This paper presents a stochastic programming approach, using a risk measure defined by conditional value-at-risk, for trading solar energy in a market environment under uncertainty. Uncertainties on electricity price and solar irradiation are considered through a set of scenarios computed by simulation and scenario-reduction. The short-term operation problem of a concentrating solar power plant is formulated as a mixed-integer linear program, which allows modelling the discrete status of the plant. To improve the operational productivity of the plant during the non-insulation periods, energy storage systems are considered. The goal is to obtain the optimal operation planning that maximizes the total expected profits while evaluating trading risks. For risk evaluation, the conditional value-at-risk is used to maximize the expected profits of the least profitable scenarios. A case study is used to illustrate the usefulness and the proficiency of the proposed approach.