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The liberalization of electricity markets and the rapid establishment of renewable power sources in the power system increases the need for decision support tools for planning, trading and operation. A suitable approach to model such decision problems is the use of stochastic optimization, where uncertain parameters, e.g. prices, are represented by a scenario tree. A scenario tree consists of possible outcomes of the stochastic prices. This paper describes a probabilistic model of real-time balancing prices. Real-time here refers to market places where balance power is traded with the TSO close to real-time. The proposed model reflects real-time markets applying hourly marginal pricing with different prices for upward and downward balancing. Further, the model is aimed for Monte Carlo simulation and generation of scenario trees. The model is based on nonlinear time series processes and use hourly day-ahead spot prices and real-time balancing demands as exogenous variables. By adjusting these prices and demands, future production mixes can be considered.