DOI: https://doi.org/10.14710/ijred.2020.31428

Composition Assessment of a Power Distribution System with Optimal Dispatching of Distributed Generation

1School of Engineering, University of Birmingham, Birmingham, United Kingdom

2Faculty of Electrical Engineering, The University of Lahore, Lahore, Pakistan

3Faculty of Engineering in Electricity and Computing, Escuela Superior Politécnica del Litoral, Guayaquil, Ecuador

4 Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur A.K, Pakistan

5 Department of Electrical and Computer Engineering, Shaqra University, Riyadh,, Saudi Arabia

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Received: 8 Jul 2020; Revised: 21 Aug 2020; Accepted: 26 Aug 2020; Available online: 27 Aug 2020; Published: 15 Oct 2020.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2020 The authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Increasing penetration of distributed generation (DG) is imminent in the new age of power distribution networks, which are smarter than the conventional grids. They enable the integration of DG into the power distribution network. This paper presents an assessment methodology for determining the optimal capacity and location of DG to ensure high reliability in a radial distribution network. The approach considers cost and the impact of aging on the DG and network topology for interconnection using genetic algorithm, which is a robust technique with wide solution space searchability and can potentially find global optima with fewer chances of getting trapped into local optima. A case study is simulated using three different scenarios to evaluate the impact of DG interconnection on the 13.8 kV power distribution network. The scenarios comprise of situations without any DG, with DG interconnection and optimization of DG interconnection. The case study shows that the penetration of DG increases the reliability of the distribution network while reducing the expected energy not supplied (EENS). Although, the difference between EENS in the optimized DG integration and non-optimized DG integration is not very significant in a small network, however, it becomes apparent with the aging curve that optimized allocation of DG possesses significant benefits.
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Keywords: aging factor; distribution generation; genetic algorithm; Monte Carlo simulation; photovoltaic system

