This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedule is carried out by a bilevel mathematical programming approach. The upper-level determines energy and spinning reserve schedule by the maximization of profit subject to all lower-level problems. Lower-level problems minimize the post-contingency power output. A controllable degree of trust on the schedule is introduced based on n - K security criterion for worst-case contingency. The approach uses duality theory and problem approximations for a conversion into an equivalent mixed-integer linear programming problem. A case study is presented to illustrate the effectiveness of the approach for power producers not only with transmission constraints, but also valuing safekeeping on the day-ahead schedule to ensure a degree trust on the satisfaction of compromises within electricity markets. (c) 2015 Elsevier Ltd. All rights reserved.