Maintaining the electrical distribution grid network reliability with distributed photovoltaic generations

Abstract

Green energy supply can be achieved by integrating intermittent renewable energy resources into the electrical distribution network. The intermittent nature of solar power generation presents significant technical challenges for integration that affect the network reliability and stability in relation to the grid power quality and voltage profile. Maximum utilization of photovoltaic in the electrical distribution network requires siting and sizing optimization. Distribution and transmission lines incur voltage drops and power losses due to their reactive and resistive properties. Application of evolutionary optimization techniques is adopted for optimal photovoltaic distributed generations placement in an electrical distribution network. Improved network voltage profile and system reliability was achieved by the application of particle swarm optimization algorithm to minimize the system’s power losses in a radial distribution network-IEEE 33-bus system. This was achieved through a MATLAB code implementation, with validation of the solution techniques and the developed model realized through a genetic algorithm case study. The active and reactive total loads linked to the network test system were 3.720 MW and 2.310 MVAr, accordingly. The conversion of solar power was modeled at a constant power factor with cut-off solar radiation ≥ 4.0 kWh/m2/day under normal operating conditions. As an initial configuration, active and reactive power losses were found as 211.02 kW and 143.04 kVAr without photovoltaic distributed generation at 0.85 pf, respectively. Integration of solar distributed generations at optimal location and capacity resulted in reduction of the network power losses by 57.98% reactive and 61.60% active. Improvement in voltage profile attained was 8.46%, while the ASAI network reliability index value before integrating solar source was 0.99734 p.u. but improved by 1.82% on installation. In conclusion, the system’s power losses reduced as acceptable voltage profile was maintained for sustained distribution network reliability.

Keywords

• Electrical distribution network
• Objective function
• Particle swarm optimization
• Real power loss
• Solar photovoltaic

References

1. [1] Banhthasit B, Jamroen C, Dechanupaprittha S. Optimal Generation Scheduling of Power System for Maximum Renewable Energy Harvesting and Power Losses Minimization. Int J Electr Comput Eng (IJECE). 2018;8(4):1954-1966. doi:10.11591/ijece.v8i4.pp1954-1966.
2. [2] Yammani C, Maheswarapu S, Matam S. Optimal Placement of Multi DGs in Distribution System with Considering the DG Bus Available Limits. Energy Power. 2012;2(1):18-23. doi:10.5923/j.ep.20120201.03.
3. [3] El-Zonkoly AM. Optimal Placement of Multi-Distributed Generation Units Including Different Load Models Using Particle Swarm Optimization. Swarm Evol Comput. 2011;1(1):50-59. doi:10.1016/j.swevo.2011.02.003.
4. [4] Rohit F, Jitendra B. Optimal Placement of Multi DG in 33 Bus System Using PSO. Int J Adv Res Electr Electron Instrum Eng. 2015;4(4):2320-3765. doi:10.15662/ijareeie.2015.0404139.
5. [5] Kumar S, Sarita K, Vardhan ASS, Elavarasan RM, Saket RK, Das N. Reliability Assessment of Wind-Solar PV Integrated Distribution System Using Electrical Loss Minimization Technique. Energies. 2020;13(21):5631. doi:10.3390/en13215631.
6. [6] Kariuki BW, Sato T. Interannual and Spatial Variability of Solar Radiation Energy Potential in Kenya Using Meteosat Satellite. Renew Energy. 2018;116:88-96. doi:10.1016/j.renene.2017.09.069.
7. [7] Cornforth D, Sayeef S, Heslop S, Moore T, Percy S, Ward JK, Berry A, Rowe D. Solar Intermittency: Australia’s Clean Energy Challenges; Characterizing the Effect of High Penetration Solar Intermittency on Australian Electricity Network. Australian University Press; 2012.
8. [8] Rind MH, Rathi MK, Hashmani AA, Lashari AA. Optimal Placement and Sizing of DG in Radial Distribution System. Sindh Univ Res J (Sci Ser). 2019;51(4):653-660.

9. [9] Pisica I, Balac C, Eremia M. Optimal Distributed Generation Location and Sizing Using Genetic Algorithms. In: 2009 15th International Conference on Intelligent System Applications to Power Systems; 08-12 November 2009; Curitiba, Brazil. p. 1-6. doi:10.1109/ISAP.2009.5352936.
10. [10] Osborn J, Kawann C. Reliability of the U.S. Electricity System: Recent Trends and Current Issues. U.S. Department of Energy; 2016.
11. [11] Intermittent Energy Source. Available from: https://en.wikipedia.org/wiki/Intermittent-energy-source. Accessed: 10 August 2022.
12. [12] Onyango AO, Ongoma V. Estimation of Mean Monthly Global Solar Radiation Using Sunshine Hours for Nairobi City, Kenya. J Renew Sustain Energy. 2015;7(5):053105.
13. [13] Kahouli O, Alsaif H, Bouteraa Y, Ali NB, Chaabene M. Power System Reconfiguration in Distribution Network for Improving Reliability Using Genetic Algorithm and Particle Swarm Optimization. Appl Sci. 2021;11(7):3092. doi:10.3390/app11073092.
14. [14] Ahmed AH, Hasan S. Optimal Allocation of Distributed Generation Units for Converting Conventional Radial Distribution System to Loop Using Particle Swarm Optimization. Energy Procedia. 2018;153:118-124. doi:10.1016/j.egypro.2018.10.073.
15. [15] Eberhart RC, Shi Y. Comparing Inertia Weights and Constraint Factor in Particle Swarm Optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation; 2000; San Diego, CA, USA. p. 84-88.
16. [16] Mohamed O, Felix AD, Benjamin B. Integration of Multiple Distributed Solar PV (DSP) into the Grid: The Case of the Distribution Network in Freetown, Sierra Leone. Energy Rep. 2024;10:100075. doi:10.1016/j.egyr.2024.100075.
17. [17] Dilini A, Jagadeesh P, Jaraka E. Performance Evaluation of PV Penetration at Different Locations in a LV Distribution Network Connected with an Off-Load Tap Changing Transformer. Indones J Electr Eng Comput Sci. 2021;21(2):987-993. doi:10.11591/ijeecs.v21.i2.pp987-993.
18. [18] Youssef E, Masoud N, Marc MG, Alberto F, Dieter B. Integration of Solar Photovoltaic Systems into Power Networks: A Scientific Evaluation Analysis. Sustainability. 2022;14(15):9249. doi:10.3390/su14159249.
19. [19] Brutton D, Kennedy J. Defining a Standard for Particle Swarm Optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium; 2007; Honolulu, HI, USA. p. 120-127.
20. [20] Hari CP, Subbaramaiah K, Sujatha P. Cost Benefit Analysis for Optimal DG Placement in Distribution System Using Elephant Herding Optimization Algorithm. J Electr Syst. 2019;15:56. doi:10.1186/s40807-019-0056-9.
21. [21] Wihartiko FD, Wijayanti H, Virgantari F. Performance Comparison of Genetic Algorithms and Particle Swarm Optimization for Model Integer Programming Bus Timetabling Program. IOP Conf Ser Mater Sci Eng. 2018;332:012020. doi:10.1088/1757-899X/332/1/012020.