# Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

- The most advisable values of the weight factors of the developed objective function are discovered.
- WSO as a successful optimization tool to handle the issue of the optimum position and size of PVs and WTs in RDSs is adopted to reduce power losses and reinforce the system voltage profile.
- The economic charge is examined to find the power losses and net savings after placing the DGs in the standard IEEE 33, 69, and 85 point systems. Moreover, the voltage stability index (VSI) is inspected for all RDSs.
- The sensibleness of the method for real implementation has been validated and investigated by calculation of the losses and voltage profile before and after installing the DG strategy with achieving the restrictions and working constraints.

## 2. White Shark Optimizer

#### 2.1. Inspiration

#### 2.2. Track Victim

#### 2.3. Exploration

#### 2.4. Exploitation

#### 2.5. Algorithm Steps

#### 2.6. Initialization of WSO

#### 2.7. Movement Speed toward Prey

#### 2.8. Movement toward Optimum Kill

#### 2.9. Motion toward the Great White Shark

#### 2.10. Fish-School Attitude

## 3. Objective Charge Function

#### 3.1. Equality and Inequality Restrictions

#### 3.1.1. Equality Restriction

#### 3.1.2. Inequality Restrictions

## 4. Outcomes and Discussion

#### 4.1. The 33-Node Test System

#### 4.1.1. Outcomes for Establishing 1 Unit in the 33-Node Grid

#### 4.1.2. Outcomes for Establishing Two DGs in the 33-Node System

#### 4.1.3. Outcomes for Establishing Three DGs in 33-Node System

#### 4.2. Simulation Results for the IEEE-69 Bus RDS

#### 4.2.1. Outcomes for Establishing One DG in the 69-Node Grid

#### 4.2.2. Outcomes for Establishing Two DGs in the 69-Node Grid

#### 4.2.3. Outcomes of the 69-Bus System and Three DGs

#### 4.3. The 85-Node Test System

## 5. Conclusions

- A multi-objective function was developed with an accurate choice of weighting factors to reduce the net power losses and improve the voltage profiles and VSIs of various RDSs.
- WT installation provides much better results compared with PVs.
- As the number of penetrated DGs was increased to three units, the percentages of power losses increased to 94.57%, 98.23%, and 93.52% for WTs, while these percentages were 67.068%, 69.465%, and 52.267% for PVs for 33, 69, and 85, respectively.
- With the increasing number of penetrated DGs, the rate of improvement in the percentage of loss reductions decreased. These rates were 63.766, 4.912, and 0.787 for PV, which was less than 89.7, 7.19, and 1.34, respectively, for WTs used for the 69-node system.
- The notability of WSO was assured, compared to other recent studies, in terms of power losses. The enhancement reached 27.78%, 70%, and 39.27% for the three used systems.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

DG | Distributed Generation |

WSO | White Shark Optimization |

PV | Photovoltaic |

WT | Wind Turbine |

GA | Genetic Approach |

PSO | Particle Swarm Optimization |

EVPSO | Escape Velocity Particle Swarm Optimization |

PSOPC | Particle Swarm Optimization with Passive Congregation |

AEPSO | Area Extension with Particle Swarm Optimization |

ADPSO | Adaptive Dissipative Particle Swarm Optimization |

DAPSO | Dynamic Adaptation of Particle Swarm Optimization |

ALOA | Ant Lion Optimization Algorithm |

QOSIMBO_Q | Quasi-Oppositional Swine Influenza Model-Based Optimization with Quarantine |

