Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-Constrained Model

Controlled islanding plays an essential role in preventing blackout of power systems. Although there are several studies on this topic in the past, the uncertainty brought by Renewable Energy Sources (RES) that may cause unpredictable unbalanced power and the observability of systems after islanding that is essential for backup black-start measures, are not paid enough attention. Therefore, a novel Mixed Integer Second Order Cone Chance-constrained Programming (MISOCCP) based model for controlled islanding is proposed in this work to address these issues. First, the uncertainty of RES is characterized by their possibility distribution models with chance constraints and the requirements (e.g., system observability) for rapid back-up black-start measures are also considered. Then, a Law of Large Numbers (LLN)-based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE-39 and −118 bus systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed MISOCCP model for controlled islanding. The results show that the proposed MISOCCP model can result in less unbalanced power and better observability after islanding when compared with other models.