This paper proposes a bi-probability-interval optimization (BPIO) model for wind-solar power day-ahead scheduling (WSP-DAS) under uncertainties, which aims to obtain the optimal trade-off planning by balancing profit and risk brought by uncertain wind power and solar power penetration. First, the confidence intervals of wind power and solar power are formulated under given confidence levels of wind speed and solar radiation, respectively. Then, the distribution probabilities of each wind power and solar power are obtained accordingly based on the cumulative distribution function (CDF) of wind speed and solar radiation, respectively. Finally, the framework of BPIO based WSP-DAS is developed to balance the profit and risk, considering a conditional expectation based optimization objective. Comparative experiments are conducted on two day-ahead scheduling systems under the dynamic uncertain wind and solar power penetration. The empirical results fully demonstrate that the proposed BPIO can significantly improve the reliability and effectiveness of evaluating WSP-DAS, in terms of obtaining a trade-off planning between profit and risk against the integration of uncertain wind-solar power. (C) 2017 Elsevier Ltd. All rights reserved.