Optimal black start strategy for microgrids considering the uncertainty using a data-driven chance constrained approach

Microgrids may suffer from full blackouts when confronted with unexpected disruptions due to man-made faults or natural disasters. How to quickly restore the power supply of microgrids by making use of local distributed energy resources (DERs) is therefore a practical issue to help microgrids ride through full blackouts. Accordingly, this study proposes a novel black start strategy for microgrids to determine the restoration sequence and optimally allocate the DERs after full blackouts. In particular, the uncertainty of power output of renewable energy sources is considered using a data-driven chance-constrained approach when renewable energy sources are integrated. The proposed approach only utilises historical data and does not need any prior knowledge about the true probability distribution of the uncertainty. In addition, affine-based techniques and sample-dependent band functions are developed to convert the chance-constrained problem into a tractable mixed-integer linear programming problem. Finally, numerical experiments based on two microgrid test systems are performed to validate the effectiveness of the proposed model.