A survey and comparison of leading-edge uncertainty handling methods for power grid modernization

The power grid infrastructure encounters multiple uncertainties such as unprecedented energy generation from non-dispatchable resources, erratic load, intensifying energy demand, the transition towards electric mobility, and the electricity market that exaggerate the decision-maker's difficulties in the power system. How to deal with massive real-time uncertain data is a pressing and challenging issue. To date, the art to tackle contingencies and ambiguous events has globally advanced and attained great assiduity, whereas, substantial work has been conducted on the optimization problems under uncertainties. In this regard, a comprehensive review of contemporary research is presented to identify future research trends. Moreover, the literature on the state-of-the-art uncertainty modeling methods is scrutinized, whilst a comparative assessment is stated to provide a broader overview evidencing that, so far, there is no particular preeminent uncertainty handling technique. The presented work may be adopted for the selection of the most suitable methodology in each application. In comparison to traditional approaches, robust optimization is one of the recent and adaptive uncertainty handling techniques for optimization problems owing to its salient features. Furthermore, its contributions in five crucial categories of power grid optimization problems are reviewed to highlight additional challenges and the scope of future research in the context of envisioned power networks.

Automated summary

The article discusses the uncertainties faced by the power grid infrastructure and the challenges of dealing with massive real-time uncertain data. It reviews the state-of-the-art uncertainty modeling methods and highlights the potential of robust optimization as a new approach. The article also provides insights into future research trends.