Robust Short-Term Electrical Distribution Network Planning Considering Simultaneous Allocation of Renewable Energy Sources and Energy Storage Systems

  • Ozy D. Melgar-DominguezEmail author
  • Mahdi Pourakbari-Kasmaei
  • José Roberto Sanches Mantovani


The short-term electrical distribution network (EDN) planning is a strategy that aims to enhance the efficiency of the system and to provide high-quality service to end users. This strategy uses some classical actions and devices to effectively control the system power factor, reactive power, and the voltage magnitude of the network. Over the past decades, trends in this decision-making process have changed due to the integration of modern technologies. Therefore, this chapter investigates a short-term EDN planning problem considering classical investment alternatives with sizing and placement of energy storage systems and distributed generation sources based on renewable energy. Since this optimization problem is inherently a non-convex mixed-integer nonlinear programming model, there is no guarantee in finding the global solution. Therefore, proper linearization techniques are used to find a mixed-integer linear programming (MILP) model. On the other hand, to address the uncertainty in electricity demand and renewable output power, this deterministic MILP model is transformed into a two-stage robust optimization model. To handle this complex trilevel optimization problem, the column-and-constraint generation algorithm (C&CG) is employed in a hierarchical environment. To assess the performance of the proposed approach, a 42-node distribution network is studied under different operational conditions. Numerical results of different case studies show the robustness and applicability of the proposed approach.


Energy storage systems Renewable energy-based distributed generation sources Short-term planning problem Two-stage robust optimization 



The authors would like to thank the Brazilian institutions CAPES (Finance code 001), CNPq (Grant NO. 305318/2016-0), and FAPESP (Grant NO. 2015/21972-6) for the financial support.


