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Electric demand and renewable power are highly variable, and the solution ofa planning model relies on capturing this variability. This paper proposes ahybrid multi-area method that effectively captures both the intraday andinterday chronology of real data considering extreme values, using a limitednumber of representative days, and time points within each day. Anoptimization-based representative extraction method is proposed to improveintraday chronology capturing. It ensures higher precision in preserving datachronology and extreme values than hierarchical clustering methods. Theproposed method is based on a piecewise linear demand and supplyrepresentation, which reduces approximation errors compared to the traditionalpiecewise constant formulation. Additionally, sequentially linked day blockswith identical representatives, created through a mapping process, are employedfor interday chronology capturing. To evaluate the efficiency of the proposedmethod, a comprehensive expansion co-planning model is developed, includingtransmission lines, energy storage systems, and wind farms.

Representative Days and Hours with Piecewise Linear Transitions for Power System Planning