access icon free Optimal sizing and placement of rooftop solar photovoltaic at Kabul city real distribution network

Renewable energy resources (RERs) such as wind and solar are said to be considerable promising of the power system worldwide, and Afghanistan is evaluated for abundant and feasible electricity generation capacity from these resources. It fortifies merging of RER to the electric power system of Afghanistan where power quality issue sums up with scheduled and unscheduled load shedding due to the shortage of electricity. This research study presents an optimal solution comprising of rooftop solar photovoltaic (PV) as distributed generation to a real and substantial 162-bus electric distribution network (EDN) in Kabul, the capital of Afghanistan. Genetic algorithm (GA) based on Newton–Raphson power flow with the objective of power loss minimisation is put forward for sizing and placement of the solar PV at practically available locations or candidate buses of the network. This approach tends to reduce the dependency on the import power and at the same time improves the performance of the current system through minimisation of the total power loss and voltage deviation. The proposed method is simulated by MATLAB® software to compare and demonstrate the performance of the system under different scenarios of the PV allocations.

Inspec keywords: distribution networks; solar power stations; genetic algorithms; power supply quality; building integrated photovoltaics; power generation scheduling; load flow; load shedding; Newton-Raphson method

Other keywords: solar PV placement; solar PV sizing; optimal rooftop solar photovoltaic placement; real 162-bus electric distribution network; renewable energy resources; GA; distributed generation; electric power system; MATLAB software; Newton-Raphson power flow; Afghanistan; electricity generation capacity; power quality issue; substantial 162-bus electric distribution network; power loss minimisation; voltage deviation minimisation; optimal rooftop solar photovoltaic sizing; total power loss minimisation; electricity shortage; wind; unscheduled load shedding; RER; Kabul city real distribution network; genetic algorithm; scheduled load shedding

Subjects: Power supply quality and harmonics; Photoelectric conversion; solar cells and arrays; Numerical approximation and analysis; Solar energy; Buildings (energy utilisation); Power system management, operation and economics; Interpolation and function approximation (numerical analysis); Distribution networks; Solar power stations and photovoltaic power systems; Optimisation techniques

