A Model Predictive Control for Microgrids Considering Battery Aging

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10.35833:MPCE.2019.000804.pdf

Date

2020-03-01

Author

Yilmaz, Ugur Can
SEZGİN, MUSTAFA ERDEM
Göl, Murat

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The increasing number of distributed energy resources (DERs), advancing communication and computation technologies, and reliability concerns of the customers have caused an intense interest in the concept of microgrid. Although DERs are the biggest motivation of the microgrids due to their intermittent generation characteristics, they constitute a risk for system reliability. Battery storage systems (BSSs) stand as one of the most effective solutions for this reliability problem. However, the inappropriate use of BSS creates other operational problems in power systems. In order to deal with these concerns explicitly in microgrids, an optimized microgrid central controller (MGCC) is the key factor, which controls the realtime operation of a microgrid. This work proposes a model predictive control (MPC) based MGCC that will provide optimal control of the microgrid, considering economic and operational constraints. The proposed system will minimize the energy cost of the microgrid by utilizing mixed-integer linear programming (MILP) assuming the presence of DERs and BSS as well as the bi-directional grid connection. Moreover, the aging effect of BSS will be considered in the proposed optimization problem which will provide an up-to-date system model. The proposed method is evaluated using real load and photovoltaic (PV) generation data.

Subject Keywords

Microgrid, Optimization, Battery storage, Model predictive control, Mixed-integer linear programming

URI

https://hdl.handle.net/11511/35274

Journal

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY

DOI

https://doi.org/10.35833/mpce.2019.000804

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

U. C. Yilmaz, M. E. SEZGİN, and M. Göl, “A Model Predictive Control for Microgrids Considering Battery Aging,” JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, pp. 296–304, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35274.