# Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Problem Formulation

#### 2.1. Problem Statement

#### 2.2. Optimization Objective and Constraints

#### 2.3. Metaheuristic Algorithms

#### 2.3.1. Genetic Algorithm

#### 2.3.2. Particle Swarm Optimization

#### 2.3.3. Moth Flame Optimization

#### 2.3.4. Salp Swarm Algorithm

#### 2.3.5. Whale Optimization Algorithm

#### 2.3.6. Grey Wolf Optimization

#### 2.4. Performance Evaluation

## 3. Simulation Results and Discussions

#### 3.1. Simulation Settings

#### 3.2. Metaheuritic Algorithm Analysis

#### 3.3. Voltage Stability Results

_{3}= 0.2) over the other two factors—voltage deviation (w

_{1}= 0.5) and power loss (w

_{2}= 0.3), given the potential need for the timely execution of charging scenarios for MDHD EVs in the future. In this paper, the weight of computational time is 0.2, but when considering the unit difference between time (seconds) and voltage deviation (p.u.) and power loss (p.u.), respectively, the effect of computational time is highlighted and then voltage deviation and finally power loss. Nevertheless, the weight distribution can be flexibly adjusted to align with specific operator requirements or preferences.

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Bus Number | Voltage Magnitude (p.u.) | Active Power (kW) | Reactive Power (kVar) |
---|---|---|---|

1 | 1.000000 | −3917.677 | −2435.141 |

2 | 0.997032 | 100.000 | 60.000 |

3 | 0.982938 | 90.000 | 40.000 |

4 | 0.975456 | 120.000 | 80.000 |

5 | 0.968059 | 60.000 | 30.000 |

6 | 0.949658 | 60.000 | 20.000 |

7 | 0.946173 | 200.000 | 100.000 |

8 | 0.941328 | 200.000 | 100.000 |

9 | 0.935059 | 60.000 | 20.000 |

10 | 0.929244 | 60.000 | 20.000 |

11 | 0.928384 | 45.000 | 30.000 |

12 | 0.926885 | 60.000 | 35.000 |

13 | 0.920772 | 60.000 | 35.000 |

14 | 0.918505 | 120.000 | 80.000 |

15 | 0.917093 | 60.000 | 10.000 |

16 | 0.915725 | 60.000 | 20.000 |

17 | 0.913698 | 60.000 | 20.000 |

18 | 0.913090 | 90.000 | 40.000 |

19 | 0.996504 | 90.000 | 40.000 |

20 | 0.992926 | 90.000 | 40.000 |

21 | 0.992222 | 90.000 | 040.000 |

22 | 0.991584 | 90.000 | 040.000 |

23 | 0.979352 | 90.000 | 050.000 |

24 | 0.972681 | 420.000 | 200.000 |

25 | 0.969356 | 100.000 | 200.000 |

26 | 0.947729 | 90.000 | 25.000 |

27 | 0.945165 | 120.000 | 25.000 |

28 | 0.933726 | 60.000 | 20.000 |

29 | 0.925507 | 60.000 | 70.000 |

30 | 0.921950 | 200.000 | 600.000 |

31 | 0.917789 | 200.000 | 70.000 |

32 | 0.916873 | 60.000 | 100.000 |

33 | 0.916590 | 60.000 | 40.000 |

## References

- Razmjoo, A.; Ghazanfari, A.; Jahangiri, M.; Franklin, E.; Denai, M.; Marzband, M.; Garcia, D.A.; Maheri, A. A Comprehensive Study on the Expansion of Electric Vehicles in Europe. Appl. Sci.
**2022**, 12, 11656. [Google Scholar] [CrossRef] - Langbroek, J.H.; Franklin, J.P.; Susilo, Y.O. The effect of policy incentives on electric vehicle adoption. Energy Policy
**2016**, 94, 94–103. [Google Scholar] [CrossRef] - Kwong, J.; Salah, S.; Deboever, J.; Zhao, A.; Dunckley, J. 36th International Electric Vehicle Symposium and Exhibition: Medium and Heavy Duty Fleet Electrification Planning and Assessment, Sacramento, CA, USA, June 2023. Available online: https://evs36.com/wp-content/uploads/finalpapers/FinalPaper_Kwong_Jennifer%20(2).pdf (accessed on 20 September 2023).
- Taylor, T. The Advance of the Advanced Clean Truck (ACT) Rule. Available online: https://www.atlasevhub.com/weekly-digest/the-advance-of-the-act/#:~:text=The%20ACT%20requires%20manufacturers%20who,of%20the%20Clean%20Air%20Act (accessed on 30 September 2023).
- Painuli, S.; Rawat, M.S.; Rao, G.K.; Rayudu, D.R. Effects on Distribution System Voltage Stability including Electric Vehicles and Its Enhancement by Placing DG at Optimal Location. 2018. Available online: https://www.researchgate.net/publication/324648024 (accessed on 15 August 2023).
- El Helou, R.; Sivaranjani, S.; Kalathil, D.; Schaper, A.; Xie, L. The impact of heavy-duty vehicle electrification on large power grids: A synthetic Texas case study. Adv. Appl. Energy
**2022**, 6, 100093. [Google Scholar] [CrossRef] - Hong, W.; Jenn, A.; Wang, B. Electrified autonomous freight benefit analysis on fleet, infrastructure and grid leveraging Grid-Electrified Mobility (GEM) model. Appl. Energy
**2023**, 335, 120760. [Google Scholar] [CrossRef] - Vandael, S.; Claessens, B.; Ernst, D.; Holvoet, T.; Deconinck, G. Reinforcement Learning of Heuristic EV Fleet Charging in a Day-Ahead Electricity Market. IEEE Trans. Smart Grid
**2015**, 6, 1795–1805. [Google Scholar] [CrossRef] - Danese, A.; Torsæter, B.N.; Sumper, A.; Garau, M. Planning of High-Power Charging Stations for Electric Vehicles: A Review. Appl. Sci.
**2022**, 12, 3214. [Google Scholar] [CrossRef] - Mahmud, N.; Zahedi, A. Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation. Renew. Sustain. Energy Rev.
**2016**, 64, 582–595. [Google Scholar] [CrossRef] - Roy, N.; Pota, H.; Hossain, M. Reactive power management of distribution networks with wind generation for improving voltage stability. Renew. Energy
**2013**, 58, 85–94. [Google Scholar] [CrossRef] - Schiffer, J.; Seel, T.; Raisch, J.; Sezi, T. Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids With Consensus-Based Distributed Voltage Control. IEEE Trans. Control Syst. Technol.
**2015**, 24, 96–109. [Google Scholar] [CrossRef] - Nanibabu, S.; Shakila, B.; Prakash, M. Reactive Power Compensation using Shunt Compensation Technique in the Smart Distribution Grid. In Proceedings of the 2021 6th International Conference on Computing, Communication and Security (ICCCS), Las Vegas, NV, USA, 4–6 October 2021; pp. 1–6. [Google Scholar] [CrossRef]
- Adewuyi, O.B.; Shigenobu, R.; Ooya, K.; Senjyu, T.; Howlader, A.M. Static voltage stability improvement with battery energy storage considering optimal control of active and reactive power injection. Electr. Power Syst. Res.
**2019**, 172, 303–312. [Google Scholar] [CrossRef] - Fusco, G.; Russo, M.; De Santis, M. Decentralized Voltage Control in Active Distribution Systems: Features and Open Issues. Energies
**2021**, 14, 2563. [Google Scholar] [CrossRef] - Mishra, M.K.; Lal, V.N. An improved methodology for reactive power management in grid integrated solar PV system with maximum power point condition. Sol. Energy
**2020**, 199, 230–245. [Google Scholar] [CrossRef] - De Santis, M.; Di Fazio, A.R.; Russo, M.; Harighi, T.; Borghetti, A. Voltage Optimization in Distribution Networks using EV Parking Lots and PV systems as flexibility options. In Proceedings of the 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Madrid, Spain, 6–9 June 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Kisacikoglu, M.C.; Ozpineci, B.; Tolbert, L.M. EV/PHEV Bidirectional Charger Assessment for V2G Reactive Power Operation. IEEE Trans. Power Electron.
**2013**, 28, 5717–5727. [Google Scholar] [CrossRef] - Vittorias, I.; Metzger, M.; Kunz, D.; Gerlich, M.; Bachmaier, G. A bidirectional battery charger for electric vehicles with V2G and V2H capability and active and reactive power control. In Proceedings of the 2014 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA, 15–18 June 2014; pp. 1–6. [Google Scholar] [CrossRef]
- Lenka, R.K.; Panda, A.K.; Dash, A.R.; Venkataramana, N.N.; Tiwary, N. Reactive Power Compensation using Vehicle-to-Grid enabled Bidirectional Off-Board EV Battery Charger. In Proceedings of the 2021 1st International Conference on Power Electronics and Energy (ICPEE), Bhubaneswar, India, 2–3 January 2021; pp. 1–6. [Google Scholar] [CrossRef]
- Li, Y.; Li, L.; Peng, C.; Zou, J. An MPC based optimized control approach for EV-based voltage regulation in distribution grid. Electr. Power Syst. Res.
**2019**, 172, 152–160. [Google Scholar] [CrossRef] - Hu, J.; Yin, W.; Ye, C.; Bao, W.; Wu, J.; Ding, Y. Assessment for Voltage Violations considering Reactive Power Compensation Provided by Smart Inverters in Distribution Network. Front. Energy Res.
**2021**, 9, 713510. [Google Scholar] [CrossRef] - Nazaripouya, H.; Pota, H.R.; Chu, C.-C.; Gadh, R. Real-Time Model-Free Coordination of Active and Reactive Powers of Distributed Energy Resources to Improve Voltage Regulation in Distribution Systems. IEEE Trans. Sustain. Energy
**2019**, 11, 1483–1494. [Google Scholar] [CrossRef] - Nassef, A.M.; Abdelkareem, M.A.; Maghrabie, H.M.; Baroutaji, A. Review of Metaheuristic Optimization Algorithms for Power Systems Problems. Sustainability
**2023**, 15, 9434. [Google Scholar] [CrossRef] - Lu, P.; Ye, L.; Zhao, Y.; Dai, B.; Pei, M.; Tang, Y. Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges. Appl. Energy
**2021**, 301, 117446. [Google Scholar] [CrossRef] - Antarasee, P.; Premrudeepreechacharn, S.; Siritaratiwat, A.; Khunkitti, S. Optimal Design of Electric Vehicle Fast-Charging Station’s Structure Using Metaheuristic Algorithms. Sustainability
**2022**, 15, 771. [Google Scholar] [CrossRef] - Mazumder, M.; Debbarma, S. EV Charging Stations with a Provision of V2G and Voltage Support in a Distribution Network. IEEE Syst. J.
**2020**, 15, 662–671. [Google Scholar] [CrossRef] - Gandhi, O.; Zhang, W.; Rodriguez-Gallegos, C.D.; Srinivasan, D.; Reindl, T. Continuous optimization of reactive power from PV and EV in distribution system. In Proceedings of the 2013 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), Bangalore, India, 10–13 November 2013; Volume 9, pp. 1–6, 281–287. [Google Scholar] [CrossRef]
- Hemmatpour, M.H.; Koochi, M.H.R.; Dehghanian, P.; Dehghanian, P. Voltage and energy control in distribution systems in the presence of flexible loads considering coordinated charging of electric vehicles. Energy
**2021**, 239, 121880. [Google Scholar] [CrossRef] - Bakirtzis, A.G.; Biskas, P.N.; Zoumas, C.E.; Petridis, V. Optimal Power Flow by Enhanced Genetic Algorithm. IEEE Trans. Power Syst.
**2002**, 17, 229–236. [Google Scholar] [CrossRef] - del Valle, Y.; Venayagamoorthy, G.K.; Mohagheghi, S.; Hernandez, J.-C.; Harley, R.G. Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems. IEEE Trans. Evol. Comput.
**2008**, 12, 171–195. [Google Scholar] [CrossRef] - Taher, M.A.; Kamel, S.; Jurado, F.; Ebeed, M. An improved moth-flame optimization algorithm for solving optimal power flow problem. Int. Trans. Electr. Energy Syst.
**2018**, 29, e2743. [Google Scholar] [CrossRef] - Mirjalili, S.; Gandomi, A.H.; Mirjalili, S.Z.; Saremi, S.; Faris, H.; Mirjalili, S.M. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv. Eng. Softw.
**2017**, 114, 163–191. [Google Scholar] [CrossRef] - Mirjalili, S.; Lewis, A. The Whale Optimization Algorithm. Adv. Eng. Softw.
**2016**, 95, 51–67. [Google Scholar] [CrossRef] - Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw.
**2014**, 69, 46–61. [Google Scholar] [CrossRef] - Al-Hanahi, B.; Ahmad, I.; Habibi, D.; Masoum, M.A.S. Charging Infrastructure for Commercial Electric Vehicles: Challenges and Future Works. IEEE Access
**2021**, 9, 121476–121492. [Google Scholar] [CrossRef] - Liimatainen, H.; van Vliet, O.; Aplyn, D. The potential of electric trucks—An international commodity-level analysis. Appl. Energy
**2018**, 236, 804–814. [Google Scholar] [CrossRef] - Zhang, C.; Sheinberg, R.; Gowda, S.N.; Sherman, M.; Ahmadian, A.; Gadh, R. A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM. Front. Energy Res.
**2023**, 11, 1078027. [Google Scholar] [CrossRef] - Baran, M.E.; Wu, F.F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv.
**1989**, 4, 1401–1407. [Google Scholar] [CrossRef] - Thurner, L.; Scheidler, A.; Schafer, F.; Menke, J.-H.; Dollichon, J.; Meier, F.; Meinecke, S.; Braun, M. Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems. IEEE Trans. Power Syst.
**2018**, 33, 6510–6521. [Google Scholar] [CrossRef] - Smith, D.; Ozpineci, B.; Graves, R.L.; Jones, P.T.; Lustbader, J.; Kelly, K.; Walkowicz, K.; Birky, A.; Payne, G.; Sigler, C.; et al. Medium-and Heavy-Duty Vehicle Electrification An Assessment of Technology and Knowledge Gaps; Oak Ridge National Lab.(ORNL): Oak Ridge, TN, USA, 2019. [CrossRef]
- National Renewable Energy Laboratory. End-Use Load Profiles for the U.S. Building Stock. Available online: https://www.nrel.gov/buildings/end-use-load-profiles.html#dataset (accessed on 15 February 2023).

