Multivariate Predictive Analytics Of Wind Power Data For Robust Control Of Energy Storage

Keywords

Data analytics; energy storage; forecasting; microgrid; prediction intervals (PIs); predictive ensembles; robust optimization; smart grid; wind power

Abstract

Short-term forecasting is frequently identified as an important tool for the effective management of wind generation. However, forecasting errors, inherent to the point forecasts, increase requirements for energy storage and can affect optimal system operation. Probabilistic forecasts can help tackle this issue by providing a proper characterization of forecasting errors in the optimization process. This paper proposes a multivariate model of forecasting data for wind generation. Predictive uncertainty intervals of wind power can be obtained by sampling from the proposed model. The main goal is to use empirical data models without linear or Gaussian approximations of the distributional or temporal variations. The predictive modeling is utilized within a case study of an energy storage system. A modified robust convex programming is used to maintain the practical robustness and feasibility of the solution based on the sampled scenarios from the model.

Publication Date

8-1-2016

Publication Title

IEEE Transactions on Industrial Informatics

Volume

12

Issue

4

Number of Pages

1350-1360

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TII.2016.2569531

Socpus ID

84983742563 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84983742563

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