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A Comparison Between GSA and IGSA for Optimal Allocation and Sizing of DG and Impact to Voltage Stability Margin in Electrical Distribution System

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Abstract

Optimal placement and sizing of Distributed Generation (DG) are essential for future power planning of distribution networks. The performance of Gravitational Search Algorithm (GSA) and an Improved version of Gravitational Search Algorithm (IGSA) are compared in solving the optimization problem. The multi-objective function optimization includes power loss minimization (Ploss), Minimum bus voltage (Vbusmin), and an average voltage total harmonic distortion (THDv) are considered in this optimization problem and the IEEE 13-bus and IEEE 69-bus radial distribution network were applied on this study. The benefits due to the optimal placement and sizing of DG include power loss reduction, minimization of total harmonic distortion, and improvement of bus voltages, especially the weakest bus in the distribution network. The impact of DG installation at the proposed location with the proposed sizing on voltage stability margin also presented. The results show the Voltage Stability Margin (VSM) for both real power (P) and reactive power (Q) at the weakest bus after DG have improvement with present of DG.

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References

  1. Kadir A, Mohamed A, Shareef H, Ibrahim AA, Khatib T, Elmenreich W (2014) An improved gravitational search algorithm for optimal placement and sizing of renewable distributed generation units in a distribution system for power quality enhancement. J Renew Sustain Energy 6:033112–1. https://doi.org/10.1063/1.4878997

    Article  Google Scholar 

  2. Kadir AFA, Khatib T, Elmenreich W (2014) Integrating photovoltaic systems in power system: power quality impacts and optimal planning challenges. Int J Photoenergy. https://doi.org/10.1155/2014/321826

    Article  Google Scholar 

  3. Daud S et al (2015) Optimal placement and sizing of renewable distributed generation via gravitational search algorithm. Appl Mech Mater 785:556–560. https://doi.org/10.4028/www.scientific.net/amm.785.556

    Article  Google Scholar 

  4. Daud S, Kadir AFA, Gan CK (2015) The impacts of distributed Photovoltaic generation on power distribution networks losses. In: 2015 IEEE student conference on research and development, SCOReD 2015, pp 11–15. https://doi.org/10.1109/SCORED.2015.7449305

  5. Jamil M, Anees AS (2016) Optimal sizing and location of SPV (solar photovoltaic) based MLDG (multiple location distributed generator) in distribution system for loss reduction, voltage profile improvement with economical benefits. 103. https://doi.org/10.1016/j.energy.2016.02.095.

  6. Lqwr Q, Dvhg ULG (2017) Impact analysis for high-penetration distributed photovoltaic generation into grid based on DIgSILENT. In: 2017 IEEE conference on energy internet and energy system integration (EI2), pp 2–7

  7. Jayasree MS, Sreejaya P, Bindu GR (2019) Multi-objective metaheuristic algorithm for optimal distributed generator placement and profit analysis. Technol Econ Smart Grids Sustain Energy 4(1):201. https://doi.org/10.1007/s40866-019-0067-z

    Article  Google Scholar 

  8. Murty VVSN, Kumar A (2015) Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth. Int J Electr Power Energy Syst 69:246–256. https://doi.org/10.1016/j.ijepes.2014.12.080

    Article  Google Scholar 

  9. Sambaiah KS (2018) A review on optimal allocation and sizing techniques for DG in distribution systems. Int J Renew Energy Res 8(3):1236–1256

    Google Scholar 

  10. Yeo SM et al (2003) Analysis of system impact of the distributed generation using emtp with particular reference to voltage sag. IFAC Proceedings Volumes (IFAC-PapersOnline), pp 821–826. https://doi.org/10.1016/S14746670(17)34573-1

  11. Bala Krishna K, Mercy Rosalina K, Ramaraj N (2018) Complete and incomplete observability analysis by optimal PMU placement techniques of a network. J Electr Eng Technol 13(5):1814–1820. https://doi.org/10.5370/JEET.2018.13.5.1814

    Article  Google Scholar 

  12. Dulău LI, Abrudean M, Bică D (2014) Effects of distributed generation on electric power systems. Procedia Technol. https://doi.org/10.1016/j.protcy.2013.12.549

    Article  Google Scholar 

  13. Kayal P, Chanda CK (2013) Placement of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2013.05.047

    Article  Google Scholar 

  14. El-Amin IM, Ahmed MK (2014) Impact of a PV system on a power grid. In: International symposium on power electronics, electrical drives, automation and motion. https://doi.org/10.1109/SPEEDAM.2014.6872082

  15. Pesaran HAM, Huy PD, Ramachandaramurthy VK (2017) A review of the optimal allocation of distributed generation: objectives, constraints, methods, and algorithms. Renew Sustain Energy Rev 75:293–312

