Representative days and hours with piecewise linear transitions for power system planning.

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. • Transmission and generation expansion problems are often based on time series. • Time series aggregation methods are used to reduce computational complexity. • We propose to use representative days with adaptively selected representative hours. • The efficiency is improved by using a piecewise linear dispatch representation. • Long-duration energy storages are modeled by tracking the daily state of charge. [ABSTRACT FROM AUTHOR]