Voltage constraint-oriented management of low carbon technologies in a large-scale distribution network

Şengör, İbrahim; Mehigan, Laura; Zehir, Mustafa Alparslan; Cuenca, Juan; Geaney, Ciaran; Hayes, Barry

doi:10.1016/j.jclepro.2023.137160

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…The network analysis tool GAIA was employed to carry out the grid simulations, and the case studies took into account the hourly (day/night) and seasonal (summer/winter) load profiles. Sengor et al [19] presented a variety of voltage constraint-oriented management algorithms to identify potential under-voltage problems caused by the widespread use of HPs, EVs, PVs, and BSS and, eventually, increase the grid's hosting capacity; the investigation was conducted in a real MV/LV grid located in Ireland, considering different penetration levels of these loads. Damianakis et al [20] evaluated the influence of diverse grid regions (urban, suburban, and rural), seasons (winter/summer), and both singular and combined EV-HP-PV penetrations on the voltage deviations and line/transformer overloadings in six real Dutch LV networks.…”

Section: Introductionmentioning

confidence: 99%

Systematic Evaluation of Possible Maximum Loads Caused by Electric Vehicle Charging and Heat Pumps and Their Effects on Common Structures of German Low-Voltage Grids

Fakhrooeian,

Pitz,

Scheppat

2024

WEVJ

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In this paper, we present a comprehensive assessment of the effects of residential loads, electric vehicles (EVs), and electric heat pumps (HPs) on low-voltage (LV) grids in urban, suburban, and rural areas of Germany. Firstly, real data are used to determine the typical structures for each LV grid region. Secondly, nine scenarios are defined with different levels of EV and HP penetration. Thirdly, the Low Voltage Load Flow Calculation in the DIgSILENT PowerFactory is performed for all scenarios while taking the simultaneity factor (SF) for each load type into consideration to calculate the minimum voltage and maximum loadings of transformer and lines in each grid; this allows for the grid’s potential bottlenecks to be identified. The network simulations are carried out with the consideration of charging powers of 11 kW and 22 kW in order to evaluate how an increasing EV load in the future may affect the grid’s parameters. To the best of our knowledge, no study in the literature has simultaneously addressed all of the aforementioned topics. The results of this study provide a useful framework that distribution system operators (DSOs) may apply to anticipate the forthcoming challenges and figure out when grid reinforcement will be required.

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Section: Introductionmentioning

confidence: 99%