# Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households

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## Abstract

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## 1. Introduction

#### 1.1. Background and Scope

#### 1.2. Structure

## 2. Innovation Compared to Previous Research

#### 2.1. Literature Review

#### 2.2. Innovation and Development in This Research

- The interdependency of mobility parameters such as commuting distance, travel behavior, and probability of vehicle usage for each day have been considered in the charging profile simulation using weighted distributions from the standard dataset package of the survey “Mobilität in Deutschland 2017” [22]. This allows a much more precise charging demand prediction than most previous studies.
- The division into spatial and socioeconomic mobility groups and the assignment of probability distributions of the mentioned parameters to these groups allow an exact spatial selection to specific households so that calculations for real low-voltage networks can be performed.
- The consideration of temperature and seasonal interdependencies and a consistent database for weather-dependent devices PV, household, and EVs inside the optimization framework allow a precise prediction of expected grid load.
- The complex optimization framework combined with the comprehensive databases for weather data, PV generation, EV charging profiles, household load, and future electricity prices enable a detailed understanding of implications on charging behavior and grid load.
- Future electricity price estimations from energy system optimization depict the potential user benefits for different charging strategies.

## 3. Materials and Methods

#### 3.1. Charging Strategies

#### 3.2. Charging Profile Generation

#### 3.2.1. Driving and Charging Profiles

#### 3.2.2. Impact of Temperature

#### 3.3. Input Data

#### 3.3.1. Electric Vehicle

#### 3.3.2. Photovoltaic

#### 3.3.3. Dynamic Electricity Price

#### 3.3.4. Household Load

#### 3.4. Optimization Model

## 4. Results

#### 4.1. Effects of Optimized Charging Configurations on Load Shifting

#### 4.2. Occurrence and Extent of Charging Peaks

#### 4.3. Economical Aspects on Households

#### 4.4. Effect on Grid Load Considering Aggregated Configuration with Household Load

## 5. Sensitivity Analysis

#### 5.1. Number of Households and EVs

#### 5.2. Yearly Driven Distance

#### 5.3. Electricity Price Volatility

## 6. Discussion and Outlook

- Home battery storage systems could reduce the grid load significantly further. This should be taken into account in future investigations.
- Further aspects of grid load should be addressed, especially voltage issues, that can be approached by the spread of maximum positive and negative load inside a grid area.
- However, in terms of system convenience, further feedback effects with the energy system and eventually with the transmission grid must be considered.
- An option to meet this demand would be direct central control of decentralized flexibility by an aggregator marketer. This could address both the challenges regarding market integration and congestion management.
- To address these questions of system design, further calculations with central control should be compared to the results in this study. Furthermore, in this context, the load and flexible operation of heat pumps should be considered.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

BTOEIt | Bundestarifordnung Elektrizität |

CMS | charging management system |

DSO | distribution system operator |

EEA | European Environment Agency |

EnWG | Energiewirtschaftsgesetz |

ESS | energy storage system |

EV | electric vehicle |

GHG | greenhouse gas emissions |

HEMS | home energy management system |

LCoE | levelized cost of electricity |

PV | photovoltaic |

RL | reinforcement learning |

SoC | state of charge |

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**Figure 2.**Procedure for the generation of the charging load time series and the grid load caused by the households.

**Figure 5.**Maximum possible charging load of all 100 EVs, based on the presence of EVs at their home parking lot and the maximum charging power of every single EV.

**Figure 11.**Aggregated average for EV charging energy and corrensponding PV generation and household load on different type days: “direct charging” and “PV optimized charging”.

**Figure 12.**Aggregated average for EV charging energy and corresponding electricity price on different type days: “direct charging”, “price optimized charging” and “combined price + PV optimized charging”.

**Figure 13.**Aggregated average for EV charging power throughout the year for the different charging configurations.

**Figure 14.**Aggregated maximum for EV charging power throughout the year for different charging configurations.

**Figure 16.**(

**Left**): Total charging costs for all charging configurations and the 100 vehicles. (

**Right**): Distribution of charging costs for all charging configurations and the 100 vehicles in EUR ct/kWh.

**Figure 17.**(

**Left**): Charging costs in EUR ct/kWh for all charging configurations sorted by ascending driven distance. (

**Right**): Percentage of the PV-utilized energy out of the total charging load for all charging configurations.

**Figure 18.**Aggregated maximum for grid load throughout the year for the different charging configurations.

**Figure 19.**Largest 1% values of sorted grid load for each charging configuration and household load minus PV generation. Annotations show the increase of grid load compared to household load minus PV generation.

