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Application of Artificial Intelligence in the Unit Commitment System in the Application of Energy Sustainability

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  • Bohumír Garlík

    (Faculty of Civil Engineering, Czech Technical University, 166 29 Prague, Czech Republic)

Abstract

This article approaches the optimal solution of energy sustainability based on the use of artificial intelligence (AI). The application of renewable energy sources (RES) and unit commitment (UC) is the basic idea of this concept. Therefore, a new approach to solving the UC problem is introduced. The proposed method has a simple procedure to obtain the popular solutions in an acceptable time interval, by creating a basic model of the schedule of the state of energy units RES. It is obvious that individual consumer units, of an operational nature, take hourly performance values by performing economic evaluations on them in the sense of cost optimization. This is conducted through an artificial intelligence (AI) algorithm by optimizing the dedicated cost function, simulated by annealing. Despite the acceptable solution obtained from these two steps, another shift is proposed, called the TDD process in a given consumer area. This process in the application of AI in the system of selection of universal load TDD from hundreds of possible ones is based on the use of artificial neural networks and cluster analysis, which is represented by the application of the Kohonen map. This logical process to achieve a modified solution is a self-organizing map (SOM). It is a software tool for visualizing high-dimensional data. Converts complex, nonlinear statistical relationships (functions) between high-dimensional data to simple geometric relationships, low-dimensional representation. The output of SOM is an optimized load TDD on the basis of which the process of automatic control of UC in the local urban area is built. The results of the AI application in the case of sustainable energy solutions confirm that this UC method provides a robust solution to an almost optimal solution.

Suggested Citation

  • Bohumír Garlík, 2022. "Application of Artificial Intelligence in the Unit Commitment System in the Application of Energy Sustainability," Energies, MDPI, vol. 15(9), pages 1-33, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:2981-:d:797136
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    References listed on IDEAS

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    1. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    2. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    3. Howell, Shaun & Rezgui, Yacine & Hippolyte, Jean-Laurent & Jayan, Bejay & Li, Haijiang, 2017. "Towards the next generation of smart grids: Semantic and holonic multi-agent management of distributed energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 193-214.
    4. Cicea, Claudiu & Marinescu, Corina & Popa, Ion & Dobrin, Cosmin, 2014. "Environmental efficiency of investments in renewable energy: Comparative analysis at macroeconomic level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 555-564.
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