# Techno-Environmental Evaluation and Optimization of a Hybrid System: Application of Numerical Simulation and Gray Wolf Algorithm in Saudi Arabia

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Case Study

#### 2.2. Technical Specifications of the Hybrid System

#### 2.2.1. Wind Turbine

#### 2.2.2. Photovoltaic Panel

#### 2.2.3. Diesel Generator

#### 2.2.4. Battery and Converter

#### 2.3. Gray Wolf Optimization (GWO)

- Hunting method gray wolves

- (a)
- Observing, hunting, tracking, and pursuing prey;
- (b)
- Approaching, encircling, and misleading prey until it ceases to move;
- (c)
- An assault on prey during hunting [48].

_{2}emissions, and ${N}_{i}(x$) and ${M}_{j}\left(x\right)$ are the equal and unequal limits. Limitations included the amount of energy consumed in each production unit having to be less than or equal to the amount of energy produced by the hybrid system configuration in that unit. Upon determining the objective function, conducting sensitivity analysis, and identifying the optimal values of the influential parameters of the algorithms, the decision variables of the problem were computed. CO

_{2}emissions could not be less than zero, and the other limitations are presented in Table 6. Figure 5 illustrates the convergence pattern of the objective function value during the whole operational period of the hybrid system, utilizing the optimal population derived from the operational model. We set the number of program executions at to 1000 and the initial population size at 100.

## 3. Results and Discussion

_{2}emissions. The resulting configuration comprises a total of 1728 simulation cases and 3456 sensitivity analysis cases. The HOMER software, upon conducting an analysis of various modes, proposes an optimal configuration for the integrated system. The outcomes of numerical simulation and optimization model indicate that employing a diesel generator is the most cost-effective approach for the designated regions. Furthermore, a comparative analysis of the outcomes obtained from the simulation and GWO reveals that the utilization of the proposed GWO algorithm to achieve the optimal configuration of the hybrid system yields superior results than the numerical simulation.

- Load requirements: Different loads require different power outputs and energy storage capacities. Therefore, the configuration must be chosen based on the specific load requirements of the system.
- Resource availability: The availability of solar radiation and the wind speed varies across different geographic locations. The configuration should be based on the availability and predictability of these resources at the installation site to ensure optimal utilization.
- System efficiency: Each component of the hybrid system operates at a different efficiency. The configuration should be designed to maximize the overall system efficiency by selecting components that complement each other and minimize energy losses.
- Redundancy and reliability: To ensure continuous power supply, the system should incorporate redundancy and reliability measures. This outcome can be achieved by selecting configurations that provide backup power sources and allow seamless switching between different energy sources.
- Cost-effectiveness: The installation and maintenance costs of different components can significantly vary. The configuration must strike a balance between performance and cost-effectiveness to make the system financially viable.
- Environmental impact: Hybrid off-grid systems aim to reduce reliance on fossil fuels and minimize carbon emissions. Configurations should prioritize renewable energy sources, such as solar and wind, to minimize environmental impacts and promote sustainability.
- Scalability: The system may need to be expanded in the future to accommodate increasing power demand. The chosen configuration should have the flexibility to scale up or down without significant disruptions or additional investments.

#### 3.1. Diesel Generator Configuration

#### 3.2. Diesel Generator–Photovoltaic Configuration

#### 3.3. Diesel Generator–Wind Turbine Configuration

#### 3.4. Diesel Generator–Wind Turbine–Photovoltaic Configuration

#### 3.5. Sensitivity Analysis

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Characteristics of electric power generation by the turbine. (

**b**) Cost curve for the wind turbine.

**Figure 3.**Cost curves for the (

**a**) photovoltaic panel, (

**b**) diesel generator, (

**c**) battery, and (

**d**) converter.

**Figure 6.**Environmental data: (

**a**) GHI; (

**b**) wind speed from 2017 to 2019 for Najran, Saudi Arabia (available on https://nsrdb.nrel.gov/, accessed on 1 August 2023).

**Figure 8.**(

**a**) Power generation in each month of year, and (

**b**) NPC breakdown for diesel–generator–photovoltaic configuration for GWO results.

**Figure 9.**(

**a**) Power generation in each month of year, and (

**b**) NPC breakdown for diesel–generator–wind turbine configuration of GWO results.

**Figure 10.**(

**a**) Power generation in each month of year, and (

**b**) NPC breakdown for diesel–generator–wind turbine–photovoltaic configuration for GWO results.

**Figure 11.**The results of the average monthly electricity power produced by the wind turbine diesel generator configuration (wind turbine height is 30 m).