References

    1. 1)
      • 20. David, E.G.: ‘Genetic algorithms in search optimization & machine learning’ (Addison-Wesley Publishing Company, Inc, 1989).
    2. 2)
      • 25. ‘NREL, Afghanistan Resource Maps and Toolkit’. Available at http://www.nrel.gov/international/raafghanistan.html, accessed 12 Feb 2017.
    3. 3)
      • 23. Hadi, S.: ‘Power system analysis’ (PSA Publishing LLC, 2011, 3rd edn.).
    4. 4)
      • 7. Meysam, K., Ahad, K.: ‘Placement of distributed generation unit and capacitor allocation in distribution systems using genetic algorithm’. 10th Int. Conf. Environment and Electrical Engineering (EEEIC), Italy, 2011.
    5. 5)
      • 9. Kenichi, T., Masato, O., Shohei, T., et al: ‘Decentralised control of voltage in distribution systems by distributed generators’, IET Gen. Trans. Dist., 2010, 4, (11), pp. 12511260.
    6. 6)
      • 4. Prabhjot, K., Sandeep, K., Rintu, K.: ‘Optimal placement and sizing of DG comparison of different techniques of DG placement’. 1st IEEE Int. Conf. Power Electronics, Intelligent Control, and Energy Systems (ICPEICES) 2016, 2016, pp. 14.
    7. 7)
      • 19. Masatoshi, S.: ‘Genetic algorithms and fuzzy multiobjective optimization’ (Kluwer Academic Publishers, 2002).
    8. 8)
      • 12. Benjamin, K., Pankaj, K., Keith, M.: ‘Optimum sizing and placement of distributed and renewable energy sources in electric power distribution systems’, IEEE Trans. Ind. Appl., 2013, 49, pp. 27412752.
    9. 9)
      • 11. Masato, O., Kenichi, T., Akie, U., et al: ‘Optimal voltage control in distribution systems with coordination of distribution installations’, Renew. Energy, 2010, 32, (10), pp. 11251134.
    10. 10)
      • 13. Tara, M.J., Geoffery, R.W., Nadarajah, M.: ‘Integrating PV systems into distribution networks with battery energy storage systems’. Australasian Universities Power Engineering Conf. (AUPEC), Australia, 2014, pp. 17.
    11. 11)
      • 22. Jizhong, Z.: ‘Optimization of power system operation’ (IEEE Wiley Presses, 2009).
    12. 12)
      • 17. Mathworks: ‘Search for global minimum of highly nonlinear problem’. Available at https://www.mathworks.com/discovery/genetic-algorithm.html, accessed 10 April 2017.
    13. 13)
      • 21. ‘IEA-ETSAP, IRENA Technology Brief E15’. Available at www.irena.org/Publications.pdf, accessed 14 February 2017.
    14. 14)
      • 1. ‘USAID, South Asia Regional Initiative for Energy (SARI/EI), Afghanistan energy sector overview’. Available at http://sari-energy.org/oldsite/PageFiles/Countries/Afghanistan_Energy_detail.html, accessed 25 March 2017.
    15. 15)
      • 6. Geev, M., Pierluigi, S.: ‘Optimal siting and sizing of wind turbines based on genatic algorithm and optimal power flow’, in Jahangir, H., Apel, M. (Eds.): ‘Renewable energy integration challenges and solutions’ (Springer Singapore, 2014, 1st edn.), pp. 125144.
    16. 16)
      • 8. Vaiju, K., Rajesh, K., Rohit, B.: ‘Optimal sizing of PV-battery for loss reduction and intermittency mitigation’. IEEE Int. Conf. Recent Advances and Innovations in Engineering (ICRAIE), India, 2014.
    17. 17)
      • 16. Ryuto, S., Ahmad, N., Cirio, M., et al: ‘Optimal operation and management for smart grid subsumed high penetration of renewable energy, electric vehicle, and battery energy storage system’, Int. Emerging Electric Power Syst., 2016, 17, (2), pp. 173189.
    18. 18)
      • 10. Mohamed, A., Mamdouh, A., Zakaria, Z., et al: ‘Assessment of reactive power contribution of photovoltaic energy systems on voltage profile and stability of distribution systems’, Int. J. Electric. Power Eng. Syst., 2014, 61, pp. 665672.
    19. 19)
      • 18. Bo, Y., Yunping, C., Zunlian, Z.: ‘A hybrid evolutionary algorithm by combination of PSO and GA for unconstrained and constrained optimization problems’. IEEE Int. Conf. Control and Automation, China, 2007, pp. 166170.
    20. 20)
      • 5. Mohammadhafez, B., Nikolaos, G.: ‘Placing and sizing distributed photovoltaic generators for optimal reactive power compensation’. IEEE Global Conf. Symp. Signal and Information Processing (GlobalSIP) 2015, 2015, pp. 11361140.
    21. 21)
      • 15. Adnan, A., Pota, H.R.: ‘Loss reduction of power distribution network using optimum size and location of distributed generation’. Power Engineering Conf. (AUPEC), Australia, 2011.
    22. 22)
      • 14. Juan, A.M., Gerardo, G.: ‘Reliability analysis of distribution systems with photovoltaic generation using a power flow simulator and a parallel Monte Carlo approach’, Energies, 2016, 9, (7), pp. 121.
    23. 23)
      • 24. Hui, Y., Fushuan, W., Liping, W.: ‘Newton-Raphson on power flow algorithm and Broyden method in the distribution system’. 2nd IEEE Int. Conf. Power and Energy (PECon 08), Malaysia, 2008, pp. 16131618.
    24. 24)
      • 2. Nicolae, G., George, C.L., Mariacristina, R., et al: ‘Power quality assessment in small scale renewable energy sources supplying distribution systems’, Energies, 2013, 2, pp. 634645.
    25. 25)
      • 3. Kabir, M.N., Mishra, Y., Ledwich, G., et al: ‘Improving voltage profile of residential distribution systems using rooftop PVs and battery energy storage systems’, Appl. Energy., 2014, 134, pp. 290300.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2017.0687
Loading

Related content

content/journals/10.1049/iet-gtd.2017.0687
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading