Uncertainty borne balancing cost modeling for wind power forecasting. Issue 7 (2nd September 2019)

• Record Type: Journal Article
• Authors: Sarathkumar, Tirunagaru V.; Banik, Abhishek; Goswami, Arup Kumar; Dey, Shiladitya; Chatterjee, Abhishek; Rakshit, Sagarika; Basumatary, Sanjay; Saloi, Jayashri
• Is Part Of: Energy sources. Volume 14:Issue 7/9(2019)
• Page Start: 291
• Page End: 303
• Publication Date: 2019-09-02
• Subjects: Wind power forecasting -- wind power cost modelling -- Relevance vector machine (RVM) -- wind power mismatch balancing cost -- renewable energy sources; Natural resources -- Periodicals; Energy consumption -- Periodicals; Energy consumption -- Climatic factors -- Periodicals; Energy conversion -- Periodicals; Energy conversion -- Environment aspects -- Periodicals; 333.7905
• DOI: 10.1080/15567249.2019.1693664
• Languages: English
• ISSNs: 1556-7249

Abstract

ABSTRACT: Renewable sources, especially the wind power, are volatile in nature which introduce non-linear uncertainties in wind power forecasting (WPF). These non-linear uncertainties hinder the way for the integration of wind power with the electric power grid. Generally, point forecast methods are used for WPF, which are less reliable as they do not offer any uncertainty-related information. In this study, a probabilistic forecasting methodology based on relevance vector machine (RVM) is used in a novel approach for WPF. Based on the forecast, wind power mismatch balancing cost (WPMBC) is formulated to offset the power balancing costs induced by wind power uncertainties (WPU). In addition to that, the probabilistic risk (PR) of failing to meet the contracted dispatch is also formulated. A realistic case study has been adopted for implementing the proposed RVM-based model. It has been found that the RVM model provides superior performance for WPF than other state-of-the-art machine learning models.