Advanced chance-constrained predictive control for the efficient energy management of renewable power systems. (February 2019)

• Record Type: Journal Article
• Authors: Vergara-Dietrich, José D.; Morato, Marcelo M.; Mendes, Paulo R.C.; Cani, Alex A.; Normey-Rico, Julio E.; Bordons, Carlos
• Is Part Of: Journal of process control. Volume 74(2019)
• Page Start: 120
• Page End: 132
• Publication Date: 2019-02
• Subjects: Model Predictive Control -- Chance Constraints -- Disturbance forecasting -- Energy management -- Microgrids -- Renewable sources; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.11.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The study proposes an advanced control structure in 3 layers aiming the optimal efficiency energy management for Grid-Connected Hybrid Power Plant. The control structure is based on a Stochastic Model Predictive Control and an optimal finite-state machine to take into account disturbances variations. Disturbance estimation techniques based on of NAR Neural Networks are applied to have prediction of renewable sources. The studied control method is compared, through simulation, to a deterministic MPC approach to illustrate an improved performance. Abstract: This study presents a complete advanced control structure aimed at the optimal and most efficient energy management for a Grid-Connected Hybrid Power plant. This control scheme is composed of process supervision and process control layers, and it is a possible technology to enable improvements in the energy consumption of industrial systems subject to constraints and process demands. The proposed structure consists of the combination of a Model-Based Predictive Controller, formulated within the Chance Constraints framework to deal with stochastic disturbances (renewable sources, as solar irradiance), an optimal finite-state machine decision system and the use of disturbance estimation techniques for the prediction of renewable sources. The predictive controller uses feedforward compensation of estimated future disturbances, obtained by the use of Nonlinear Auto-Regressive Neural Networks with time delays. The proposed controller aims to perform the management of which energy system to use and to decide where to store energy between multiple storage options. This has to be done while always maximizing the use of renewable energy and optimizing energy generation due to contract rules (maintain maximal economic profit). The proposed method is applied to a case study of energy generation in a sugar cane power plant, with non-dispatchable renewable sources (such as photovoltaic and wind power generation), as well as dispatchable sources (as biomass and biogas). This hybrid power system is subject to operational constraints, as to produce steam in different pressures, sustain internal demands and, imperiously, produce and maintain an amount of electric power throughout each month, defined by strict contract rules with a local Distribution Network Operator ( DNO ). This paper aims to justify the use of this novel approach to optimal energy generation in hybrid microgrids through simulation, illustrating the performance improvement for different cases.