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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
Chapter

Abstract

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.

Keywords

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

Notes

Acknowledgments

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.

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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

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