With the rapid growth of renewable energy integration, power systems are facing increasing uncertainty and variability in operation. The intermittent and uncontrollable nature of wind and solar generation requires operational decisions to anticipate future fluctuations, creating strong temporal coupling across days. This leads to large-scale mixed-integer linear programming (MILP) with a large number of binary variables, which is computationally intensive—especially in year-long simulations. As a result, there is a growing need for efficient modeling approaches that can reduce complexity while preserving key temporal features. This paper proposes a temporal–spatial acceleration framework for long-term power system operation simulation. In the temporal dimension, a monthly K-means clustering algorithm is applied to reconstruct typical scenario days from 8760 h time series, preserving the characteristics of seasonal and intraday variability. In the spatial dimension, thermal units with similar characteristics are aggregated, and binary decision variables are relaxed into continuous variables, transforming the MILP into a tractable LP model, and thereby reducing computational burden. Case studies are performed based on the six-bus and the IEEE RTS-79 systems to validate the framework, being able to provide a practical solution for renewable-integrated power system planning and dispatch applications.
Wang et al. (2025) studied this question.