ($) | Total ($) |
---|---|---|---|---|---|

Wind turbine | 20,000 | 0 | 45,309 | 0 | 65,309 |

PV system | 52,000 | 0 | 36,247 | −42,347 | 45,900 |

Hydrokinetic turbine | 35,000 | 0 | 54,371 | 0 | 89,371 |

Converter | 7500 | 15,134 | 0 | 0 | 22,634 |

Battery | 12,000 | 0 | 906 | 0 | 12,906 |

Electrolyzer | 40,000 | 114,657 | 45,309 | −108,581 | 91,385 |

Hydrogen Tank | 30,000 | 0 | 0 | −24,431 | 5569 |

System | 196,500 | 129,791 | 182,142 | −175,359 | 333,074 |

System | PV (kW) | WT (#) | HKT (#) | NPC ($) | LCOE ($/kWh) | LCOH ($/kg) |
---|---|---|---|---|---|---|

Single source | 0 | 0 | 3 | 400,607 | 0.1389 | 4.82 |

Single source | 0 | 7 | 0 | 589,657 | 0.2230 | 8.27 |

Double source | 0 | 1 | 2 | 376,545 | 0.1306 | 4.95 |

Double source | 0 | 3 | 1 | 417,792 | 0.1454 | 5.53 |

Double source | 40 | 0 | 2 | 357,136 | 0.1239 | 4.10 |

Double source | 198 | 0 | 1 | 449,072 | 0.1558 | 6.37 |

Double source | 40 | 4 | 0 | 439,631 | 0.1547 | 5.74 |

Double source | 157 | 2 | 0 | 443,272 | 0.1551 | 6.29 |

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**MDPI and ACS Style**

Xia, T.; Rezaei, M.; Dampage, U.; Alharbi, S.A.; Nasif, O.; Borowski, P.F.; Mohamed, M.A. Techno-Economic Assessment of a Grid-Independent Hybrid Power Plant for Co-Supplying a Remote Micro-Community with Electricity and Hydrogen. *Processes* **2021**, *9*, 1375.
https://doi.org/10.3390/pr9081375

**AMA Style**

Xia T, Rezaei M, Dampage U, Alharbi SA, Nasif O, Borowski PF, Mohamed MA. Techno-Economic Assessment of a Grid-Independent Hybrid Power Plant for Co-Supplying a Remote Micro-Community with Electricity and Hydrogen. *Processes*. 2021; 9(8):1375.
https://doi.org/10.3390/pr9081375

**Chicago/Turabian Style**

Xia, Tian, Mostafa Rezaei, Udaya Dampage, Sulaiman Ali Alharbi, Omaima Nasif, Piotr F. Borowski, and Mohamed A. Mohamed. 2021. "Techno-Economic Assessment of a Grid-Independent Hybrid Power Plant for Co-Supplying a Remote Micro-Community with Electricity and Hydrogen" *Processes* 9, no. 8: 1375.
https://doi.org/10.3390/pr9081375