Mohebbi-Gharavanlou, M., Nojavan, S., Zareh, K. (2019). Energy management of virtual power plant to participate in the electricity market using robust optimization. Journal of Operation and Automation in Power Engineering, (), -. doi: 10.22098/joape.2019.5362.1400

M. Mohebbi-Gharavanlou; S. Nojavan; K. Zareh. "Energy management of virtual power plant to participate in the electricity market using robust optimization". Journal of Operation and Automation in Power Engineering, , , 2019, -. doi: 10.22098/joape.2019.5362.1400

Mohebbi-Gharavanlou, M., Nojavan, S., Zareh, K. (2019). 'Energy management of virtual power plant to participate in the electricity market using robust optimization', Journal of Operation and Automation in Power Engineering, (), pp. -. doi: 10.22098/joape.2019.5362.1400

Mohebbi-Gharavanlou, M., Nojavan, S., Zareh, K. Energy management of virtual power plant to participate in the electricity market using robust optimization. Journal of Operation and Automation in Power Engineering, 2019; (): -. doi: 10.22098/joape.2019.5362.1400

Energy management of virtual power plant to participate in the electricity market using robust optimization

^{1}Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

^{2}Department of Electrical Engineering, University of Bonab, Bonab, Iran.

Abstract

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.

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