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Neural Network Based Cogeneration Dispatch nder Deregulation

Co-generation is an efficient energy system that generates steam and electricity simultaneously. In ordinary operation, fuel cost accounts for more than 60% of the operational cost. As a result, the boiler efficiency and optimization level of co-generation are both high. To achieve further energy conservation, objectives of this thesis are to find the Profit-maximizing dispatch and efficiency enhancing strategy of the co-generation systems under deregulation.
In a coexistent environment of both Bilateral and Poolco-based power market, there are bid-based spot dispatch, and purchases and sales agreement-based contract dispatch. For profit-maximizing dispatch, the steam of boilers, fuels and generation output will be obtained by using the SQP(Sequential Quadratic Programming ) method. In order to improve the boiler efficiency, this thesis utilizes artificial neural networks(ANN) and evolutionary programming(EP) methods to search for the optimal operating conditions of boilers.
A co-generation system (back-pressure type and extraction type) is used to illustrate the effectiveness of the proposed method.
Date03 August 2005
CreatorsChou, Yu-ching
ContributorsWhei-Min Lin, Fu-Sheng Cheng, Ta-Peng Tsao, Shi-Jaw Chen
Source SetsNSYSU Electronic Thesis and Dissertation Archive
Detected LanguageEnglish
Rightsnot_available, Copyright information available at source archive

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