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Energy management of virtual power plant to participate in the electricity market using robust optimization | ||
| Journal of Operation and Automation in Power Engineering | ||
| مقاله 15، دوره 8، شماره 1، اردیبهشت 2020، صفحه 43-56 اصل مقاله (1.17 M) | ||
| نوع مقاله: Research paper | ||
| شناسه دیجیتال (DOI): 10.22098/joape.2019.5362.1400 | ||
| نویسندگان | ||
| M. Mohebbi-Gharavanlou1؛ S. Nojavan* 2؛ K. Zareh1 | ||
| 1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. | ||
| 2Department of Electrical Engineering, University of Bonab, Bonab, Iran. | ||
| چکیده | ||
| Virtual power plant (VPP) can be studied to investigate how energy is purchased or sold in the presence of electricity market price uncertainty. The VPP uses different intermittent distributed sources such as wind turbine, flexible loads, and locational marginal prices (LMPs) in order to obtain profit. VPP should propose bidding/offering curves to buy/sell from/to day-ahead market. In this paper, robust optimization approach is proposed to achieve the optimal offering and bidding curves which should be submitted to the day-ahead market. This paper uses mixed-integer linear programming (MILP) model under GAMS software based on robust optimization approach to make appropriate decision on uncertainty to get profit which is resistance versus price uncertainty. The offering and bidding curves of VPP are obtained based on derived data from results. The proposed method, due to less computing, is also easy to trace the problem for the VPP operator. Finally, the price curves are obtained in terms of power for each hour, which operator uses the benefits of increasing or decreasing market prices for its plans. Also, results of comparing deterministic and RO cases are presented. Results demonstrate that profit amount in maximum robustness case is reduced 25.91 % and VPP is resisted against day-ahead market price uncertainty. | ||
| کلیدواژه‌ها | ||
| Virtual power plant؛ Electricity market uncertainty؛ Robust optimization approach؛ Bidding and offering curves؛ Distributed energy resources | ||
| مراجع | ||
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[1] S. Nojavan, K. Zare, and B. Mohammadi-Ivatloo, “Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program,” Appl. Energy, vol. 187, pp. 449-464, 2017.
[2] A. Hatefi einaddin, 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, no. 1, pp. 1-10, 2017.
[3] S. H. Hosseinian, P. Karimyan, R. Khatami, and M. Abedi, “Stochastic approach to represent distributed energy resources in the form of a virtual power plant in energy and reserve markets,” IET Gener. Transm. Distrib., vol. 10, no. 8, pp. 1792–1804, 2016.
[4] H. M. Samakoosh; M. Jafari-Nokandi; A. Sheikholesl-ami, “Coordinated resource scheduling in a large scale virtual power plant considering demand response and energy storages”, J. Oper. Autom. Power Eng., vol. 6, no. 1, pp. 50-60, 2018.
[5] A. Hasankhani and G. B. Gharehpetian, “Virtual power plant management in presence of renewable energy resources,” Proce. 2016 24th Iran. Conf. Electr. Eng. ICEE 2016, pp. 665-669, 2016.
[6] M. Peik-Herfeh, H. Seifi, and M. K. Sheikh-El-Eslami, “Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method,” Int. J. Electr. Power Energy Syst., vol. 44, no. 1, pp. 88-98, 2013.
[7] M. Peik-Herfeh, H. Seifi, and M. Kazem Sheikh-El-Eslami, “Two-stage approach for optimal dispatch of distributed energy resources in distribution networks considering virtual power plant concept,” Int. Trans. Electr. Energy Syst., vol. 24, no. 1, pp. 43-63, Jan. 2014.
[8] S. M. Nosratabadi, R. A. Hooshmand, and E. Gholipour, “A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems,” Renew. Sustain. Energy Rev., vol. 67, pp. 341–363, 2017.
[9] N. Pourghaderi, M. Fotuhi-Firuzabad, M. Kabirifar, and M. Moeini-Aghtaie, “Energy scheduling of a technical virtual power plant in presence of electric vehicles,” Proce. 2017 25th Iran. Conf. Electr. Eng. ICEE 2017, pp. 1193-1198, 2017.
[10] S. Ghavidel, L. Li, J. Aghaei, T. Yu, and J. Zhu, “A review on the virtual power plant: Components and operation systems,” Proce. 2016 IEEE Int. Conf. Power Syst. Technol., 2016.
[11] T. Adefarati and R. C. Bansal, “Reliability and economic assessment of a microgrid power system with the integration of renewable energy resources,” Appl. Energy, vol. 206, pp. 911-933, 2017.
[12] M. Shabanzadeh, M. K. Sheikh-El-Eslami, and M. R. Haghifam, “A medium-term coalition-forming model of heterogeneous DERs for a commercial virtual power plant,” Appl. Energy, vol. 169, pp. 663-681, 2016.
[13] M. A. Rostami and M. Raoofat, “Optimal operating strategy of virtual power plant considering plug-in hybrid electric vehicles load,” Int. Trans. Electr. Energy Syst., vol. 26, no. 2, pp. 236-252, Feb. 2016.
