Rashidizadeh-Kermani, H., Najafi, H., Anvari-Moghaddam, A., M. Guerrero, J. (2018). Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets. Journal of Operation and Automation in Power Engineering, 6(2), 157-168. doi: 10.22098/joape.2006.3608.1288

H. Rashidizadeh-Kermani; H. R. Najafi; A. Anvari-Moghaddam; J. M. Guerrero. "Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets". Journal of Operation and Automation in Power Engineering, 6, 2, 2018, 157-168. doi: 10.22098/joape.2006.3608.1288

Rashidizadeh-Kermani, H., Najafi, H., Anvari-Moghaddam, A., M. Guerrero, J. (2018). 'Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets', Journal of Operation and Automation in Power Engineering, 6(2), pp. 157-168. doi: 10.22098/joape.2006.3608.1288

Rashidizadeh-Kermani, H., Najafi, H., Anvari-Moghaddam, A., M. Guerrero, J. Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets. Journal of Operation and Automation in Power Engineering, 2018; 6(2): 157-168. doi: 10.22098/joape.2006.3608.1288

Optimal Decision Making Framework of an Electric Vehicle Aggregator in Future and Pool markets

^{1}Department of Electrical & Computer Engineering, University of Birjand, Birjand, Iran

^{2}Department of Energy Technology, Aalborg University, Aalborg, Denmark

Abstract

Electric vehicle (EV) aggregator, as an agent between the electricity market and EV owners, participates in the future and pool market to supply EVs’ requirement. Because of the uncertain nature of pool prices and EVs’ behaviour, this paper proposed a two-stage scenario-based model to obtain optimal decision making of an EV aggregator. To deal with mentioned uncertainties, the aggregator’s risk aversion is applied using conditional value at risk (CVaR) method in the proposed model. The proposed two-stage risk-constrained decision-making problem is applied to maximize EV aggregator’s expected profit in an uncertain environment. The aggregator can participate in the future and pool market to buy the required energy of EVs and offer optimal charge/discharge prices to the EV owners. In this model, in order to assess the effects of EVs owners’ reaction to the aggregator’s offered prices on the purchases from electricity markets, a sensitivity analysis over risk factor is performed. The numerical results demonstrate that with the application of the proposed model, the aggregator can supply EVs with lower purchases from markets.

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