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Section: Original Research Article
Language : EN
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  1. Ahadi, A., Ghadimi, N., & Mirabbasi, D. (2014). Reliability assessment for components of large scale photovoltaic systems. Journal of Power Sources, 264, 211-219. https://doi.org/10.1016/j.jpowsour.2014.04.041
  2. Altamimi, A., & Jayaweera, D. (2017). Reliability performances of grid-integrated PV systems with varying climatic conditions. In IET International Conference on Resilience of Transmission and Distribution Networks (RTDN 2017), Birmingham, UK, 2017. IET Publishing. https://doi.org/10.1049/cp.2017.0336
  3. Bodenhofer, U. (2003). Genetic algorithms: theory and applications. Lecture notes, Fuzzy Logic Laboratorium Linz-Hagenberg, Winter
  4. Borges, C. L. T., & Falcao, D. M. (2006). Optimal distributed generation allocation for reliability, losses, and voltage improvement. International Journal of Electrical Power & Energy Systems, 28(6), 413-420. https://doi.org/10.1016/j.ijepes.2006.02.003
  5. Bouktir, T., Slimani, L., & Belkacemi, M. (2004). A genetic algorithm for solving the optimal power flow problem. Leonardo Journal of Sciences, 4, 44-58
  6. Calais, M., Myrzik, J., Spooner, T., & Agelidis, V. G. (2002). Inverters for single-phase grid connected photovoltaic systems-an overview. In Power Electronics Specialists Conference, 2002. pesc 02. 2002 IEEE 33rd Annual (Vol. 4, pp. 1995-2000). IEEE. https://doi.org/10.1109/PSEC.2002.1023107
  7. Celli, G., Ghiani, E., Mocci, S., & Pilo, F. (2005). A multiobjective evolutionary algorithm for the sizing and siting of distributed generation. IEEE Transactions on Power Systems, 20(2), 750-757. https://doi.org/10.1109/TPWRS.2005.846219
  8. Chen, P.-H., & Chang, H.-C. (1995). Large-scale economic dispatch by genetic algorithm. IEEE Transactions on Power Systems, 10(4), 1919-1926. https://doi.org/10.1109/59.476058
  9. Dellosa, J. (2016). PotentialEffectand Analysisof High Residential Solar Photovoltaic (PV) Systems Penetration to an Electric Distribution Utility (DU). Int. Journal of Renewable Energy Development, 5(3),179-185, doi: 10.14710/ijred.5.3.179-185https://doi.org/10.14710/ijred.5.3.179-185
  10. Eltawil, M. A., & Zhao, Z. (2010). Grid-connected photovoltaic power systems: Technical and potential problems-A review. Renewable and Sustainable Energy Reviews, 14(1), 112-129. https://doi.org/10.1016/j.rser.2009.07.015
  11. Gerbex, S., Cherkaoui, R., & Germond, A. J. (2001). Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms. IEEE Transactions on Power Systems, 16(3), 537-544. https://doi.org/10.1109/59.932292
  12. Goyal, V., & Mahapatra, S. (2011). Application of genetic algorithm in the optimum placement of distributed generator in distributed power system. International Journal of Computer Applications, 30(6), 1-5
  13. International Energy Agency. (2017). Trends 2017 in Photovoltaic Applications Executive Summary. Retrieved from http://www.iea-pvps.org/fileadmin/dam/public/report/statistics/IEA-PVPS_-_Trends_in_PV_Applications_2017_-_EXECUTIVE_SUMMARY.pdf
  14. Jantsch, M., Real, M., Häberlin, H., Whitaker, C., Kurokawa, K., Blässer, G., Kremer, P & Verhoeve, C. W. G. (1997). Measurement of PV maximum power point tracking performance. Netherlands Energy Research Foundation ECN. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download doi=10.1.1.621.4660&rep=rep1&type=pdf
  15. Jayaweera, D., & Islam, S. (2014). Security of energy supply with change in weather conditions and dynamic thermal limits. IEEE Transactions on Smart Grid, 5(5), 2246-2254. https://doi.org/10.1109/TSG.2014.2316523
  16. Kishore, L. N., & Fernandez, E. (2011). Reliability well-being assessment of PV-wind hybrid system using Monte Carlo simulation. In Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on (pp. 63-68). IEEE. https://doi.org/10.1109/ICETECT.2011.5760092
  17. Li, W. (2013). Reliability assessment of electric power systems using Monte Carlo methods. Springer Science & Business Media
  18. Premkumar, M., Karthick, K and Sowmya, R(2018). A Review on Solar PV Based Grid Connected Microinverter Control Schemes and TopologiesInt. Journal of Renewable Energy Development, 7(2),171-182, doi.org/10.14710/ijred.7.2.171-182 https://doi.org/10.14710/ijred.7.2.171-182
  19. Puttgen, H. B., Macgregor, P. R., & Lambert, F. C. (2003). Distributed generation: Semantic hype or the dawn of a new era? IEEE Power and Energy Magazine, 99(1), 22-29. https://doi.org/10.1109/MPAE.2003.1180357
  20. Quezada, V. H. M., Abbad, J. R., & Roman, T. G. S. (2006). Assessment of energy distribution losses for increasing penetration of distributed generation. IEEE Transactions on Power Systems, 21(2), 533-540. https://doi.org/10.1109/TPWRS.2006.873115
  21. Rodríguez-Gallegos, C. D., Gandhi, O., Bieri, M., Reindl, T., & Panda, S. K. (2018). A diesel replacement strategy for off-grid systems based on progressive introduction of PV and batteries: An Indonesian case study. Applied Energy, 229, 1218-1232. https://doi.org/10.1016/j.apenergy.2018.08.019
  22. Rodríguez-Gallegos, C. D., Gandhi, O., Yang, D., Alvarez-Alvarado, M. S., Zhang, W., Reindl, T., & Panda, S. K. (2017). A siting and sizing optimization approach for PV-battery-diesel hybrid systems. IEEE Transactions on Industry Applications, 54(3), 2637-2645. https://doi.org/10.1109/TIA.2017.2787680
  23. Rodríguez-Gallegos, C. D., Yang, D., Gandhi, O., Bieri, M., Reindl, T., & Panda, S. K. (2018). A multi-objective and robust optimization approach for sizing and placement of PV and batteries in off-grid systems fully operated by diesel generators: An Indonesian case study. Energy, 160, 410-429. https://doi.org/10.1016/j.energy.2018.06.185
  24. Rújula, A. A. B., Amada, J. M., Bernal-Agustin, J. L., Loyo, J. M. Y., & Navarro, J. A. D. (2005). Definitions for distributed generation: a revision. In International Conference on Renewable Energy and Power Quality March (pp. 16-18)
  25. Grigg, C., Wong, P., Albrecht, P., Allan, R., Bhavaraju, M., Billinton, R., Chen Q., Fong C., Haddad S., Kuruganty S., Mukerji R., Li, W., Patton D., Rau N., Reppen D., Schneider A., Shahidehpour M., & C. Singh (1999). The IEEE reliability test system-1996. A report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE Transactions on power systems, 14(3), 1010-1020. https://doi.org/10.1109/59.780914
  26. Stember, L. H., Huss, W. R., & Bridgman, M. S. (1982). A methodology for photovoltaic system reliability & economic analysis. IEEE Transactions on Reliability, 31(3), 296-303. https://doi.org/10.1109/TR.1982.5221344
  27. Walters, D. C., & Sheble, G. B. (1993). Genetic algorithm solution of economic dispatch with valve point loading. IEEE Transactions on Power Systems, 8(3), 1325-1332. https://doi.org/10.1109/59.260861
  28. Wang, Y., Zhang, P., Li, W., & Kan'an, N. H. (2012). Comparative analysis of the reliability of grid-connected photovoltaic power systems. In Power and Energy Society General Meeting, 2012 IEEE (pp. 1-8). IEEE. https://doi.org/10.1109/PESGM.2012.6345373
  29. Zhang, P., Li, W., Li, S., Wang, Y., & Xiao, W. (2013). Reliability assessment of photovoltaic power systems: Review of current status and future perspectives. Applied Energy, 104, 822-833. https://doi.org/10.1016/j.apenergy.2012.12.010
  30. Zulu, E. & Jayaweera, D. (2014). Reliability assessment in active distribution networks with detailed effects of PV systems. Journal of Modern Power Systems and Clean Energy, 2(1), 59-68. https://doi.org/10.1007/s40565-014-0046-2

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