Voltage Control in Smart Distribution Network with Deep Penetration of Electric Vehicle

Petinrin Olawole Oluyemi, Fasina Taiwo Emmanuel, Petinrin Olawole Joseph, Adebanji Bankole, Olulope Kehinde Paul, Oyedokun Aremu Jelili

Abstract


The deep integration of renewable energy resources (RES) in the smart distribution network (SDN) has presented more technical challenges and uncertainties in the operation of the network system degraded protection, voltage variation,  increased fault level, and two-way power flow. Hence, the high penetration of the (RES), most especially the variability of PV irradiance and wind speed will certainly affect the power distribution Network (DN) control and operation. It is therefore essential to investigate the effect of high penetration of RES on the design requirements for DN and appropriate voltage control strategy to be taken. To accommodate deep integration of solar PV in SDN with its fluctuating and variability characteristic, different conceptualization based on control schemes are proposed. Hence the application of battery electric vehicle (EV) and plug hybrid electric vehicle (PHEV) are used to overcome voltage rise or variation problems with minimum network reinforcement. A modified IEEE 13-bus test feeder of total load distributed among residential and commercial electricity consumers is used as a test feeder network. The network, including all conductors, feeder regulators, service transformers, and customer loads are simulated in OpenDSS interfaced with Matlab. This is utilized to compute the random variables and regulate the execution of the procedure. The solar PVs integrated produce as much power as the available energy resources permit. Excess energy produced is stored in battery electric vehicle (EV).


Keywords


Electric vehicle; OPENDSS; Renewable energy; PV; Smart distribution.

Full Text:

PDF

References


X. Lv, Y. Sun, S. Cao, and V. Dinavahi, “Event-triggered load frequency control for multi-area power systems based on Markov model: A global sliding mode control approach,” IET Gener. Transm. Distrib., vol. 14, pp. 4878–4887, Nov. 2020, doi: 10.1049/iet-gtd.2020.0186.

A. Rafinia, J. Moshtagh, and N. Rezaei, “Towards an enhanced power system sustainability: An MILP under-frequency load shedding scheme considering demand response resources,” Sustain. Cities Soc., vol. 59, p. 102168, Aug. 2020, doi: 10.1016/j.scs.2020.102168.

K. Naidu, H. Mokhlis, and A. H. A. Bakar, “Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control,” Int. J. Electr. Power Energy Syst., vol. 55, pp. 657–667, Feb. 2014, doi: 10.1016/j.ijepes.2013.10.022.

M. Kh. AL-Nussairi, S. D. Al-Majidi, A. R. Hussein, and R. Bayindir, “Design of a Load Frequency Control based on a Fuzzy logic for Single Area Networks,” in 2021 10th International Conference on Renewable Energy Research and Application (ICRERA), Sep. 2021, pp. 216–220. doi: 10.1109/ICRERA52334.2021.9598593.

F. Mazouz, S. Belkacem, and I. Colak, “DPC-SVM of DFIG Using Fuzzy Second Order Sliding Mode Approach,” Int. J. Smart Grid-IjSmartGrid, vol. 5, no. 4, pp. 174–82, 2021.

A. Shahid, “Smart grid integration of renewable energy systems,” in 2018 7th International conference on renewable energy research and applications (ICRERA), IEEE, 2018, pp. 944–948.

R. Z. Caglayan, K. Kayisli, N. Zhakiyev, A. Harrouz, and I. Colak, “A review of hybrid renewable energy systems and MPPT methods,” Int. J. Smart Grid-IjSmartGrid, vol. 6, no. 3, pp. 72–78, 2022.

S. Pahadasingh, “TLBO based CC-PID-TID controller for load frequency control of multi area power system,” in 2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), IEEE, 2021, pp. 1–7.

A. Sahu and S. K. Hota, “Performance comparison of 2-DOF PID controller based on Moth-flame optimization technique for load frequency control of diverse energy source interconnected power system,” in 2018 Technologies for Smart-City Energy Security and Power (ICSESP), IEEE, 2018, pp. 1–6.

L. C. Saikia and N. Sinha, “Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller,” Int. J. Electr. Power Energy Syst., vol. 80, pp. 52–63, 2016.

D. Guha, P. K. Roy, and S. Banerjee, “Load frequency control of interconnected power system using grey wolf optimization,” Swarm Evol. Comput., vol. 27, pp. 97–115, 2016.

Aditya, “Design of load frequency controllers using genetic algorithm for two area interconnected hydro power system,” Electr. Power Compon. Syst., vol. 31, no. 1, pp. 81–94, 2003.

I. A. Chidambaram and B. Paramasivam, “Optimized load-frequency simulation in restructured power system with redox flow batteries and interline power flow controller,” Int. J. Electr. Power Energy Syst., vol. 50, pp. 9–24, 2013.

Y. Arya and N. Kumar, “Design and analysis of BFOA-optimized fuzzy PI/PID controller for AGC of multi-area traditional/restructured electrical power systems,” Soft Comput., vol. 21, pp. 6435–6452, 2017.

A. Saha and L. C. Saikia, “Utilisation of ultra-capacitor in load frequency control under restructured STPP-thermal power systems using WOA optimised PIDN-FOPD controller,” IET Gener. Transm. Distrib., vol. 11, no. 13, pp. 3318–3331, 2017.

