# A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model

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## Abstract

**:**

## 1. Introduction

#### State of the Art

## 2. Materials and Methods

#### 2.1. The Two Case Studies

#### 2.2. The Modelling Framework

#### 2.2.1. The Simstadt Model

#### 2.2.2. The KomMod Model

#### 2.2.3. The Heating Grid Disaggregation Algorithm

- The street segment with the global highest heating density (taken in this paper).
- A street segment adjacent to a potential site for a future heating station.
- The street segment with the highest heating density among all segments adjacent to an existing grid.

_{1}, C

_{2}) shall be adjusted for inflation (see also Section 3.1).

#### 2.2.4. Computing the Optimal Grid

- The two curves have an intersection. In that case, the intersection point yields the optimal amount of grid-supplied heat, as at this point, the specific grid costs from KomMod and the cost determined via the grid distribution algorithm are approximately the same (KomMod case 1 in Figure 3).
- The two curves do not intersect, with the KomMod curve generally yielding smaller values for the grid-supplied heat. In this case, KomMod suggests using district heating only at costs that are lower than the costs associated with heating grid installations in the studied area. Therefore, district heating is economically not feasible. (KomMod case 2 in Figure 3).

## 3. Results

#### 3.1. Linear Heating Density and Grid Costs

#### 3.2. Optimal Grid Layout

#### 3.2.1. Case Study Stuttgart-Stöckach: Fuel Cost Variation

#### 3.2.2. Case Study Rainau: Fuel Cost Variation

#### 3.2.3. Case Study Stöckach: Grid Connection

#### 3.3. Summary of the Results

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Flow Chart and data relations between SimStadt, KomMod and the heating grid distribution algorithm.

**Figure 2.**Illustration of the search algorithm for the next street segment in the grid distribution algorithm with Rainau as an example. The darkness of the street segments indicates the sequence of grid expansion. The darker the color, the later the street segments are included in the grid (lookahead 1 =

**left**, lookahead 2 =

**right**).

**Figure 3.**Illustrative relation of specific grid costs to grid supplied heat for the two options of the relative position of the algorithm curve and the KomMod curve.

**Figure 4.**Specific heating demand for every building in Stuttgart-Stöckach and linear heating density for every street segment.

**Figure 6.**Results for Stuttgart-Stöckach of specific grid costs over grid-supplied heat with CHPs as central heating supplier.

**Figure 7.**Visualization of the different heating grid configurations for Stuttgart-Stöckach (orange: EUR 0.11/kWh gas price, +grey: EUR 0.08/kWh, +yellow: EUR 0.05/kWh, +blue: EUR 0.03/kWh).

**Figure 8.**Results for Rainau for specific grid costs over grid supplied heat with CHPs and heat pumps as central heating supplier.

**Figure 9.**Sensitivity analysis for the share of heating demand that is supplied via the heating grid in Stuttgart-Stöckach (hd = heating demand).

Urban: Stuttgart-Stöckach | Rural: Rainau | |
---|---|---|

Buildings included in the model [−] | 1858 | 1838 |

Area of the case study [m^{2}] | 2,064,000 | 29,840,000 |

Heating demand calculated with Simstadt for medium refurbishment scenario [GWh/a] | 106 | 40 |

Areal heating demand density [kWh/(m^{2} a)] | 51.4 | 1.3 |

Number of street segments (part of street between two intersections) [−] | 439 | 498 |

Stöckach | Rainau | ||
---|---|---|---|

Possible technologies | Electricity converters | Photovoltaic | |

Gas-fired CHP | |||

Import | |||

Wind power plants | |||

Decentral thermal converters | Gas and oil boilers | Gas, wood and oil boilers | |

Air-sourced heat pumps | Air and ground-sourced heat pumps | ||

Solar heaters | |||

Central thermal converters | Gas-fired CHP | ||

Ground-sourced heat pumps | |||

Cost data | Import electricity price [EUR/kWh] | 0.15 | |

Technology installation and maintenance costs | According to [52] | ||

Natural gas price [EUR/kWh] | Varied between 0.03 and 0.11 | ||

Price for oil and wood | 0.02 EUR/kWh higher than natural gas price (based on historic price differences between gas, oil and wood) |

Fuel Price in EUR/kWh | S-Stöckach CHP | S-Stöckach CHP + Heat Pump | Rainau CHP | Rainau CHP + Heat Pump |
---|---|---|---|---|

0.03 | 51.1% | 95.0% | 4.5% | 8.4% |

0.05 | 49.4% | 86.2% | 20.3% | |

0.08 | 26.5% | 91.2% | 41.8% | |

0.11 | 6.9% | 87.6% | 47.3% |

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## Share and Cite

**MDPI and ACS Style**

Steingrube, A.; Bao, K.; Wieland, S.; Lalama, A.; Kabiro, P.M.; Coors, V.; Schröter, B. A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model. *Resources* **2021**, *10*, 52.
https://doi.org/10.3390/resources10050052

**AMA Style**

Steingrube A, Bao K, Wieland S, Lalama A, Kabiro PM, Coors V, Schröter B. A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model. *Resources*. 2021; 10(5):52.
https://doi.org/10.3390/resources10050052

**Chicago/Turabian Style**

Steingrube, Annette, Keyu Bao, Stefan Wieland, Andrés Lalama, Pithon M. Kabiro, Volker Coors, and Bastian Schröter. 2021. "A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model" *Resources* 10, no. 5: 52.
https://doi.org/10.3390/resources10050052