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An algorithm facilitating automated purchase of energy on real-time balancing market is proposed in this paper. The main inputs of the algorithm are a prediction of the area control error, its uncertainty in the form of a standard deviation and a set of previously procured ancillary services (contracted power reserves) available for power balance control. The algorithm evaluates all possible power balance scenarios for the closest hour in a probabilistic manner and gives a cost-optimal recommendation for the real-time balancing market demand. A case study on realistic data for the Czech Republic control area was performed by simulation of the power balance control with a Monte Carlo simulator of the Czech transmission system. The results showed potential for savings on the regulation energy costs.