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A proposed optimization scheme for the Egyptian electrical network generation mix based on cost reduction

  • Said A. KotbEmail author
  • A. Sadat
  • Ahmed R. Adly
Original Paper
  • 48 Downloads

Abstract

The energy strategy was updated in Egypt until 2035 in cooperation with the European Union. This strategy dealt with a study of all the potentials and scenarios of the energy in Egypt, where the generation mix includes nuclear power and renewable energy in addition to the traditional energy from gas and oil with a focus on renewable energy uses to reach about 42% from the generation mix. This paper includes a framework strategy to provide an optimal yearly mix from generation sources that gives minimum cost with an acceptable range from the emitted pollution and satisfying the forecasted load. The paper objective is achieved by calculating the overall generation mix in parallel with the fair sharing from each available source that gives minimum cost against the emitted pollution. The particle swarm optimization (PSO) scheme is used to implement the proposed strategy and subjected to the generation capability limit of each type of generation from solar, wind, nuclear and conventional (thermal and hydro) as given by the government. The effectiveness of this scheme is verified by comparing the results with the Egyptian network data and with conventional optimization methodology. The scheme structure was built using MATLAB library. According to the results, it has been proved that the designed scheme used with the help of the PSO can robustly and efficiently generate great economic benefits. The study concludes the importance of the framework strategy to achieve a reliable and sustainable future energy supply.

Keywords

Power generation mix Renewable energy Nuclear power Emitted pollution Levelized cost of electricity Particle swarm optimization 

