|
Optimal mathematical operation of a hybrid microgrid in islanded mode for improving energy efficiency using deep learning and demand side management
|
Mohsen Aryan nezhad *    |
| Technical and Vocational University (TVU) |
|
|
Abstract: (1074 Views) |
Deep learning method is used to predict the future value of load demand. Based on obtained results, a new model based on the forward-backward load shifting and unnecessary load shedding is presented. As well, to increase energy efficiency, excess renewable energy has been used to produce green hydrogen. For this purpose, GAMS optimization software has been used for optimal operation of the microgrid in the presence of renewable energy sources, battery, diesel generator, aqua electrolyzer, and fuel cell considering demand side management (DSM) restrictions. The obtained results from the proposed model of the considered microgrid show that the huge amount of excess electricity can be saved to enhance energy efficiency. This issue increases green hydrogen production that can be used for fuel cell consumption. As well, the proposed model provides lower cost of operation cost. In addition, the diesel generator consumes lower diesel fuel.
|
|
| Keywords: Deep learning, Demand side management, Neural network, Optimal operation, Renewable energy. |
|
|
Full-Text [PDF 1758 kb]
(279 Downloads)
|
Type of Study: Research |
Subject:
General
Received: 2023/01/20 | Accepted: 2023/06/21 | Published: 2023/10/2
|
|
|
|
|
|
|
| References |
1. [1] T. L. Vandoorn, B. Renders, L. Degroote, B. Meersman, and L. Vandevelde, "Active load control in islanded microgrids based on the grid voltage," Smart Grid, IEEE Transactions on, vol. 2, pp. 139-151, 2011. [ DOI:10.1109/TSG.2010.2090911] 2. [2] Z. Jiang, L. Gao, and R. A. Dougal Sr, "Adaptive control strategy for active power sharing in hybrid fuel cell/battery power sources," Energy conversion, ieee transactions on, vol. 22, pp. 507-515, 2007. [ DOI:10.1109/TEC.2005.853747] 3. [3] M. Fathi and H. Bevrani, "Adaptive energy consumption scheduling for connected microgrids under demand uncertainty," Power Delivery, IEEE Transactions on, vol. 28, pp. 1576-1583, 2013. [ DOI:10.1109/TPWRD.2013.2257877] 4. [4] T. Hamajima, M. Tsuda, D. Miyagi, H. Amata, T. Iwasaki, K. Son, et al., "Advanced superconducting power conditioning system with SMES for effective use of renewable energy," Physics Procedia, vol. 27, pp. 396-399, 2012. [ DOI:10.1016/j.phpro.2012.03.494] 5. [5] X. Tan, Q. Li, and H. Wang, "Advances and trends of energy storage technology in microgrid," International Journal of Electrical Power & Energy Systems, vol. 44, pp. 179-191, 2013. [ DOI:10.1016/j.ijepes.2012.07.015] 6. [6] I. Şerban and C. Marinescu, "Aggregate load-frequency control of a wind-hydro autonomous microgrid," Renewable Energy, vol. 36, pp. 3345-3354, 2011. [ DOI:10.1016/j.renene.2011.05.012] 7. [7] S. y. Obara, "Analysis of a fuel cell micro-grid with a small-scale wind turbine generator," International journal of Hydrogen energy, vol. 32, pp. 323-336, 2007. [ DOI:10.1016/j.ijhydene.2006.07.032] 8. [8] S. Nasri, S. B. Slama, I. Yahyaoui, B. Zafar, and A. Cherif, "Autonomous hybrid system and coordinated intelligent management approach in power system operation and control using hydrogen storage," International Journal of Hydrogen Energy, 2017. [ DOI:10.1016/j.ijhydene.2017.01.098] 9. [9] X. Zhang, C. Huang, and J. Shen, "Energy Optimal Management of Microgrid with High Photovoltaic Penetration," IEEE Transactions on Industry Applications, 2022. [ DOI:10.1109/CIEEC50170.2021.9510403] 10. [10] D. H. Vu, K. M. Muttaqi, and D. Sutanto, "An integrated energy management approach for the economic operation of industrial microgrids under uncertainty of renewable energy," IEEE Transactions on Industry Applications, vol. 56, pp. 1062-1073, 2020. [ DOI:10.1109/TIA.2020.2964635] 11. [11] W.-G. Lee, T.-T. Nguyen, H.-J. Yoo, and H.-M. Kim, "Consensus-Based Hybrid Multiagent Cooperative Control Strategy of Microgrids Considering Load Uncertainty," IEEE Access, vol. 10, pp. 88798-88811, 2022. [ DOI:10.1109/ACCESS.2022.3198949] 12. [12] X. Guan, Z. Xu, and Q.