Impact of different electric vehicle charging strategies onto required distribution grid reinforcement

Matrose, Claas; Helmschrott, Thomas; Gödde, Markus; Szczechowicz, Eva; Schnettler, Armin

doi:10.1109/itec.2012.6243435

Cited by 6 publications

(10 citation statements)

References 3 publications

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“…Simulation of representative grid structures and scaling of their results: [5,7,10,19-25] -synthetic grids modeled based on real-life grid data [5,19,22-24] -real-life grids selected from the area of investigation [7,10,20,21,25] -classification of grids into representative classes [5,7,19,20] Large-scale grid simulation of numerous real-life grid models [6,10,26] Others perform load flow simulations in all grids in the investigated area. The total amount of grid reinforcement measures equals the sum of all grids' results (Section 2.2).…”

Section: Methods To Quantify Total Grid Reinforcement Costs Studiesmentioning

confidence: 99%

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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)

Thormann

Kienberger

2022

Energies

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The 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.

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Section: Methods To Quantify Total Grid Reinforcement Costs Studiesmentioning

confidence: 99%

“…Matrose et al [21] investigated grid reinforcement costs caused by EV charging in five countries. On both the medium-voltage (MV) and LV levels, most representative grids are chosen by experts for grid planning and operation.…”

Section: Simulation Of Representative Grid Structures and Scaling Of ...mentioning

confidence: 99%

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)

Thormann

Kienberger

2022

Energies

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“…To remove the network constraints, the DSO is required to reinforce the lines of grid. The measures for grid reinforcement can be installation of an additional cable, or replacement of overhead lines with higher crosssection cables, or reactive power compensation ( [9]). All of them have considerable costs for the DSO.…”

Section: Voltage Issue On An Active Distuibution Gridmentioning

confidence: 99%

Valorization of demand response for voltage control in MV distribution grids with distributed generation

Petit

2016

2016 IEEE International Energy Conference (ENERGYCON)

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This paper presents a methodology for the contribution of demand response in reducing the cost of voltage control in radial distribution grid with distributed generators (DG). The bidirectional power flow in the active distribution network can introduce voltage issues that impede the integration of DG. Due to this fact, the distribution system operator (DSO) has to enhance the lines in the grid or curtail the power output of DG in case of constraint. The possibility of using the flexibility of demand to cope with the voltage constraint is investigated in this paper. The results are promising that by using the demand response, it is able to reduce the active curtailment of DG and postpone the investment of grid reinforcement.

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“…However, as EV penetration increases, the extra demand due to EV charging will have a considerable impact on the design and operation of electrical power systems [4]. In [5][6][7][8][9][10], it has been shown that the widespread adoption of EVs could negatively impact the distribution network if charging is not properly coordinated. Grid impacts of uncoordinated charging include, but are not limited to, increased voltage imbalance [7], increased grid losses [8], overloading [6], fluctuation of grid frequencies [11], and increased harmonic distortion [12].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown in [4,10] that by using suitable charging strategies, it is possible to mitigate some of the adverse impacts of charging, which could in turn reduce or postpone the need for infrastructure reinforcement. To date, there have been several different strategies proposed for charging groups of EVs connected to low-voltage distribution networks [9,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Residential electrical vehicle charging strategies: the good, the bad and the ugly

Liu

McNamara

Shorten

et al. 2015

J. Mod. Power Syst. Clean Energy

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In recent years, a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles (EVs). In this paper, we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system. Using this framework and a realistic distribution network simulation testbed, we provide a comparative evaluation of a range of different residential EV charging strategies, highlighting in each case positive and negative characteristics.

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Impact of different electric vehicle charging strategies onto required distribution grid reinforcement

Cited by 6 publications

References 3 publications

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)

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)

Valorization of demand response for voltage control in MV distribution grids with distributed generation

Residential electrical vehicle charging strategies: the good, the bad and the ugly

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