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Abstract-In this paper, electrical parameters of a microgrid containing distributed generation wind and fuel cell units are controlled in islanding mode operation. Islanding operation refers to isolation of a part of power system including distributed generation from the grid having continuous and independent operation. This has negative impacts on the isolated network including voltage and frequency dependence on load. These impacts and their solutions are studied in this paper. A proper controller using power electronics converters has been proposed. In order to verify the validity of the proposed control approach, it has been implemented and simulated on a 13-bus IEEE distribution power system containing a combination of distributed generation wind and fuel cell units.Index Terms-Frequency controd, inverter, islanding operation, microgrids, voltage control.

This paper presents a decision‐driven stochastic adaptive‐robust microgrid operation optimization model considering the uncertainties of wind and solar generations, electricity price, and demand as well as the availability uncertainties of microgrid's components. Unlike previous works, this paper utilizes stochastic adaptive‐robust optimization approach to model both continuous and binary uncertainties simultaneously. To do so, adaptive‐robust optimization is used to model the continuous uncertainties, while the binary uncertainties are modelled by means of stochastic programming. An operating dispatchable unit usually exhibits a higher forced outage rate than a de‐committed one. Hence, due to the effect of the optimization decisions on the availability uncertainties, this research work proposes an intrinsic scenario production technique to model these binary uncertainties. In addition, a tri‐level decomposition method is introduced to solve the proposed microgrid operation optimization problem. In this decomposition method, the worst‐case realization of continuous uncertain parameters and unit commitment decisions are determined at each iteration considering the produced scenarios in the previous iteration. Case studies on the IEEE 69‐bus test system exhibit the effectiveness of the proposed decision‐driven stochastic adaptive‐robust model and the proposed solution method.

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