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:: Volume 11, Issue 4 (11-2022) ::
ieijqp 2022, 11(4): 15-27 Back to browse issues page
Optimal operation of energy hub with the presence of electricity to gas technology and hydrogen loads, considering demand response programs and air pollution
Amin Namvar1 , Javad Salehi * 2, Navid Taghizadegan Kalantari2
1- Ph.D. Student, Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
2- Associate Professor, Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Abstract:   (1189 Views)
Energy supply is the most important need of human societies because life is impossible without energy. Therefore, the operation of energy resources is a substantial subject in the management of these resources. On the other hand, energy resources are often interdependent, which can help their management. In other words, the integrated operation of energy resources can be useful in energy management. In this regard, “the energy hub” has been introduced as a new concept for the integrated operation of energy resources. Using the concept of an energy hub, this paper tries to reduce the energy supply costs of consumers and manage these resources by integrated and simultaneous operation of electricity, natural gas, and water. In modeling, various pieces of equipment, such as energy storage devices, combined heat and power systems, and renewable sources, are used. Power-to-gas technology is also used to produce hydrogen and natural gas from water and electricity to supply hydrogen loads and inject the natural gas produced into the gas network. Power-to-gas technology uses excess electricity produced by renewable sources to produce hydrogen, which is obtained from the breakdown of water molecules. Given that the technology requires carbon dioxide to produce natural gas, this can reduce air pollution. In addition, a demand response program is implemented to shift a part of the electricity and heat consumption from peak hours to off-peak hours in order to reduce operating costs. Load transfer can be done with different methods, such as incentive plans or load management on the demand side. This modeling is a mixed integer linear programming. As mentioned, the model presented in this article is linear, so it is necessary to linearize the nonlinear equations. In this modeling, a method called "the Cartesian" method is used for linearization. After linearization of the nonlinear equations, this problem has been solved by GAMS software using the CPLEX solver. The results show that the proposed model has a significant impact on reducing operating costs and air pollution. In other words, devices such as electric heaters, combined heat and power, and power-to-gas units could reduce operating costs by 22% by converting energy carriers into different forms of energy, thereby significantly reducing pollutant emissions into the air so that, in the presence of the power-to-gas unit, the amount of pollution in the air can be decreased by 26%.
Keywords: Air pollution, Demand response program, Electricity to gas, Energy hub, Hydrogen loads
Full-Text [PDF 1315 kb]   (498 Downloads)    
Type of Study: Research |
Received: 2022/02/10 | Accepted: 2023/01/16 | Published: 2023/02/22
References
1. Alipour, M., Zare, K., & Abapour, M. (2017). MINLP probabilistic scheduling model for demand response programs integrated energy hubs. IEEE Transactions on Industrial Informatics, 14(1), 79-88. [DOI:10.1109/TII.2017.2730440]
2. Amiri, S., & Honarvar, M. (2018). Providing an integrated Model for Planning and Scheduling Energy Hubs and preventive maintenance. Energy, 163, 1093-1114. [DOI:10.1016/j.energy.2018.08.046]
3. Eveloy, V., & Gebreegziabher, T. (2018). A review of projected power-to-gas deployment scenarios. Energies, 11(7), 1824. [DOI:10.3390/en11071824]
4. Gazijahani, F. S., Ravadanegh, S. N., & Salehi, J. (2018). Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies. ISA transactions, 73, 100-111. [DOI:10.1016/j.isatra.2017.12.004]
5. Gazijahani, F. S., & Salehi, J. (2017). Robust design of microgrids with reconfigurable topology under severe uncertainty. IEEE Transactions on Sustainable Energy, 9(2), 559-569. [DOI:10.1109/TSTE.2017.2748882]
6. Gazijahani, F. S., & Salehi, J. (2018). Integrated DR and reconfiguration scheduling for optimal operation of microgrids using Hong's point estimate method. International Journal of Electrical Power & Energy Systems, 99, 481-492. [DOI:10.1016/j.ijepes.2018.01.044]
7. Geidl, M., Koeppel, G., Favre-Perrod, P., Klockl, B., Andersson, G., & Frohlich, K. (2006). Energy hubs for the future. IEEE power and energy magazine, 5(1), 24-30. [DOI:10.1109/MPAE.2007.264850]
8. Gu, C., Tang, C., Xiang, Y., & Xie, D. (2019). Power-to-gas management using robust optimisation in integrated energy systems. Applied Energy, 236, 681-689. [DOI:10.1016/j.apenergy.2018.12.028]
9. Hou, W., Liu, Z., Ma, L., & Wang, L. (2020). A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling. Sustainable Cities and Society, 62, 102417. [DOI:10.1016/j.scs.2020.102417]
10. Huang, W., Zhang, N., Yang, J., Wang, Y., & Kang, C. (2017). Optimal configuration planning of multi-energy systems considering distributed renewable energy. IEEE Transactions on Smart Grid, 10(2), 1452-1464. [DOI:10.1109/TSG.2017.2767860]