Data | Value |
---|---|

Inner temperature | 20 °C |

Annual electrical power for heating | 11,080 kWh |

Annual electrical power for air conditioner | 9850 kWh |

Annual water consumption | 60 m^{3} |

Annual electrical power irrigation | 75 kWh |

Feature | Value |
---|---|

Rated power | 3.0 kW |

Maximum output power | 3.5 kW |

Cut-in wind speed | 2.5 m/s |

Rated wind speed | 10 m/s |

Working wind speed | 4–25 m/s |

Survival wind speed | 50 m/s |

Battery bank voltage | 180 Vdc |

Generator efficiency | ˃0.8 |

Wind energy utilizing ratio | 0.4 Cp |

Generator weight | 81 Kg |

Blade material/quantity | GRP/3 |

Blade diameter | 4.8 m |

Feature | Value |
---|---|

Power output (P_{max}) | 250 W |

Power output tolerance (ΔP_{max}) | ±3% |

Module efficiency (η_{m}) | 0.2 |

Voltage at Pmax (V_{mpp}) | 27.8 V |

Current at Pmax (I_{mpp}) | 8.99 A |

Open-circuit voltage (V_{oc}) | 34.9 V |

Short-circuit current (I_{sc}) | 9.58 A |

Diesel | |
---|---|

Motor volume | 1.473 Liter |

Motor power | 18 Hp |

Fuel consumption | 244.8 g/kWh |

Weight | 185 kg |

Generator | |

Voltage | 220 V |

Power | 15 kW |

Phase | Single-phase electric power |

Nom. Voltage (V) | Nom. Capacity (20 h) (Ah) | Dimension | Weight (kg) | ||
---|---|---|---|---|---|

L (mm) | W (mm) | H (mm) | |||

12 | 200 | 522 | 238 mm | 218 mm | 65 |

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

Number of wolves | 12 |

Lower limitation | −30 |

Upper limitation | 30 |

Maximum iteration | 100 |

Optimal System ID | N_{PV} | N_{DG} | N_{WT} | N_{batt} | N_{Conv} | NPC (USD) | CO_{2} Emission (kg/year) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Sim | GWO | Sim | GWO | Sim | GWO | Sim | GWO | Sim | GWO | Sim | GWO | Sim | GWO | |

1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 17,755 | 17,350 | 3621 | 3517 |

2 | 20 | 16 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 26,091 | 25,337 | 3138 | 3017 |

3 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 28,725 | 28,237 | 3320 | 3231 |

4 | 12 | 10 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 37,016 | 36,224 | 3289 | 3241 |

Pollution Material | Emission (kg/year) | |||
---|---|---|---|---|

Scenario 1 (Diesel–Generator) | Scenario 2 (Diesel–Generator–Photovoltaic) | Scenario 3 (Diesel–Generator–Wind Turbine) | Scenario 4 (Diesel–Generator–Wind Turbine–Photovoltaic) | |

CO_{2} | 3517 | 3017 | 3231 | 3241 |

CO | 10.21 | 8.89 | 9.09 | 6.06 |

NO | 80.17 | 73.06 | 76.11 | 61.92 |

SO_{2} | 10.22 | 9.93 | 10.45 | 8.81 |

Other | 2.05 | 1.91 | 2.00 | 1.15 |

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**MDPI and ACS Style**

Alghamdi, H.; Alviz-Meza, A.
Techno-Environmental Evaluation and Optimization of a Hybrid System: Application of Numerical Simulation and Gray Wolf Algorithm in Saudi Arabia. *Sustainability* **2023**, *15*, 13284.
https://doi.org/10.3390/su151813284

**AMA Style**

Alghamdi H, Alviz-Meza A.
Techno-Environmental Evaluation and Optimization of a Hybrid System: Application of Numerical Simulation and Gray Wolf Algorithm in Saudi Arabia. *Sustainability*. 2023; 15(18):13284.
https://doi.org/10.3390/su151813284

**Chicago/Turabian Style**

Alghamdi, Hisham, and Aníbal Alviz-Meza.
2023. "Techno-Environmental Evaluation and Optimization of a Hybrid System: Application of Numerical Simulation and Gray Wolf Algorithm in Saudi Arabia" *Sustainability* 15, no. 18: 13284.
https://doi.org/10.3390/su151813284