Two-Stage Distributionally Robust Optimization for Secure Operation of Provincial Power Grids with High Renewable Penetration
Abstract
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.