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

TL;DR

This paper introduces a hybrid multi-area approach for power system planning that uses a novel piecewise linear demand and supply model to accurately capture demand variability and extreme values with fewer representative days and hours.

Contribution

It presents an optimization-based representative extraction method that improves intraday and interday data chronology capturing over traditional clustering techniques.

Findings

Higher precision in data chronology preservation.

Reduced approximation errors with piecewise linear modeling.

Effective representation of demand and supply variability.

Abstract

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.