Probabilistic weather forecasting for dynamic line rating studies

Abstract

Abstract-This paper aims to describe methods to determine short term probabilistic forecasts of weather conditions experienced at overhead lines (OHLs) in order to predict percentiles of dynamic line ratings of OHLs which can be used by a system operator within a chosen risk policy with respect to probability of a rating being exceeded. Predictive probability distributions of air temperature, wind speed and direction are assumed to be normal, truncated normal and von Mises respectively. Predictive centres are estimated as a sum of residuals predicted by a univariate auto-regressive model or a vector auto-regressive model and temporal trends fitted by a Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. Parameters of the probabilistic models are determined to minimize the average value of continuous ranked probability score which is a summary indicator to assess performance of probabilistic models. The conditionally heteroscedastic models are shown to have appropriate sharpness and better calibration than the respective homoscedastic models.

A Short-Term Electricity Price Forecasting Scheme for Power Market

“…Alternatively, other statistical models, e.g., an artificial neural network (ANN) model or an AR model combined with trend modelling [16] will be established and their forecast accuracies will be compared with the ARIMA models. The optimum models will be employed to generate the electricity price forecasts of high reliability and accuracy.…”

Probabilistic Real-Time Thermal Rating Forecasting for Overhead Lines by Conditionally Heteroscedastic Auto-Regressive Models

“…Then the wind direction forecast is determined based on predictions of the easterly and northerly components. Please refer to [17] where the AR and VAR models are defined for wind direction forecasting.…”

“…In order to address the circular property of wind direction, the predictive distribution of wind direction is assumed to be von Mises denoted by ( ) which is regarded as the circular analogue of the Gaussian distribution [20]. Their probability density functions (PDFs) can be found in [17].…”

“…However, due to some extremely dispersive distributions for conditionally heteroscedastic models, most of the average widths of 50% CPIs modelled by homoscedastic models are smaller on average as shown in Fig. 5, at the cost of losing a certain calibration which could be inspected from their PIT histograms [17]. The calculated CRPS values suggest that the VAR(2)-CH, AR(4)-CH, and AR(4)-CH models should be employed to estimate probabilistic 1-step-ahead forecasts for , and respectively.…”

Placement of large‐scale utility‐owned wind distributed generation based on probabilistic forecasting of line congestion

“…The wind power generated at each state and their corresponding probabilities are shown in Table 2. The average out power of the wind turbine and its CF are calculated using (14) and (15), respectively. The average wind power of the 1.5 MW wind turbine and its CF are found to be 0.5026 MW and 33.5%, respectively.…”