QOCSOS | Quasi-Oppositional Chaotic Symbiotic Organisms Search |

CSCA | Chaotic Sine Cosine Approach |

HHO | Harris Hawks Optimizer |

SFSA | Stochastic Fractal Search Algorithm |

QOTLBO | Quasi-Oppositional Teaching–Learning-Based Optimization |

GWO | Gray Wolf Optimization |

IGWO | Improved Gray Wolf Optimization |

ABC | Artificial Bee Colony |

CSA | Cuckoo Search Approach |

SGA | Simple Genetic Algorithm |

MTLBO | Modified Teaching–Learning-Based Optimization |

BB-BC | Big Bang–Big Crunch |

SFSA | Stochastic Fractal Search Algorithm |

IHHO | Improved Harris Hawks Optimizer |

BFOA | Bacterial Foraging Optimization Algorithm |

LSFSA | Loss Sensitivity Factor-Simulated Annealing |

CABC | Chaotic Artificial Bee Colony |

WOA | Whale Optimization Algorithm |

WCA | Water Cycle Algorithm |

MFF | Modified Firefly |

ROA | Rider Optimization Algorithm |

HGSO | Henry Gas Solubility Optimization |

COA | Coyote Optimization Algorithm |

ECOA | Enhanced Coyote Optimization Algorithm |

SFO | Sunflower Optimization |

VSI | Voltage Stability Index |

NR | Not Reported |

## References

- Ali, E.S.; Abd-Elazim, S.M.; Abd-Elaziz, A.Y. Improved Harmony Algorithm and Power Loss Index for Optimal Locations and Sizing of Capacitors in Radial Distribution Systems. Int. J. Electr. Power Energy Syst.
**2016**, 80, 252–263. [Google Scholar] [CrossRef] - Abd-Elaziz, Y.; Ali, E.S.; Abd-Elazim, S.M. Flower Pollination Algorithm for Optimal Capacitor Placement and Sizing in Distribution Systems. Electr. Power Compon. Syst.
**2016**, 44, 544–555. [Google Scholar] [CrossRef] - Ali, E.S.; Abd-Elazim, S.M. Optimal Locations and Sizing of Capacitors in Radial Distribution Systems Using Mine Blast Algorithm. Electr. Eng.
**2018**, 100, 1–9. [Google Scholar] - Abd-Elazim, S.M.; Ali, E.S. Optimal Network Restructure via Improved Whale Optimization Approach. Int. J. Commun. Syst.
**2021**, 34, e4617. [Google Scholar] - Rao, R.S.; Ravindra, K.; Satish, K.; Narasimham, S.V.L. Power Loss Minimization in Distribution System Using Network Reconfiguration in the Presence of Distributed Generation. IEEE Trans. Power Syst.
**2013**, 28, 317–325. [Google Scholar] [CrossRef] - Elraouf, M.O.A.; Aljohani, M.; Mosaad, M.I.; Fattah, T.A.A. Mitigating Misfire and Fire-through Faults in Hybrid Renewable Energy Systems Utilizing Dynamic Voltage Restorer. Energies
**2022**, 15, 5769. [Google Scholar] [CrossRef] - Bawazir, R.O.; Cetin, N.S. Comprehensive Overview of Optimizing PV-DG Allocation in Power System and Solar Energy Resource Potential Assessments. Energy Rep.
**2020**, 6, 173–208. [Google Scholar] [CrossRef] - Kiehbadroudinezhad, M.; Merabet, A.; Abo-Khalil, A.G.; Salameh, T.; Ghenai, C. Intelligent and Optimized Microgrids for Future Supply Power from Renewable Energy Resources: A Review. Energies
**2022**, 15, 3359. [Google Scholar] [CrossRef] - Mosaad, M.I. Grid-Connected PV System Statistics and Evaluation; Review. YJES
**2022**, 19, 1–10. [Google Scholar] [CrossRef] - Ali, E.S.; Abd-Elazim, S.M.; Abd-Elaziz, A. Ant Lion Optimization Algorithm for Renewable Distributed Generations. Energy
**2016**, 116, 445–458. [Google Scholar] [CrossRef] - Alhejji, A.K.; Salem, F.; Mosaad, M.I. Optimal Location and Size of SVC Devices in Interconnected Electrical Power System Using Quadratic Adaptive Bacterial Foraging Algorithm. In Proceedings of the 2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, USA, 3–5 May 2018; pp. 0817–0821. [Google Scholar] [CrossRef]
- Smallwood, L. Distributed Generation in Autonomous and Nonautonomous Micro Grids. In Proceedings of the IEEE Rural Electric Power Conference, Colorado Springs, CO, USA, 5–7 May 2002; pp. D1–D6. [Google Scholar]
- Aman, M.; Jasmon, G.B.; Mokhlis, H.; Bakar, A.H. Optimal Placement and Sizing of a DG based on A New Power Stability Index and Line Losses. Int. J. Electr. Power Energy Syst.
**2012**, 43, 1296–1304. [Google Scholar] [CrossRef] - Moravej, Z.; Akhlaghi, A. A Novel Approach based on Cuckoo Search for DG Allocation in Distribution NetWork. Int. J. Electr. Power Energy Syst.
**2013**, 44, 672–679. [Google Scholar] [CrossRef] - Chakravorty, M.; Das, D. Voltage Stability Analysis of Radial Distribution Networks. Int. J. Electr. Power Energy Syst.
**2001**, 23, 129–135. [Google Scholar] [CrossRef] - Khalesi, N.; Rezaei, N.; Haghifam, M.R. DG Allocation with Application of Dynamic Programming for Loss Reduction and Reliability Improvement. Int. J. Electr. Power Energy Syst.
**2011**, 33, 288–295. [Google Scholar] [CrossRef] - Gopu, P.; Naaz, S.; Aiman, K. Optimal Placement of Distributed Generation using Genetic Algorithm. In Proceedings of the International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 19–20 February 2021; pp. 1–6. [Google Scholar]
- Syahputra, R.; Robandi, I.; Ashari, M. PSO Based Multiobjective Optimization for Reconfiguration of Radial Distribution Network. Int. J. Appl. Eng. Res.
**2015**, 10, 14573–14586. [Google Scholar] - Ali, E.S.; El-Sehiemy, R.A.; El-Ela, A.A.A.; Kamel, S.; Khan, B. Optimal Planning of Uncertain Renewable Energy Sources in Unbalanced Distribution Systems by a Multi-Objective Hybrid PSO–SCO Algorithm. IET Renew. Power Gener.
**2022**, 16, 2111–2124. [Google Scholar] [CrossRef] - Devi, S.; Geethanjali, M. Application of Modified Bacterial Foraging Optimization Algorithm for Optimal Placement and Sizing of Distributed Generation. Expert Syst. Appl.
**2014**, 41, 2772–2781. [Google Scholar] [CrossRef] - Prakash, R.; Lokeshgupta, B.; Sivasubramani, S. Multi-Objective Bat Algorithm for Optimal Placement and Sizing of DG. In Proceedings of the 20th National Power Systems Conference (NPSC), Tiruchirappalli, India, 14–16 December 2018; pp. 1–6. [Google Scholar]
- Sudharani, D.; Subrahmanyam, N.; Sydulu, M. Multiobjective Invasive Weed Optimization An Application to Optimal Network Reconfiguration in Radial Distribution systems. Int. J. Electr. Power Energy Syst.