-S. Jia, "Energy-efficient buildings facilitated by microgrid," IEEE Transactions on smart grid, vol. 1, pp. 243-252, 2010. [ DOI:10.1109/TSG.2010.2083705] 13. [13] L. Ali, S. Muyeen, H. Bizhani, and A. Ghosh, "Optimal planning of clustered microgrid using a technique of cooperative game theory," Electric Power Systems Research, vol. 183, p. 106262, 2020. [ DOI:10.1016/j.epsr.2020.106262] 14. [14] L. Wen, K. Zhou, and S. Yang, "Load demand forecasting of residential buildings using a deep learning model," Electric Power Systems Research, vol. 179, p. 106073, 2020. [ DOI:10.1016/j.epsr.2019.106073] 15. [15] C. Fan, F. Xiao, and Y. Zhao, "A short-term building cooling load prediction method using deep learning algorithms," Applied energy, vol. 195, pp. 222-233, 2017. [ DOI:10.1016/j.apenergy.2017.03.064] 16. [16] A. H. V.-H. B. H.-M. Kim, "Resilience-Oriented Optimal Operation of Networked Hybrid Microgrids," IEEE Transactions on Smart Grid, vol. 10, 2019 [ DOI:10.1109/TSG.2017.2737024] 17. [17] K. R. C. C. C. R. Zhang, "Energy Cooperation Optimization in Microgrids With Renewable Energy Integration," IEEE Transactions on Smart Grid, vol. 9, 2018. [ DOI:10.1109/TSG.2016.2600863] 18. [18] C. J. Dongfeng Yang, Guowei Cai, Deyou Yang, Xiaojun Liu,, "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, vol. 277, 2020. [ DOI:10.1016/j.apenergy.2020.115491] 19. [19] Z. X. Xiaohong Guan, and Qing-Shan Jia, "Energy-Efficient Buildings Facilitated by Microgrid," IEEE Transaction on Smart Grid, vol. 1, 2010. [ DOI:10.1109/PES.2010.5590040] 20. [20] S. M. M. Liaqat Ali, Hamed Bizhani, Arindam Ghosh,, "Optimal planning of clustered microgrid using a technique of cooperative game theory,," Electric Power Systems Research, vol. 183, 2020. [ DOI:10.1016/j.epsr.2020.106262] 21. [21] P. L. Mansour Alramlawi, "Design Optimization of a Residential PV-Battery Microgrid With a Detailed Battery Lifetime Estimation Model," IEEE Transactions on Industry Applications, vol. 46, 2020. [ DOI:10.1109/TIA.2020.2965894] 22. [22] T. N. Duc, K. Goshome, N. Endo, and T. Maeda, "Optimization strategy for high efficiency 20 kW-class direct coupled photovoltaic-electrolyzer system based on experiment data," International Journal of Hydrogen Energy, vol. 44, pp. 26741-26752, 2019. [ DOI:10.1016/j.ijhydene.2019.07.056] 23. [23] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A search space odyssey," IEEE transactions on neural networks and learning systems, vol. 28, pp. 2222-2232, 2016. [ DOI:10.1109/TNNLS.2016.2582924] 24. [1] T. L. Vandoorn, B. Renders, L. Degroote, B. Meersman, and L. Vandevelde, "Active load control in islanded microgrids based on the grid voltage," Smart Grid, IEEE Transactions on, vol. 2, pp. 139-151, 2011. [ DOI:10.1109/TSG.2010.2090911] 25. [2] Z. Jiang, L. Gao, and R. A. Dougal Sr, "Adaptive control strategy for active power sharing in hybrid fuel cell/battery power sources," Energy conversion, ieee transactions on, vol. 22, pp. 507-515, 2007. [ DOI:10.1109/TEC.2005.853747] 26. [3] M. Fathi and H. Bevrani, "Adaptive energy consumption scheduling for connected microgrids under demand uncertainty," Power Delivery, IEEE Transactions on, vol. 28, pp. 1576-1583, 2013. [ DOI:10.1109/TPWRD.2013.2257877] 27. [4] T. Hamajima, M. Tsuda, D. Miyagi, H. Amata, T. Iwasaki, K. Son, et al., "Advanced superconducting power conditioning system with SMES for effective use of renewable energy," Physics Procedia, vol. 27, pp. 396-399, 2012. [ DOI:10.1016/j.phpro.2012.03.494] 28. [5] X. Tan, Q. Li, and H. Wang, "Advances and trends of energy storage technology in microgrid," International Journal of Electrical Power & Energy Systems, vol. 44, pp. 179-191, 2013. [ DOI:10.1016/j.ijepes.2012.07.015] 29. [6] I. Şerban and C. Marinescu, "Aggregate load-frequency control of a wind-hydro autonomous microgrid," Renewable Energy, vol. 36, pp. 3345-3354, 2011. [ DOI:10.1016/j.renene.2011.05.012] 