# Representative Days for Expansion Decisions in Power Systems

## TL;DR

This paper introduces a modified K-means clustering method to select representative days for power system expansion planning, effectively capturing extreme load and renewable production values to improve decision accuracy.

## Contribution

A novel K-means based approach that emphasizes extreme data points for better modeling of uncertainties in power system expansion planning.

## Key findings

- The proposed method better captures extreme load and renewable production values.
- It improves the accuracy of expansion planning decisions.
- The method outperforms traditional K-means in case study simulations.

## Abstract

Short-term uncertainty should be properly modeled when the expansion planning problem in a power system is analyzed. Since the use of all available historical data may lead to intractability, clustering algorithms should be applied in order to reduce computer workload without renouncing accuracy representation of historical data. In this paper, we propose a modified version of the traditional K-means method that seeks to attain the representation of maximum and minimum values of input data, namely, the electric load and the renewable production in several locations of an electric energy system. The crucial role of depicting extreme values of these parameters lies in the fact that they can have a great impact on the expansion and operation decisions taken. The proposed method is based on the traditional K-means algorithm that represents the correlation between electric load and wind-power production. Chronology of historical data, which influences the performance of some technologies, is characterized though representative days, each one composed of 24 operating conditions. A realistic case study based on the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System is analyzed applying representative days and comparing the results obtained using the traditional K-means technique and the proposed method.