**2015**, 73, 932–942. [Google Scholar] [CrossRef] - Mohamed, A.; Ali, S.; Alkhalaf, S.; Senjyu, T.; Hemeida, A.M. Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm. Sustainability
**2019**, 11, 6550. [Google Scholar] [CrossRef] - Omar, S.; Manan, M.N.A.; Siam, M.N.M.; Samat, A.A.A.; Daud, K.A. Optimum Location of DG for Loss Reduction with Ant Colony Algorithm. In Proceedings of the First International Conference on Electrical Energy and Power Engineering (ICEEPE 2020), Penang, Malaysia, 25–26 August 2020; Volume 1045, pp. 1–12. [Google Scholar]
- Fetanat, A.; Khorasaninejad, E. Size Optimization for Hybrid Photovoltaic-Wind Energy System Using Ant Colony Algorithm for Continuous Domains based Integer Programming. Appl. Soft Comput.
**2015**, 31, 196–209. [Google Scholar] [CrossRef] - García, J.A.M.; Mena, A.J.G. Optimal Distributed Generation Location and Size Using a Modified Teaching–Learning based Optimization Algorithm. Int. J. Electr. Power Energy Syst.
**2013**, 50, 65–75. [Google Scholar] [CrossRef] - Ahmadi, S.; Abdi, S. Application of the Hybrid Big Bang-Big Crunch Algorithm for Optimal Sizing of a Stand-Alone Hybrid PV/Wind/Battery System. Sol. Energy
**2016**, 134, 366–374. [Google Scholar] [CrossRef] - Ansari, M.M.; Guo, C.; Shaikh, M.S.; Chopra, N.; Haq, I.; Shen, L. Planning for Distribution System with Grey Wolf Optimization Method. J. Electr. Eng. Technol.
**2020**, 15, 1485–1499. [Google Scholar] [CrossRef] - Nguyen, T.T.; Truong, A.V. Distribution Network Reconfiguration for Power Loss Minimization and Voltage Profile Improvement Using Cuckoo Search Algorithm. Int. J. Electr. Power Energy Syst.
**2015**, 68, 233–242. [Google Scholar] [CrossRef] - Nguyen, T.T.; Truong, A.V.; Phung, T.A. A Novel Method based on Adaptive Cuckoo Search for Optimal Network Reconfiguration and Distributed Generation Allocation in Distribution Network. Int. J. Electr. Power Energy Syst.
**2016**, 78, 801–815. [Google Scholar] [CrossRef] - Kumar, J.S.; Raja, S.C.; Nesamalar, J.J.D.; Venkatesh, P. Optimizing Renewable based Generations in AC/DC Microgrid System Using Hybrid Nelder-Mead-Cuckoo Search Algorithm. Energy
**2018**, 158, 204–215. [Google Scholar] [CrossRef] - Purlu, M.; Turka, B.E. Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index. IEEE Power Energy Soc. Sect.
**2022**, 10, 21455–21474. [Google Scholar] [CrossRef] - Saha, S.; Mukherjee, V. A Novel Multiobjective Chaotic Symbiotic Organisms Search Algorithm to Solve Optimal DG Allocation Problem in Radial Distribution System. Int. Trans. Electr. Energy Syst.
**2019**, 29, e2839. [Google Scholar] [CrossRef] - Bayoumi, S.A.; El-Sehiemy, R.A.; Abaza, A. Effective PV Parameter Estimation Algorithm Based on Marine Predators Optimizer Considering Normal and Low Radiation Operating Conditions. Arab. J. Sci. Eng.
**2022**, 47, 3089–3104. [Google Scholar] [CrossRef] - Hassan, A.; Fahmy, F.; Nafeh, A.; Abuelmagd, M. Genetic Single Objective Optimisation for Sizing and Allocation of Renewable DG Systems. Int. J. Sustain. Energy
**2017**, 36, 545–562. [Google Scholar] [CrossRef] - Manafi, H.; Ghadimi, N.; Ojaroudi, M.; Farhadi, P. Optimal Placement of Distributed Generations in Radial Distribution Systems Using Various PSO and DE Algorithms. Elektron. Ir Elektrotechnika
**2013**, 19, 53–57. [Google Scholar] [CrossRef] - Acharya, N.; Mahat, P.; Mithulananthan, N. An Analytical Approach for DG Allocation in Primary Distribution Network. Int. J. Electr. Power Energy Syst.
**2006**, 28, 669–678. [Google Scholar] [CrossRef] - Shukla, T.; Singh, S.; Srinivasaraob, V.; Naik, K. Optimal Sizing of Distributed Generation Placed on Radial Distribution Systems. Electr. Power Compon. Syst.
**2010**, 38, 260–274. [Google Scholar] [CrossRef] - Naik, S.; Khatod, D.; Sharma, M. Optimal Allocation of Combined DG and Capacitor for Real Power Loss Minimization in Distribution Networks. Int. J. Electr. Power Energy Syst.
**2013**, 53, 967–973. [Google Scholar] [CrossRef] - Ali, S.; Abd-Elazim, S.M.; Abd-Elaziz, A.Y. Ant Lion Optimization Algorithm for Optimal Location and Sizing of Renewable Distributed Generations. Renew. Energy
**2017**, 101, 1311–1324. [Google Scholar] [CrossRef] - Reddy, P.; Reddy, V.; Manohar, T. Ant Lion Optimization Algorithm for Optimal Sizing of Renewable Energy Resources for Loss Reduction in Distribution Systems. J. Electr. Syst. Inf. Technol.
**2018**, 5, 663–680. [Google Scholar] - Khasanov, M.; Kamel, S.; Rahmann, C.; Hasanien, H.M.; Al-Durra, A. Optimal Distributed Generation and Battery Energy Storage Units Integration in Distribution systems Considering Power Generation Uncertainty. IET Gener. Transm. Distrib.
**2021**, 15, 3400–3422. [Google Scholar] [CrossRef] - Pham, T.D.; Nguyen, T.T.; Dinh, B.H. Find Optimal Capacity and Location of Distributed Generation Units in Radial Distribution Networks by Using Enhanced Coyote Optimization Algorithm. Neural Comput. Appl.
**2021**, 33, 4343–4371. [Google Scholar] [CrossRef] - Nowdeh, S.A.; Davoudkhani, I.F.; Moghaddam, M.J.H.; Najmi, E.S.; Abdelaziz, A.Y.; Ahmadi, A.; Razavi, S.E.; Gandoman, F.H. Fuzzy Multi-Objective Placement of Renewable Energy Sources in Distribution System with Objective of Loss Reduction and Reliability Improvement Using A Novel Hybrid Method. Appl. Soft Comput. J.
**2019**, 77, 761–779. [Google Scholar] [CrossRef] - Sharma, S.; Bhattacharjee, S.; Bhattacharya, A. Quasi Oppositional Swine Influenza Model Based Optimization with Quarantine for Optimal Allocation of DG in Radial Distribution Network. Int. J. Electr. Power Energy Syst.
**2016**, 74, 348–373. [Google Scholar] [CrossRef] - Truong, K.H.; Nallagownden, P.; Elamvazuthi, I.; Vo, D.N. A Quasi-Oppositional-Chaotic Symbiotic Organisms Search Algorithm for Optimal Allocation of DG in Radial Distribution Networks. Appl. Soft Comput. J.
**2020**, 88, 106067. [Google Scholar] [CrossRef] - Selim, A.; Kamel, S.; Jurado, F. Efficient Optimization Technique for Multiple DG Allocation in Distribution Networks. Appl. Soft Comput. J.
**2020**, 86, 105938. [Google Scholar] [CrossRef] - Selim, A.; Kamel, S.; Alghamdi, A.S.; Jurado, F. Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- And Multi-Objective Approaches. IEEE Access
**2020**, 8, 52815–52829. [Google Scholar] [CrossRef] - Nguyen, T.P.; Vo, D.N. A Novel Stochastic Fractal Search Algorithm for Optimal Allocation of Distributed Generators in Radial Distribution Systems. Appl. Soft Comput. J.
**2018**, 70, 773–796. [Google Scholar] [CrossRef] - Sultana, S.; Roy, P.K. Multi-Objective Quasi-Oppositional Teaching Learning based Optimization for Optimal Location of Distributed Generator in Radial Distribution Systems. Int. J. Electr. Power Energy Syst.
**2014**, 63, 534–545. [Google Scholar] [CrossRef] - Akbar, M.I.; Kazmi, S.A.A.; Alrumayh, O.; Khan, Z.A.; Altamimi, A.; Malik, M.M. A Novel Hybrid Optimization-based Algorithm for The Single and Multi-Objective Achievement with Optimal DG Allocations in Distribution Networks. IEEE Access
**2022**, 10, 25669–25687. [Google Scholar] [CrossRef] - Abu-Mouti, S.; El-Hawary, M.E. Optimal Distributed Generation Allocation and Sizing in Distribution Systems via Artificial Bee Colony Algorithm. IEEE Trans. Power Deliv.
**2011**, 26, 2090–2101. [Google Scholar] [CrossRef] - Gozel, T.; Hocaoglu, M. An Analytical Method for The Sizing and Siting of Distributed Generators in Radial Systems. Int. J. Electr. Power Syst. Res.
**2009**, 79, 912–918. [Google Scholar] [CrossRef] - Pisica, I.; Bulac, C.; Eremia, M. Optimal Distributed Generation Location and Sizing Using Genetic Algorithms. In Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems (ISAP 09), Curitiba, Brazil, 8–12 November 2009; pp. 1–6. [Google Scholar]
- Wong, L.Y.; Rahim, S.R.A.; Sulaiman, M.H.; Aliman, O. Distributed Generation Installation Using Particle Swarm Optimization. In Proceedings of the 4th International Conference on Power Engineering and Optimization, Shah Alam, Malaysia, 23–24 June 2010; pp. 159–163. [Google Scholar]
- Tan, W.; Hassan, M.; Majid, M.; Rahman, H. Allocation and Sizing of DG Using Cuckoo Search Algorithm. In Proceedings of the IEEE International Conference on Power and Energy, Kota Kinabalu, Malaysia, 2–5 December 2012; pp. 133–138. [Google Scholar]
- Abdelaziz, A.; Hegazy, Y.; El-Khattam, W.; Othman, M. A Multi-objective Optimization for Sizing and Placement of Voltage-Controlled Distributed Generation Using Supervised Big Bang-Big Crunch Method. Electr. Power Compon. Syst.
**2015**, 43, 105–117. [Google Scholar] [CrossRef] - Ali, S.; Abd-Elazim, S.M.; Abd-Elaziz, A.Y. Optimal Allocation and Sizing of Renewable Distributed Generation Using Ant Lion Optimization Algorithm. Electr. Eng.
**2018**, 100, 99–109. [Google Scholar] [CrossRef] - Imran, A.M.; Kowsalya, M. Optimal Size and Siting of Multiple Distributed Generators in Distribution System Using Bacterial Foraging Optimization. Swarm Evolut. Compt.
**2014**, 15, 58–65. [Google Scholar] [CrossRef] - Injeti, S.K.; Kumar, N.P. A Novel Approach to Identify Optimal Access Point and Capacity of Multiple DGs in a Small, Medium and Large Scale Radial Distribution Systems. Int. J. Electr. Power Energy Syst.
**2013**, 45, 142–151. [Google Scholar] [CrossRef] - Natarajan, M.; Balamurugan, R.; Lakshminarasimman, L. Optimal Placement and Sizing of DGs in the Distribution System for Loss Minimization and Voltage Stability Improvement Using CABC. Int. J. Electr. Eng. Inform.
**2015**, 7, 679–690. [Google Scholar] [CrossRef] - Reddy, P.; Reddy, V.; Manohar, T. Optimal Renewable Resources Placement in Distribution Networks by Combined Power Loss Index and Whale Optimization Algorithms. J. Electr. Syst. Inf. Technol.
**2018**, 5, 175–191. [Google Scholar] - Reddy, P.; Reddy, V.; Manohar, T. Whale Optimization Algorithm for Optimal Sizing of Renewable Resources for Loss Reduction in Distribution Systems. Renewables
**2017**, 4, 3. [Google Scholar] [CrossRef] - Latreche, Y.; Bouchekara, H.R.E.H.; Mokhlis, H.; Naidu, K.; Kerrour, F.; Javaid, M.S. Optimal Multi-DG Units Incorporation in Distribution Systems Using Single and Multi-Objective Approaches based on Water Cycle Algorithm. J. Electr. Syst.
**2020**, 16, 530–549. [Google Scholar] - Sukraj, K.; Yuvaraj, T.; Hariharan, R.; Thirumalai, M. Simultaneous Allocation of Shunt Capacitor and Distributed Generator in Radial Distribution Network Using Modified Firefly Algorithm. In Proceedings of the IEEE 6th International Conference on Smart Structures and Systems ICSSS 2019, Chennai, India, 14–15 March 2019; pp. 1–5. [Google Scholar]
- Braik, M.; Hammouri, A.; Atwan, J.; Al-Betar, M.A.; Awadallah, M.A. White Shark Optimizer: A Novel Bio-inspired Meta-Heuristic Algorithm for Global Optimization Problems. Knowl.-Based Syst.
**2022**, 243, 108457. [Google Scholar] [CrossRef] - Haddad, O.B. Advanced Optimization by Nature-Inspired Algorithms. In Studies in Computational Intelligence; Springer: Berlin/Heidelberg, Germany, 2018. [Google Scholar]
- Kamarzaman, A.; Sulaiman, S.I.; Ibrahim, I.R. Adaptive Mechanism for Enhanced Performance of Shark Smell Optimization. J. Electr. Electron. Syst. Res.
**2021**, 18, 9–17. [Google Scholar] [CrossRef] - Wang, L.; Wang, X.; Sheng, Z.; Lu, S. Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation. Energies
**2020**, 13, 714. [Google Scholar] [CrossRef] - Wang, D.; Wang, X.C.; Liu, K.W. An Improved Multi-objective Shark Smell Optimization Algorithm for Automatic Train Operation Based on Angle Cosine and Fusion Distance. J. Comput.
**2020**, 31, 141–156. [Google Scholar] - Zhou, Y.; Ye, J.; Du, Y.; Sheykhahmad, F.R. New Improved Optimized Method for Medical Image Enhancement Based on Modified Shark Smell Optimization Algorithm. Sens. Imaging
**2020**, 21, 20. [Google Scholar] [CrossRef] - Ali, A.; Kamel, S.; Hassan, M.H.; Ahmed, E.M.; Alanazi, M. Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm. Sustainability
**2022**, 14, 6049. [Google Scholar] [CrossRef] - Alhumade, H.; Rezk, H.; Louzazni, M.; Moujdin, I.A.; Al-Shahrani, S. Advanced Energy Management Strategy of Photovoltaic/PEMFC/Lithium-Ion Batteries/Supercapacitors Hybrid Renewable Power System Using White Shark Optimizer. Sensors
**2023**, 23, 1534. [Google Scholar] [CrossRef] [PubMed]