30. [7] S. y. Obara, "Analysis of a fuel cell micro-grid with a small-scale wind turbine generator," International journal of Hydrogen energy, vol. 32, pp. 323-336, 2007. [ DOI:10.1016/j.ijhydene.2006.07.032] 31. [8] S. Nasri, S. B. Slama, I. Yahyaoui, B. Zafar, and A. Cherif, "Autonomous hybrid system and coordinated intelligent management approach in power system operation and control using hydrogen storage," International Journal of Hydrogen Energy, 2017. [ DOI:10.1016/j.ijhydene.2017.01.098] 32. [9] X. Zhang, C. Huang, and J. Shen, "Energy Optimal Management of Microgrid with High Photovoltaic Penetration," IEEE Transactions on Industry Applications, 2022. [ DOI:10.1109/CIEEC50170.2021.9510403] 33. [10] D. H. Vu, K. M. Muttaqi, and D. Sutanto, "An integrated energy management approach for the economic operation of industrial microgrids under uncertainty of renewable energy," IEEE Transactions on Industry Applications, vol. 56, pp. 1062-1073, 2020. [ DOI:10.1109/TIA.2020.2964635] 34. [11] W.-G. Lee, T.-T. Nguyen, H.-J. Yoo, and H.-M. Kim, "Consensus-Based Hybrid Multiagent Cooperative Control Strategy of Microgrids Considering Load Uncertainty," IEEE Access, vol. 10, pp. 88798-88811, 2022. [ DOI:10.1109/ACCESS.2022.3198949] 35. [12] X. Guan, Z. Xu, and Q.-S. Jia, "Energy-efficient buildings facilitated by microgrid," IEEE Transactions on smart grid, vol. 1, pp. 243-252, 2010. [ DOI:10.1109/TSG.2010.2083705] 36. [13] L. Ali, S. Muyeen, H. Bizhani, and A. Ghosh, "Optimal planning of clustered microgrid using a technique of cooperative game theory," Electric Power Systems Research, vol. 183, p. 106262, 2020. [ DOI:10.1016/j.epsr.2020.106262] 37. [14] L. Wen, K. Zhou, and S. Yang, "Load demand forecasting of residential buildings using a deep learning model," Electric Power Systems Research, vol. 179, p. 106073, 2020. [ DOI:10.1016/j.epsr.2019.106073] 38. [15] C. Fan, F. Xiao, and Y. Zhao, "A short-term building cooling load prediction method using deep learning algorithms," Applied energy, vol. 195, pp. 222-233, 2017. [ DOI:10.1016/j.apenergy.2017.03.064] 39. [16] A. H. V.-H. B. H.-M. Kim, "Resilience-Oriented Optimal Operation of Networked Hybrid Microgrids," IEEE Transactions on Smart Grid, vol. 10, 2019 [ DOI:10.1109/TSG.2017.2737024] 40. [17] K. R. C. C. C. R. Zhang, "Energy Cooperation Optimization in Microgrids With Renewable Energy Integration," IEEE Transactions on Smart Grid, vol. 9, 2018. [ DOI:10.1109/TSG.2016.2600863] 41. [18] C. J. Dongfeng Yang, Guowei Cai, Deyou Yang, Xiaojun Liu,, "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, vol. 277, 2020. [ DOI:10.1016/j.apenergy.2020.115491] 42. [19] Z. X. Xiaohong Guan, and Qing-Shan Jia, "Energy-Efficient Buildings Facilitated by Microgrid," IEEE Transaction on Smart Grid, vol. 1, 2010. [ DOI:10.1109/PES.2010.5590040] 43. [20] S. M. M. Liaqat Ali, Hamed Bizhani, Arindam Ghosh,, "Optimal planning of clustered microgrid using a technique of cooperative game theory,," Electric Power Systems Research, vol. 183, 2020. [ DOI:10.1016/j.epsr.2020.106262] 44. [21] P. L. Mansour Alramlawi, "Design Optimization of a Residential PV-Battery Microgrid With a Detailed Battery Lifetime Estimation Model," IEEE Transactions on Industry Applications, vol. 46, 2020. [ DOI:10.1109/TIA.2020.2965894] 45. [22] T. N. Duc, K. Goshome, N. Endo, and T. Maeda, "Optimization strategy for high efficiency 20 kW-class direct coupled photovoltaic-electrolyzer system based on experiment data," International Journal of Hydrogen Energy, vol. 44, pp. 26741-26752, 2019. [ DOI:10.1016/j.ijhydene.2019.07.056] 46. [23] K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, "LSTM: A search space odyssey," IEEE transactions on neural networks and learning systems, vol. 28, pp. 2222-2232, 2016. [ DOI:10.1109/TNNLS.2016.2582924] |
|
Aryan nezhad M. Optimal mathematical operation of a hybrid microgrid in islanded mode for improving energy efficiency using deep learning and demand side management. ieijqp 2023; 12 (3) :53-60 URL: http://ieijqp.ir/article-1-947-en.html
|
