A Hybrid bVAR-NARX Wind Power Forecasting Model Based on Wind and Load Demand Correlation: A Case Study of ERCOT's System from an ISO's Perspective. Issue 14 (14th September 2018)

• Record Type: Journal Article
• Authors: Heistrene, Leena; Mishra, Poonam; Lokhande, Makarand
• Is Part Of: Electric power components and systems. Volume 46:Issue 14/15(2018)
• Page Start: 1634
• Page End: 1649
• Publication Date: 2018-09-14
• Subjects: wind power forecasting -- wind and load correlation -- multivariate modeling -- neural network (NARX) -- vector autoregressive model -- hybrid model -- ERCOT wind data set -- descriptive analysis -- objective analysis -- correlation functions; Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2018.1511006
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: Wind power forecasting tools are as crucial for independent system operators (ISO) as it is for individual wind farm owners. ISO integrate wind power forecasting tools into power system operations for a number of applications and processes such as balancing energy, regulation and energy reserve services. To deal with the challenges that have surfaced in implementing wind power forecasting tools from an ISO's perspective, a hybrid model is proposed which is based on the correlation between wind power and load demand. The proposed model has used a bi-sectional vector auto-regressive model in conjunction with a nonlinear auto-regressive with exogenous output model forming a hybrid model that gives better results on real-world data sets as well. The case study taken for validating the model is the Electric Reliability Council of Texas system that has seen tremendous increase in the installed capacity of wind power generation sources in the recent past.