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Energy and reserve management of a smart distribution system by incorporating responsive-loads /battery/wind turbines considering uncertain parameters

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  • Ghahramani, Mehrdad
  • Nazari-Heris, Morteza
  • Zare, Kazem
  • Mohammadi-Ivatloo, Behnam

Abstract

Uncertainties of load demand and power output of renewable-based energy sources as well as participation of responsive loads in energy supply can be identified as the main issues of the future power networks. Accordingly, it is essential to develop practical approaches for dealing with the uncertainties of wind power and load in optimal scheduling of such systems. This paper proposes a new uncertainty-modeling approach based on Hong's two-point estimate method (T-PEM) for optimal day-ahead scheduling (ODAS) of a smart distribution system (SDS). The proposed method seeks to minimize the functional cost of energy and reserve requirements of SDS in the presence of wind turbines, diesel generators and battery energy storage system considering uncertainties of wind production and load demand. Also, according to importance of enabling consumers to contribute in energy and reserve supply of SDSs, the present work studies the implementation of two various demand response (DR) programs in energy and reserve management of a SDS. The proposed method is applied on IEEE 33-bus distribution test system to investigate the efficiency and performance of the proposed model, which confirms the validity and practicality of the presented model.

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  • Ghahramani, Mehrdad & Nazari-Heris, Morteza & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2019. "Energy and reserve management of a smart distribution system by incorporating responsive-loads /battery/wind turbines considering uncertain parameters," Energy, Elsevier, vol. 183(C), pages 205-219.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:205-219
    DOI: 10.1016/j.energy.2019.06.085
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    References listed on IDEAS

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    5. Rawat, Tanuj & Niazi, K.R. & Gupta, Nikhil & Sharma, Sachin, 2022. "A linearized multi-objective Bi-level approach for operation of smart distribution systems encompassing demand response," Energy, Elsevier, vol. 238(PC).
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