پیوندهای مفید
آمار
| تعداد نشریات | 27 |
| تعداد شماره‌ها | 364 |
| تعداد مقالات | 3,222 |
| تعداد مشاهده مقاله | 4,739,797 |
| تعداد دریافت فایل اصل مقاله | 3,237,666 |
A Bi-Level Optimization Approach for Optimal Operation of Distribution Networks with Retailers and Micro-grids | ||
| Journal of Operation and Automation in Power Engineering | ||
| مقاله 8، دوره 8، شماره 1، اردیبهشت 2020، صفحه 15-21 اصل مقاله (568.51 K) | ||
| نوع مقاله: Research paper | ||
| شناسه دیجیتال (DOI): 10.22098/joape.2019.5432.1407 | ||
| نویسندگان | ||
| H. Fateh1؛ A. Safari2؛ S. Bahramara* 1 | ||
| 1Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran. | ||
| 2Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran. | ||
| چکیده | ||
| Distributed energy resources (DERs) including distributed generators (DGs) and controllable loads (CLs) are managed in the form of several microgrids (MGs) in active distributions networks (ADNs) to meet the demand locally. On the other hand, some loads of distribution networks (DNs) can be supplied by retailers which participate in wholesale energy markets. Therefore, there are several decision makers in DNs which their cooperation should be modeled for optimal operation of the network. For this purpose, a bi-level optimization approach is proposed in this paper to model the cooperation between retailers and MGs in DNs. In the proposed model, the aim of the upper level (leader) and lower level (follower) problems are to maximize the profit of retailers and to minimize the cost of MGs, respectively. To solve the proposed multi-objective bi-level optimization model, multi-objective Particle Swarm Optimization (MOPSO) algorithm is employed. The effectiveness of the proposed bi-level model and its solution methodology is investigated in the numerical results. | ||
| کلیدواژه‌ها | ||
| Bi-level Optimization؛ Micro-grids؛ Particle Swarm Optimization؛ Retailer | ||
| مراجع | ||
|
[1] A. Rastgou, J. Moshtagh, and S. Bahramara, “Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm,” J. Oper. Autom. Power Eng., vol. 6, pp. 111-125, 2018. [2] P. Sheikhahmadi, S. Bahramara, J. Moshtagh, and M. Yazdani Damavandi, “A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market,” Appl. Energy, vol. 214, pp. 24-38, 2018. [3] S. Bahramara, M. Yazdani-Damavandi, J. Contreras, M. Shafie-Khah, and J. P. Catalão, “Modeling the strategic behavior of a distribution company in wholesale energy and reserve markets,” IEEE Trans. Smart Grid, vol. 9, pp. 3857-3870, 2018. [4] A. Sadeghi Yazdankhah and R. Kazemzadeh, “Power management in a utility connected micro-grid with multiple renewable energy sources,” J. Oper. Autom. Power Eng., vol. 5, pp. 1-10, 2017. [5] A. A. Algarni and K. Bhattacharya, “A generic operations framework for discos in retail electricity markets,” IEEE Trans. Power Syst., vol. 24, pp. 356-367, 2009. [6] A. Safdarian, M. Fotuhi-Firuzabad, and M. Lehtonen, “A stochastic framework for short-term operation of a distribution company,” IEEE Trans. Power Syst., vol. 28, pp. 4712-4721, 2013. [7] C. Zhang, Q. Wang, J. Wang, M. Korpås, P. Pinson, J. Østergaard, “Trading strategies for distribution company with stochastic distributed energy resources,” Appl. Energy, vol. 177, pp. 625-635, 2016. [8] S. M. Larimi, M. Haghifam, and A. Ghadiri, “Determining the guaranteed energy purchase price for Distributed Generation in electricity distribution networks,” Util. Policy, vol. 41, pp. 118-127, 2016. [9] H. Haghighat and S. W. Kennedy, “A bilevel approach to operational decision making of a distribution company in competitive environments,” IEEE Trans. Power Syst., vol. 27, pp. 1797-1807, 