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The paper presents the optimization algorithm which may eventually be used by electric energy suppliers to coordinate charging and discharging of electric-drive vehicles (EDVs) exploited in electricity markets. The research is focused on a day-ahead market and a provision of system regulation in an ancillary-service market. The proposed optimization minimizes the charging costs that can be partly compensated with profits obtained from participation in the energy markets. Due to the stochastic nature of transportation patterns, the Monte Carlo simulation is applied to model uncertainties presented by numerous scenarios. To reduce the problem complexity, the simulated driving patterns are not individually considered in the optimization but clustered into fleets using the GAMS/SCENRED tool. Uncertainties of energy requirements in the market and energy prices are presented by statistical central moments that are further considered in Hong's 2 - point +1 estimation method in order to define points considered in the optimization. Finally, each energy supplier has to offer competitive energy prices to EDV users for transportation. Due to uncertainties, the final prices cannot be deterministically calculated; thus, the paper proposes the risk-based approach applying value at risk. Case studies illustrate the application of the proposed optimization in achieving competitive prices for EDV users.