${\mathit{w}}_{\mathbf{1}}$ | ${\mathit{w}}_{\mathbf{2}}$ | ${\mathit{w}}_{\mathbf{3}}$ | Cost Function |
---|---|---|---|

0.5 | 0.1 | 0.4 | 0.317 |

0.5 | 0.2 | 0.3 | 0.362 |

0.5 | 0.3 | 0.2 | 0.407 |

0.5 | 0.4 | 0.1 | 0.452 |

0.5 | 0.25 | 0.25 | 0.385 |

0.6 | 0.1 | 0.3 | 0.365 |

0.6 | 0.2 | 0.2 | 0.41 |

0.6 | 0.3 | 0.1 | 0.455 |

0.6 | 0.25 | 0.15 | 0.432 |

0.6 | 0.15 | 0.25 | 0.387 |

0.7 | 0.1 | 0.2 | 0.412 |

0.7 | 0.2 | 0.1 | 0.457 |

0.7 | 0.15 | 0.15 | 0.435 |

0.8 | 0.1 | 0.1 | 0.46 |

Items | Without DG | DG (kVA/p.f) | ||
---|---|---|---|---|

1 PV | 2 PVs | 3 PVs | ||

Net losses (kW) | 210.98 | 102.7915 | 82.6 | 69.4808 |

Loss reduction (%) | - | 51.28 | 60.85 | 67.068 |

Lower voltage/bus | 0.9134/18 | 0.9525/18 | 0.9732/33 | 0.9726/33 |

Net DG/p.f/bus | - | 2600/1/6 | 850/1/13 1191.1/1/30 | 790/1/13 1070/1/24 1080/1/30 |

VSI | 25.887 | 28.8655 | 29.4794 | 29.6384 |

Charge of losses (USD) | 110,891.08 | 54,027.212 | 43,414.56 | 36,519.1085 |

Saving (USD/year) | - | 56,863.87 | 67,476.52 | 74,371.9715 |

1 WT | 2 WTs | 3 WTs | ||

Net losses (kW) | 210.98 | 65.1426 | 28.4 | 11.45 |

Loss reduction (%) | - | 69.124 | 86.54 | 94.57 |

Lower voltage/bus | 0.9134 | 0.9581/18 | 0.9803/25 | 0.985/33 |

Net DG/p.f/bus | - | 2550/0.825/6 | 945/0.9/13 1550/0.73/30 | 800/0.88/13 1100/0.9/24 1200/0.73/30 |

VSI | 25.887 | 29.2610 | 30.8679 | 30.4412 |

Charge of losses (USD) | 110,891.08 | 34,238.95 | 14,927.04 | 6018.12 |

Saving (USD/year) | - | 76,652.129 | 95,964.04 | 104,872.96 |

DG Type | Mechanism | Year | DG Installation | Power Loss (kW) | Lower Voltage | ||
---|---|---|---|---|---|---|---|

Size (Kva/p.f) | Bus | Value | Percentage | ||||

PV | Without | - | - | 210.98 | - | 0.9134 | |

GA [35] | 2017 | 2580/1 | 6 | 105.481 | 48.21 | NR | |

EVPSO [36] | 2013 | 763/1 | 11 | 140.19 | 33.55 | 0.9284 | |

PSOPC [36] | 2013 | 1000/1 | 15 | 136.75 | 35.18 | 0.9318 | |

AEPSO [36] | 2013 | 1200/1 | 14 | 131.43 | 37.7 | 0.9347 | |

ADPSO [36] | 2013 | 1210/1 | 13 | 129.53 | 38.60 | 0.9348 | |

DAPSO [36] | 2013 | 1212/1 | 8 | 127.17 | 39.7 | 0.9349 | |

Analytical [37] | 2006 | 2490/1 | 6 | 111.24 | 47.27 | NR | |

GA [38] | 2010 | 2380/1 | 6 | 132.64 | 37.13 | NR | |

[39] | 2013 | 1000/1 | 18 | 142.34 | 33.29 | 0.9311 | |

ALOA [40] | 2017 | 2450/1 | 6 | 103.053 | 51.15 | 0.9503 | |

ALOA [41] | 2018 | 1542.67/1 | 30 | 125.161 | 40.67 | 0.9272 | |

ROA [42] | 2021 | 2590.2/1 | 6 | 111.027 | 47.37 | 0.7886 | |

HHO [42] | 2021 | 2590.2/1 | 6 | 111.03 | 47.3717 | NR | |

HGSO [42] | 2021 | 2616.8/1 | 6 | 111.038 | 47.3703 | NR | |

ECOA [43] | 2021 | 1000/1 | 30 | 127.28 | 39.67 | 0.9285 | |

Proposed | - | 2600/1 | 6 | 102.7915 | 51.28 | 0.9525 | |

WT | ALOA [41] | 2018 | 2238.8/0.87 | 6 | 71.75 | 65.99 | 0.9528 |

GWO [44] | 2019 | 1000/0.8011 | 30 | 81.43 | 61.404 | NR | |

ROA [42] | 2021 | 2558.4/0.82 | 6 | 67.83 | 67.85 | NR | |

Proposed | - | 2550/0.825 | 6 | 65.1426 | 69.123 | 0.9581 |

DG Type | Mechanism | Year | DG Installation | Power Loss (kW) | Lower Voltage | ||
---|---|---|---|---|---|---|---|

Size (kVA)/p.f | Bus | Value | Percentage | ||||

- | Without | - | - | 210.98 | - | 0.9134 | |

PV | GA [35] | 2017 | 837.5/1 | 13 | 82.7 | 60.8 | 0.96846 |

1212.2/1 | 29 | ||||||

PSOPC [36] | 2013 | 916/1 | 8 | 111.45 | 47.17 | 0.9418 | |

767/1 | 12 | ||||||

EVPSO [36] | 2013 | 540/1 | 14 | 108.05 | 48.78 | 0.9457 | |

569/1 | 31 | ||||||

AEPSO [36] | 2013 | 600/1 | 14 | 106.38 | 49.57 | 0.9447 | |

600/1 | 29 | ||||||

ADPSO [36] | 2013 | 550/1 | 15 | 106.24 | 49.64 | 0.9467 | |

621/1 | 30 | ||||||

DAPSO [36] | 2013 | 1227/1 | 13 | 95.93 | 54.53 | 0.9651 | |

738/1 | 32 | ||||||

GA [38] | 2010 | 1718/1 | 6 | 96.580 | 54.22 | NR | |

840/1 | 8 | ||||||

ROA [42] | 2021 | 851.5/1 | 13 | 87.165 | 58.68 | 0.96 | |

1157.6/1 | 30 | ||||||

HHO [42] | 2021 | 855.93/1 | 13 | 87.1682 | 58.684 | NR | |

1150.6/1 | 30 | ||||||

HGSO [42] | 2021 | 1128.8/1 | 11 | 89.999 | 57.342 | NR | |

806.199/1 | 30 | ||||||

ECOA [43] | 2021 | 893/1 | 10 | 86.55 | 58.977 | 0.9629 | |

1000/1 | 30 | ||||||

Proposed | 850/1 | 13 | 82.6 | 60.85 | 0.9732 | ||

1191.1/1 | 30 | ||||||

WT | ALOA [41] | 2018 | 1039.5/0.862 | 13 | 30.9251 | 85.34 | NR |

1463/0.837 | 30 | ||||||

ROA [42] | 2021 | 858.4/0.91 | 13 | 28.50 | 86.49 | NR | |

1089.09/0.7 | 30 | ||||||

GWO [44] | 2019 | 861/0.8742 | 10 | 32.17 | 84.75 | NR | |

1000/0.8091 | 30 | ||||||

Proposed | 945/0.9 | 13 | 28.4 | 86.539 | 0.9803 | ||

1550/0.73 | 30 |

DG Type | Mechanism | Year | DG Installation | Power Loss (kW) | Minimum Voltage | ||
---|---|---|---|---|---|---|---|

Size (kVA)/p.f | Bus | Value | Percentage | ||||

- | Without | - | - | 210.98 | - | 0.9134 | |

PV | QOSIMBO_Q [45] | 2016 | 801.6/1 | 13 | 72.8 | 65.49 | NR |

1090.6/1 | 24 | ||||||

1054.2/1 | 30 | ||||||

QOCSOS [46] | 2020 | 801.7/1 | 13 | 72.7869 | 65.5 | NR | |

1091.3/1 | 24 | ||||||

1053.7/1 | 30 | ||||||

CSCA [47] | 2020 | 871/1 | 13 | 71.94 | 65.9 | NR | |

1091.5/1 | 24 | ||||||

954.1/1 | 30 | ||||||

HHO [48] | 2020 | 775.5/1 | 14 | 72.79 | 65.5 | NR | |

1080.8/1 | 24 | ||||||

1066.7/1 | 30 | ||||||

SFSA [49] | 2018 | 802/1 | 13 | 72.785 | 65.5 | NR | |

1092/1 | 24 | ||||||

1053.7/1 | 30 | ||||||

2014 | 880.8/1 | 12 | 74.101 | 64.88 | NR | ||

1059.2/1 | 24 | ||||||

1059.2 /1 | 30 | ||||||

I-GWO [51] | 2022 | 758/1 | 14 | 70.64 | 66.51 | NR | |

1073/1 | 24 | ||||||

1099/1 | 30 | ||||||

ROA [42] | 2021 | 790.3/1 | 14 | 72.786 | 65.5 | 0.96 | |

870/1 | 24 | ||||||

1119.51/1 | 30 | ||||||

HGSO [42] | 2021 | 919.2/1 | 12 | 83.981 | 60.19 | NR | |

1237.1/1 | 27 | ||||||

504.8/1 | 24 | ||||||

ECOA [43] | 2021 | 737.6/1 | 14 | 74.6 | 64.64 | 0.9666 | |

651.8/1 | 25 | ||||||

1070.5/1 | 30 | ||||||

COA [43] | 2021 | 709.6/1 | 14 | 76 | 63.977 | 0.9637 | |

595.4/1 | 25 | ||||||

997.2/1 | 30 | ||||||

Proposed | 790/1 | 13 | 69.4808 | 67.068 | 0.9726 | ||

1070/1 | 24 | ||||||

1080/1 | 30 | ||||||

WT | ROA [42] | 2021 | 793.8/0.9 | 13 | 11.74 | 94.43 | NR |

1069.9/0.9 | 24 | ||||||

1029.8/0.71 | 30 | ||||||

GWO [44] | 2019 | 1000/0.8122 | 13 | 13.68 | 93.5 | NR | |

789/0.8726 | 24 | ||||||

997/0.8659 | 30 | ||||||

Proposed | 800/0.88 | 13 | 11.45 | 94.57 | 0.985 | ||

1100/0.9 | 24 | ||||||

1200/0.73 | 30 |

Items | Without DG | With DG (kVA/p.f) | ||
---|---|---|---|---|

1 PV | 2 PVs | 3 PVs | ||

Net losses (kW) | 224.94 | 81.5033 | 70.4556 | 68.6857 |

Loss reduction (%) | - | 63.766 | 68.678 | 69.465 |

Lower voltage/bus | 0.9102 | 0.9685/27 | 0.9828/65 | 0.9836/65 |

Net DG/p.f/bus | - | 1890/1/61 | 525/1/17 1775/1/61 | 480/1/11 380/1/17 1740/1/61 |

VSI | 61.2379 | 64.5914 | 65.8988 | 66.1004 |

Cost of losses (USD) | 118,228.46 | 42,838.1345 | 37,031.463 | 36,101.203 |

Saving (USD/year) | - | 75,390.325 | 81,196.996 | 82,127.257 |

1 WT | 2 WTs | 3 WTs | ||

Net losses (kW) | 224.94 | 23.1551 | 6.98 | 3.98 |

Loss reduction (%) | - | 89.7 | 96.89 | 98.23 |

Lower voltage/bus | 0.9102 | 0.9718/27 | 0.9851/65 | 0.9878/65 |

Net DG/p.f/bus | - | 2250/0.82/61 | 680/0.83/17 1795/0.814/61 | 528/0.81/11 527/0.83/17 1800/0.814/61 |