    Article  Google Scholar 

  16. Aman MM, Jasmon GB, Mokhlis H, Bakar AHA (2012) Optimal placement and sizing of a DG based on a new power stability index and line losses. Int J Electr Power Energy Syst 43:1296–1304

    Article  Google Scholar 

  17. Gözel T, Hocaoglu MH (2009) An analytical method for the sizing and siting of distributed generators in radial systems. Electr Power Syst Res 79:912–918

    Article  Google Scholar 

  18. Hung DQ, Mithulananthan N, Bansal RC (2010) Analytical expressions for DG allocation in primary distribution networks. IEEE Trans Energy Convers 25:814–820

    Article  Google Scholar 

  19. Mena AJG, Martín García JA (2015) An efficient approach for the siting and sizing problem of distributed generation. Int J Electr Power Energy Syst 69:167–172

    Article  Google Scholar 

  20. Park C, Hong JH, Jang G (2010) Assessment of system voltage sag performance based on the concept of area of severity. IET Gener Transm Distrib 4:683–693

    Article  Google Scholar 

  21. Dias B, Oliveira LW, Gomes FV, Silva IC, Oliveira EJ (2012) Hybrid heuristic optimization approach for optimal distributed generation placement and sizing. IEEE Power and Energy Society General Meeting 2012:1–6

    Google Scholar 

  22. Nekooei K, Farsangi MM, Nezamabadi-pour H, Lee KY (2013) An improved multi-objective harmony search for optimal placement of DG in distribution systems. IEEE Trans Smart Grid 4:557–567

    Article  Google Scholar 

  23. Duong Quoc H, Mithulananthan N (2013) Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Ind Electron 60:1700–1708

    Article  Google Scholar 

  24. Georgilakis P, Hatziargyriou ND (2013) Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Trans Power Syst 28:3420–3428

    Article  Google Scholar 

  25. Atwa Y, El-Saadany EF (2011) Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems. IET Renew Power Gener 5:79–88

    Article  Google Scholar 

  26. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  27. Al Abri RS, El-Saadany EF, Atwa YM (2013) Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation. IEEE Trans Power Syst 28(1):326–334. https://doi.org/10.1109/TPWRS.2012.2200049

    Article  Google Scholar 

  28. Dinakara Prasad Reddy P, Veera Reddy VC, Gowri Manohar T (2017) Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renew Wind Water Solar 4:3

    Article  Google Scholar 

  29. Dinakara Prasad Reddy P, Veera Reddy VC, Gowri Manohar T (2018) Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. J Electr Syst Inf Technol 5(3):663–680

    Article  Google Scholar 

  30. Sultana U, Khairuddin AB, Mokhtar AS, Zareen N, Sultana B (2016) Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system. Energy 111:525–536

    Article  Google Scholar 

  31. Fateh A, Nor M, Sulaiman M (2019) Identification of weak buses in electric power system based on modal analysis and load power margin 14(7):1377–1384

    Google Scholar 

  32. Nor AFM, Sulaiman M, Omar R (2016) Study of voltage and power stability margins of electrical power system using ANN. IET Conference Publications, (Ceat) 1–7

  33. Abu-Hashim R, Liu Y et al (1999) Test systems. IEEE Trans Power Delivery 14(2):579–587

    Article  Google Scholar 

  34. Fazliana A et al (2014) Integrating photovoltaic systems in power system: power quality impacts and optimal planning challenges. Int J Photoenergy

  35. Georgilakis PS, Member S, Hatziargyriou ND (2013) Power distribution networks: models, methods, and future research. IEEE Trans Power Syst 28(3):3420–3428. https://doi.org/10.1109/TPWRS.2012.2237043

    Article  Google Scholar 

  36. HassanzadehFard H, Jalilian A (2018) Optimal sizing and location of renewable energy based DG units in distribution systems considering load growth. Int J Electr Power Energy Syst 101:356–370. https://doi.org/10.1016/j.ijepes.2018.03.038

    Article  Google Scholar 

  37. Jamil M, Anees AS (2016) Optimal sizing and location of SPV (solar photovoltaic) based MLDG (multiple location distributed generator) in distribution system for loss reduction, voltage profile improvement with economical benefits. Energy 103:231–239. https://doi.org/10.1016/j.energy.2016.02.095

    Article  Google Scholar 

  38. Zubo RHA et al (2017) Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review. Renew Sustain Energy Revr 72:1177–1198. https://doi.org/10.1016/j.rser.2016.10.036

