Multi-Stage Stochastic Planning for Transmission Systems Considering Dynamic Frequency Stability

The increasing penetration of renewable generations (RGs) poses significant challenges to the operation of transmission power systems, especially for not only steady-stage uncertainties but also dynamic-state stability. Hence, the symmetry of RGs and synchronous generations (SGs) should be improved by a proper planning strategy. For this purpose, a multi-stage stochastic planning method is proposed for transmission power systems (TPSs) considering dynamic frequency stability. Specifically, the progressive uncertainties of RGs over stages are addressed with a multi-stage stochastic method with nonanticipativity constraints. In this scheme, the uncertainty is represented by probability-weighted scenarios, and the planning decisions are only dependent on current realizations of RG uncertainties according to nonanticipativity constraints to improve planning efficiency. Moreover, the dynamic frequency stability constraints are derived considering the frequency regulation strategy of RGs, and these nonconvex constraints are further linearized with high accuracy by a maximal affine function-based method to improve the compatibility with the planning model. Subsequently, a progressive hedging algorithm (PHA) is designed to reduce the computational burden by decomposing the multi-scenario high-dimensional model into multiple small-scale models. Finally, the effectiveness of the proposed method, as well as its superiority over existing approaches, are validated through numerical experiments.