Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia.

Challenging frequency control issues, such as the reliability and security of the power system, arise when increasing penetration levels of inverter-interfaced generation are imposed. As a result of the displacement of convention generation in favour of renewable energy sources, the reduction of frequency response capabilities can be seen. A promising method of overcoming the aforementioned challenges is to utilise Battery Energy Storage Systems (BESS), which provides frequency support by injecting instantaneous power to the grid and back up the conventional generation systems. However, large battery systems increase the cost while inadequate battery capacities result in poor performance. This paper, therefore, proposes an approach for finding the optimum BESS size for an islanded microgrid power system. The determination of the optimum BESS size is based on an existing case study, under which the most severe contingencies of generation loss and load loss have been accounted for, as well as different levels of penetration of renewable energy sources. As a result of using meta-heuristic optimization (Grey Wolf Optimization), the constraint optimization problem has been identified as BESS sizing. Through the use of real-time simulation DIgSILENT PowerFactory software, estimated BESS size can be applied to a standalone microgrid to test the frequency of support capabilities. The simulation has made it apparent that through the selection of the optimum BESS size, the system frequency response is not only mitigated, but improved. • Proposing a novel optimization model for determining the optimal size of the battery devices of standalone microgrids. • Propose an intelligent approach to mitigate the frequency response of standalone microgrids based on hybridization sources. • Combining the advantages of the BESS controller and the meta-heuristic Grey Wolf Optimizer (GWO) algorithm. • Validate the system performance based on a real case study under different scenarios. [ABSTRACT FROM AUTHOR]