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Robust Short-Term Scheduling of Smart Distribution Systems Considering Renewable Sources and Demand Response Programs

  • Mehrdad GhahramaniEmail author
  • Morteza Nazari-Heris
  • Kazem Zare
  • Behnam Mohammadi-ivatloo
Chapter
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Abstract

The distribution system operator (DSO) needs an optimal day-ahead scheduling (ODAS) for the economic and sustainable supply of electrical energy considering input parameters such as the price of the upstream grid. Next-generation distribution networks or smart distribution networks are the future networks where responsive loads are available. Renewable sources include wind turbines and solar panels have expanded. The presence of electric vehicles and the purchase from the electricity upstream market is provided, and the network can be programmed and controlled through the devices which record and transmit information. In this chapter, a robust optimization (RO) method has been proposed to minimize the cost of ODAS of smart distribution system (SDS) considering load-responsive and renewable energy sources (RESs) such as wind turbine (WT) and nonrenewable sources such as diesel generators (DGs) and battery energy storage system (BESS). The proposed method considers all the technical constraints of the upstream grid and DGs and the utilized BESS. In order to model SDSs, a 33-base IEEE test system has been used in the evaluation of the proposed model. The proposed ODAS concept determines the optimal level of exchange with the upstream network, the production of each dispersed generation unit, and the participation of demand response (DR) programs. It also provides an optimal layout for charging and discharging the BESS. It can be observed that the proposed model has the optimal scheduling capabilities of the SDSs considering the uncertainties of power market price. Moreover, it is observed that the resilient optimization method reduces the cost of network operation and confronts the price of electricity with uncertainty.

Keywords

Smart distribution system Day-ahead scheduling Demand response programs Distributed generation Wind turbine Battery energy storage system 
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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehrdad Ghahramani
    • 1
    Email author
  • Morteza Nazari-Heris
    • 1
  • Kazem Zare
    • 1
  • Behnam Mohammadi-ivatloo
    • 1
  1. 1.Faculty of Electrical and Computer EngineeringUniversity of TabrizTabrizIran

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