Large-scale grid integration of renewable energy increases the uncertainty and volatility of power systems, which brings difficulties to output planning and reserve decision-making of power system units. In this paper, we innovatively combined the non-parametric kernel density estimation method and scenario method to describe the uncertainty of renewable energy outputs, and obtained a representative set of renewable energy output scenarios. In addition, we proposed a new method to determine the reserve capacity demand. Further, we derived the quantitative relationship between the reserve demand and the power system reliability index, which was used as the constraint condition of a day-ahead power generation dispatch. Finally, a coordinated dispatching model of power generation and reserve was established, which had the lowest penalty for curtailment of wind power and photovoltaic, as well as the lowest total operating cost for thermal power units, gas power units, and pumped storage power station. By simulating three different working conditions, the proposed model was compared with the traditional deterministic model. Results showed that our proposed method significantly improved system efficiency while maintaining system reliability.