AIMS Energy, 2019, 7(2): 211-226. doi: 10.3934/energy.2019.2.211.

Research article

Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Optimal siting and sizing of renewable sources in distribution system planning based on life cycle cost and considering uncertainties

1 Thainguyen University of Technology (TNUT), Thai Nguyen, Vietnam
2 School of Electric Power, South China University of Technology, Guangzhou, China

The renewable sources have made an impact on economic benefits and the electrical quality of the distribution system. Therefore, a multi-scenario optimization model is proposed, targeting to optimize the investment of renewable sources considering uncertainties based on the minimum life cycle cost. The mathematic model allows selecting the siting, sizing and type of renewable sources in distribution system. The objective function is minimizing life-cycle cost of the project during the planning period, including the investment and operation cost of renewable sources, the cost of purchasing energy from the grid, the emission taxes and residual value of the equipment at the end of the planning period. The nonlinear power flow model alternating current is utilized to concurrently balance both active and reactive power at each state as well as the constraints for the limit capacity of feeders. Connectable substation and the constraints in selecting the renewable sources are also represented and thus improving the accuracy of the calculated results of the distribution system. The uncertainty parameters of renewable sources (photovoltaic and wind turbine), electricity price and load were modeled by the probability density functions and are considered in the optimization model. The clustering technique was utilized to divide each parameter into states then all states of parameters are integrated by the combined model. The general algebraic modeling system (GAMS) was applied to solve the optimization problem with the test system and demonstrated the advantages of the proposed model.
  Article Metrics

Keywords Distribution system; GAMS; Life cycle cost; optimization; renewable sources; uncertainties

Citation: V. V. Thang, Thanhtung Ha. Optimal siting and sizing of renewable sources in distribution system planning based on life cycle cost and considering uncertainties. AIMS Energy, 2019, 7(2): 211-226. doi: 10.3934/energy.2019.2.211


