# Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Case Study

#### 2.2. Problem Statement

#### 2.3. Canal-Top PV Accommodation

#### 2.4. System Modeling

#### 2.4.1. Canal-Top PV

^{2}) and ${T}_{ref}$ (25 °C) are reference irradiation and temperature, respectively [34,37].

#### 2.4.2. DC/AC Inverter

#### 2.4.3. Load Demand Profile

#### 2.5. Metaheuristic Optimizers

#### 2.5.1. Particle Swarm Optimization (PSO)

#### 2.5.2. Genetic Algorithm (GA)

- The algorithm randomly starts reading the LCOE, and then an initial population (POPo) is assumed.
- The constraints in (POPo) are checked. The solutions that are outside the constraints are eliminated with a large penalty.
- The objective function (i.e., LCOE) is checked and evaluated at (POPo), and a new solution is generated (POPk).
- Another population is generated (POPk+1) via GA elitism, selection, crossover, and mutation.
- The constraints are checked at a solution (POPk+1). The simulation is stopped for a previously determined number of iterations. Ultimately, the best solution is printed.

#### 2.5.3. Cuckoo Search (CS)

- The number of host nests is decided. Adult cuckoo birds choose random nests to lay the eggs. An adult cuckoo bird can lay an egg one at a time.
- Only eggs with better quality that the host parents cannot discover are moved to the upcoming generation.
- Provided that the probability (Pa) lies between 0 and 1, the host nest can delineate a cuckoo egg according to the Pa. Cuckoos’ eggs discovered by the host birds are thrown away. Sometimes, the host bird might abandon the nest.

## 3. Problem Formulation

_{t}is the total yearly costs, and E

_{l}is the total yearly demand in kWh.

_{int}is the total initial or CAPEX costs, C

_{OM}is the operational and maintenance costs, and C

_{rep}is the replacement costs.

_{PV}is canal-top solar PV initial costs.

#### 3.1. GHG Emissions

#### 3.2. Loss of Power Supply Probability (LPSP)

#### 3.3. Constraints

#### 3.4. Evaporation Estimation

^{2}). Water loss variability (W

_{L}) % was estimated at a daily time scale using Equation (19):

#### 3.5. Optimizers Implementation

- For satisfactory evaporation reduction and environmental improvement, the PVs have a higher priority than the primary grid to meet the load demands,
- Canal-top PVs can provide the load demands; consequently, the excess energy is sold to the primary grid, as demonstrated in Figure 4, and
- Canal-top PVs cannot meet load demands. The deficit load energy is purchased from the primary grid.

## 4. Results and Discussion

#### 4.1. Water Loss Due to Evaporation

#### 4.2. Energy Production and Profitability

#### 4.2.1. Scenario 1: Impact of Tilt Angle

#### 4.2.2. Scenario 2: Economical Solution

^{3}/day for all optimizers.

#### 4.2.3. Scenario 3: GIS Investigations

^{2}area of 35 ft average length with dimensions of 46.6, 34.4, 48.9, and 5.8 ft, respectively, to yield 10.6 kWp. Furthermore, Helioscope displays the system loss sources for the area under investigation, as in Figure 13.

#### 4.2.4. Scenario 4: Sensitivity of the Top Canal PV Accommodation

## 5. Discussions

- -
- The developed CS optimizer is validated through an impartial comparison with prior research, as demonstrated in Table 3. In [56], several battery-mix technologies were employed via a hybrid PSO-GOA algorithm. In [34], a bi-objective ant colony was conducted. Despite the figures in Table 3 depending on location, meteorological data, initial costs, microgrid configuration, salvage market, and the optimizer’s ability to find a near-optimal solution, the developed CS seems to be competitive the other algorithms in the literature.

Algorithm | LCOE ($/kWh) | GHG (ton) | TNPC (k$) |

PSO_{GOA} [56] | 0.658 | 141.8 | 118.8 |

BOACA [34] | 1.082 | 118.3 | 121.6 |

HOMER [34] | 0.809 | - | 30,033 |

NGSA [37] | 0.19–0.25 | 8.87 | - |

GWO [57] | 0.78–1.59 | - | - |

Decision making | 0.12–0.17 | 100.3–130 | - |

CS | 0.707 | 67.3 | 140.6 |

- -
- According to the Helioscope investigation, the ambient temperature might significantly affect the canal-top PV accommodation performance, as in Figure 13. Contrarily, the soil in such agricultural areas has a negligible influence.
- -
- The canal-top PV rating has a negligible influence compared to the load impact on the LCOE, as in Figure 14.
- -
- The simulated results show that the PVs are superior to the main grid to meet the load demand, as illustrated in the annual energy share in Figure 11.
- -
- The developed CS outperforms the other algorithms (Figure 10) as it relaxed fast towards the optimal solution.
- -
- Water-saving through canal-top PV is a significant benefit; however, a quantitative analysis of the actual evaporation reduction and the quantity of the water saved will be part of future research.
- -
- Another issue of the current work is related to the nature of the renewables, which could influence the utility grid frequency [58]. The modern controllers are to be designed to handle the canal-top PV injected energy and the main grid frequency oscillations.

