# Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids

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## Abstract

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## 1. Introduction

- How is it possible to integrate transmission and distribution grid planning?
- Is it (systematically) cost-efficient to align distribution grid planning with the overall systematic optimization of power plant dispatch, grid and storage expansion?

## 2. State of the Art

#### 2.1. Transmission Grid Planning

#### 2.2. Distribution Grid planning

## 3. Methods

#### 3.1. Data Model

#### 3.2. Optimization of Extra-High and High Voltage Levels

- Guess k cluster centers
- Repeat until converged
- (a)
- E-Step: assign points to the nearest cluster center
- (b)
- M-Step: set the cluster centers to the mean [65]

#### 3.3. Incorporation of the Medium and Low Voltage Levels

#### 3.3.1. Interface Design

#### 3.3.2. Complexity Reduction

#### 3.3.3. Simulation and Optimization of Medium and Low Voltage Levels

- Allocation of curtailment requirements
- Grid-supportive storage integration
- Grid expansion measures to solve remaining grid issues

#### Grid Expansion Methodology

#### Curtailment Allocation Methodology

#### Storage Integration Methodology

## 4. Results

#### 4.1. Scenario NEP 2035

#### 4.2. Scenario eGo 100

## 5. Discussion and Critical Appraisal

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Installed generation capacities for Germany (GER) and the entire model region (total) per scenario and technology/fuel [59].

**Figure 2.**The Status Quo grid topology model for the EHV and HV level (

**a**). In (

**b**) one of the 3376 underlying MV grid topologies and its connection to the HV level is displayed. Its location corresponds to the zoom box in (

**a**). The connected generation (except for MV generators in (

**b**)) and demand as well as transformers are not displayed for better visualization. A visualized bus which seems to connect two voltage levels is actually modelled as two buses connected by a transformer (see also [14]).

**Figure 3.**Exemplary visualization of the E–M algorithm for k-means clustering [65]. During the E-Step, all data points are assigned to the nearest (initially randomly set) cluster center. Afterwards each cluster center is reset to the centroid of its current cluster. These steps are repeated until convergence.

**Figure 4.**Exemplary allocation of battery storage expansion before (

**a**) and after (

**b**) disaggregation. In (

**a**) the EHV and HV grid is reduced to 300 buses. In (

**b**) the original complete complexity is displayed. For better visualization it was zoomed in to a subregion of southern Bavaria next to the border to the Czech Republic and Austria. The basic grid topology is visualized by grey lines and black buses.

**Figure 5.**Relative deviation of grid expansion costs of different k cluster approximations compared to the calculation of all MV grids using the simple worst-case method.

**Figure 6.**MV grid clusters with 600 representative MV grids (black-rimmed) and their weighting for the eGo 100 scenario. The MV grid districts are displayed without any grid topology.

**Figure 7.**Grid expansion measures to solve overloading and voltage issues in MV and LV grids based on [31].

**Figure 8.**Linear relation between relative curtailment and bus voltage used to allocate curtailment requirements.

**Figure 9.**Illustration of the approach of finding the storage size that minimizes the maximum load in a feeder.

**Figure 10.**Allocation of grid and storage expansion in the EHV and HV level for the scenario NEP 2035. s_nom refers to the original nominal thermal limit capacity of each line. A net 300% extension is synonymous with a new optimized capacity of four times the original capacity.

**Figure 11.**Allocation of grid and storage expansion in the EHV and HV level for the scenario eGo 100. s_nom refers to the original nominal thermal limit capacity of each line. A net extension of 300% is synonymous with a new optimized capacity of four times the original capacity.

**Figure 12.**Allocation of grid and storage expansion for a representative MV grid for the scenario eGo 100. Orange colored nodes represent battery storage units. Color of other nodes and lines represents expansion costs of stations and lines.

Component | Overnight Costs | Unit EUR/ |
---|---|---|

Overhead Line, 380 kV | 85 | MVA*km |

Overhead Line, 220 kV | 290 | MVA*km |

Overhead Line, 110 kV | 230 | MVA*km |

EHV DC-Link (underground) | 375 | MVA*km |

DC-Converter | 200,000 | MVA |

Transformer, 380–220 kV | 14,166 | MVA |

Transformer, 380–110 kV | 17,333 | MVA |

Transformer, 220–110 kV | 7,500 | MVA |

Battery storage, P-E ratio: 1/6 | 918,500/678,000 ${}^{1}$ | MW |

Hydrogen storage, P-E ratio: 1/168 | 890,600/650,600 ${}^{1}$ | MW |

**Table 2.**Standard equipment for grid expansion in MV and LV grids [3]. ${}^{a}$ costs include earthwork costs in area with population density of ≤500 people/km${}^{2}$. ${}^{b}$ costs include earthwork costs in area with population density of >500 people/km${}^{2}$.