[14] S. Skarvelis-Kazakos, E. Rikos, E. Kolentini, L. M. Cipcigan, and N. Jenkins, “Implementing agent-based emissions trading for controlling Virtual Power Plant emissions,” Electr. Power Syst. Res., vol. 102, pp. 1-7, 2013.
[15] A. Mehdizadeh, N. Taghizadegan, and J. Salehi, “Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management,” Appl. Energy, vol. 211, pp. 617-630, 2018.
[16] V. Davatgaran, M. Saniei, and S. S. Mortazavi, “Optimal bidding strategy for an energy hub in energy market,” Energy, vol. 148, pp. 482-493, 2018.
[17] M. E. Wainstein, R. Dargaville, and A. Bumpus, “Social virtual energy networks: Exploring innovative business models of prosumer aggregation with virtual power plants,” Proce. 2017 IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf., 2017.
[18] H. Steiniger, “Virtual Power Plants: Bringing the Flexibility of Decentralized Loads and Generation to Power Markets,” Innov. Disrupt. Grid’s Edge How Distrib. Energy Resour. are Disrupting Util. Bus. Model, pp. 331-362, 2017.
[19] O. Arslan and O. E. Karasan, “Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks,” Energy, vol. 60, pp. 116-124, 2013.
[20] M. Shabanzadeh, M. K. Sheikh-El-Eslami, and M. R. Haghifam, “The design of a risk-hedging tool for virtual power plants via robust optimization approach,” Appl. Energy, vol. 155, pp. 766-777, 2015.
[21] M. Mazidi, H. Monsef, and P. Siano, “Robust day-ahead scheduling of smart distribution networks considering demand response programs,” Appl. Energy, vol. 178, pp. 929-942, 2016.
[22] S. Rahmani-Dabbagh and M. K. Sheikh-El-Eslami, “A profit sharing scheme for distributed energy resources integrated into a virtual power plant,” Appl. Energy, vol. 184, pp. 313-328, 2016.
[23] H. Bai, S. Miao, X. Ran, and C. Ye, “Optimal dispatch strategy of a virtual power plant containing battery switch stations in a unified electricity market,” Energies, vol. 8, no. 3, pp. 2268-2289, 2015.
[24] M. Majidi, S. Nojavan, and K. Zare, “Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program,” Energy Convers. Manag., vol. 144, pp. 132-142, 2017.
[25] A. Shayegan-Rad, A. Badri, and A. Zangeneh, “Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties,” Energy, vol. 121, pp. 114-125, 2017.
[26] S. Nojavan and H. A. Aalami, “Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program,” Energy Convers. Manag., vol. 103, pp. 1008-1018, 2015.
[27] S. Nojavan, B. Mohammadi-Ivatloo, and K. Zare, “Robust optimization based price-taker retailer bidding strategy under pool market price uncertainty,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 955-963, 2015.
[28] J. F. Toubeau, Z. De Grève, and F. Vallée, “Medium-Term Multimarket Optimization for Virtual Power Plants: A Stochastic-Based Decision Environment,” IEEE Trans. Power Syst., vol. 33, no. 2, pp. 1399-1410, 2018.
[29] A. A. Thatte, D. E. Viassolo, and L. Xie, “Robust bidding strategy for wind power plants and energy storage in electricity markets,” IEEE Power Energy Soc. Gen. Meet., 2012.
[30] S. Nojavan, K. Zare, and B. Mohammadi-Ivatloo, “Robust bidding and offering strategies of electricity retailer under multi-tariff pricing,” Energy Econ., vol. 68, pp. 359-372, 2017.
[31] J. M. Buhmann, M. Mihalak, R. Sramek, and P. Widmayer, “Robust optimization in the presence of uncertainty,” dl.acm.org, p. 505, 2013.
[32] D. Bertsimas and M. Sim, “Robust discrete optimization and network flows,” Math. Program., vol. 98, no. 1-3, pp. 49-71, Sep. 2003.
[33] R. Dominguez, L. Baringo, and A. J. Conejo, “Optimal offering strategy for a concentrating solar power plant,” Appl. Energy, vol. 98, pp. 316-325, 2012.
[34] “Pg_Tca30Bus.” [Online]. Available: https://www2.e-e.washington.edu/research/pstca/pf30/pg_tca30bus.hm, [Accessed: 19-Apr-2019], “404,” /404.
[35] “2002.” [Online]. Available: https://www.iea.org-/newsroom/news/2002/.[Accessed: 19-Apr-2019].
[36] Y. Wang, X. Ai, Z. Tan, L. Yan, and S. Liu, “Interactive dispatch modes and bidding strategy of multiple virtual power plants based on demand response and game theory,” IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 510-519, 2016.
[37] A. Baringo and L. Baringo, “A Stochastic Adaptive Robust Optimization Approach for the Offering Strategy of a Virtual Power Plant,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3492-3504, 2017.
[38] D. Koraki and K. Strunz, “Wind and solar power integration in electricity markets and distribution networks through service-centric virtual power plants,” IEEE Trans. Power Syst., vol. 33, no. 1, pp. 473-485, 2018. | ||
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