A Genetic Algorithm to Solve Power System Expansion Planning with Renewable Energy

  • Lourdes Martínez-Villaseñor
  • Hiram PonceEmail author
  • José Antonio Marmolejo
  • Juan Manuel Ramírez
  • Agustina Hernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)


In this paper, a deterministic dynamic mixed-integer programming model for solving the generation and transmission expansion-planning problem is addressed. The proposed model integrates conventional generation with renewable energy sources and it is based on a centralized planned transmission expansion. Due a growing demand over time, it is necessary to generate expansion plans that can meet the future requirements of energy systems. Nowadays, in most systems a public entity develops both the short and long of electricity-grid expansion planning and mainly deterministic methods are employed. In this study, an heuristic optimization approach based on genetic algorithms is presented. Numerical results show the performance of the proposed algorithm.


Genetic algorithms Generation and transmission problem Power system planning 


  1. 1.
    Abbasi, A.R., Seifi, A.R.: Energy expansion planning by considering electrical and thermal expansion simultaneously. Energy Convers. Manag. 83, 9–18 (2014)CrossRefGoogle Scholar
  2. 2.
    Alizadeh, B., Jadid, S.: Reliability constrained coordination of generation and transmission expansion planning in power systems using mixed integer programming. IET Gener., Transm. Distrib. 5(9), 948–960 (2011)CrossRefGoogle Scholar
  3. 3.
    Barati, F., Seifi, H., Sepasian, M.S., Nateghi, A., Shafie-khah, M., Catalão, J.P.: Multi-period integrated framework of generation, transmission, and natural gas grid expansion planning for large-scale systems. IEEE Trans. Power Syst. 30(5), 2527–2537 (2015)CrossRefGoogle Scholar
  4. 4.
    Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11(6), 4135–4151 (2011)CrossRefGoogle Scholar
  5. 5.
    Bussieck, M.R., Meeraus, A.: General algebraic modeling system (GAMS). In: Kallrath, J. (ed.) Modeling Languages in Mathematical Optimization. APOP, vol. 88, pp. 137–157. Springer, Boston (2004). Scholar
  6. 6.
    Cadini, F., Zio, E., Petrescu, C.A.: Optimal expansion of an existing electrical power transmission network by multi-objective genetic algorithms. Reliab. Eng. Syst. Saf. 95(3), 173–181 (2010)CrossRefGoogle Scholar
  7. 7.
    Chen, S.L., Zhan, T.S., Tsay, M.T.: Generation expansion planning of the utility with refined immune algorithm. Electr. Power Syst. Res. 76(4), 251–258 (2006)CrossRefGoogle Scholar
  8. 8.
    Conejo, A., Baringo, L., Kazempour, S., Siddiqui, A.: Investment in Electricity Generation and Transmission: Decision Making under Uncertainty, 1st edn. Springer, Heidelberg (2016). Scholar
  9. 9.
    Cortes-Carmona, M., Palma-Behnke, R., Moya, O.: Transmission network expansion planning by a hybrid simulated annealing algorithm. In: 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP 2009, pp. 1–7. IEEE (2009)Google Scholar
  10. 10.
    Faria, H., Binato, S., Resende, M.G., Falcão, D.M.: Power transmission network design by greedy randomized adaptive path relinking. IEEE Trans. Power Syst. 20(1), 43–49 (2005)CrossRefGoogle Scholar
  11. 11.
    Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Gallego, R., Monticelli, A., Romero, R.: Transmision system expansion planning by an extended genetic algorithm. IEE Proc.-Gener., Transm. Distrib. 145(3), 329–335 (1998)CrossRefGoogle Scholar
  13. 13.
    Gallego, R.A., Romero, R., Monticelli, A.J.: Tabu search algorithm for network synthesis. IEEE Trans. Power Syst. 15(2), 490–495 (2000)CrossRefGoogle Scholar
  14. 14.
    Haupt, R.L., Haupt, S.E., Haupt, S.E.: Practical Genetic Algorithms, vol. 2. Wiley, New York (1998)zbMATHGoogle Scholar
  15. 15.
    Hemmati, R., Hooshmand, R.A., Khodabakhshian, A.: State-of-the-art of transmission expansion planning: comprehensive review. Renew. Sustain. Energy Rev. 23, 312–319 (2013)CrossRefGoogle Scholar
  16. 16.
    Hemmati, R., Hooshmandd, R.A., Khodabakhshian, A.: Comprehensive review of generation and transmission expansion planning. IET Gener., Transm. Distrib. 7(9), 955–964 (2013)CrossRefGoogle Scholar
  17. 17.
    Jadidoleslam, M., Ebrahimi, A.: Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm. Int. J. Electr. Power Energy Syst. 64, 743–751 (2015)CrossRefGoogle Scholar
  18. 18.
    Jalilzadeh, S., Shabani, A., Azadru, A.: Multi-period generation expansion planning using genetic algorithm. In: 2010 International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 358–363. IEEE (2010)Google Scholar
  19. 19.
    Javadi, M.S., Saniei, M., Mashhadi, H.R., Gutiérrez-Alcaraz, G.: Multi-objective expansion planning approach: distant wind farms and limited energy resources integration. IET Renew. Power Gener. 7(6), 652–668 (2013)CrossRefGoogle Scholar
  20. 20.
    Jin, Y.X., Cheng, H.Z., Yan, J., Zhang, L.: New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electr. Power Syst. Res. 77(3), 227–233 (2007)CrossRefGoogle Scholar
  21. 21.