VSI | 61.2379 | 65.3928 | 66.6257 | 66.9666 |

Cost of losses (USD) | 118,228.46 | 12,170.32 | 3668.688 | 2091.888 |

Saving (USD/year) | - | 106,058.14 | 114,559.77 | 116,136.57 |

DG Type | Mechanism | Year | DG Installation | Power Loss (kW) | ||
---|---|---|---|---|---|---|

Size (kVA/p.f) | Bus | Amount | Percentage | |||

- | Without | - | - | - | 224.94 | - |

PV | ABC [52] | 2011 | 1900/1 | 61 | 83.31 | 62.96 |

GA [35] | 2017 | 1872/1 | 61 | 83.18 | 63.02 | |

Analytical [37] | 2006 | 1810/1 | 61 | 81.54 | 63.64 | |

Analytical [53] | 2009 | 1807.8/1 | 61 | 92 | 59.1 | |

Grid search [53] | 2009 | 1876.1/1 | 61 | 83 | 63.1 | |

GA [54] | 2009 | 1794/1 | 61 | 83.4252 | 62.91 | |

PSO [55] | 2010 | 1337.8/1 | 61 | 83.206 | 63.01 | |

CSA [56] | 2012 | 2000/1 | 61 | 83.8 | 62.74 | |

SGA [56] | 2012 | 2300/1 | 61 | 89.4 | 60.3 | |

PSO [56] | 2012 | 2000/1 | 61 | 83.8 | 62.75 | |

MTLBO [26] | 2013 | 1819.691/1 | 61 | 83.323 | 62.95 | |

BB-BC [57] | 2015 | 1872.5/1 | 61 | 83.2246 | 63 | |

ALOA [58] | 2018 | 1800/1 | 61 | 81.776 | 63.645 | |

ROA [42] | 2021 | 1872.7/1 | 61 | 83.19 | 63.01 | |

HHO [42] | 2021 | 1901/1 | 61 | 83.24 | 62.99 | |

HGSO [42] | 2021 | 1890/1 | 61 | 83.25 | 62.99 | |

ECOA [43] | 2021 | 1000/1 | 61 | 111.56 | 50.40 | |

Proposed | 1890/1 | 61 | 81.5033 | 63.766 | ||

WT | ALOA [41] | 2018 | 2227.9/0.82 | 61 | 23.1622 | 89.7 |

ROA [42] | 2021 | 1828.47/0.814 | 61 | 23.1681 | 89.7 | |

GWO [44] | 2019 | 1000/0.8 | 61 | 58.8 | 73.86 | |

Proposed | 2250/0.82 | 61 | 23.1551 | 89.706 |

DG Type | Mechanism | DG Installation | Power Loss (kW) | |||
---|---|---|---|---|---|---|

Size (kVA/p.f) | Bus | Value | Percentage | |||

- | Without | - | - | 224.94 | - | |

PV | GA [38] | 2010 | 1777/1 | 61 | 71.7912 | 68.08 |

555/1 | 11 | |||||

GA [54] | 2009 | 6/1 | 1 | 84.233 | 62.55 | |

1794/1 | 62 | |||||

CSA [56] | 2012 | 600/1 | 22 | 76.4 | 66 | |

2100/1 | 61 | |||||

SGA [56] | 2012 | 1000/1 | 17 | 82.9 | 63.1 | |

2400/1 | 61 | |||||

PSO [56] | 2012 | 700/1 | 14 | 78.8 | 64.97 | |

2100/1 | 62 | |||||

MTLBO [26] | 2013 | 519.705/1 | 17 | 71.776 | 68.09 | |

1732.004/1 | 61 | |||||

ALOA [58] | 2018 | 538.777/1 | 17 | 70.750 | 68.547 | |

1700/1 | 61 | |||||

ROA [42] | 2021 | 531.48/1 | 17 | 71.674 | 68.13 | |

1781.5/1 | 61 | |||||

HHO [42] | 2021 | 814/1 | 12 | 72.52 | 67.76 | |

1735.3/1 | 61 | |||||

HGSO [42] | 2021 | 502/1 | 17 | 72.9 | 67.59 | |

1998/1 | 61 | |||||

ECOA [43] | 2021 | 1000/1 | 61 | 83.34 | 62.95 | |

863/1 | 62 | |||||

Proposed | 525/1 | 17 | 70.4556 | 68.678 | ||

1775/1 | 61 | |||||

WT | ALOA [58] | 2018 | 726.637/0.83 | 17 | 20.9342 | 90.69 |

1500/0.8 | 61 | |||||

ROA [42] | 2021 | 432.3717/0.7 | 17 | 7.19 | 96.80 | |

1750.06/0.8195 | 61 | |||||

GWO [44] | 2019 | 1000/0.8 | 61 | 23.28 | 89.65 | |

820/0.8328 | 62 | |||||

Proposed | 680/0.83 | 17 | 6.98 | 96.89 | ||

1795/0.814 | 61 |

DG Type | Mechanism | Year | DG Installation | Power Loss (kW) | ||
---|---|---|---|---|---|---|

