Climate change is progressively reshaping the spatiotemporal dynamics of renewable energy sources such as wind and solar, intensifying the complexity and uncertainty of long-term power system planning. Existing planning frameworks are largely focused on climate mitigation strategies but often overlook the critical dimension of climate adaptation, limiting their efficacy in managing evolving climatic risks. In response, this article proposes a multistage stochastic low-carbon planning framework that incorporates climate-related uncertainties into system planning decision-making. By embedding climate evolution trajectories into the planning horizon, the proposed approach determines optimal stage-wise planning pathways that jointly accommodate mitigation goals and adaptation imperatives under long-term climate uncertainties. First, a systematic climate uncertainty modeling approach is developed to capture both scenario uncertainty and climate response uncertainty through the construction of a representative scenario tree. Second, to reconcile the temporal mismatch between coarse-resolution climate projections and the finegrained requirements of power system planning, a climate-consistent temporal downscaling method is proposed to transform long-term climate projections into high-resolution, hourly level data. Third, to address the computational complexity inherent in the multistage planning problem, a tailored decomposition-based stochastic dual dynamic programming algorithm is developed, which operates on a stage-wise clustered scenario tree to leverage the tree’s structural compactness for accelerated convergence and scalable optimization under climate-related uncertainties. Numerical studies demonstrate that the proposed climate-adaptive planning framework enhances the power system’s ability to manage climate-induced risks while maintaining cost-effectiveness across a wide range of plausible climate futures.

Following the recent devastating blackouts in North America, UK and Italy, blackout prevention has attracted significant attention, though it is known as a notoriously difficult task. To prevent the blackout, it is essential to accurately predict the instable status of power network components. In the large-scale power network however, existing analysis tools fail to perform accurate and in-time prediction of component instability, because of the sophisticated structure of real-world power networks and the huge amount of system variables to be analyzed. To prevent the blackout, we need an accurate and efficient method that (a) can discover interesting features and patterns relevant to the blackout, from the highly complex structure and ten thousands of system variables of a power network, and (b) can give accurate and fast prediction of system instability whenever required, so that the network operator can take necessary actions in time. In this paper, we report our tool developed for power network instability prediction. The proposed method consists of two major stages. In the first stage,a novel type of patterns namely Local Correlation Network Pattern (LCNP) is mined from the structure and system variables of the power network. Correlation rules, which are useful for the network operator to locate potentially instable components, can be further generated from the LCNP. In the second stage, a kernel based network classification method is developed to predict the system instability. By testing on a real world power network (the New England system), we demonstrate that the proposed tool is effective in predicting system instability and thus highly useful for blackout prevention.