    Kannan, S., Baskar, S., McCalley, J.D., Murugan, P.: Application of NSGA-II algorithm to generation expansion planning. IEEE Trans. Power Syst. 24(1), 454–461 (2009)CrossRefGoogle Scholar
  22. 22.
    Khakpoor, M., Jafari-Nokandi, M., Akbar Abdoos, A.: A new hybrid GA-fuzzy optimization algorithm for security-constrained based generation and transmission expansion planning in the deregulated environment. J. Intell. Fuzzy Syst. 33(6), 3789–3803 (2017)CrossRefGoogle Scholar
  23. 23.
    Moradi, M., Abdi, H., Lumbreras, S., Ramos, A., Karimi, S.: Transmission expansion planning in the presence of wind farms with a mixed AC and DC power flow model using an imperialist competitive algorithm. Electr. Power Syst. Res. 140, 493–506 (2016)CrossRefGoogle Scholar
  24. 24.
    Motamedi, A., Zareipour, H., Buygi, M.O., Rosehart, W.D.: A transmission planning framework considering future generation expansions in electricity markets. IEEE Trans. Power Syst. 25(4), 1987–1995 (2010)CrossRefGoogle Scholar
  25. 25.
    Murugan, P., Kannan, S., Baskar, S.: Application of NSGA-II algorithm to single-objective transmission constrained generation expansion planning. IEEE Trans. Power Syst. 24(4), 1790–1797 (2009)CrossRefGoogle Scholar
  26. 26.
    Murugan, P., Kannan, S., Baskar, S.: NSGA-II algorithm for multi-objective generation expansion planning problem. Electr. Power Syst. Res. 79(4), 622–628 (2009)CrossRefGoogle Scholar
  27. 27.
    Neshat, N., Amin-Naseri, M.: Cleaner power generation through market-driven generation expansion planning: an agent-based hybrid framework of game theory and particle swarm optimization. J. Clean. Prod. 105, 206–217 (2015)CrossRefGoogle Scholar
  28. 28.
    Pereira, A.J., Saraiva, J.T.: Generation expansion planning (GEP)-a long-term approach using system dynamics and genetic algorithms (GAs). Energy 36(8), 5180–5199 (2011)CrossRefGoogle Scholar
  29. 29.
    Rajesh, K., Bhuvanesh, A., Kannan, S., Thangaraj, C.: Least cost generation expansion planning with solar power plant using differential evolution algorithm. Renew. Energy 85, 677–686 (2016)CrossRefGoogle Scholar
  30. 30.
    Rezende, L.S., Leite da Silva, A.M., de Mello Honório, L.: Artificial immune system applied to the multi-stage transmission expansion planning. In: Andrews, P.S., et al. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 178–191. Springer, Heidelberg (2009). Scholar
  31. 31.
    Romero, R., Gallego, R., Monticelli, A.: Transmission system expansion planning by simulated annealing. In: 1995 IEEE Proceedings of the Conference on Power Industry Computer Application, pp. 278–283. IEEE (1995)Google Scholar
  32. 32.
    Sadegheih, A., Drake, P.: System network planning expansion using mathematical programming, genetic algorithms and tabu search. Energy Convers. Manag. 49(6), 1557–1566 (2008)CrossRefGoogle Scholar
  33. 33.
    Sadeghi, H., Rashidinejad, M., Abdollahi, A.: A comprehensive sequential review study through the generation expansion planning. Renew. Sustain. Energy Rev. 67, 1369–1394 (2017)CrossRefGoogle Scholar
  34. 34.
    Saka, M.P., Hasançebi, O., Geem, Z.W.: Metaheuristics in structural optimization and discussions on harmony search algorithm. Swarm Evol. Comput. 28, 88–97 (2016)CrossRefGoogle Scholar
  35. 35.
    Shayeghi, H., Mahdavi, M., Bagheri, A.: Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem. Energy Convers. Manag. 51(1), 112–121 (2010)CrossRefGoogle Scholar
  36. 36.
    da Silva, A.M.L., Freire, M.R., Honório, L.M.: Transmission expansion planning optimization by adaptive multi-operator evolutionary algorithms. Electr. Power Syst. Res. 133, 173–181 (2016)CrossRefGoogle Scholar
  37. 37.
    da Silva, A.M.L., Rezende, L.S., da Fonseca Manso, L.A., de Resende, L.C.: Reliability worth applied to transmission expansion planning based on ant colony system. Int. J. Electr. Power Energy Syst. 32(10), 1077–1084 (2010)CrossRefGoogle Scholar
  38. 38.
    da Silva, E.L., Gil, H.A., Areiza, J.M.: Transmission network expansion planning under an improved genetic algorithm. In: Proceedings of the 21st 1999 IEEE International Conference on Power Industry Computer Applications, PICA 1999, pp. 315–321. IEEE (1999)Google Scholar
  39. 39.
    Sorensen, K., Sevaux, M., Glover, F.: A history of metaheuristics. arXiv preprint arXiv:1704.00853 (2017)
  40. 40.
    Verma, A., Panigrahi, B., Bijwe, P.: Harmony search algorithm for transmission network expansion planning. IET Gener., Transm. Distrib. 4(6), 663–673 (2010)CrossRefGoogle Scholar
  41. 41.
    Yoza, A., Yona, A., Senjyu, T., Funabashi, T.: Optimal capacity and expansion planning methodology of PV and battery in smart house. Renew. Energy 69, 25–33 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lourdes Martínez-Villaseñor
    • 1
  • Hiram Ponce
    • 1
    Email author
  • José Antonio Marmolejo
    • 1
  • Juan Manuel Ramírez
    • 2
  • Agustina Hernández
    • 2
  1. 1.Facultad de IngenieríaUniversidad PanamericanaMexico CityMexico
  2. 2.Centro de Investigación y de Estudios Avanzados Instituto Politécnico NacionalZapopanMexico

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