11. Huo, D., Gu, C., Ma, K., Wei, W., Xiang, Y., & Le Blond, S. (2018). Chance-constrained optimization for multienergy hub systems in a smart city. IEEE Transactions on Industrial Electronics, 66(2), 1402-1412. [DOI:10.1109/TIE.2018.2863197]
12. Jiang, Y., & Guo, L. (2019). Research on wind power accommodation for an electricity-heat-gas integrated microgrid system with power-to-gas. IEEE Access, 7, 87118-87126. [DOI:10.1109/ACCESS.2019.2924577]
13. Juanwei, C., Tao, Y., Yue, X., Xiaohua, C., Bo, Y., & Baomin, Z. (2019). Fast analytical method for reliability evaluation of electricity-gas integrated energy system considering dispatch strategies. Applied Energy, 242, 260-272. [DOI:10.1016/j.apenergy.2019.03.106]
14. Kienzle, F., Favre-Perrod, P., Arnold, M., & Andersson, G. (2008). Multi-energy delivery infrastructures for the future. 2008 First international conference on infrastructure systems and services: building networks for a brighter future (INFRA), [DOI:10.1109/INFRA.2008.5439681]
15. Lewandowska-Bernat, A., & Desideri, U. (2018). Opportunities of power-to-gas technology in different energy systems architectures. Applied Energy, 228, 57-67. [DOI:10.1016/j.apenergy.2018.06.001]
16. Liu, T., Zhang, D., Wang, S., & Wu, T. (2019). Standardized modelling and economic optimization of multi-carrier energy systems considering energy storage and demand response. Energy Conversion and Management, 182, 126-142. [DOI:10.1016/j.enconman.2018.12.073]
17. Ma, L., Liu, N., Zhang, J., & Wang, L. (2018). Real-time rolling horizon energy management for the energy-hub-coordinated prosumer community from a cooperative perspective. IEEE Transactions on Power Systems, 34(2), 1227-1242. [DOI:10.1109/TPWRS.2018.2877236]
18. Mohammadi, M., Noorollahi, Y., Mohammadi-Ivatloo, B., & Yousefi, H. (2017). Energy hub: from a model to a concept-a review. Renewable and Sustainable Energy Reviews, 80, 1512-1527. [DOI:10.1016/j.rser.2017.07.030]
19. Moradi, S., Ghaffarpour, R., Ranjbar, A. M., & Mozaffari, B. (2017). Optimal integrated sizing and planning of hubs with midsize/large CHP units considering reliability of supply. Energy Conversion and Management, 148, 974-992. [DOI:10.1016/j.enconman.2017.06.008]
20. Pazouki, S., Haghifam, M.-R., & Moser, A. (2014). Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response. International Journal of Electrical Power & Energy Systems, 61, 335-345. [DOI:10.1016/j.ijepes.2014.03.038]
21. Qu, K., Yu, T., Huang, L., Yang, B., & Zhang, X. (2018). Decentralized optimal multi-energy flow of large-scale integrated energy systems in a carbon trading market. Energy, 149, 779-791. [DOI:10.1016/j.energy.2018.02.083]
22. Šumbera, J. (2012). Modelling generator constraints for the self-scheduling problem. Vedecký seminár doktorandu FIS-únor.
23. Wang, X., Liu, Y., Liu, C., & Liu, J. (2020). Coordinating energy management for multiple energy hubs: From a transaction perspective. International Journal of Electrical Power & Energy Systems, 121, 106060. [DOI:10.1016/j.ijepes.2020.106060]
24. Wang, Y., Wang, X., Yu, H., Huang, Y., Dong, H., Qi, C., & Baptiste, N. (2019). Optimal design of integrated energy system considering economics, autonomy and carbon emissions. Journal of Cleaner Production, 225, 563-578. [DOI:10.1016/j.jclepro.2019.03.025]
25. Wang, Y., Zhang, N., Kang, C., Kirschen, D. S., Yang, J., & Xia, Q. (2017). Standardized matrix modeling of multiple energy systems. IEEE Transactions on Smart Grid, 10(1), 257-270. [DOI:10.1109/TSG.2017.2737662]
26. Weng, Y.-T., & Hsu, Y.-Y. (2016). Reactive power control strategy for a wind farm with DFIG. Renewable energy, 94, 383-390. [DOI:10.1016/j.renene.2016.03.072]
27. Xu, Z., Guan, X., Jia, Q.-S., Wu, J., Wang, D., & Chen, S. (2012). Performance analysis and comparison on energy storage devices for smart building energy management. IEEE Transactions on Smart Grid, 3(4), 2136-2147. [DOI:10.1109/TSG.2012.2218836]
28. Zhang, K., Zhou, B., Li, C., Voropai, N., Li, J., Huang, W., & Wang, T. (2021). Dynamic modeling and coordinated multi-energy management for a sustainable biogas-dominated energy hub. Energy, 220, 119640. [DOI:10.1016/j.energy.2020.119640]
29. Zhang, X., Chan, K., Wang, H., Hu, J., Zhou, B., Zhang, Y., & Qiu, J. (2019). Game-theoretic planning for integrated energy system with independent participants considering ancillary services of power-to-gas stations. Energy, 176, 249-264. [DOI:10.1016/j.energy.2019.03.154]
30. Zhang, X., & Yu, T. (2019). Fast stackelberg equilibrium learning for real-time coordinated energy control of a multi-area integrated energy system. Applied Thermal Engineering, 153, 225-241. [DOI:10.1016/j.applthermaleng.2019.02.053]


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Namvar A, Salehi J, Taghizadegan Kalantari N. Optimal operation of energy hub with the presence of electricity to gas technology and hydrogen loads, considering demand response programs and air pollution. ieijqp 2022; 11 (4) :15-27
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Volume 11, Issue 4 (11-2022) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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