  1. 1.
    Szuvovivski, I., Fernandes, T. S. P., & Aoki, A. R. (2012). Simultaneous allocation of capacitors and voltage regulators at distribution networks using genetic algorithms and optimal power flow. International Journal of Electrical Power and Energy Systems, 40, 62–69.CrossRefGoogle Scholar
  2. 2.
    Pereira Junior, B. R., Cossi, A. M., & Mantovani, J. R. S. (2013). Multiobjective short-term planning of electric power distribution systems using NSGA-II. Journal of Control Automation and Electrical Systems, 24, 286–299.CrossRefGoogle Scholar
  3. 3.
    Asrari, A., Lotfifard, S., & Ansari, M. (2016). Reconfiguration of smart distribution systems with time varying loads using parallel computing. IEEE Transaction on Smart Grid, 7, 2713–2723.CrossRefGoogle Scholar
  4. 4.
    Rupolo, D., Pereira, B. R., Contreras, J., et al. (2017). Medium- and low-voltage planning of radial electric power distribution systems considering reliability. IET Generation Transmission and Distribution, 11, 2212–2221.CrossRefGoogle Scholar
  5. 5.
    Resener, M., Haffner, S., Pereira, L. A., et al. (2018). Optimization techniques applied to planning of electric power distribution systems: A bibliographic survey. Energy System, 9(3), 473–509.CrossRefGoogle Scholar
  6. 6.
    Adefarati, T., & Bansal, R. C. (2016). Integration of renewable distributed generators into the distribution system: A review. IET Renewable Power Generation, 10, 873–884.CrossRefGoogle Scholar
  7. 7.
    Pereira, B. R., Martins Da Costa, G. R. M., Contreras, J., et al. (2016). Optimal distributed generation and reactive power allocation in electrical distribution systems. IEEE Transactions on Sustainable Energy, 7, 975–984.CrossRefGoogle Scholar
  8. 8.
    Jannat, M. B., & Savić, A. S. (2016). Optimal capacitor placement in distribution networks regarding uncertainty in active power load and distributed generation units production. IET Generation Transmission and Distribution, 10, 3060–3067.CrossRefGoogle Scholar
  9. 9.
    Quijano, D. A., Wang, J., Sarker, M. R., et al. (2017). Stochastic assessment of distributed generation hosting capacity and energy efficiency in active distribution networks. IET Generation Transmission and Distribution, 11, 4617–4625.CrossRefGoogle Scholar
  10. 10.
    Ortiz, J. M. H., Pourakbari-Kasmaei, M., López, J., et al. (2018). A stochastic mixed-integer conic programming model for distribution system expansion planning considering wind generation. Energy System, 9, 551–571.CrossRefGoogle Scholar
  11. 11.
    Nazari-Heris, M., & Mohammadi-Ivatloo, B. (2018). Application of robust optimization method to power system problems. In Classical and recent aspects of power system optimization (pp. 19–32). Imprint: Academic Press: Elsevier.
  12. 12.
    Wang, Z., Chen, B., Wang, J., et al. (2014). Robust optimization based optimal DG placement in microgrids. IEEE Transactions on Smart Grid, 5, 2173–2182.CrossRefGoogle Scholar
  13. 13.
    Ruiz, C., & Conejo, A. J. (2015). Robust transmission expansion planning. European Journal of Operational Research, 242, 390–401.CrossRefGoogle Scholar
  14. 14.
    Jabr, R. A., Dzafic, I., & Pal, B. C. (2015). Robust optimization of storage investment on transmission networks. IEEE Transactions on Power Systems, 30, 531–539.CrossRefGoogle Scholar
  15. 15.
    Yuan, W., Wang, J., Qiu, F., et al. (2016). Robust optimization-based resilient distribution network planning against natural disasters. IEEE Transactions on Smart Grid, 7, 2817–2826.CrossRefGoogle Scholar
  16. 16.
    Amjady, N., Attarha, A., Dehghan, S., et al. (2018). Adaptive robust expansion planning for a distribution network with DERs. IEEE Transactions on Power Systems, 33, 1698–1715.CrossRefGoogle Scholar
  17. 17.
    Melgar Dominguez, O. D., Pourakbari Kasmaei, M., & Mantovani, J. R. S. (2018). Adaptive robust short-term planning of electrical distribution systems considering siting and sizing of renewable energy-based DG units. IEEE Transactions on Sustainable Energy, 1.Google Scholar
  18. 18.
    Saboori, H., Hemmati, R., Ghiasi, S. M. S., et al. (2017). Energy storage planning in electric power distribution networks – A state-of-the-art review. Renewable and Sustainable Energy Reviews, 79, 1108–1121.CrossRefGoogle Scholar
  19. 19.
    Babacan, O., Torre, W., & Kleissl, J. (2017). Siting and sizing of distributed energy storage to mitigate voltage impact by solar PV in distribution systems. Solar Energy, 146, 199–208.CrossRefGoogle Scholar
  20. 20.
    Melgar Dominguez, O. D., Pourakbari Kasmaei, M., Lavorato, M., et al. (2018). Optimal siting and sizing of renewable energy sources, storage devices, and reactive support devices to obtain a sustainable electrical distribution systems. Energy Systems, 9, 529–550.CrossRefGoogle Scholar
  21. 21.
    Khalid Mehmood, K., Khan, S. U., Lee, S.-J., et al. (2017). Optimal sizing and allocation of battery energy storage systems with wind and solar power DGs in a distribution network for voltage regulation considering the lifespan of batteries. IET Renewable Power Generation, 11, 1305–1315.CrossRefGoogle Scholar
  22. 22.
    Pourakbari-Kasmaei, M., & Sanches Mantovani, J. R. (2018). Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model. International Journal Electrical Power and Energy Systems, 97, 240–249.CrossRefGoogle Scholar
  23. 23.
    Shirmohammadi, D., Hong, H. W., Semlyen, A., et al. (1988). A compensation-based power flow method for weakly meshed distribution and transmission networks. IEEE Transactions on Power Systems, 3, 753–762.CrossRefGoogle Scholar
  24. 24.
    Cespedes, R. G. (1990). New method for the analysis of distribution networks. IEEE Transactions on Power Delivery, 5, 391–396.MathSciNetCrossRefGoogle Scholar
  25. 25.
    Alguacil, N., Motto, A. L., & Conejo, A. J. (2003). Transmission expansion planning: A mixed-integer LP approach. IEEE Transaction on Power Systems, 18, 1070–1077.CrossRefGoogle Scholar
  26. 26.
    Bertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98, 49–71.MathSciNetCrossRefGoogle Scholar
  27. 27.
    Ben-Tal, A., Goryashko, A., Guslitzer, E., et al. (2004). Adjustable robust solutions of uncertain linear programs. Mathematical Programming, 99, 351–376.MathSciNetCrossRefGoogle Scholar
  28. 28.
    Zeng, B., & Zhao, L. (2013). Solving two-stage robust optimization problems using a column-and-constraint generation method. Operations Research Letters, 41, 457–461.MathSciNetCrossRefGoogle Scholar
  29. 29.
    Stephen Boyd, L. V. (2004). Convex optimization. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  30. 30.
    Rebennack, S. (2016). Computing tight bounds via piecewise linear functions through the example of circle cutting problems. Mathematical Methods of Operations Research, 84, 3–57.MathSciNetCrossRefGoogle Scholar
  31. 31.
    LaPSEE Power System Test Cases Repository, [Online]. Available:!/lapsee
  32. 32.
    Masteri, K., Venkatesh, B., & Freitas, W. (2018). A fuzzy optimization model for distribution system asset planning with energy storage. IEEE Transactions on Power Systems, 33(5), 5114–5123.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ozy D. Melgar-Dominguez
    • 1
    • 2
    Email author
  • Mahdi Pourakbari-Kasmaei
    • 2
  • José Roberto Sanches Mantovani
    • 1
  1. 1.Department of Electrical EngineeringSão Paulo State University-(UNESP)Ilha Solteira, São PauloBrazil
  2. 2.Department of Electrical Engineering and AutomationAalto UniversityEspooFinland

Personalised recommendations