**Figure 4.**The voltage deviation values of different algorithms for the first 20 iterations within a single implementation.

**Figure 5.**Voltage magnitude profile of the 33-bus system with/without MDHD EVs and with MDHD EVs plus reactive power compensation.

**Figure 6.**Voltage magnitude profile of bus 11 with/without reactive power compensation from MDHD EVs.

Ref | Reactive Power Provider | Optimization Algorithm |
---|---|---|

[14] | Battery storage | Single metaheuristic algorithm (PSO) |

[15] | DERs | Lagrange multipliers |

[11] | Shunt capacitors, wind | Deterministic method |

[16] | PVs | Closed-loop control |

[20,21,22] | Regular EVs | Closed-loop control, MPC control, N/A |

Our paper | MDHD EVs | Multiple metaheuristic algorithms |

Parameters | Values |
---|---|

Population size | 50 |

Maximum iterations | 100 |

Termination criteria | ${10}^{-6}$ |

**Table 3.**Average values for the voltage variation, power loss, and computational time of the different metaheuristic algorithms.

$\mathit{A}\mathit{v}\_\stackrel{~}{\mathit{V}}$ (p.u.) | $\mathit{A}\mathit{v}\_\mathit{P}\mathit{L}$ (p.u.) | $\mathit{A}\mathit{v}\_\mathit{C}\mathit{T}$ (s) | I | |
---|---|---|---|---|

GA | 0.4294 | 0.4037 | 418.6772 | 84.0716 |

PSO | 117.09 (penalized value) | 0.4063 | 221.1575 | 102.8984 |

MFO | 0.4265 | 0.4031 | 621.9836 | 124.3309 |

SSA | 0.4296 | 0.4046 | 40.1610 | 8.3683 |

WOA | 0.4383 | 0.4101 | 217.5731 | 43.8586 |

GWO | 0.4300 | 0.4042 | 83. 2494 | 16.9861 |

No MDHD EVs | MDHD EVs | MDHD EVs with Reactive Power Compensation | Improved Percentage | |
---|---|---|---|---|

Voltage variation | 0.3421 p.u. | 0.5055 p.u. | 0.4291 p.u. | 15.13% |

Power loss | 0.2026 p.u. | 0.4692 p.u. | 0.4142 p.u. | 11.73% |

Bus Number | Reactive Power (kVar) |
---|---|

11 | 448.11 |

20 | 380.75 |

33 | 479.61 |

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## Share and Cite

**MDPI and ACS Style**

Zhang, C.; Sedghisigarchi, K.; Sheinberg, R.; Narayana Gowda, S.; Gadh, R.
Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs. *World Electr. Veh. J.* **2023**, *14*, 310.
https://doi.org/10.3390/wevj14110310

**AMA Style**

Zhang C, Sedghisigarchi K, Sheinberg R, Narayana Gowda S, Gadh R.
Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs. *World Electric Vehicle Journal*. 2023; 14(11):310.
https://doi.org/10.3390/wevj14110310

**Chicago/Turabian Style**

Zhang, Chen, Kourosh Sedghisigarchi, Rachel Sheinberg, Shashank Narayana Gowda, and Rajit Gadh.
2023. "Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs" *World Electric Vehicle Journal* 14, no. 11: 310.
https://doi.org/10.3390/wevj14110310