  Electric demand and renewable power are highly variable, and the solution of
a planning model relies on capturing this variability. This paper proposes a
hybrid multi-area method that effectively captures both the intraday and
interday chronology of real data considering extreme values, using a limited
number of representative days, and time points within each day. An
optimization-based representative extraction method is proposed to improve
intraday chronology capturing. It ensures higher precision in preserving data
chronology and extreme values than hierarchical clustering methods. The
proposed method is based on a piecewise linear demand and supply
representation, which reduces approximation errors compared to the traditional
piecewise constant formulation. Additionally, sequentially linked day blocks
with identical representatives, created through a mapping process, are employed
for interday chronology capturing. To evaluate the efficiency of the proposed
method, a comprehensive expansion co-planning model is developed, including
transmission lines, energy storage systems, and wind farms.
