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Use of Modified Cuckoo Search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms

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  • Piechocki, Janusz
  • Ambroziak, Dominik
  • Palkowski, Aleksander
  • Redlarski, Grzegorz

Abstract

In the face of increasingly stringent pollutant emission regulations, designing an agricultural holding becomes a difficult challenge of connecting a large number of coefficients that describe an energy system of a farm in regard to its ecological and economic efficiency. One way to cope with this issue is to design an energy self-sufficient farm that integrates various technologies, including renewable energy. However, the selection of appropriate components of such a system may be difficult. Large selection of facilities for management of heating and water systems and the choice of appropriate building technology makes it difficult to solve the problem of optimizing characteristics of such a holding by using standard methods. In this paper the issue of computer-aided design of energy systems for farms is dealt with. The solution proposed use the Modified Cuckoo Search algorithm in the process of optimizing the selection of particular components that influence performance of the power system, such as energy sources, water preparation systems or structure of walls. Presented results of the optimization process with the use of different fitness functions allow to state that the system developed achieved very satisfactory results and is capable to cope with the task. Through the use of the swarm algorithm it is possible to search for solutions in a large feature space and achieve optimality in terms of energy, economy and pollutant emission simultaneously.

Suggested Citation

  • Piechocki, Janusz & Ambroziak, Dominik & Palkowski, Aleksander & Redlarski, Grzegorz, 2014. "Use of Modified Cuckoo Search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms," Applied Energy, Elsevier, vol. 114(C), pages 901-908.
  • Handle: RePEc:eee:appene:v:114:y:2014:i:c:p:901-908
    DOI: 10.1016/j.apenergy.2013.07.057
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    Cited by:

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    2. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    3. Nesamalar, J. Jeslin Drusila & Venkatesh, P. & Raja, S. Charles, 2016. "Energy management by generator rescheduling in congestive deregulated power system," Applied Energy, Elsevier, vol. 171(C), pages 357-371.
    4. Baklacioglu, Tolga & Turan, Onder & Aydin, Hakan, 2015. "Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks," Energy, Elsevier, vol. 86(C), pages 709-721.
    5. Coelho, Leandro dos Santos & Klein, Carlos Eduardo & Sabat, Samrat L. & Mariani, Viviana Cocco, 2014. "Optimal chiller loading for energy conservation using a new differential cuckoo search approach," Energy, Elsevier, vol. 75(C), pages 237-243.

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