Y. K. Bhateshvar, H. D. Mathur, H. Siguerdidjane, and R. C. Bansal, “Ant colony optimized fuzzy control solution for frequency oscillation suppression,” Electr. Power Compon. Syst., vol. 45, no. 14, pp. 1573–1584, 2017.

S. Vadi, F. B. Gurbuz, S. Sagiroglu, and R. Bayindir, “Optimization of pi based buck-boost converter by particle swarm optimization algorithm,” in 2021 9th International Conference on Smart Grid (icSmartGrid), IEEE, 2021, pp. 295–301.

H. R. Chamorro, I. Riano, R. Gerndt, I. Zelinka, F. Gonzalez-Longatt, and V. K. Sood, “Synthetic inertia control based on fuzzy adaptive differential evolution,” Int. J. Electr. Power Energy Syst., vol. 105, pp. 803–813, 2019.

L. Kumar, M. K. Kar, and S. Kumar, “Statistical analysis based reactive power optimization using improved differential evolutionary algorithm,” Expert Syst., vol. 40, no. 1, p. e13091, 2023.

S. Zheng, X. Tang, B. Song, S. Lu, and B. Ye, “Stable adaptive PI control for permanent magnet synchronous motor drive based on improved JITL technique,” Isa Trans., vol. 52, no. 4, pp. 539–549, 2013.

H. Bevrani and P. R. Daneshmand, “Fuzzy logic-based load-frequency control concerning high penetration of wind turbines,” IEEE Syst. J., vol. 6, no. 1, pp. 173–180, 2011.

A. AlKassem, M. Al Ahmadi, and A. Draou, “Modeling and Simulation Analysis of a Hybrid PV-Wind Renewable Energy Sources for a Micro-Grid Application,” in 2021 9th International Conference on Smart Grid (icSmartGrid), Jun. 2021, pp. 103–106.

J. Yang, Z. Zeng, Y. Tang, J. Yan, H. He, and Y. Wu, “Load frequency control in isolated micro-grids with electrical vehicles based on multivariable generalized predictive theory,” Energies, vol. 8, no. 3, pp. 2145–2164, 2015.

R. R. Kumar, A. K. Yadav, and M. Ramesh, “Hybrid PID plus LQR based frequency regulation approach for the renewable sources based standalone microgrid,” Int. J. Inf. Technol., vol. 14, no. 5, pp. 2567–2574, Aug. 2022, doi: 10.1007/s41870-022-01019-3.

Y. Nagaraja, T. Devaraju, and M. Vijaykumar, “Optimal distributed control of renewable energy-based microgrid – an energy management approach,” Int. J. Ambient Energy, vol. 42, no. 14, pp. 1635–1642, Oct. 2021

M. Regad, M. Helaimi, R. Taleb, A. M. Othman, and H. A. Gabbar, “Frequency Control in Microgrid Power System with Renewable Power Generation Using PID Controller Based on Particle Swarm Optimization,” in Smart Energy Empowerment in Smart and Resilient Cities, M. Hatti, Ed., in Lecture Notes in Networks and Systems. Cham: Springer International Publishing, 2020, pp. 3–13.

A. Singh and S. Suhag, “Frequency regulation in an AC microgrid interconnected with thermal system employing multiverse-optimised fractional order-PID controller,” Int. J. Sustain. Energy, vol. 39, no. 3, pp. 250–262, 2020.

A. Annamraju and S. Nandiraju, “Load frequency control of an autonomous microgrid using robust fuzzy PI controller,” in 2019 8th International Conference on Power Systems (ICPS), IEEE, 2019, pp. 1–6.

A. Naderipour, Z. Abdul-Malek, I. F. Davoodkhani, H. Kamyab, and R. R. Ali, “Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller,” Environ. Sci. Pollut. Res., pp. 1–12, 2021.

N. K. Jena, S. Sahoo, and B. K. Sahu, “Fractional order cascaded controller for AGC study in power system with PV and diesel generating units,” J. Interdiscip. Math., vol. 23, no. 2, pp. 425–434, 2020.

D. Mishra, M. K. Maharana, and A. Nayak, “Frequency amelioration of an interconnected microgrid system,” Open Eng., vol. 12, no. 1, pp. 349–358, 2022.

M. S. Ayas and E. Sahin, “FOPID controller with fractional filter for an automatic voltage regulator,” Comput. Electr. Eng., vol. 90, p. 106895, 2021.

H. Li, X. Wang, and J. Xiao, “Differential evolution-based load frequency robust control for micro-grids with energy storage systems,” Energies, vol. 11, no. 7, p. 1686, 2018.

B. Khokhar, S. Dahiya, and K. S. Parmar, “Load frequency control of a microgrid employing a 2D Sine Logistic map based chaotic sine cosine algorithm,” Appl. Soft Comput., vol. 109, p. 107564, 2021.




DOI (PDF): https://doi.org/10.20508/ijrer.v13i2.13666.g8728

Refbacks

  • There are currently no refbacks.


Online ISSN: 1309-0127

Publisher: Gazi University

IJRER is cited in SCOPUS, EBSCO, WEB of SCIENCE (Clarivate Analytics);

IJRER has been cited in Emerging Sources Citation Index from 2016 in web of science.

WEB of SCIENCE in 2025; 

h=35,

Average citation per item=6.59

Last three Years Impact Factor=(1947+1753+1586)/(146+201+78)=5286/425=12.43

Category Quartile:Q4