Optimal energy management of microgrid in day-ahead and intra-day markets using a copula-based uncertainty modeling method

Document Type : Research paper

Authors

1 Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

2 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

3 Department of Energy Technology, Aalborg University, Aalborg, Denmark

4 Electrical Engineering Department, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran

5 Department of Electromechanical, Systems and Metal Engineering, Ghent University, Ghent, Belgium

Abstract

Recently, economic and environmental problems have created a strong attitude toward utilizing renewable energy sources (RESs). Nevertheless, uncertainty of wind and solar power leads to a more complicated energy management (EM) of RESs in microgrids. This paper models and solves the EM problem of microgrid from the generation point of view. To do this, mathematical formulation of a grid- connected microgrid including wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC) and energy storage system (ESS) is presented. Furthermore an improved incentive-based demand response program (DRP) is applied in microgrid EM problem to flatten the load pattern. Comprehensive studying of EM in both intra-day and day-ahead markets is another contribution of this paper. However, the main novelty of this paper is proposing a new uncertainty modeling technique which is based on copula function and scenario generation. This paper tries to optimize operational cost and environmental pollution as the objective functions and solve them using group search optimization (GSO) algorithm. Numerical results approve the efficiency of the proposed method in solving microgrid EM problem.

Keywords

Main Subjects

References

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Journal of Operation and Automation in Power Engineering
Volume 8, Issue 2
August 2020
Pages 86-96

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History

  • Receive Date: 10 December 2018
  • Revise Date: 17 January 2019
  • Accept Date: 26 January 2019
  • First Publish Date: 01 August 2020

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