2012. [10] R. Palma-Behnke, L. S. Vargas, and A. Jofré, “A distribution company energy acquisition market model with integration of distributed generation and load curtailment options,” IEEE Trans. Power Syst., vol. 20, pp. 1718-1727, 2005. [11] J. Vasiljevska, J. P. Lopes, and M. Matos, “Evaluating the impacts of the multi-microgrid concept using multicriteria decision aid,” Electr. Power Syst. Res., vol. 91, pp. 44-51, 2012. [12] N. Hatziargyriou, A. Anastasiadis, A. Tsikalakis, and J. Vasiljevska, “Quantification of economic, environmental and operational benefits due to significant penetration of Microgrids in a typical LV and MV Greek network,” Eur. Trans. Electr. Power., vol. 21, pp. 1217-1237, 2011. [13] A. K. Marvasti, Y. Fu, S. DorMohammadi, and M. Rais-Rohani, “Optimal operation of active distribution grids: A system of systems framework,” IEEE Trans. Smart Grid, vol. 5, pp. 1228-1237, 2014. [14] S. Bahramara, M. P. Moghaddam, and M. Haghifam, “A bi-level optimization model for operation of distribution networks with micro-grids,” Int. J. Electr. Power Energy Syst., vol. 82, pp. 169-178, 2016. [15] S. Bahramara, M. P. Moghaddam, and M. R. Haghifam, “Modelling hierarchical decision making framework for operation of active distribution grids,”IET Gener. Transm. Distrib., vol. 9, pp. 2555-2564, 2015. [16] H. Algarvio, F. Lopes, J. Sousa, and J. Lagarto, “Multi-agent electricity markets: Retailer portfolio optimization using Markowitz theory,” Electr. Power Syst. Res., vol. 148, pp. 282-294, 2017. [17] M. Khojasteh and S. Jadid, “Decision-making framework for supplying electricity from distributed generation-owning retailers to price-sensitive customers,” Util. Policy, vol. 37, pp. 1-12, 2015. [18] S. Nojavan, K. Zare, and B. Mohammadi-Ivatloo, “Risk-based framework for supplying electricity from renewable generation-owning retailers to price-sensitive customers using information gap decision theory,” Int. J. Electr. Power Energy Syst., vol. 93, pp. 156-170, 2017. [19] N. Mahmoudi, T. K. Saha, and M. Eghbal, “Modelling demand response aggregator behavior in wind power offering strategies,” Appl. Energy, vol. 133, pp. 347-355, 2014. [20] M. Zugno, J. M. Morales, P. Pinson, and H. Madsen, “A bilevel model for electricity retailers' participation in a demand response market environment,” Energy Econ., vol. 36, pp. 182-197, 2013. [21] M. Marzband, A. Sumper, A. Ruiz-Álvarez, J. L. Domínguez-García, and B. Tomoiagă, “Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets,” Appl. Energy, vol. 106, pp. 365-376, 2013. T. Lv, Q. Ai, and Y. Zhao, “A bi-level multi-objective optimal operation of grid-connected microgrids,” Electr. Power Syst. Res., vol. 131, pp. 60-70, 2016. [22] A. Rastgou, S. Bahramara, and J. Moshtagh, “Flexible and robust distribution network expansion planning in the presence of distributed generators,” Int. Trans. Electr. Energy Syst., p. e2637. [23] A. Rastgou, J. Moshtagh, and S. Bahramara, “Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators,” Energy, vol. 151, pp. 178-202, 2018. [24] M. Azaza and F. Wallin, “Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden,” Energy, vol. 123, pp. 108-118, 2017. [25] C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 8, pp. 256-279, 2004. [26] B. Colson, P. Marcotte, and G. Savard, “An overview of bilevel optimization,” Annal. Oper. Res., vol. 153, pp. 235-256, 2007. [27] D. E. Goldberg, “Genetic algorithm,” Search, Optim. Mach. Learn., pp. 343-349, 1989. [28] J. Horn, “Handbook of evolutionary computation,” Publishing Ltd Oxford University Press, England, 1997. | ||
|
آمار تعداد مشاهده مقاله: 1,696 تعداد دریافت فایل اصل مقاله: 1,554 |
||