TY - GEN
T1 - Graph partitioning-based clustering for the planning of distribution network topology using spatial- temporal load forecasting
AU - Zambrano-Asanza, S.
AU - Cando, Diego J.
AU - Chuqui, Freddy H.
AU - Sanango, Juan
AU - Franco, John F.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/15
Y1 - 2021/9/15
N2 - Planning the expansion and the new topology of distribution networks requires knowing the location and characterization of the load as well as its future growth. Spatial load forecasting is a key tool in this task, providing high spatial resolution and adequate temporal granularity. Nowadays, with the penetration of distributed energy resources, multiple microgrid connection strategies, and implementation of self-healing and protection schemes, it is necessary to identify load blocks to plan the new active network architecture. Based on spatial load forecasting information, this paper proposes a graph partitioning technique to create load clusters in the distribution feeders. A weighted graph is constructed by means of a minimum spanning tree that allows to consider adjacency relations. The results of the simulation, carried out in a real distribution network, have demonstrated the effectiveness of the proposed method.
AB - Planning the expansion and the new topology of distribution networks requires knowing the location and characterization of the load as well as its future growth. Spatial load forecasting is a key tool in this task, providing high spatial resolution and adequate temporal granularity. Nowadays, with the penetration of distributed energy resources, multiple microgrid connection strategies, and implementation of self-healing and protection schemes, it is necessary to identify load blocks to plan the new active network architecture. Based on spatial load forecasting information, this paper proposes a graph partitioning technique to create load clusters in the distribution feeders. A weighted graph is constructed by means of a minimum spanning tree that allows to consider adjacency relations. The results of the simulation, carried out in a real distribution network, have demonstrated the effectiveness of the proposed method.
KW - Clustering
KW - Distribution planning
KW - Graph partitioning
KW - Microgrids
KW - Minimal spanning tree
KW - Spatial load forecasting
UR - https://www.scopus.com/pages/publications/85117610692
U2 - 10.1109/ISGTLatinAmerica52371.2021.9543010
DO - 10.1109/ISGTLatinAmerica52371.2021.9543010
M3 - Contribución a la conferencia
AN - SCOPUS:85117610692
T3 - 2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021
BT - 2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021
Y2 - 15 September 2021 through 17 September 2021
ER -