Energy generation in district heating (DH) systems is usually done in combined heat and power (CHP) units which can efficiently produce both useful heat and electric power. There can also exist heat only boilers, electric heaters, heat pumps and heat storage tanks. The coupling of heat and power generation in the CHP units and the possibility to store heat for later use makes the short-term operation scheduling of such systems quite challenging. Furthermore, big DH systems produce power that is sold in the electricity markets. This makes the operation scheduling problem even more complex as the uncertainty of the electricity prices in the markets should be considered. To make optimal decisions under uncertainty, various mathematical optimization tools were developed, such as stochastic programming and robust optimization. In this paper, an approach based on a Monte Carlo simulation is followed. Initially, a model of DH system short-term operation and power trading is mathematically formulated. Then, this model is used to run a Monte Carlo simulation for a case study system where the values of stochastic parameters are simulated using autoregressive models. Results demonstrate that simulation is fast, taking 300-400 runs to converge. A comparison of two system configurations shows that the use of heat storage increases the daily expected profit by 11%. Finally, the electricity price volatility in this case study is such that mainly two CHP units are operating for most of the time.