پیوندهای مفید
آمار
| تعداد نشریات | 26 |
| تعداد شماره‌ها | 306 |
| تعداد مقالات | 2,643 |
| تعداد مشاهده مقاله | 3,969,436 |
| تعداد دریافت فایل اصل مقاله | 2,673,801 |
Optimal Operation Management of Grid-connected Microgrid Using Multi-Objective Group Search Optimization Algorithm | ||
| Journal of Operation and Automation in Power Engineering | ||
| مقاله 12، دوره 5، شماره 2، اسفند 2017، صفحه 227-239 اصل مقاله (1009.58 K) | ||
| نوع مقاله: Research paper | ||
| شناسه دیجیتال (DOI): 10.22098/joape.2017.3659.1290 | ||
| نویسندگان | ||
| H. Shayeghi* 1؛ E. Shahryari2 | ||
| 1Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran. | ||
| 2Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran | ||
| چکیده | ||
| Utilizing distributed generations (DGs) near load points has introduced the concept of microgrid. However, stochastic nature of wind and solar power generation as well as electricity load makes it necessary to utilize an energy management system (EMS) to manage hourly power of microgrid and optimally supply the demand. As a result, this paper utilizes demand response program (DRP) and battery to tackle this difficulty. To do so, an incentive-based DRP has been utilized and the effects of applying DRP on microgrid EMS problem have been studied. The objective functions of microgrid EMS problem include the total cost and emission. These metrics are combined in a multi-objective formulation and solved by the proposed multi-objective group search optimization (MOGSO) algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using fuzzy decision making (FDM) technique. Studies have been employed on a test microgrid composed of a wind turbine, photovoltaic, fuel cell, micro turbine and battery while it is connected to the upper-grid. Simulation results approve the efficiency of the proposed method in hourly operation management of microgrid components. | ||
| کلیدواژه‌ها | ||
| Microgrid؛ demand response program؛ MOGSO؛ Fuzzy decision making؛ wind turbine | ||
| مراجع | ||
|
[1] C. Yin, H. Wu, F. Locment, and M. Sechilariu, “Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor,” Energy Convers. Manage., vol. 132, pp. 14-27, 2017. [2] E. Hossain, E. Kabalci, R. Bayindir, and R. Perez, “Microgrid testbeds around the world: State of art,” Energy Convers. Manage., vol. 86, pp. 132-153, 2014. [3] A. Deihimi, B. Keshavarz Zahed, and R. Iravani, “An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm,” Energy, vol. 106, pp. 482-509, 2016. [4] E. E. Sfikas, Y. A. Katsigiannis, and P. S. Georgilakis, “Simultaneous capacity optimization of distributed generation and storage in medium voltage microgrids,” Int. J. Electr. Power Energy Syst., vol. 67, pp. 101-113, 2015. [5] M. Marzband, F. Azarinejadian, M. Savaghebi, and J. M. Guerrero, “An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with markov chain,” IEEE Syst. J., vol. PP, pp. 1-11, 2015. [6] T. Wu, Q. Yang, Z. Bao, and W. Yan, “Coordinated energy dispatching in microgrid with wind power generation and plug-in electric vehicles,” IEEE Trans. Smart Grid, vol. 4, pp. 1453-1463, 2013. [7] J. Garcia-Gonzalez, R. M. R. d. l. Muela, L. M. Santos, and A. M. Gonzalez, “stochastic joint optimization of wind generation and pumped-storage units in an electricity market,” IEEE Trans. Power Syst., vol. 23, pp. 460-468, 2008. [8] L. Guo, W. Liu, X. Li, Y. Liu, B. Jiao, W. Wang, “Energy management system for stand-alone wind-powered-desalination microgrid,” IEEE Trans. Smart Grid, vol. 7, pp. 1079-1087, 2016. [9] M. Motevasel and A. R. Seifi, “Expert energy management of a micro-grid considering wind energy uncertainty,” Energy Convers. Manage., vol. 83, pp. 58-72, 2014. [10] C.S. Karavas, G. Kyriakarakos, K. G. Arvanitis, and G. Papadakis, “A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous poly generation microgrids,” Energy Convers. Manage., vol. 103, pp. 166-179, 2015. [11] S. A. Alavi, A. Ahmadian, and M. Aliakbar-Golkar, “Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method,” Energy Convers. Manage., vol. 95, pp. 314-325, 2015. [12] M. A. Fotouhi Ghazvini, J. Soares, N. Horta, R. Neves, R. Castro, and Z. Vale, “A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers,” Appl. Energy, vol. 151, pp. 102-118, 2015. [13] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Demand response modeling considering Interruptible/ Curtailable loads and capacity market programs,” Appl. Energy, vol. 87, pp. 243-250, 2010. [14] L. Wang, Q. Li, R. Ding, M. Sun, and G. Wang, “Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach,” Energy, vol. 130, pp. 1-14, 2017. [15] M. Mazidi, A. Zakariazadeh, S. Jadid, and P. Siano, “Integrated scheduling of renewable generation and demand response programs in a microgrid,” Energy Convers. Manage., vol. 86, pp. 1118-1127, 2014. [16] G. R. Aghajani, H. A. Shayanfar, and H. Shayeghi, “Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management,” Energy Convers. Manage., vol. 106, pp. 308-321, 2015. [17] R. Wang, P. Wang, G. Xiao, and S. Gong, “Power demand and supply management in microgrids with uncertainties of renewable energies,” Int. J. Electr. Power Energy Syst., vol. 63, pp. 260-269, 2014. [18] H. Safamehr and A. Rahimi-Kian, “A cost-efficient and reliable energy management of a micro-grid using intelligent demand-response program,” Energy, vol. 91, pp. 283-293, 2015. [19] Y. Simmhan, S. Aman, A. Kumbhare, R. Liu, S. Stevens, Q. Zhou, “Cloud-based software platform for big data analytics in smart grids,” Comput. Sci. Eng., vol. 15, pp. 38-47, 2013. [20] W. Alharbi and K. Raahemifar, “Probabilistic coordination of microgrid energy resources operation considering uncertainties,” Electr. Power Syst. Res., vol. 128, pp. 1-10, 2015. [21] M. Marzband, M. Ghadimi, A. Sumper, and J. L. Domínguez-García, “Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode,” Appl. Energy, vol. 128, pp. 164-174, 2014. [22] B. Zhao, M. Xue, X. Zhang, C. Wang, and J. Zhao, “An MAS based energy management system for a stand-alone microgrid at high altitude,” Appl. Energy, vol. 143, pp. 251-261, 2015. [23] S. He, Q. H. Wu, and J. R. Saunders, “Group search optimizer: an optimization algorithm inspired by animal searching behavior,” IEEE Trans. Evol. Comput., vol. 13, pp. 973-990, 2009. [24] M. Moradi-Dalvand, B. Mohammadi-Ivatloo, A. Najafi, and A. Rabiee, “Continuous quick group search optimizer for solving non-convex economic dispatch problems,” Electr. Power Syst. Res., vol. 93, pp. 93-105, 2012. [25] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Modeling and prioritizing demand response programs in power markets,” Electr. Power Syst. Res., vol. 80, pp. 426-435, 2010. | ||
|
آمار تعداد مشاهده مقاله: 1,580 تعداد دریافت فایل اصل مقاله: 1,241 |
||