To address the difficulty in accurately characterizing the fault current response of renewable energy grid-connected devices during high-/low-voltage ride-through, this paper proposes a fault current response modeling and parameter identification method based on adaptive nonlinear compensation. First, with the fault voltage and pre-fault operating point as input variables, a basic quadratic equivalent model is established to describe the main variation characteristics of active and reactive currents during high-/low-voltage ride-through. Second, nonlinear compensation terms are introduced into the basic model to correct the response deviation caused by the simplification of fast electromagnetic control links in the electromechanical transient equivalent process, thereby improving the representation capability of the model for complex fault current characteristics. Furthermore, considering that the structural parameters of the nonlinear compensation terms are difficult to directly identify using the traditional least squares method, a differential evolution–ridge regression (DE–Ridge) hierarchical identification method is proposed. In this method, the differential evolution algorithm is used in the outer layer to adaptively optimize the nonlinear structural parameters, while ridge regression is used in the inner layer to solve the corresponding linear coefficients. Case study results show that, compared with the traditional quadratic equivalent model and the fixed nonlinear compensation model, the proposed method further reduces the fault current identification error on the validation set and improves the identification accuracy and generalization capability of fault current responses during high-/low-voltage ride-through.

Provincial power grids with high wind and photovoltaic penetration face poor out-of-sample security under limited data, whereas robust scheduling can be overly conservative. This study develops an integrated scheduling framework for secure provincial-grid operation using established two-stage Wasserstein distributionally robust optimization. The first stage determines the day-ahead schedule and reserves flexibility. After renewable forecast errors are observed, the second stage adjusts generation, renewable curtailment, reserve deployment, and energy storage charging and discharging. A Wasserstein ambiguity set is constructed from synchronized error samples. The model jointly enforces plant output, ramping, line flow, incremental loss, reserve, and storage state constraints. Dual reformulation and linearization yield a mixed-integer linear program. For the four selected day-specific cases, the estimated reductions in power-shortage frequency relative to sample-based stochastic optimization are 61.24%, 67.39%, 39.01%, and 56.11% in spring, summer, autumn, and winter, respectively. The maximum winter reserve shortfall decreases from 32.69 MW to 5.51 MW, accompanied by lower real-time balancing requirements. Compared with the robust optimization benchmark, the reported schedules and ex post revenue profiles indicate greater revenue opportunities for some renewable generators. These results demonstrate the framework’s potential to balance operational security and renewable generation revenue opportunities in provincial-grid scheduling under limited data.