# Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

_{2}equivalent has to stay below 1000 gigatons (from 2000 to 2050) [1,2]. To achieve this goal, it is essential to decarbonise various aspects of daily life. The transportation sector for example is responsible for a large share of the total energy consumption. In Austria, for instance, its share is as high as 25% of the gross inland consumption (~400 TWh per year) [3]. The decarbonisation of the transport sector, which requires an immediate changeover to alternative power sources (e.g., battery or hydrogen powered vehicles) is a necessary step in the right direction. Although, the traffic related new registrations of private vehicles show an increasing share of alternative drive systems, around 90% are still using technology based on fossil fuels [4]. The change to alternative drive systems will be accompanied by an increasing demand of electrical energy, which must be satisfied by renewable sources in order to achieve the decarbonisation targets. The additional energy demand of electric vehicles (EVs) in combination with the decentralised energy production from renewable energy source (RES) can have challenging effects on the power grid (e.g., a reduction of grid stability and reduced security of supply) [5,6,7,8]. The analysis of the impact of a large amount of EVs as well as effects caused by an increasing amount of (decentralised) RES on the power grid is necessary in order to optimise grid expansion projects and to avoid grid instability.

## 2. Methods

#### 2.1. Development of Simplified Distribution Grid Models Based on a Cellular Approach

- Consideration of grid topology before geographic topology.
- Resulting grid model should correspond to a radial grid.
- Prevention of closed ring structure (use of lines with normally open points to connect two cells with each other).
- Existing rings in the reference grid should be combined in one cell.

#### 2.2. Determination of Load and Production Profiles

#### 2.3. Determination of Synthetic Charging Load Profiles of Electric Vehicles

- In charging strategy 1, the charging power is selected separately for each user group. All charging processes within a user group thus charge with the same charging power. For example, as is quite common with today’s charging stations, charging powers can vary between 3.7 and 50 kW.
- For charging strategy 2, the charging power is determined for each charging process based on a probability distribution function. This distribution function can, for example, be determined from the data of charging stations and therefore describes how many users have charged with which charging power.
- Charging strategy 3 simulates the possibility of reduced charging power under consideration of the available charging time, corresponding to the duration of stay. The determination of the reduced charging power takes place in step 3. Since each loading process is simulated independently of the previous or subsequent one, it is not possible at this point to shift the charging process within the duration of stay as part of this strategy. The influence on the time shift would have to be taken into account when preparing the distributions for arrival and departure times.

_{const})). At the changeover point (s = 80% SOC) constant voltage charging follows. In this charging phase, the charging current decreases automatically. The charging power as a function of the state of charge (SOC) is therefore calculated by Equation (7): [52]

_{L}is the correction factor, which considers the nominal battery capacity and the switch off of the charging power. It has to be mentioned that Equation (7) is only valid for lithium-ion batteries, which are widely used in EVs.

## 3. Results and Discussion

#### 3.1. Calculation Times

#### 3.2. Case Study: Impact of E-Mobility Integration

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Flowchart showing the five steps of the distribution grid model development using the cellular approach.

**Figure 2.**Schematic representation of the cell classification (Step 1). (

**a**) Topography of the artificial sample area; (

**b**) location and topology of the distribution grid; (

**c**) and cell classification.

**Figure 4.**Schematic representation of the principle of line implementation. (

**a**) “Interconnectors” and “missing elements”; (

**b**) energy nodes of the geographically located power grid model and their connection according to the reference grid.

**Figure 5.**Choice of cell boundaries with a negative influence on the cell-based grid model accuracy (

**a**) reference grid and (

**b**) developed grid model based on (

**a**), leading to a load flow shift. Optimised (

**c**) reference grid and (

**d**) developed grid model based on (

**c**).

**Figure 6.**Five kV reference grid including the selected cell division used to develop the simplified grid model.

**Figure 7.**Cell-based grid model representing the reference grid based on nine aggregated energy nodes shown in Figure 8.

**Figure 8.**Structure of the E-RLC modules for consideration of the reactive power losses of the “missing elements”.

**Figure 9.**Comparison of the (

**a**) absolute and (

**b**) relative deviation of the reactive power load flow with and without consideration of the reactive power losses of the “missing elements” for selected “interconnectors”.

**Figure 10.**Comparison of the absolute and relative deviations of the active power flows between grid model and reference grid.

**Figure 11.**Synthetic load profiles provided by e-control for ULA (hot water tank) and ULC (night heat storage tank), both without day recharging.

**Figure 13.**Original destination matrice according to Bosserhoff [49] and cumulative distribution function. (

**a**) Trip home; (

**b**) trip to work—shift operation.

**Figure 15.**(

**a**) 10 charging processes (CP); (

**b**) aggregation of the charging processes from (

**a**); (

**c**) aggregated charging curves of all charging processes within a cell per user group (UG); and (

**d**) aggregation of all charging curves.

**Figure 16.**Comparison duration of the load flow (

**a**) calculation time and (

**b**) calculation time per day of the reference grid with the grid model for different simulation periods—all calculations were performed with the same PC using the same software version (NEPLAN 5.5.6).

