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This paper proposes a novel method for analytically assessing the value of renewable forecasting accuracy in power system operation. Different from previous assessment methods where renewable forecasting accuracy is perturbed numerically, the proposed method directly derives sensitivity formulae of the operation model with respect to (w.r.t.) renewable forecasting error distribution change. Compared with conventional analytical sensitivity methods dealing with only deterministic parameters, deriving such sensitivity formulae is challenging and necessitates the closed-form expression of forecasting error distribution parameters in the operation model. To tackle this challenge, we propose to model and reformulate the two-stage stochastic unit commitment for wind-penetrated power systems based on the Point Estimate Method (PEM). Then, leveraging the explicitly expressed wind power statistical moments in the PEM-based model, the sensitivity formulae of expected operation costs w.r.t. both global and nodal wind power forecasting error distribution change are derived. The proposed method provides to assess the value of renewable forecasting accuracy and identify critical nodes with uncertain injections that are most influential on system operation without exhausting numerical simulations. Case studies on modified IEEE systems verify the effectiveness of the proposed method.