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

CS | Cuckoo Search |

D | Saturation Deficit |

DHI | Diffuse Horizontal Irradiation |

E | Evaporation |

GA | Genetic Algorithm |

GHG | Greenhouse Gas Emissions |

GHI | Global Horizontal Irradiation |

GIS | Graphical Information System |

HMG | Hybrid Microgrids |

LCOE | Levelized Cost of Energy |

LPSP | Loss of Power Supply Probability |

NPC | Net Present Costs |

PSO | Particle Swarm Optimization |

PV | Photovoltaic |

Rh | Relative humidity |

SDGs | Sustainable Development Goals |

Ta | Air temperature |

V | Wind speed |

## Appendix A

**Figure A1.**Notched boxplots show the daily variability of GHI for each month; the dashed line shows the mean values.

**Figure A2.**Notched boxplots show the daily variability of DHI for each month; the dashed line shows the mean values.

**Figure A3.**Notched boxplots show the daily variability of temperature for each month; the dashed line shows the mean values.

**Figure A4.**Notched boxplots show the daily variability of relative humidity for each month; the dashed line shows the mean values.

**Figure A5.**Notched boxplots show the daily variability of wind speed each month; the dashed line shows the mean values.

**Figure A6.**Notched boxplots show the daily variability of evaporation for each month; the dashed line shows the mean values.

## Appendix B

- CS parameters: number of iterations = 10, number of host nests = 25, number of generations = 25, Pa = 0.25.
- PSO parameters: number of iterations = 10, population = 25, wo = 0.1, c1 = 0.25, c2 = 0.99, r1 = 0.3, r2 = 0.45.
- GA parameters: number of iterations = 10, population = 25, crossover probability = 0.6, crossover probability = 0.5.
- The technical and economic parameters of system compounds are given in Table A1.

**Table A1.**Technical and economic parameters of system components according to Abo-Elyousr and Elnozahy [34].

Type | Value | Unit |
---|---|---|

PV | ||

Lifetime | 20 | year |

Initial cost | 600 | $/kW |

Operational and maintenance cost | 0.01 | $/kW |

CO2 emissions | 0.0225 | Kg/kWh |

Grid | ||

CO2 emissions | 0.143 | Kg/kWh |

Converter | ||

Lifetime | 20 | Years |

Initial costs | 515 | $/kWh |

Efficiency | 95 | % |

Others | ||

Project lifetime | 20 | Years |

Interest rate | 13 | % |

Inflation rate | 5 | % |

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**Figure 1.**The Nile River flowing through Egyptian territory; (

**a**) irrigation system in Egypt adapted after Abdrabbo [46] and (

**b**) location of the case study site.

**Figure 5.**Notched boxplots show the daily variability of water loss for each month; the dashed line shows the mean value.

**Figure 8.**Tilt angle impact for solar power: upper the first group angles, and lower the second group.

Month | Avg. GHI [Wh/m^{2}] | Avg. DHI [W/m^{2}] | Avg. Temp. (°C) | Avg. R_{h} (%) | Avg. Wind Speed (m/s) | Avg. E (mm) |
---|---|---|---|---|---|---|

Jan | 4003.6 | 6.91 | 11.4 | 38.5 | 2.6 | 3.6 |

Feb | 4905.1 | 10.4 | 13.7 | 40.8 | 3.1 | 4.4 |

Mar | 6189.4 | 11 | 16.8 | 31.2 | 3.8 | 6.3 |

Apr | 6684.4 | 14.7 | 21.9 | 25.6 | 3.9 | 8.7 |

May | 7482.4 | 20.7 | 29.6 | 15.2 | 3.5 | 12.5 |

Jun | 7515.3 | 27.8 | 32.2 | 21.6 | 4.2 | 14.9 |

Jul | 7637.2 | 29.8 | 32.2 | 23.2 | 4.7 | 15.9 |

Aug | 7174.5 | 29.8 | 31.9 | 24.4 | 4.0 | 14.0 |

Sep | 6461.1 | 25.9 | 28.6 | 31.0 | 4.8 | 12.7 |

Oct | 5351.7 | 20.8 | 26.3 | 32.0 | 3.8 | 9.9 |

Nov | 4477.5 | 16.6 | 21.0 | 37.1 | 2.9 | 6.1 |

Dec | 3979.2 | 10.2 | 14.1 | 50.8 | 3.3 | 4.0 |

Algorithm | Canal-top PV (kW) | LCOE ($/kWh) | GHG (ton) | TNPC (k$) | LPSP | Yearly Evaporation * (m^{3}) |
---|---|---|---|---|---|---|

GA | 6.1 | 0.811 | 64.9 | 161.1 | 0 | 6.9 |

PSO | 10.4 | 0.707 | 67.3 | 140.6 | 0 | 6.9 |

CS | 10.4 | 0.707 | 67.3 | 140.6 | 0 | 6.9 |

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**MDPI and ACS Style**

Alhejji, A.; Kuriqi, A.; Jurasz, J.; Abo-Elyousr, F.K.
Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals. *Energies* **2021**, *14*, 2620.
https://doi.org/10.3390/en14092620

**AMA Style**

Alhejji A, Kuriqi A, Jurasz J, Abo-Elyousr FK.
Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals. *Energies*. 2021; 14(9):2620.
https://doi.org/10.3390/en14092620

**Chicago/Turabian Style**

Alhejji, Ayman, Alban Kuriqi, Jakub Jurasz, and Farag K. Abo-Elyousr.
2021. "Energy Harvesting and Water Saving in Arid Regions via Solar PV Accommodation in Irrigation Canals" *Energies* 14, no. 9: 2620.
https://doi.org/10.3390/en14092620