MIMO auto-regressive modeling-based generalized predictive control for grid-connected hybrid systems. (January 2022)

• Record Type: Journal Article
• Authors: Franco, Ricardo A.P.; Cardoso, Álisson A.; Filho, Gilberto Lopes; Vieira, Flávio H.T.
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Alternate energies -- Autoregressive modeling -- Energy management model -- Generalized predictive control -- Integrated grids; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107636
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, we propose an energy management model for grid-connected hybrid systems. This model is based on the coupling of a multiple input multiple output (MIMO) AutoRegressive Moving Average with eXogenous inputs (ARMAX) model with generalized predictive control (GPC). The ARMAX model performs input–output mapping of the electrical system data in terms of energy costs and power values. The GPC system receives the input–output mapping provided by the ARMAX model and performs energy management by minimizing the consumed energy cost in the load. Cost minimization is accomplished by optimizing the use of renewable energy sources, thus consuming less energy from the grid. The effectiveness of the proposed model is verified, comparing its computational and experimental results to those of a conventional control model and a model predictive control approach presented in the literature. Graphical abstract: Highlights: The ARMAX-MIMO-GPC model is an energy management model for hybrid electrical systems. The model is based on the ARMAX associated with generalized predictive control. The model minimizes energy supplied by grid and the load consumed energy cost. Results show that load demand is attained avoiding using energy supplied by the grid.