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In the electricity market environment, it is very important for generation companies (GENCOs) to make the optimal mid-term generation operation planning (MTGOP) which includes the trading strategies in the spot market and the contract market as well as the suitable unit maintenance scheduling (UMS). In making the decision of MTGOP, GENCOs are subject to risk due to uncertain factors, and hence should manage the inevitable risk rationally. Given this background, a new MTGOP model is first developed for a GENCO as a price taker so as to maximize its profit and minimize its risk measured by the conditional value-at-risk (CVaR). In this model, the bilateral physical contracts are taken into consideration, together with the transmission congestion and the operation constraints of generating units. Then, a solving method is given by integrating the genetic algorithm and the Monte Carlo method. Finally, a numerical example is used to show the features of the proposed method.