TY - THES

T1 - A Decomposition Method for the Deterministic Flow Refueling Location Problem (DFRLP)

AU - Höller, Marcel

N1 - no embargo

PY - 2024

Y1 - 2024

N2 - The transport sector is a significant driver of climate change and is responsible for a substantial proportion of global CO2 emissions. Battery electric vehicles offer a promising solution for reducing emissions, but require a well-developed charging network for widespread acceptance. The Deterministic Flow Refueling Location Problem (DFRLP) deals with optimizing the placement of charging stations considering traffic flows in order to maximize the coverage of charging demand. This thesis addresses the combination of two existing extensions of the DFRLP. These consider the sizing of charging stations with limited capacity, as well as the cost heterogeneity in urban, suburban and rural areas. A problem-specific decomposition method is developed and applied to efficiently solve this extended DFRLP. The developed decomposition method decomposes a given graph by removing the edges with the smallest traffic volume until the graph is decomposed into smaller clusters to which the extended DFRLP can be efficiently applied. The effectiveness of the decomposition method is demonstrated through extensive numerical experiments. The results show that the solution quality is close to the optimal solution of the full data sets with a significant reduction in runtime. This work contributes to the optimization of electric vehicle charging infrastructure and provides a practical tool for decision making in the field of transportation planning. The proposed method can assist decision makers, infrastructure planners and private investors in optimizing the placement and sizing of charging stations to enable a sustainable transportation future.

AB - The transport sector is a significant driver of climate change and is responsible for a substantial proportion of global CO2 emissions. Battery electric vehicles offer a promising solution for reducing emissions, but require a well-developed charging network for widespread acceptance. The Deterministic Flow Refueling Location Problem (DFRLP) deals with optimizing the placement of charging stations considering traffic flows in order to maximize the coverage of charging demand. This thesis addresses the combination of two existing extensions of the DFRLP. These consider the sizing of charging stations with limited capacity, as well as the cost heterogeneity in urban, suburban and rural areas. A problem-specific decomposition method is developed and applied to efficiently solve this extended DFRLP. The developed decomposition method decomposes a given graph by removing the edges with the smallest traffic volume until the graph is decomposed into smaller clusters to which the extended DFRLP can be efficiently applied. The effectiveness of the decomposition method is demonstrated through extensive numerical experiments. The results show that the solution quality is close to the optimal solution of the full data sets with a significant reduction in runtime. This work contributes to the optimization of electric vehicle charging infrastructure and provides a practical tool for decision making in the field of transportation planning. The proposed method can assist decision makers, infrastructure planners and private investors in optimizing the placement and sizing of charging stations to enable a sustainable transportation future.

KW - Electromobility

KW - E-Mobility

KW - Charging Stations

KW - Deterministic Flow Refueling Location Problem

KW - DFRLP

KW - Decomposition

KW - ILP

KW - Elektromobilität

KW - E-Mobilität

KW - Ladestationen

KW - Deterministic Flow Refueling Location Problem

KW - DFRLP

KW - Dekomposition

KW - ILP

U2 - 10.34901/mul.pub.2024.124

DO - 10.34901/mul.pub.2024.124

M3 - Master's Thesis

ER -