**Figure 20.**Largest 1% of minimum values of sorted grid load for each charging configuration against household load. Annotations show the increase of grid load compared to household load.

**Figure 21.**Grid load of the aggregated pool of all 100 households for (

**a**) “direct charging” configuration, (

**b**) “PV optimized charging”, (

**c**) “price optimized charging”, (

**d**) “combined price + PV optimized charging”.

**Figure 22.**Sorted aggregated charging power for all charging configurations and peak shaving potential after cutting off 3% of energy (dashed line).

**Figure 23.**Largest 1% values of additional grid load for each charging configuration and variation of the number of households with EVs.

**Figure 24.**Largest 1% values of additional grid load for three samples with different annual distances.

Charging Strategy | Incentive | Description | |
---|---|---|---|

Uncontrolled | “Direct Charging” | None | Users charge their car directly to the maximum capacity as soon as they arrive back home. |

Controlled | ”PV Optimized Charging” | PV generation | Users with a home PV system shift the charging process to maximize self-sufficiency while considering home demand. |

”Price Optimized Charging” | Electricity costs | Users directly receive the signal of the wholesale electricity prices and adapt the charging configuration to minimize electricity costs for charging. | |

”Combined Price + PV Optimized Charging” | PV generation & Electricity costs | Users receive price signal of the wholesale electricity prices and adapt the charging configuration to minimize total household electricity costs. |

Spatial Type | |
---|---|

Urban | Rural |

Metropolis | Central city |

Bigger city | Urban area |

Suburbia | Village area |

Village area | |

Household | |

Net Income | Type |

EUR < 2000 | Single |

EUR 2000–4000 | Multi-person without children |

EUR >4000 € | Multi-person with children |

**Table 3.**Overview of characteristics and amount of EVs [25].

Type | Capacity [kWh] | Consumption [kWh/km] | Qty. |
---|---|---|---|

BEV small | 35.00 | 0.16 | 28 |

BEV small (second car) | 25.00 | 0.16 | 11 |

BEV medium | 60.00 | 0.20 | 28 |

BEV medium (second car) | 50.00 | 0.20 | 1 |

BEV big | 80.00 | 0.25 | 9 |

BEV light utility vehicle | 45.00 | 0.28 | 4 |

PHEV small | 8.80 | 0.16 | 1 |

PHEV small (second car) | 8.80 | 0.16 | 1 |

PHEV medium | 11.50 | 0.20 | 12 |

PHEV medium (second car) | 11.50 | 0.20 | 5 |

Total Quantity | 100 |

Charging Configuration | Reference at ${\mathit{I}}_{\mathit{F}}$= 1 | Volatility at ${\mathit{I}}_{\mathit{F}}$= 2 | Volatility at ${\mathit{I}}_{\mathit{F}}$= 4 | ||
---|---|---|---|---|---|

Total costs [EUR 1000], Avg. Charging costs [EUR ct/kWh] | Total costs [EUR 1000], Avg. Charging costs [EUR ct/kWh] | +/− [%] | Total costs [EUR 1000], Avg. Charging costs [EUR ct/kWh] | +/− [%] | |

Direct | 49.21, 30.11 | 50.92, 31.19 | +3.37%, +3.44% | 54.21, 33.23 | +9.22%, +9.37% |

PV Optimized | 32.63, 19.59 | 32.97, 19.91 | +1.02%, +1.66% | 33.11, 20.01 | +1.44%, +2.08% |

Price Optimized | 40,12, 26.72 | 40.00, 26.68 | −0.30%, −0.18% | 37.08, 24.37 | −8.20%, −9.66% |

Price + PV Optimized | 32.91, 19.88 | 32.76, 19.80 | −0.45%, −0.40% | 32.90, 19.89 | −0.02%, +0.03% |

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## Share and Cite

**MDPI and ACS Style**

von Bonin, M.; Dörre, E.; Al-Khzouz, H.; Braun, M.; Zhou, X.
Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households. *Energies* **2022**, *15*, 1079.
https://doi.org/10.3390/en15031079

**AMA Style**

von Bonin M, Dörre E, Al-Khzouz H, Braun M, Zhou X.
Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households. *Energies*. 2022; 15(3):1079.
https://doi.org/10.3390/en15031079

**Chicago/Turabian Style**

von Bonin, Michael, Elias Dörre, Hadi Al-Khzouz, Martin Braun, and Xian Zhou.
2022. "Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households" *Energies* 15, no. 3: 1079.
https://doi.org/10.3390/en15031079