**Figure 18.**Month of occurrence for the maximum line utilisation of selected lines during the simulation period of one year.

**Table 1.**Comparison of the deviations with and without consideration of the reactive power losses of the “missing elements” for four selected lines.

L31_1 | L35_1 | L37_1 | L63_1 | ||||||
---|---|---|---|---|---|---|---|---|---|

Orig. ^{a} | Comp. ^{b} | Orig. ^{a} | Comp. ^{b} | Orig. ^{a} | Comp. ^{b} | Orig. ^{a} | Comp. ^{b} | ||

Q_{Flow} | (kvar) | −7 | −7 | 0 | 0 | 14 | 14 | −3 | −3 |

Reference grid | |||||||||

Q_{Flow} | (kvar) | 0 | −7 | 24 | 0 | 5 | 14 | −26 | −8 |

Grid model | |||||||||

Absolute deviation | (kvar) | −7 | 0 | −24 | 0 | −36 | 0 | 23 | 5 |

Relative deviation | (%) | 100 | 0 | - | 0 | −257 | 0 | −767 | −167 |

^{a}Original (Orig.): without E-RLC module;

^{b}Compensated (Comp.): with E-RLC module.

P_{imp} | Q_{imp} | P_{gen} | Q_{gen} | |
---|---|---|---|---|

(MW) | (Mvar) | (MW) | (Mvar) | |

Reference grid | 12.215 | 1.683 | 27.851 | 1.553 |

Grid model | 12.106 | 1.656 | 27.737 | 1.526 |

Absolute deviation | 0.109 | 0.027 | 0.114 | 0.027 |

Relative deviation | 0.89% | 1.60% | 0.41% ^{(a)} | 1.74% |

^{(a)}The relative deviation of 0.41% of the active power generated in the grid can be traced back to rounding errors that already occur when the loads are aggregated into energy nodes.

L35_1 | L37_1 | L62_1 | L63_1 | ||
---|---|---|---|---|---|

Reactive power flow—reference grid | (kW) | 1478 | −782 | 64 | 98 |

Reactive power flow—grid model | (kW) | 1455 | −756 | 28 | 151 |

Absolute deviation | (kW) | 23 | −26 | 36 | −53 |

Relative deviation | (%) | 2 | 3 | 56 | −54 |

Reference Grid | Grid Model | |
---|---|---|

Number of nodes | 148 | 9 |

Number of consumer/producer | 752/8 | 126/5 |

Number of sum load profiles ^{(a)} | 274 | 24 |

Modelling duration of annual sum load profiles ^{(b)} | 16 h 53 min | 2 h 54 min |

^{(a)}Sum load profiles of consumers and producers;

^{(b)}all calculations were performed with the same PC using the same software version (MATLAB R2017a).

**Table 5.**Maximum utilisation and their occurrence of the line L37_1 as well as the load, producer and the difference of load and producer of cell 7. All calculations are based on Data of the year 2014.

EV Penetration | (%) | 0 | 25 | 50 | 75 | 100 |
---|---|---|---|---|---|---|

Date | 31 March 08:00 | 3 April 08:00 | 31 March 21:30 | 23 August 18:00 | 23 August 18:00 | |

Utilisation of L37_1 | (%) | 83.733 | 68.957 | 57.456 | 93.468 | 134.860 |

Load of cell 7 | (MW) | 1.029 | 1.374 | 0.930 | 3.380 | 4.324 |

Producer of cell 7 | (MW) | 3.007 | 2.998 | 2.282 | 1.188 | 1.188 |

Load—Producer of cell 7 | (MW) | −1.978 | −1.624 | −1.352 | 2.192 | 3.136 |

**Table 6.**Duration of the utilisations over 80% and 100% for the four overloaded lines during the simulation period of one year.

EV Penetration (%) | Duration of the Utilisation | ||||||||
---|---|---|---|---|---|---|---|---|---|

L23_1 | L31_1 | L37_1 | L68_2 | ||||||

>80% | >100% | >80% | >100% | >80% | >100% | >80% | >100% | ||

0 | (h) | 0.00 | 0.00 | 0.00 | 0.00 | 16.00 | 0.00 | 0.00 | 0.00 |

25 | (h) | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

50 | (h) | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

75 | (h) | 0.00 | 0.00 | 4.00 | 0.00 | 17.00 | 0.00 | 94.00 | 16.75 |

100 | (h) | 163.00 | 14.00 | 92.00 | 5.75 | 121.00 | 58.00 | 995.00 | 216.50 |

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**MDPI and ACS Style**

Vopava, J.; Koczwara, C.; Traupmann, A.; Kienberger, T.
Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach. *Energies* **2020**, *13*, 39.
https://doi.org/10.3390/en13010039

**AMA Style**

Vopava J, Koczwara C, Traupmann A, Kienberger T.
Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach. *Energies*. 2020; 13(1):39.
https://doi.org/10.3390/en13010039

**Chicago/Turabian Style**

Vopava, Julia, Christian Koczwara, Anna Traupmann, and Thomas Kienberger.
2020. "Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach" *Energies* 13, no. 1: 39.
https://doi.org/10.3390/en13010039