Model predictive control of fuel cells system within hybrid renewable energy generation
| Autor: | Min-Sen Chiu, Xiaonan Wang, Scarlett Chen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Energy management Computer science 020209 energy Control engineering 02 engineering and technology 021001 nanoscience & nanotechnology Renewable energy Model predictive control Control and Systems Engineering Control theory Control system 0202 electrical engineering electronic engineering information engineering Fuel cells Solid oxide fuel cell 0210 nano-technology business Energy (signal processing) |
| Zdroj: | IFAC-PapersOnLine. 51:856-861 |
| ISSN: | 2405-8963 |
| Popis: | This paper presents model predictive control (MPC) strategies with a shrinking horizon approach to track local control systems when subject to supervisory trajectories. The supervisory trajectories are generated using economic receding horizon optimization based on energy management in energy-intensive industries (e.g., chlor-alkali process) with a hybrid renewable energy system (HRES), including solar, wind, and fuel cell sub-systems to provide sustainable power supply. A planer solid oxide fuel cell system is adopted in this study, and its power output is regulated using a constrained shrinking horizon MPC controller. The feasibility of MPC control algorithm in regulating energy sub-systems within a supervisory MPC framework will be studied and evaluated at different parameters. The main contribution of this paper is to provide practical control options when addressing technical viability concerns of hybrid energy system implementation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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