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Kumisbekovna, S.R., Kakimzhan, G., Darimbayeva, N., Besterekova, A., Toygozhinova, Z., Darkenbaeva, E., Sakitzhanov, M.. (1401). Deep Learning Based Optimal Energy Management in the Presence of Renewable Energy. سامانه مدیریت نشریات علمی, 11(Special Issue), -. doi: 10.22098/joape.2024.14720.2126
S.R. Kumisbekovna; G. Kakimzhan; N. Darimbayeva; A. Besterekova; Z. Toygozhinova; E. Darkenbaeva; M. Sakitzhanov. "Deep Learning Based Optimal Energy Management in the Presence of Renewable Energy". سامانه مدیریت نشریات علمی, 11, Special Issue, 1401, -. doi: 10.22098/joape.2024.14720.2126
Kumisbekovna, S.R., Kakimzhan, G., Darimbayeva, N., Besterekova, A., Toygozhinova, Z., Darkenbaeva, E., Sakitzhanov, M.. (1401). 'Deep Learning Based Optimal Energy Management in the Presence of Renewable Energy', سامانه مدیریت نشریات علمی, 11(Special Issue), pp. -. doi: 10.22098/joape.2024.14720.2126
Kumisbekovna, S.R., Kakimzhan, G., Darimbayeva, N., Besterekova, A., Toygozhinova, Z., Darkenbaeva, E., Sakitzhanov, M.. Deep Learning Based Optimal Energy Management in the Presence of Renewable Energy. سامانه مدیریت نشریات علمی, 1401; 11(Special Issue): -. doi: 10.22098/joape.2024.14720.2126

Deep Learning Based Optimal Energy Management in the Presence of Renewable Energy

Journal of Operation and Automation in Power Engineering
دوره 11، Special Issue، خرداد 2023    اصل مقاله (1.53 M)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2024.14720.2126
نویسندگان
S.R. Kumisbekovna* 1؛ G. Kakimzhan2؛ N. Darimbayeva3؛ A. Besterekova3؛ Z. Toygozhinova3؛ E. Darkenbaeva3؛ M. Sakitzhanov3
1Institute of Engineering and Technology. Educational Programs of «Electric Power Engineering,Technosphere Safety and Ecology», Kyzylorda, Kazakhstan.
2Almaty University Power Engineering and Communications, Associate Professor, Almaty, Kazakhstan.
3Institute of Energy and Green Technologies, Almaty University of Power Engineering and Telecommunications, Almaty, Kazakhstan.
چکیده
Traditional energy management focuses on ensuring a reliable and sustainable energy supply through meticulous planning, coordination, and optimization of resources. However, integrating renewable energy sources like solar, wind, and hydropower introduces a new layer of complexity. These sources, while environmentally friendly, are inherently intermittent and variable in their production, posing unique challenges for energy management. Effective energy management in the presence of renewable energy requires strategies to balance supply and demand, optimize energy use, and ensure grid stability. This study introduces a new model designed to significantly improve the accuracy of estimating both energy production and demand. This enhanced level of precision plays a decisive role in the decision-making process for energy management. This innovative model employs a fuzzy neural network trained on historical energy production data, integrating weather information through fuzzy functions to improve precision in estimating energy production for future intervals. The objective functions prioritize renewable energy use to minimize overall system costs. The simulations evaluated the total system cost under various conditions. The results showed that more accurate estimation and maximized utilization of renewable energy sources led to a significant reduction in the cost per kilowatt-hour. In essence, this study offers a promising approach to managing energy systems that heavily rely on renewable sources. By improving estimation accuracy and prioritizing renewable energy use, the model paves the way for a more reliable, sustainable, and cost-effective energy future.
کلیدواژه‌ها
Energy Management؛ Renewable Energy؛ LSTM-fuzzy Network؛ Power Production Forecast
مراجع
  1. S. G. Malla, J. M. R. Malla, P. Malla, S. Ramasamy, S. K. Doniparthi, M. K. Sahu, P. K. Subudhi, and H. Awad, “Coordinated power management and control of renewable energy sources based smart grid,” Int. J. Emerging Electr. Power Syst., vol. 23, no. 2, pp. 261–276, 2022.
  2. Z. Yang, F. Yang, H. Min, H. Tian, W. Hu, J. Liu, and N. Eghbalian, “Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources,” Energy, vol. 263, p. 125695, 2023.
  3. J. J. Quiñones, L. R. Pineda, J. Ostanek, and L. Castillo, “Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources,” Energy Convers. Manage., vol. 293, p. 117440, 2023.
  4. M. Farrokhifar, F. H. Aghdam, A. Alahyari, A. Monavari, and A. Safari, “Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic milp model,” Electr. Power Syst. Res., vol. 187, p. 106483, 2020.
  5. M. M. Alam, M. H. Rahman, M. F. Ahmed, M. Z. Chowdhury, and Y. M. Jang, “Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system,” Sci. Rep., vol. 12, no. 1, p. 15133, 2022.
  6. H. Mohammadi, S. Jokar, M. Mohammadi, A. Kavousi Fard, M. Dabbaghjamanesh, and M. Karimi, “A deep learning-tolearning based control system for renewable microgrids,” IET Renewable Power Gener., 2023.
  7. T. Hai, J. Zhou, and K. Muranaka, “Energy management and operational planning of renewable energy resources-based microgrid with energy saving,” Electr. Power Syst. Res., vol. 214, p. 108792, 2023.
  8. M. M. Alam and Y. M. Jang, “Deep learning based optimal energy management framework for community energy storage system,” ICT Express, vol. 9, no. 3, pp. 333–340, 2023.
  9. A. K. Erenoglu,˘ ˙I. S¸engör, O. Erdinç, A. Tas¸cıkaraoglu,˘ and J. P. Catalão, “Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation,” Int. J. Electr. Power Energy Syst., vol. 136, p. 107714, 2022.
  10. M. M. Alam and Y. M. Jang, “Deep learning based optimal energy management framework for community energy storage system,” ICT Express, vol. 9, no. 3, pp. 333–340, 2023.
  11. S. M. Hosseini, R. Carli, and M. Dotoli, “Robust optimal energy management of a residential microgrid under uncertainties on demand and renewable power generation,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 2, pp. 618–637, 2020.
  12. M. F. Elmorshedy, M. Elkadeem, K. M. Kotb, I. B. Taha, and D. Mazzeo, “Optimal design and energy management of an isolated fully renewable energy system integrating batteries and supercapacitors,” Energy Convers. Manage., vol. 245, p. 114584, 2021.
  13. H. Karimi, G. Gharehpetian, R. Ahmadiahangar, and A. Rosin, “Optimal energy management of grid-connected multimicrogrid systems considering demand-side flexibility: A two-stage multi-objective approach,” Electr. Power Syst. Res., vol. 214, p. 108902, 2023.
  14. S. Chupradit, G. Widjaja, S. Mahendra, M. Ali, M. Tashtoush, A. Surendar, M. Kadhim, A. Oudah, I. Fardeeva, and F. Firman, “Modeling and optimizing the charge of electric vehicles with genetic algorithm in the presence of renewable energy sources,” J. Oper. Autom. Power Eng., vol. 11, no. 1, pp. 33–38, 2023.
  15. V. Arumugham, H. M. Ghanimi, D. A. Pustokhin, I. V. Pustokhina, V. S. Ponnam, M. Alharbi, P. Krishnamoorthy, and S. Sengan, “An artificial-intelligence-based renewable energy prediction program for demand-side management in smart grids,” Sustainability, vol. 15, no. 6, p. 5453, 2023.
  16. J. Napitupulu, A. Al-Khalidi, Z. Al-Lami, A. Ibrahim, M. Arabi, A. Ali, M. Abdulhasan, K. Nematovich, D. Sholeha, and Y. Yerkin, “Hybrid energy systems optimization using firefly algorithm: Integration of various resources for enhanced stability and flexibility,” J. Oper. Autom. Power Eng., vol. 11, no. Special Issue, 2024.
  17. G. Aghajani and I. Heydari, “Energy management in microgrids containing electric vehicles and renewable energy sources considering demand response,” J. Oper. Autom. Power Eng., vol. 9, no. 1, pp. 34–48, 2021.
  18. E. Shahryari, H. Shayeghi, B. Mohammadi-Ivatloo, and M. Moradzadeh, “Optimal energy management of microgrid in day-ahead and intra-day markets using a copula-based uncertainty modeling method,” J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 86–96, 2020.
  19. F. R. Badal, P. Das, S. K. Sarker, and S. K. Das, “A survey on control issues in renewable energy integration and microgrid,” Prot. Control Mod. Power Syst., vol. 4, no. 1, pp. 1–27, 2019.
  20. R. Anaadumba, Q. Liu, B. D. Marah, F. M. Nakoty, X. Liu, and Y. Zhang, “A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid,” Cybersecur., vol. 4, pp. 1–12, 2021.
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