A spatiotemporal probabilistic model‐based temperature‐augmented probabilistic load flow considering PV generations. (16th January 2019)

• Record Type: Journal Article
• Authors: Prusty, B Rajanarayan; Jena, Debashisha
• Is Part Of: International transactions on electrical energy systems. Volume 29:Number 5(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-16
• Subjects: operational planning -- probabilistic load flow -- PV generation -- spatiotemporal correlation -- steady‐state forecasting; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.2819
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: The probabilistic steady‐state forecasting of a PV‐integrated power system requires a suitable forecasting model capable of accurately characterizing the uncertainties and correlations among multivariate inputs. The critical and foremost difficulties in the development of such a model include the accurate representation of the characterizing features such as complex nonstationary pattern, non‐Gaussianity, and spatial and temporal correlations. This paper aims at developing an improved high‐dimensional multivariate spatiotemporal model through enhanced preprocessing, transformation techniques, principal component analysis, and a suitable time series model that is capable of accurately modeling the trend in the variance of uncertain inputs. The proposed model is applied to the probabilistic load flow carried out on the modified Indian utility 62‐bus transmission system using temperature‐augmented system model for an operational planning study. A detailed discussion of various results has indicated the effectiveness of the proposed model in capturing the aforesaid characterizing features of uncertain inputs.