Robust Optimal Scheduling with a Grid-Connected Microgrid Installed in a Large-Scale Power Consumer

Park, Yong-Gi; Park, Jong-Bae

doi:10.1007/s42835-019-00227-5

Cited by 6 publications

(2 citation statements)

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“…Therefore, there are potential hazards for successful islanding, since traditional islanding strategies are all based on the condition that the power generation can be dispatched. To address this issue, several optimization models such as robust optimization and stochastic optimization models considering the uncertainty of RESs are proposed [16] - [19]. In [16], the robust optimization model is presented to obtain the optimal operation schedule for power grid connection.…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, several optimization models such as robust optimization and stochastic optimization models considering the uncertainty of RESs are proposed [16] - [19]. In [16], the robust optimization model is presented to obtain the optimal operation schedule for power grid connection. In [17], a robust secondary cooperative control model is introduced for islanded microgrids.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Zhang

Lin

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, no enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs 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-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

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Section: Introductionmentioning

confidence: 99%