Equipment | Overnight Investment Costs in kEUR | Unit |
---|---|---|

MV cable, NA2XS2Y 3 × 1 × 185 RM/25 | 20/80 ${}^{a}$/140 ${}^{b}$ | km |

LV cable, NAYY 4 × 1 × 150 | 9/60 ${}^{a}$/100 ${}^{b}$ | km |

HV/MV Transformer, 40 MVA | 1000 | - |

MV/LV Transformer, 630 kVA | 10 | - |

**Table 3.**Allowed equipment load factors used to identify thermal overloading [31].

Equipment | Load Factor in HLF | Load Factor in RPF |
---|---|---|

MV cable | 50% | 100% |

LV cable | 100% | 100% |

HV/MV Transformer | 50% | 100% |

MV/LV Transformer | 100% | 100% |

**Table 4.**Allowed voltage deviations used to identify voltage issues [31].

Voltage Level | Voltage Deviation in HLF | Voltage Deviation in RPF |
---|---|---|

MV | 1.5% | 5.0% |

MV/LV | 2.0% | 1.5% |

LV | 6.5% | 3.5% |

**Table 5.**Used Simultaneity factors of loads and generators in MV and LV grids for calculation of worst-case grid expansion costs based on [3].

Design Case | Load | Generation |
---|---|---|

HLF | 100% | 0% |

RPF | 15% (MV)/10% (LV) | 85% (Solar)/100% (Other) |

**Table 6.**Comparison of grid expansion costs per scenario in the MV and LV level based on a calculation of 600 cluster grids. The costs of the transformation levels HV-MV and MV-LV are included in the MV and LV level.

Scenario | Grid Investment bn EUR per Voltage Level | Comparison to Flex in % | ||
---|---|---|---|---|

MV | LV | |||

NEP 2035 | Flex | 8.17 | 1.69 | - |

Reference | 8.17 | 1.70 | +0 | |

Worst-Case-600 | 12.6 | 2.60 | +54 | |

Worst-Case-3591 | 12.5 | 2.40 | +51 | |

eGo 100 | Flex | 10.2 | 2.39 | - |

Reference | 10.3 | 2.42 | +0 | |

Worst-Case-600 | 15.3 | 3.76 | +51 | |

Worst-Case-3591 | 15.2 | 3.62 | +49 |

**Table 7.**Overview of overnight investment costs for grid and storage expansion for each scenario and grid level.The MV+LV results refer to the Flex approach. Transformer costs are included in each aggregated grid level - HV/MV transformer are assigned to the MV+LV level.

Scenario | Grid Level | Grid Investment bn EUR | Storage Investment bn EUR | |||||
---|---|---|---|---|---|---|---|---|

Battery | Hydrogen | |||||||

National | Cross-Border | Abroad | National | Abroad | National | Abroad | ||

NEP 2035 | EHV+HV | 3.1 | 1.5 | 2.7 | 0.013 | 0 | 0 | 0 |

MV+LV | 9.9 | - | - | - | - | - | - | |

eGo 100 | EHV+HV | 4.1 | 1.7 | 2.8 | 2.0 | 0 | 9.1 | 2.2 |

MV+LV | 12.6 | - | - | 0.014 | - | - | - |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Müller, U.P.; Schachler, B.; Scharf, M.; Bunke, W.-D.; Günther, S.; Bartels, J.; Pleßmann, G.
Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids. *Energies* **2019**, *12*, 2091.
https://doi.org/10.3390/en12112091

**AMA Style**

Müller UP, Schachler B, Scharf M, Bunke W-D, Günther S, Bartels J, Pleßmann G.
Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids. *Energies*. 2019; 12(11):2091.
https://doi.org/10.3390/en12112091

**Chicago/Turabian Style**

Müller, Ulf Philipp, Birgit Schachler, Malte Scharf, Wolf-Dieter Bunke, Stephan Günther, Julian Bartels, and Guido Pleßmann.
2019. "Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids" *Energies* 12, no. 11: 2091.
https://doi.org/10.3390/en12112091