TY - GEN
T1 - Heuristic Kalman Algorithm applied to the Dynamic AC Transmission Network Expansion Planning
AU - Torres Contreras, Santiago Patricio
AU - Chillogalli, Jose E.
AU - Llivisaca Mejía, Mateo David
AU - Romero, Ruben R.
AU - Ergun, Hakan
AU - Van Hertem, Dirk
AU - Torres Contreras, Santiago Patricio
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/10/6
Y1 - 2025/10/6
N2 - The transmission network is an essential part of the power system for delivering generated power to load centers, primarily through an interconnected system of transmission lines. Significant investment is required for network expansion to maintain system reliability and address congestion issues. Transmission Network Expansion Planning (TNEP) is thus essential and involves complex and large-scale mathematical and computational challenges. This research introduces a Memetic Heuristic Kalman Algorithm (HKA) combined with the Variable Neighborhood Search (VNS) meta-heuristic to solve static and dynamic TNEP. This approach is based on the AC model of the transmission network, integrating reactive power compensation planning to enhance the system’s performance. Tests were conducted on the 6-bus Garver test system, demonstrating the algorithm’s effectiveness in addressing the nonlinear nature of AC networks and underscoring the importance of advanced optimization techniques.
AB - The transmission network is an essential part of the power system for delivering generated power to load centers, primarily through an interconnected system of transmission lines. Significant investment is required for network expansion to maintain system reliability and address congestion issues. Transmission Network Expansion Planning (TNEP) is thus essential and involves complex and large-scale mathematical and computational challenges. This research introduces a Memetic Heuristic Kalman Algorithm (HKA) combined with the Variable Neighborhood Search (VNS) meta-heuristic to solve static and dynamic TNEP. This approach is based on the AC model of the transmission network, integrating reactive power compensation planning to enhance the system’s performance. Tests were conducted on the 6-bus Garver test system, demonstrating the algorithm’s effectiveness in addressing the nonlinear nature of AC networks and underscoring the importance of advanced optimization techniques.
KW - AC model
KW - heuristic Kalman algorithm
KW - memetic algorithms
KW - transmission network expansion planning
KW - AC model
KW - Heuristic Kalman algorithm
KW - Memetic algorithms
KW - Transmission network
KW - Expansion planning
UR - https://www.scopus.com/pages/publications/105019304318
UR - https://ieeexplore.ieee.org/abstract/document/11180338
UR - https://www.mendeley.com/catalogue/023de342-0b1b-3ba8-b568-4875bd5c38e7/
U2 - 10.1109/PowerTech59965.2025.11180338
DO - 10.1109/PowerTech59965.2025.11180338
M3 - Contribución a la conferencia
SN - 9798331543976
T3 - IEEE Kiel PowerTech, PowerTech 2025
SP - 1
EP - 6
BT - 2025 IEEE Kiel PowerTech, PowerTech 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE Kiel PowerTech, PowerTech 2025
Y2 - 29 June 2025 through 3 July 2025
ER -