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Self-scheduling of a generating company with an EV load aggregator under an energy exchange strategy

Tavakoli, A, Negnevitsky, M ORCID: 0000-0002-5130-419X, Saha, S, Haque, ME, Arif, MT, Contreras, J and Oo, A 2019 , 'Self-scheduling of a generating company with an EV load aggregator under an energy exchange strategy' , IEEE Transactions on Smart Grid, vol. 10, no. 4 , pp. 4253-4264 , doi: 10.1109/TSG.2018.2854763.

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This paper investigates an energy exchange strategy between a generating company (GenCO) and an electric vehicle load aggregator (EVLA) in the energy and ancillary services markets. The impact of the proposed strategy on the schedule ofgeneration, EV charging, payoff, and offer prices is discussed, especially when renewable energy and EV penetration grow. An optimal self-scheduling problem for a GenCO together with an EVLA and renewable generation units under an energyexchange strategy is presented. In the proposed method, offer prices and EV tariffs under a price-maker approach are calculated by simulating the market operator clearing process and considering uncertainties corresponding to the renewableforecasting errors and the driving patterns of EV owners. A stochastic intra-hour bi-level problem is developed for the upper and lower levels. In the upper level, a firm which owns conventional and wind generation plus EVLA maximizes the profit, while the lower-level problems correspond to the market clearings. The bi-level problem is solved as a mixed-integer linear program (MILP) by the CPLEX solver. Results show that the energy exchange strategy under flexible EV tariffs results inan increase of the renewable energy penetration and the profitability of the GenCO.

Item Type: Article
Authors/Creators:Tavakoli, A and Negnevitsky, M and Saha, S and Haque, ME and Arif, MT and Contreras, J and Oo, A
Keywords: energy exchange, renewable energy, electric vehicle, bi-level model, energy and ancillary services markets
Journal or Publication Title: IEEE Transactions on Smart Grid
Publisher: IEEE
ISSN: 1949-3053
DOI / ID Number: 10.1109/TSG.2018.2854763
Copyright Information:

Copyright 2018 IEEE.

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