Notes

References

  1. 1.
    Mondal M, Denich M (2010) Assessment of renewable energy resources potential for electricity generation in Bangladesh. Renew Sustain Energy Rev 14:2401–2413CrossRefGoogle Scholar
  2. 2.
    Belke A, Dobnik F, Dreger C (2011) Energy consumption and economic growth: new insights into the cointegration relationship. Energy Econ 33(5):782–789CrossRefGoogle Scholar
  3. 3.
    UN (2015) Sustainable development goals. United Nations. https://sustainabledevelopment.un.org/?menu=1300
  4. 4.
    UN-Energy (2005) The energy challenge for achieving the millennium development goals. United Nations, New YorkGoogle Scholar
  5. 5.
    Jakob M, Haller M, Marschinski R (2012) Will history repeat itself? Economic convergence and convergence in energy use patterns. Energy Econ 34(1):95–104CrossRefGoogle Scholar
  6. 6.
    Initiative GE (2014) Global electricity initiative executive summary, GEI 2014ut also to determine global optimum system configuration with relative computationalGoogle Scholar
  7. 7.
    Kuang YI, Zhang Y, Zhou B, Li C, Cao Y, Li L et al (2016) A review of renewable energy utilization in islands. Renew Sustain Energy Rev 59:504–513CrossRefGoogle Scholar
  8. 8.
    Hegazy K (2015) Egypt’s energy sector: regional cooperation outlook and prospects of furthering engagement with the energy charter. Energy Charter Secretariat, Knowledge Centre, BrusselsGoogle Scholar
  9. 9.
    International Energy Agency (IEA) (2011) CO2 emissions from fuel combustion. Imprimerie-Centrale, LuxembourgGoogle Scholar
  10. 10.
    Muis ZA, Hashim H, Manan ZA, Taha FM, Douglas PL (2010) Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target. Renew Energy 35:2562–2570CrossRefGoogle Scholar
  11. 11.
    De Jonghea C, Delarueb E, Belmansa R, D’haeseleerb W (2011) Determining optimal electricity technology mix with high level of wind power penetration. Appl Energy 88(6):2231–2238CrossRefGoogle Scholar
  12. 12.
    Pereiraa AJC, Saraivab JT (2011) Generation expansion planning (GEP)—a long-term approach using system dynamics and genetic algorithms (GAs). Energy 36(8):5180–5199CrossRefGoogle Scholar
  13. 13.
    Koltsaklis NE, Dagoumas AS, Kopanos GM, Pistikopoulos EN, Georgiadis MC (2014) A spatial multi-period long-term energy planning model: a case study of the greek power system. Appl Energy 82:115–126Google Scholar
  14. 14.
    Vidal-Amaroa JJ, Ostergaardb P, Sheinbaum-Pardoa C (2015) Optimal energy mix for transitioning from fossil fuels to renewable energy sources—the case of the Mexican electricity system. Appl Energy 150(15):80–96CrossRefGoogle Scholar
  15. 15.
    Thangavelu SR, Khambadkone AM, Karimi IA (2015) Long-term optimal energy mix planning towards high energy security and low GHG emission. Appl Energy 154(15):959–969CrossRefGoogle Scholar
  16. 16.
    Amer M, Namaane A, M’sirdi NK (2013) Optimization of hybrid renewable energy systems (HRES) using PSO for cost reduction. Energy Procedia 42:318–327CrossRefGoogle Scholar
  17. 17.
    Strnad I, Prenc R (2018) Optimal sizing of renewable sources and energy storage in low-carbon microgrid nodes. Electr Eng 100(3):1661–1674CrossRefGoogle Scholar
  18. 18.
    Kotb SA, Abdelaal MMZ (2018) Analysis of the impact of introduction of nuclear power plants on energy characteristics and environment in Egypt. Electr Eng 100:285–292CrossRefGoogle Scholar
  19. 19.
    Dawoud SM, Lin X, Okba MI (2018) Hybrid renewable microgrid optimization techniques: a review. Renew Sustain Energy Rev 82:2039–2052CrossRefGoogle Scholar
  20. 20.
    Ramli MAM, Bouchekara HREH, Alghamdi AS (2018) Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renew Energy 121:400–411CrossRefGoogle Scholar
  21. 21.
    Hong Y, Chang W, Chang Y, Lee Y, Ouyang D (2017) Optimal sizing of renewable energy generations in a community microgrid using Markov model. Energy 135:68–74CrossRefGoogle Scholar
  22. 22.
    Al-falahi MDA, Jayasinghe SDG, Enshaei H (2017) A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Convers Manag 143:252–274CrossRefGoogle Scholar
  23. 23.
    Jung J, Villaran M (2017) Optimal planning and design of hybrid renewable energy systems for microgrids. Renew Sustain Energy Rev 75:180–191CrossRefGoogle Scholar
  24. 24.
    Algabalawy MA, Abdelaziz AY, Mekhamer SF, Abdel Aleem SHE (2018) Considerations on optimal design of hybrid power generation systems using whale and sine cosine optimization algorithms. Electr Syst Inf Technol 5:312–325Google Scholar
  25. 25.
    Shaaban M (2017) The roadmap to energy security in Egypt. University at Hamburg, HamburgGoogle Scholar
  26. 26.
    Al-Riffai P, Blohmke J, Breisinger C, Wiebelt M (2015) Harnessing the sun and wind for economic development? An economy-wide assessment for Egypt. Sustainability 7(6):7714–7740CrossRefGoogle Scholar
  27. 27.
    Shaaban M, Scheffran J (2017) Selection of sustainable development indicators for the assessment of electricity production in Egypt. Sustain Energy Technol Assess 22:65–73Google Scholar
  28. 28.
    Servert JF, Cerrajero E (2015) Assessment on Egypt’s CSP components manufacturing potential. Energy Procedia 69:1498–1507CrossRefGoogle Scholar
  29. 29.
    Shaaban M, Scheffran J, Bohner J, Elsobki M (2018) Sustainability assessment of electricity generation technologies in Egypt using multi-criteria decision analysis. Energies 11(5):1105–1117CrossRefGoogle Scholar
  30. 30.
    Khalil A, Mubarak A, Kaseb S (2010) Road map for renewable energy research and development in Egypt. J Adv Res 1(1):29–38CrossRefGoogle Scholar
  31. 31.
    Li X, Deb K (2010) Comparing best PSO Niching algorithms using different position update rules. In: IEEE world congress on computational intelligence, Spain, pp 1564–1571Google Scholar
  32. 32.
    Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, New York, pp 289–358 Ch. 16 CrossRefGoogle Scholar
  33. 33.
    Egyptian electricity holding company annual report 2015/2016. www.moee.gov.eg/test_new/report.aspx
  34. 34.
    Haghighi AS, Reza SA, Taher N (2014) A modified teaching–learning based optimization for multi-objective optimal power flow problem. Energy Convers Manag 77:597–607CrossRefGoogle Scholar
  35. 35.
    Guillaume DR, John P (2011) A methodology for calculating the levelized cost of electricity in nuclear power systems with fuel recycling. Energy Econ 33:826–839CrossRefGoogle Scholar
  36. 36.
    Thoisenniklas CK, Johannes MN (2013) Levelized cost of electricity renewable energy technologies study. Fraunhofer Institute for Solar Energy Systems, Freiburg im BreisgauGoogle Scholar
  37. 37.
    Weiner AM, McGovern RK, John H, Lienhard V (2015) A new reverse electro dialysis design strategy which significantly reduces the levelized cost of electricity. Membr Sci 493:605–614CrossRefGoogle Scholar
  38. 38.
    International Energy Agency (IEA) non-OECD country reports (2008). http://www.iea.org/Textbase/country/n_country.asp?COUNTRY_CODE=EG&Submit=Submit

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.ETRR-2, Nuclear Research CenterAtomic Energy AuthorityNasr CityEgypt

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