  • 1. Sambaiah KS (2018) A review on optimal allocation and sizing techniques for DG in distributionsystems. Int J Renew Energy Res 8: 1236–1256.
  • 2. Taner G (2019) Renewable energy, non-renewable energy and sustainable development. Int J Sustainable Dev World Ecology 26: 1–10.    
  • 3. Georgilaki PS, Hatziargyriou ND (2015) A review of power distribution planning in the modern power systems era: Models, methods and future research. Electr Power Syst Res 121: 89–100.    
  • 4. Bhadoria VS, Singh N, Shrivastava V (2013) A review on distributed generation definitions and DG impacts on distribution system. International Conference on Advanced Computing and Communication Technologies 1–7.
  • 5. Theo WL, Limb JS, Ho WS, et al. (2017) Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renew Sust Energy Rev 67: 531–573.    
  • 6. Mohandes B, Moursi MSE, Hatziargyriou ND, et al. (2019) A review of power system flexibility with high penetration of renewables. IEEE Trans Power Syst (Early Access) 2019: 1–13.
  • 7. Thang VV, Trung NH (2019) Evaluating efficiency of renewable energy sources in planning micro-grids considering uncertainties. J Energy Syst 3: 14–25.
  • 8. Bayod-Rújula AA, Yuan Y, Martínez-Gracia A, et al. (2018) Modelling and simulation of a building energy hub. Proceedings 2: 1–6.    
  • 9. Deng S, Wu Q, Jing Z, et al. (2017) Optimal capacity configuration for energy hubs considering part-load characteristics of generation units. Energies 10: 2–19.
  • 10. Acharya PS, Wagh MM, Kulkarni VV (2019) Intelligent algorithmic multi-objective optimization for renewable energy system generation and integration problems: A review. Int J Renew Energy Res 9: 271–280.
  • 11. Arriag M, Cañizares CA, Kazerani M (2016) Long-term renewable energy planning model for remote communities. IEEE Trans Sust Energy 7: 221–231.    
  • 12. Wong S, Bhattacharya K, Fuller JD (2009) Electric power distribution system design and planning in a deregulated environment. IET Gener Transm. Distrib 3: 1061–1078.    
  • 13. Abapour S, Khosroshahi AE, Frakho A, et al. (2015) Optimal integration of wind power resources in distribution networks considering demand response programs. 9th International Conference on Electrical and Electronics Engineering 26–28.
  • 14. Sambaiah KS, Jayabarathi T (2019) Optimal allocation of renewable distributed generation and capacitor banks in distribution systems using Salp Swarm algorithm. Int J Renew Energy Res 9: 96–107.
  • 15. Singh R, Bansal RC, Singh AR, et al. (2019) Multi-objective optimization of hybrid renewable energy system using reformed electric system cascade analysis for islanding and grid connected modes of operation. IEEE Access 6: 1–26.
  • 16. Nurunnab M, Roy NK, Pota HR (2019) Optimal sizing of grid-tied hybrid renewable energy systems considering inverter to PV ratio - A case study. J Renew Sust Energy 11: 1–14.
  • 17. Su H, Zhang J, Liang Z, et al. (2010) Power distribution network planning optimization based on life cycle cost. China International Conference on Electricity Distribution 1–8.
  • 18. Liu L, Cheng H, Ma Z, et al. (2010) Life cycle cost estimate of power system planning. International Conference on Power System Technology 1–8.
  • 19. Yi-Song JR, Long-Li JQ (2012) Comparative study of 10kV distribution network structure based on Life Cycle Cost analysis. China International Conference on Electricity Distribution 1–5.
  • 20. Ristimäki M, Säynäjoki A, Heinonen J, et al. (2013) Combining life cycle costing and life cycle assessment for an analysis of a new residential district energy system design. Energy 63: 168–179.    
  • 21. Zhang T, Song X, Meng X, et al. (2013) A probabilistic load model base on chi-square method for distribution network. 2nd IET Renewable Power Generation Conference 1–4.
  • 22. Liu Z, Wen F, Ledwich G (2010) Optimal siting and sizing of distributed generators in distribution systems considering uncertainties. IEEE Trans Power Delivery 26: 2541–2551.
  • 23. Montoya-Bueno S, Muñoz JI, Contreras J (2015) A stochastic investment model for renewable generation in distribution systems. IEEE Trans Sust Energy 6: 1466–1474.    
  • 24. Sadeghi M, Kalantar M (2014) Probabilistic analysis of wind turbine planning in distribution systems. 19th Conference on Electrical Power Distribution Networks 1–6.
  • 25. Shaaban MF, El-Saadany EF (2014) Accommodating high penetrations of PEVs and renewable DG considering uncertainties in distribution systems. IEEE Trans Power Syst 29: 259–270.    
  • 26. Soroudi A, Mohammadi-Ivatloo B, Rabiee A (2014) Energy hub management with intermittent wind power, In: Large Scale Renewable Power Generation, Green Energy and Technology, Springer, 413–438.
  • 27. Fan M, Vittal V, Heydt GT, et al. (2012) Probabilistic power flow studies for transmission systems with photovoltaic generation using cumulants. IEEE Trans Power Syst 27: 2251–2261.    
  • 28. Atwa YM, El-Saadany EF (2011) Probabilistic approach for optimal allocation of wind based distributed generation in distribution systems. IET Renew Power Gener. 5: 79–88.    
  • 29. Atwa YM, El-Saadany EF, Salama MMA, et al. (2010) Optimal Renewable Resources Mix for Distribution System Energy Loss Minimization. IEEE Trans Power Syst 25: 360–370.
  • 30. Yu Y, Wen X, Zhao J, et al. (2018) Co-Planning of Demand response and distributed generators in an active distribution network. Energies 11: 2–18.
  • 31. Santos SF, Fitiwi DZ, Bizuayehu AW, et al. (2017) Impacts of operational variability and uncertainty on distributed generation investment planning: A comprehensive sensitivity analysis. IEEE Trans Sust Energy 8: 855–869.    
  • 32. Gkaidatzis PA, Bouhouras AS, Sgouras KI, et al. (2019) Efficient RES penetration under optimal distributed generation placement approach. Energies 12: 1–32.
  • 33. Zheng Y, Jenkins BM, Kornbluth K, et al. (2018) Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage. Renew Energ 123: 204–217.    
  • 34. Konneh DA, Howlader HOR, Shigenobu R, et al. (2019) A multi-criteria decision maker for grid-connected hybrid renewable energy systems selection using multi-objective particle swarm optimization. Sustainability 11: 1–36.
  • 35. Hosseini S, Sarder MD (2019) Development of a Bayesian network model for optimal site selection of electric vehicle charging station. Electr Power Energy Syst 105: 110–122.    
  • 36. Longo M, Foiadelli F, Yaïci W (2019) Simulation and optimisation study of the integration of distributed generation and electric vehicles in smart residential district. Int J Energy Environ Eng 8: 1–15.
  • 37. Pazouki S, Haghifamb MR, Moser A (2014) Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. Electr Power Energy Syst 61: 335–345.    
  • 38. Chen ZP, Yan Z (2018) Scenario tree reduction methods through clustering nodes. Comput Chem Eng 109: 96–111.    
  • 39. Shi Z, Wang Z, Jin Y, et al. (2018) Optimal allocation of intermittent distributed generation under active management. Energies 11: 2–19.
  • 40. Rosenthal RE (2008) A user's guide. GAMS Dev Corp 1–262.
  • 41. Abdelkader B, Linda S, Tarek B (2014) Analysis of radial distribution system load flow under uncertainties with fuzzy arithmetic algorithm. 3rd International Conference on Information Processing and Electrical Engineering 1–6.
  • 42. Khodayar ME, Barati M, Shahidehpour M (2012) Integration of high reliability distribution system in microgrid operation. IEEE Trans Smart Grid 3: 1997–2006.    
  • 43. IRENA 2018, Renewable power generation costs in 2017, In: International Renewable Energy Agency, Abu Dhabi, 1–158.
  • 44. Muneer W, Bhattacharya K, Cañizares C (2011) Large-scale solar PV investment models, tools and analysis: The Ontario case. IEEE Trans Power Syst 26: 2547–2555.    


This article has been cited by

  • 1. Abdul Matin Ibrahimi, Mohammad Masih Sediqi, Harun Or Rashid Howlader, Mir Sayed Shah Danish, Shantanu Chakraborty, Tomonobu Senjyu, Generation expansion planning considering renewable energy integration and optimal unit commitment: A case study of Afghanistan, AIMS Energy, 2019, 7, 4, 441, 10.3934/energy.2019.4.441
  • 2. Angelo Raciti, Santi Agatino Rizzo, Giovanni Susinni, Circuit Model of LED Light Bulb suitable for typical voltage THD on LV distribution networks, Energy and Buildings, 2019, 109665, 10.1016/j.enbuild.2019.109665
  • 3. Vu Van Thang, Nguyen Hien Trung, , Advances in Engineering Research and Application, 2020, Chapter 61, 534, 10.1007/978-3-030-37497-6_61

Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved