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Optimal control of energy hub systems by use of SQP algorithm and energy prediction

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10.1109/IECON.2014.7048503
 
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hdl:2117/27224

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Kampouropoulos, KonstantinosMés informacióMés informació
Sala Cardoso, EnricMés informació
Andrade, Fabio
Romeral Martínez, José LuisMés informacióMés informacióMés informació
Document typeConference report
Defense date2014
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
This paper presents an energy optimization methodology applied on industrial plants with multiple energy carriers. The methodology combines an adaptive neuro-fuzzy inference system to calculate the short-term load forecasting of a plant, and the sequential quadratic programming algorithm to optimize its energy flow. Furthermore, the mathematical models of the plant's equipment are considered into the optimization process, in order to calculate the dynamic system response and the equipment's inertias. The final algorithm optimizes the operation of the plant in order to satisfy the energy demand, minimizing several optimization criteria. The methodology has been tested and evaluated in an automotive factory plant using real production and consumption data.
CitationKampouropoulos, K. [et al.]. Optimal control of energy hub systems by use of SQP algorithm and energy prediction. A: IEEE International Conference on Industrial Electronics. "Proceedings of the 40th Annual Conference of the IEEE Industrial Electronics Society". Dallas, TX: 2014. 
URIhttp://hdl.handle.net/2117/27224
DOI10.1109/IECON.2014.7048503
ISBN978-1-4799-4033-2
Publisher versionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7048503
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  • Departament d'Enginyeria Electrònica - Ponències/Comunicacions de congressos [1.656]
  • MCIA - Motion Control and Industrial Applications Research Group - Ponències/Comunicacions de congressos [131]
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