Experimental performance assessment of an online energy management strategy for varying renewable power production suppression

Erdinç, Ozan; Elma, Onur; Uzunoğlu, Mehmet; Selamoğulları, Ugur Savaş; Vural, Bülent; Uğur, Enes; Boynueğri, Ali Rıfat; Düşmez, Serkan

doi:10.1016/j.ijhydene.2011.12.042

Cited by 46 publications

(23 citation statements)

References 38 publications

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“…The growing attention received by FLC is demonstrated by the numerous authors who have successfully applied it to hybrid renewable energy systems: (Jeong et al 2005;El-Shatter et al 2006;Erdinc and Uzunoglu 2011;Erdinc et al 2012;Hajizadeh and Golkar 2007;Bilodeau and Agbossou 2006;Stewart et al 2009;Li et al 2011;Kyriakarakos et al 2012;Chávez-Ramírez et al 2013;Safari et al 2013).…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Modeling and Control: Theory and Applications

Mata¹,

Marichal²,

Jimnez³

2014

Atlantis Computational Intelligence Systems

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Aims and scope of the seriesThe series 'Atlantis Computational Intelligence Systems' aims at covering state-of-the-art research and development in all fields where computational intelligence is investigated and applied. The series seeks to publish monographs and edited volumes on foundations and new developments in the field of computational intelligence, including fundamental and applied research as well as work describing new, emerging technologies originating from computational intelligence research. Applied CI research may range from CI applications in the industry to research projects in the life sciences, including research in biology, physics, chemistry and the neurosciences.All books in this series are co-published with Springer.For more information on this series and our other book series, please visit our website at: www.atlantis-press.com/publications/books. This book is published under the Creative Commons Attribution-Non-commercial license, meaning that copying, distribution, transmitting and adapting the book is permitted, provided that this is done for non-commercial purposes and that the book is attributed. This book, or any parts thereof, may not be reproduced for commercial purposes in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system known or to be invented, without prior permission from the Publisher.Printed on acid-free paper ForewordAlmost half a century has passed since the publication of the seminal paper by Prof. Lotfi A. Zadeh. Fifty years that have witnessed an impressive growth of fuzzy systems from both the theoretical and applied points of view. Impressive growth and at the same time following an unforeseen way, since as Prof. Zadeh has said many times, his expectations when writing his first paper on fuzzy sets in 1965 were that fuzzy sets would find its main applications in the realm of humanistic systems (economics, linguistics, psychology, etc.). Now the reality is that most of the success of fuzzy systems research (at least in its initial decades) was related to its application to mechanistic instead of humanistic systems. This book concentrates on modeling and control of mechanistic systems, as can be seen when reading the Table of Contents, first considering important theoretical aspects and then showing its application to quite interesting real-world problems.Any book on fuzzy modeling and control is somehow a tribute to those who paved the way for the topic. In this case, we need to consider three key persons and three related milestones in the history of fuzzy sets. First, the already mentioned Prof. Zadeh and his 1973 paper ''Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,'' IEEE Transactions on Systems, Man, and Cybernetics 3: 28-44 (1973), the paper where concepts as linguistic variable and fuzzy if-then rules took form, and at the same time, the paper that triggered the second hint in the road to build up the theory of fuzzy modeling and control. Ab...

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Section: Discussionmentioning

confidence: 99%

Fuzzy Modeling and Control: Theory and Applications

Mata¹,

Marichal²,

Jimnez³

2014

Atlantis Computational Intelligence Systems

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“…Additionally, whilst simulations have been extensively used to evaluate the performance of PMS applied to hydrogen and other hybrid energy systems, most studies do not include an experimental validation (or real-time testing) due to the challenges involved with the construction of a hybrid power system and the developments of suitable power electronic interfaces [38]. Moreover, no validation of an NN-based predictive PMS has been reported which includes the forecasting of renewables such as wind, and electric loads.…”

Section: Introductionmentioning

confidence: 99%

Predictive power management strategies for stand-alone hydrogen systems: Lab-scale validation

Brka

Kothapalli

Al-Abdeli

2015

International Journal of Hydrogen Energy

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“…Because of the random nature of renewable generation, it is difficult to implement renewable sources in an autonomous system without a storage element and a backup source [8,9]. The justification of the auxiliary source use, in the isolated system, is made with the hybrid indicator which is the most widely used method [10].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [9] develops a PMS based on fuzzy logic to control an autonomous hybrid WT/PV/battery/Fuel Cell system supplying a constant demand in the studied city. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Demand side management of household appliances in stand-alone hybrid photovoltaic system

2015

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a b s t r a c tIn this paper, a demand side management control (DSM) acting on the load profile for an isolated hybrid photovoltaic/diesel/battery system for residential application was investigated. This control is a new strategy that compensates the global power flow of a distributed generation system. In fact, using the DSM strategy, the renewable energy can be used effectively to satisfy the demand and reduce the size of the system components. It leads to satisfy customers' continuous requirements to minimize the loss of power supply propability (LPSP), to extend the battery life time minimizing the storage system charge cycle, and to reduce fuel consumption and CO 2 emission together with the diesel generator operation rate (DGOR). Consequently, the DSM strategy is implemented to ensure the minimum system cost. The DSM algorithm relies on the customer's energy consumption record, the instantaneous available PV energy and the state of charge (SOC) of the battery storage system. This could be achieved through switching between two action modes on the consumption profile: time shifting load mode, amplitude modulation load mode. To prove the effectiveness of the proposed control strategy, two scenarios were investigated. The first one describes the system's simulation for two typical days. The second scenario is performed using annual profiles of photovoltaic and load powers. To highlight the benefits of the proposed strategy for the residential hybrid system applications under various conditions, a comparative study between the power management strategies without and with DSM was presented.

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Experimental performance assessment of an online energy management strategy for varying renewable power production suppression

Cited by 46 publications

References 38 publications

Fuzzy Modeling and Control: Theory and Applications

Fuzzy Modeling and Control: Theory and Applications

Predictive power management strategies for stand-alone hydrogen systems: Lab-scale validation

Demand side management of household appliances in stand-alone hybrid photovoltaic system

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