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Generation Resources Planning Based on the Nodal Model

Author Affiliations

  • 1Department of Electrical Engineering, Boroujen Branch, Islamic Azad University, Boroujen, IRAN

Res. J. Recent Sci., Volume 4, Issue (6), Pages 112-115, June,2 (2015)


Generation resources planning also known as generation expansion planning (GEP) is mainly performed to denote the place, capacity, time, and technology of new installed generation units in the power system. This paper presents generation resources planning based on the nodal model. In the proposed model, all generation units are installed on one bus and network in not included. The planning is managed as an optimization programming and solved by using genetic algorithms (GA).


  1. Limouzade E., Capacitor Replacement in Distribution Networks using Genetic Algorithm, Research Journal of Recent Sciences, 2(12), 54-64 (2013)
  2. Heidari M., Nekoubin A., Heidari R. and Jafari, M., Optimum locating and Sizing of Distributed Generation Based on Artificial Ant Colony Algorithm, Research Journal of Recent Sciences, 2(12), 1-5 (2013)
  3. Kannan S., Slochanal S., Subbaraj P. and Padhy N.P., Application of particle swarm optimization technique and its variants to generation expansion planning problem, Electric Power Systems Research, 70(3), 203-210 (2004)
  4. Pereira A.J.C. and Saraiva J.T., A decision support system for generation expansion planning in competitive electricity markets, Electric Power Systems Research, 80(7), 778-787 (2010)
  5. Tohidi Y., Aminifar F. and Fotuhi-Firuzabad M., Generation expansion and retirement planning based on the stochastic programming, Electric Power Systems Research, 104(0), 138-145 (2013)
  6. Pereira A.J.C. and Saraiva J.T., A long term generation expansion planning model using system dynamics – Case study using data from the Portuguese/Spanish generation system, Electric Power Systems Research, 97(0), 41-50 (2013)
  7. Sirikum J., Techanitisawad, A. and Kachitvichyanukul, V., A new efficient GA-benders' decomposition method: For power generation expansion planning with emission controls, IEEE Transactions on Power Systems, 22(3),1092-1100 (2007)
  8. Murugan P., Kannan S. and Baskar S., NSGA-II algorithm for multi-objective generation expansion planning problem, Electric Power Systems Research, 79,622-628 (2009)
  9. Hejrati Z., Hejrati E. and Taheri A., Optimization generation expansion planning by HBMO, Optimization, 37(7), 99-108 (2012)
  10. Chen Q., Kang C., Xia Q. and Zhong J., Power generation expansion planning model towards low-carbon economy and its application in China, IEEE Transactions on Power Systems 25, 1117-1125 (2010)
  11. Moghaddas Tafreshi S., Saliminia Lahiji A., Aghaei J. and Rabiee A., Reliable generation expansion planning in pool market considering power system security, Energy Conversion and Management, 54(1), 162-168 (2012)
  12. Gitizadeh M., Kaji M. and Aghaei J., Risk based multiobjective generation expansion planning considering renewable energy sources, Energy, 50(0), 74-82 (2013)
  13. Khan S., Khan S.A. and Zaman K., Pakistan's Export Demand Income and Price Elasticity Estimates: Reconsidering the Evidence, Research Journal of Recent Sciences 2(5), 59-62 (2013)
  14. Hemmati R., Hooshmand R.A. and Khodabakhshian A., Reliability constrained generation expansion planning with consideration of wind farms uncertainties in deregulated electricity market, Energy Conversion and Management, 76(0), 517-526 (2013)
  15. Aghaei J., Akbari M.A., Roosta A. and Baharvandi A., Multiobjective generation expansion planning considering power system adequacy, Electric Power Systems Research, 102(0), 8-19 (2013)
  16. Pantoš M., Stochastic generation-expansion planning and diversification of energy transmission paths, Electric Power Systems Research, 98(0), 1-10 (2013)
  17. Yonghan Feng and Sarah Ryan, Scenario Construction and Reduction Applied to Stochastic Power Generation Expansion Planning, Computers and Operations Research, 40, 9-23 (2013)
  18. Farsani S.T., Aboutalebi M. and Motameni H., Customizing NSGAII to Optimize Business Processes Designs, Research Journal of Recent Sciences, 2(12), 74-79 (2013)
  19. Sanjay J. and Nitin A., An Inverse Optimization Model for Linear Fractional Programming, Research Journal of Recent Sciences, 2(4), 56-58 (2013)
  20. Haupt R.L. and Haupt S.E., Practical genetic algorithms, John Wiley and Sons, (2004)
  21. Moghddas-Tafreshi S., Shayanfar H., Saliminia Lahiji A., Rabiee A. and Aghaei J., Generation expansion planning in pool market: A hybrid modified game theory and particle swarm optimization, Energy Conversion and Management, 52(2), 1512-1519 (2011)