Size (kVA/p.f) | Bus | Value | Percentage | |||

- | Without | - | - | 224.94 | - | |

PV | SFSA [49] | 2018 | 527.3/1 | 11 | 69.428 | 69.14 |

380.5/1 | 18 | |||||

1719.8/1 | 61 | |||||

QOSIMBO_Q [45] | 2016 | 833.6/1 | 9 | 71.00 | 68.44 | |

451.1/1 | 18 | |||||

1500/1 | 61 | |||||

CSCA-64 [47] | 2020 | 365.9/1 | 17 | 70.07 | 68.86 | |

1675.8/1 | 61 | |||||

652.5/1 | 67 | |||||

IHHO [48] | 2020 | 527.2/1 | 11 | 69.41 | 69.15 | |

382.5/1 | 17 | |||||

1719.4/1 | 61 | |||||

QOCSOS [46] | 2020 | 526.9/1 | 11 | 69.4284 | 69.14 | |

380.3/1 | 18 | |||||

1719/1 | 61 | |||||

BFOA [59] | 2014 | 295.4/1 | 27 | 75.23 | 66.55 | |

1345.1/1 | 61 | |||||

447.6/1 | 65 | |||||

LSFSA [60] | 2013 | 420.4/1 | 18 | 77.1 | 65.72 | |

1331.1/1 | 60 | |||||

429.8/1 | 65 | |||||

CABC [61] | 2015 | 538.1/1 | 17 | 71.59 | 68.17 | |

1200/1 | 61 | |||||

535/1 | 64 | |||||

ROA [42] | 2021 | 526.9147/1 | 11 | 69.42553 | 69.135 | |

380.3464/1 | 18 | |||||

1718.8/1 | 61 | |||||

HHO [42] | 2021 | 467.148/1 | 12 | 70.01 | 68.88 | |

346.77/1 | 15 | |||||

1734.2/1 | 61 | |||||

HGSO [42] | 2021 | 598.634/1 | 15 | 72.338 | 67.84 | |

1796.9/1 | 61 | |||||

200/1 | 57 | |||||

COA [43] | 2021 | 343.9/1 | 19 | 72.5 | 67.769 | |

1438.8/1 | 61 | |||||

285.5/1 | 64 | |||||

SFO [43] | 2021 | 358.3/1 | 19 | 72.7 | 67.68 | |

30/1 | 50 | |||||

1732.3/1 | 61 | |||||

Proposed | 480/1 | 11 | 68.6857 | 69.465 | ||

380/1 | 17 | |||||

1740/1 | 61 | |||||

WT | ROA [42] | 2021 | 508.44/0.836 | 11 | 4.2 | 98.13 |

370.25/0.819 | 18 | |||||

1670.84/0.8102 | 61 | |||||

GWO [44] | 2019 | 523/0.8294 | 18 | 7.27 | 96.76 | |

1000/0.8191 | 61 | |||||

723/0.802 | 62 | |||||

Proposed | 528/0.81 | 11 | 3.98 | 98.23 | ||

527/0.83 | 17 | |||||

1800/0.814 | 61 |

Items | Without DG | With DG (kVA/p.f) | ||
---|---|---|---|---|

1 PV | 2 PVs | 3 PVs | ||

Net losses (kW) | 315.714 | 214.1204 | 157.4592 | 150.7008 |

Loss reduction (%) | - | 32.18 | 50.126 | 52.267 |

Minimum voltage/bus | 0.8743/54 | 0.9175/76 | 0.9443/76 | 0.9543/76 |

Net DG/p.f/bus | - | 1000/1/55 | 1100/1/9 900/1/34 | 950/1/9 730/1/33 440/1/61 |

VSI | 57.7845 | 67.1635 | 72.6946 | 73.1184 |

Cost of losses (USD) | 165,939.3 | 112,541.168 | 82,760.55 | 79,208.3405 |

Saving (USD/year) | - | 53,397.62 | 83,178.75 | 86,731.16 |

1 WT | 3 WTs | |||

Net losses (kW) | 315.714 | 141.4474 | 20.4612 | |

Loss reduction (%) | - | 55.197 | 93.52 | |

Minimum voltage/bus | 0.8743/54 | 0.9255/76 | 0.9790/54 | |

Net DG/p.f/bus | - | 1250/0.7/55 | 1200/0.7/9 860/0.7/33 780/0.7/61 | |

VSI | 57.7845 | 69.6909 | 79.7560 | |

Cost of losses (USD) | 165,939.3 | 74,344.7534 | 10,754.40 | |

Saving (USD/year) | - | 91,594.55 | 155,184.893 |

DG Type | Mechanism | Year | DG Installation | Power Loss (kW) | ||
---|---|---|---|---|---|---|

Size (kVA/p.f) | Bus | Value | Percentage | |||

- | Without | - | - | 315.714 | - | |

One PV | WOA [62] | 2018 | 910.075/1 | 54 | 227.105 | 28.06 |

WOA [63] | 2017 | 946.347/1 | 55 | 224.049 | 29.03 | |

Proposed | 1000/1 | 55 | 214.1204 | 32.18 | ||

Three PVs | WCA [64] | 2020 | 838.085/1 | 53 | 235.592 | 25.378 |

837.995/1 | 54 | |||||

837.328/1 | 63 | |||||

WCA [64] | 2020 | 838.093/1 | 12 | 152.583 | 51.67 | |

838.093/1 | 48 | |||||

838.093/1 | 67 | |||||

WCA [64] | 2020 | 838.093/1 | 46 | 246.568 | 21.9 | |

838.093/1 | 47 | |||||

838.093/1 | 69 | |||||

MFF [65] | 2019 | 1000/1 | 9 | 151.79 | 51.92 | |

700/1 | 33 | |||||

500/1 | 61 | |||||

COA [43] | 2021 | 831.2/1 | 34 | 152.2 | 51.79 | |

677.9/1 | 67 | |||||

421.9/1 | 80 | |||||

SFO [43] | 2021 | 354.6/1 | 12 | 153.6 | 51.348 | |

1059.2/1 | 32 | |||||

568.6/1 | 72 | |||||

Proposed | 1000/1 | 9 | 149.7321 | 52.573 | ||

800/1 | 33 | |||||

500/1 | 61 | |||||

3 WTs | WCA [64] | 2020 | 957.82/0.8 | 10 | 21.056 | 93.331 |

800.62/0.8 | 34 | |||||

606.95/0.8 | 67 | |||||

Proposed | 1200/0.7 | 9 | 20.4612 | 93.519 | ||

860/0.7 | 33 | |||||

780/0.7 | 61 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ali, E.S.; Abd Elazim, S.M.; Hakmi, S.H.; Mosaad, M.I.
Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems. *Energies* **2023**, *16*, 3983.
https://doi.org/10.3390/en16103983

**AMA Style**

Ali ES, Abd Elazim SM, Hakmi SH, Mosaad MI.
Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems. *Energies*. 2023; 16(10):3983.
https://doi.org/10.3390/en16103983

**Chicago/Turabian Style**

Ali, Ehab S., Sahar. M. Abd Elazim, Sultan H. Hakmi, and Mohamed I. Mosaad.
2023. "Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems" *Energies* 16, no. 10: 3983.
https://doi.org/10.3390/en16103983