    Article  Google Scholar 

  39. Abdul Kadir AF et al (2013) Optimal placement and sizing of distributed generations in distribution systems for minimizing losses and THDv using evolutionary programming. Turk J Electr Eng Comput Sci 21(SUPPL. 2):2269–2282. https://doi.org/10.3906/elk-1205-35

    Article  Google Scholar 

  40. Seifinajmi E, Saghi S, Najafi M (2014) Bulletin of environment, pharmacology and life sciences optimal placement of DGs in radial distribution systems considering power quality improvement and reduce losses. Environ Pharmacol Life Sci

  41. Committee D (2004) IEEE Recommended Practices and Requirements for Harmonic Control in Electrical Power Systems

  42. Omar R et al (2015) Comparative study of a three phase cascaded H-bridge multilevel inverter for comparative study of a three phase cascaded h-bridge multilevel inverter for harmonic reduction. https://doi.org/10.11591/telkomnika.v14i3.7949

  43. Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-014-1640-y

    Article  Google Scholar 

  44. Irfan M, Ashraf S, Imtiaz AR (2018) Sizing and siting of types I–IV DG units using chaos-assisted gravitational search algorithm 8(3)

  45. Daud S et al (2016) A review: optimal distributed generation planning and power quality issues. Int Rev Electr Eng 11(2):208–222. https://doi.org/10.15866/iree.v11i2.5806

    Article  MathSciNet  Google Scholar 

  46. Daud S et al (2016) A comparison of heuristic optimization techniques for optimal placement and sizing of photovoltaic based distributed generation in a distribution system. Sol Energy 140:219–226. https://doi.org/10.1016/j.solener.2016.11.013

    Article  Google Scholar 

  47. Khajehzadeh M et al (2012) A modified gravitational search algorithm for slope stability analysis. Eng Appl Artif Intell 25(8):1589–1597. https://doi.org/10.1016/j.engappai.2012.01.011

    Article  Google Scholar 

  48. Eslami M et al (2012) An efficient particle swarm optimization technique with chaotic sequence for optimal tuning and placement of PSS in power systems. Int J Electr Power Energy Syst 43(1):1467–1478. https://doi.org/10.1016/j.ijepes.2012.07.028

    Article  Google Scholar 

  49. Ibrahim AA, Mohamed A, Shareef H (2012) A novel quantum–inspired binary gravitational search algorithm in obtaining optimal power quality monitor placement. J Appl Sci 2012:10

    Google Scholar 

  50. Fateh A et al (2018) Voltage instability analysis based on adaptive neuro-fuzzy inference system and probabilistic neural network. J Eng Technol

  51. Hadavi S et al (2017) Optimal placement and sizing of DGs considering static voltage stability. 2017 Electrical power distribution networks conference. EPDC 2017:18–19. https://doi.org/10.1109/EPDC.2017.8012733

    Article  Google Scholar 

  52. Nor AFM et al (2017) Voltage stability analysis of load buses in electric power system using adaptive neuro-fuzzy inference system (anfis) and probabilistic neural network (pnn). ARPN J Eng Appl Sci 12(5):1406–1412

    Google Scholar 

  53. Nor AFM et al (2018) Determining voltage stability margin values by measuring the hypotenuse under PV and QV curves. Int J Electr Eng Appl Sci 1(1):25–30

    Google Scholar 

  54. Sulaiman M, Nor AFM, Bujal NR (2015) Voltage instability analysis on PV and QV curves for radial-type and mesh-type electrical power networks. Int Rev Electr Eng 10(1):109–115. https://doi.org/10.15866/iree.v10i1.4509

    Article  Google Scholar 

  55. Eftekharnejad S, Heydt GT, Vittal V (2015) Optimal generation dispatch with high penetration of photovoltaic generation. IEEE Trans Sustain Energy 6(3):1013–1020. https://doi.org/10.1109/TSTE.2014.2327122

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Robotics and Industrial Automation (CeRIA), Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Noor Ropidah Bujal, Marizan Sulaiman & Aida Fazliana Abd Kadir

  2. Department of Energy Engineering and Environment, An-Najah National University, Nablus, Palestine

    Tamer Khatib

  3. Higher Institute for Water Technology, Agelat, Libya

    Naji Eltawil

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  1. Noor Ropidah Bujal
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  2. Marizan Sulaiman
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  3. Aida Fazliana Abd Kadir
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  4. Tamer Khatib
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  5. Naji Eltawil
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Correspondence to Tamer Khatib.

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Bujal, N.R., Sulaiman, M., Abd Kadir, A.F. et al. A Comparison Between GSA and IGSA for Optimal Allocation and Sizing of DG and Impact to Voltage Stability Margin in Electrical Distribution System. J. Electr. Eng. Technol. 16, 2949–2966 (2021). https://doi.org/10.1007/s42835-021-00829-y

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