Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

Abstract

The incorporation of wind generation introduces challenges to the operation of the power system due to its uncertain characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal Kriging and analog approaches are used to forecast wind power generation and used as input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal Kriging takes into account the spatial and temporal information given by the database to enhance wind forecasts. We evaluate the performance of using the spatio-temporal Kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results show that the use of Kriging-based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

A Novel Hybrid Spatiotemporal Missing Value Imputation Approach for Rainfall Data: An Application to the Ratnapura Area, Sri Lanka

“…This study introduces a hybrid method by integrating multi-layer perceptron (MLP) with spatial kriging as a tool for imputing sequential missing values of spatiotemporal data. The performance of the new model was evaluated against each of the two individual models as well as the spatiotemporal kriging model, since many past studies [17,18,20] have identified the spatiotemporal kriging model as a promising tool for estimating missing values in spatiotemporal data.…”

“…Therefore, in this study, we chose MLP to capture the temporal variation of rainfall data in our hybrid model. The spatial kriging was used to model spatial variation due its promising behavior in capturing the spatial variations of the weather data and incorporation into a hybrid model [4,17,18,[20][21][22].…”

“…There have been several spatiotemporal kriging-based studies conducted with weather variables. The study in [18] developed a spatiotemporal kriging model to predict wind speed and compared the results with the autoregressive moving average (ARMA) and Monte Carlo methods. Spatiotemporal kriging modeling gave a better fit for the data.…”

“…spatial kriging was used to model spatial variation due its promising behavior in captur-ing the spatial variations of the weather data and incorporation into a hybrid model [4,17,18,[20][21][22].…”

“…where 𝑤 ≡ (𝑤 1 , 𝑤 2 , … , 𝑤 𝑛 ) 𝑇 , 𝒁 ≡ (𝑍(𝑋 1 ), 𝑍(𝑋 2 ), … , 𝑍(𝑋 𝑛 )) . The values of 𝑘 and 𝑤 are calculated by minimizing the mean squared prediction error [18]. The optimal linear predictor of 𝑍(𝑋 0 ), 𝒁 ̂(𝑿 𝟎 ), is given by:…”