Estimation of Grid Reinforcement Costs Triggered by Future Grid Customers: Influence of the Quantification Method (Scaling vs. Large-Scale Simulation) and Coincidence Factors (Single vs. Multiple Application)
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In: Energies : open-access journal of related scientific research, technology development and studies in policy and management, Vol. 15.2022, No. 4, 1383, 14.02.2022.
Research output: Contribution to journal › Article › Research › peer-review
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T1 - Estimation of Grid Reinforcement Costs Triggered by Future Grid Customers: Influence of the Quantification Method (Scaling vs. Large-Scale Simulation) and Coincidence Factors (Single vs. Multiple Application)
AU - Thormann, Bernd
AU - Kienberger, Thomas
PY - 2022/2/14
Y1 - 2022/2/14
N2 - he integration of future grid customers, e.g., electric vehicles, heat pumps, or photovoltaic modules, will challenge existing low-voltage power grids in the upcoming years. Hence, distribution system operators must quantify future grid reinforcement measures and resulting costs early. On this account, this work initially evaluates different methods to quantify future grid reinforcement needs, applied by the current state of research. Thereby, it indicates the significance of large-scale grid simulations, i.e., simulating several thousand low-voltage grids, to quantify grid reinforcements accurately. Otherwise, a selected area’s total grid reinforcement costs might be misjudged significantly. Due to its fast application, deterministic grid simulations based on coincidence factors are most commonly used in the current state of research to simulate several thousand grids. Hence, in the second step, recent studies’ approaches to applying grid customers’ coincidence factors are evaluated: While simplified approaches allow fast simulation of numerous grids, they underestimate potential grid congestion and grid reinforcement costs. Therefore, a fully automated large-scale grid simulation tool is developed in this work to allow the simulation of multiple grids applying grid customers’ coincidence factors appropriately. As a drawback, the applied deterministic framework only allows an estimation of future grid reinforcement costs. Detailed determination of each grid’s grid reinforcement costs requires time-resolved grid simulations.
AB - he integration of future grid customers, e.g., electric vehicles, heat pumps, or photovoltaic modules, will challenge existing low-voltage power grids in the upcoming years. Hence, distribution system operators must quantify future grid reinforcement measures and resulting costs early. On this account, this work initially evaluates different methods to quantify future grid reinforcement needs, applied by the current state of research. Thereby, it indicates the significance of large-scale grid simulations, i.e., simulating several thousand low-voltage grids, to quantify grid reinforcements accurately. Otherwise, a selected area’s total grid reinforcement costs might be misjudged significantly. Due to its fast application, deterministic grid simulations based on coincidence factors are most commonly used in the current state of research to simulate several thousand grids. Hence, in the second step, recent studies’ approaches to applying grid customers’ coincidence factors are evaluated: While simplified approaches allow fast simulation of numerous grids, they underestimate potential grid congestion and grid reinforcement costs. Therefore, a fully automated large-scale grid simulation tool is developed in this work to allow the simulation of multiple grids applying grid customers’ coincidence factors appropriately. As a drawback, the applied deterministic framework only allows an estimation of future grid reinforcement costs. Detailed determination of each grid’s grid reinforcement costs requires time-resolved grid simulations.
U2 - 10.3390/en15041383
DO - 10.3390/en15041383
M3 - Article
VL - 15.2022
JO - Energies : open-access journal of related scientific research, technology development and studies in policy and management
JF - Energies : open-access journal of related scientific research, technology development and studies in policy and management
SN - 1996-1073
IS - 4
M1 - 1383
ER -