Model predictive control of fuel cell micro cogeneration systems
| Autor: | Michiel Houwing, Rudy R. Negenborn, Bart De Schutter, Marija Ilic |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
0209 industrial biotechnology
Engineering model predictive control business.industry 020209 energy Control engineering 02 engineering and technology micro cogeneration Thermal energy storage 7. Clean energy PEM fuel cells Automotive engineering distributed energy resources Demand response Cogeneration Model predictive control Electric power system 020901 industrial engineering & automation demand response Distributed generation 0202 electrical engineering electronic engineering information engineering Electricity business Efficient energy use |
| Zdroj: | Proceedings of the 2009 IEEE International Conference on Networking, Sensing and Control, ICNSC 2009, 26-29 March 2009, Okayama, Japan |
| DOI: | 10.1109/icnsc.2009.4919364 |
| Popis: | With the increasing application of distributed energy resources and information technologies in the electricity infrastructure, innovative possibilities for incorporating the demand side more actively in power system operation are enabled. At the residential level energy costs could be reduced with intelligent price-based control concepts (demand response). A promising, controllable, residential distributed generation technology is micro cogeneration (micro-CHP). Micro-CHP is an energy efficient technology that simultaneously provides heat and electricity to households during operation. This paper presents a detailed model of a household using a proton exchange membrane fuel cell (PEMFC) micro-CHP system in conjunction with heat storage options to fulfil its heat and part of its electricity demand. Furthermore, a decentralised controller based on a model predictive control (MPC) strategy is proposed. MPC can take benefit of future knowledge on prizes and energy demands and can therefore lead to better system performance. In simulations the performance of the MPC-controlled PEMFC system is illustrated under different conditions regarding energy pricing, domestic energy demand, and system configuration. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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