A Optimal Quantile Selection Method of Dynamic Thermal Rating for Economic Power Dispatching

Transmission capacity is a key factor to support the economic operation of the power system. Uncertainty in dynamic thermal rating (DTR) has an impact on the assessment of transmission capacity, which can be effectively reduced by probabilistic forecasting of DTR. The quartile of predicted DTR chosen for economic dispatch directly affects the benefits and risks of the power system. In this paper, we propose a DTR optimal quantile selection method for economic dispatching of power system. First, the BILSTMDN probabilistic prediction model is used to implement the probabilistic forecasting of time series data. The predicted DTR for the different quartiles were then added as constraints to the dispatch model to evaluate the benefits when using each quartile. Stochastic optimization is performed in a large number of scenarios to take into account the risks of overload when choosing each quartile in different situations. Finally, the benefits and risks of each quartile are combined for their selection. The analysis of a dataset from a region within Liaoning Province shows that the proposed model can reduce the operating cost of the power system and enhance the capacity of wind power consumption while ensuring system security.