On the Improvement of representative demand curves via a hierarchical agglomerative clustering for power transmission network investment

This paper introduces an optimal clustering-based strategy to gain representative demand curves from hourly demand data that allow determining the power transmission network investment by solving the transmission expansion planning (TEP) problem. The proposed approach also provides a high dimensionality data optimal reduction for the representative demand curves that feed the TEP problem. The key idea behind this strategy is to extract demand patterns from the electric power system demand data through the implementation of a hierarchical agglomerative clustering algorithm (HACA) based on the Elbow's rule and a linkage criterion, such as Ward's variance. Then, a 24-h demand pattern is provided by following three different grouping strategies: seasonal, monthly, and weekly. As a second stage, this strategy includes the TEP formulation together with the transmission losses' linearised model aiming to test the representative demand curves achieved by HACA. To illustrate the efficiency, application, and superior functionality of the proposal, this is implemented over the IEEE 118-node network under several case studies. To determine the most appropriate approach, the results are compared with the well-known K-means method.

Automated summary

This study introduces an optimal clustering strategy to extract representative demand curves from hourly demand data, aiming to solve the transmission expansion planning problem. The proposed approach provides an efficient reduction of high dimensionality data for the TEP problem through the implementation of HACA algorithm. Results show that the method demonstrates high efficiency and superior functionality when implemented on the IEEE 118-node network, outperforming the K-means method.