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Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility

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  • Chang, Hsueh-Hsien
  • Yang, Hong-Tzer

Abstract

Non-intrusive energy-management (NIEM) techniques are based on energy signatures. While such approaches lack transient energy signatures, the reliability and accuracy of recognition results cannot be determined. By using neural networks (NNs) in combination with turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of NIEM results. Case studies are presented that apply various methods to compare training algorithms and classifiers in terms of artificial neural networks (ANN) due to various factors that determine whether a network is being used for pattern recognition. Additionally, in combination with electromagnetic transient program (EMTP) simulations, calculating the turn-on transient energy facilitate load can lead to identification and a significant improvement in the accuracy of NIEM results. Analysis results indicate that an NIEM system can effectively manage energy demands within economic dispatch for a cogeneration system and power utility. Additionally, a new method based on genetic algorithms (GAs) is used to develop a novel operational strategy of economic dispatch for a cogeneration system in a regulated market and approach the global optimum with typical environmental constraints for a cogeneration plant. Economic dispatch results indicate that the NIEM system based on energy demands can estimate accurately the energy contribution from the cogeneration system and power utility, and further reduce air pollution. Moreover, applying the NIEM system for economic dispatch can markedly reduce computational time and power costs.

Suggested Citation

  • Chang, Hsueh-Hsien & Yang, Hong-Tzer, 2009. "Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility," Applied Energy, Elsevier, vol. 86(11), pages 2335-2343, November.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:11:p:2335-2343
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    References listed on IDEAS

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    1. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Zare, Kazem, 2014. "Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs," Applied Energy, Elsevier, vol. 136(C), pages 393-404.
    2. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    3. Tsai, Men-Shen & Lin, Yu-Hsiu, 2012. "Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation," Applied Energy, Elsevier, vol. 96(C), pages 55-73.
    4. Canizes, Bruno & Soares, João & Faria, Pedro & Vale, Zita, 2013. "Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets," Applied Energy, Elsevier, vol. 108(C), pages 261-270.
    5. Chang, Hsueh-Hsien, 2011. "Genetic algorithms and non-intrusive energy management system based economic dispatch for cogeneration units," Energy, Elsevier, vol. 36(1), pages 181-190.
    6. Liu, Bo & Luan, Wenpeng & Yu, Yixin, 2017. "Dynamic time warping based non-intrusive load transient identification," Applied Energy, Elsevier, vol. 195(C), pages 634-645.
    7. Yan, Lei & Tian, Wei & Han, Jiayu & Li, Zuy, 2022. "Event-driven two-stage solution to non-intrusive load monitoring," Applied Energy, Elsevier, vol. 311(C).

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