# Examples

This section reproduces the [examples provided in the repository](https://github.com/IPESE/REHO/tree/main/scripts/examples).

They should give you a brief overview of REHO's capabilities and to test the various features of the tool.

:::{note}
Since the content of the `scripts/examples/` subfolder is git-tracked, you should not modify these files directly, but rather copy their contents into any other subfolder of `scripts/` that you have yourself created. The content there (code, data, results, and figures) will be ignored by the git versioning.
:::

## 1. Building-scale

### Single-optimization

```{literalinclude} ../../scripts/examples/1a_Building-scale_totex.py
:language: python
```

### Pareto curve

```{literalinclude} ../../scripts/examples/1b_Building-scale_Pareto.py
:language: python
```

## 2. District-scale

### Single-optimization

```{literalinclude} ../../scripts/examples/2a_District-scale_totex.py
:language: python
```

### Pareto curve

```{literalinclude} ../../scripts/examples/2b_District-scale_Pareto.py
:language: python
```

## 3. Specific features

### Load buildings from a .csv file

```{literalinclude} ../../scripts/examples/3a_Read_csv.py
:language: python
```

### Add diverse energy layers

```{literalinclude} ../../scripts/examples/3b_Custom_infrastructure.py
:language: python
```

### Change heat pump temperature

```{literalinclude} ../../scripts/examples/3c_HP_T_source.py
:language: python
```

### Include electric vehicles

```{literalinclude} ../../scripts/examples/3d_EVs.py
:language: python
```

### Integrate a district heating network

```{literalinclude} ../../scripts/examples/3e_DHN.py
:language: python
```

### Use custom profiles

```{literalinclude} ../../scripts/examples/3f_Custom_profiles.py
:language: python
```

### Include stochasticity into profiles

```{literalinclude} ../../scripts/examples/3g_Stochastic_profiles.py
:language: python
```

### Fix units size

The example shows the use of two methods:

- enforcing the size of specific units (method fix_units)
- considering existing units capacity in the optimization using the parameter Units_Ext

```{literalinclude} ../../scripts/examples/3h_Fix_units.py
:language: python
```

### Connect to ELCOM database for electricity prices

```{literalinclude} ../../scripts/examples/3i_Electricity_prices.py
:language: python
```

### Include capacities of networks for imports and exports

```{literalinclude} ../../scripts/examples/3j_Transformer_capacity.py
:language: python
```

### Include buildings renovation options

The "renovation" method consists in a list of renovation options. Each option contains building elements to renovate. The order does not matter. The buildings elements are: window, facade, roof and footprint.
For each option, an additional SP is run with the U-value of the renovated building, calculated based on the file `infrastructure/U_values.csv`.
The MP will receive at each iteration one solution with non-renovated buildings and one solution per renovated option.
To keep consistency, the non-renovated U-value of the buildings should be taken using the functions *reader.read_db* or *read_csv* with the option *correct_Uh=True*.
This option uses the values in `infrastructure/U_values.csv` instead of the U-values from QBuildings.
Investment costs and embodied emissions are calculated based on the file `infrastructure/renovation.csv`.

```{literalinclude} ../../scripts/examples/3k_Renovation.py
:language: python
```

### Data centers and Organic Rankine Cycles

Liquid cooled data centers (direct-on-chip cooling) produce heat at a temperature of 60-75 °C, which can be used for district heating networks or valorized with Organic Rankine Cycles. This example shows how the demand for data can be set through an average known value of electricity consumption of data centers in an urban energy hub and how the waste heat from such district-level data centers can be used through an Organic Rankine Cycle. Assuming a temperature of 75°C, a cycle efficiency is used to abstract the model.

```{literalinclude} ../../scripts/examples/3l_Datacenter.py
```

## 4. Global features

### Compare various scenarios

```{literalinclude} ../../scripts/examples/4a_Progressive_scenarios.py
:language: python
```

### Conduct a sensitivity analysis

```{literalinclude} ../../scripts/examples/4b_Sensitivity_analysis.py
:language: python
```

## 5. Photovoltaics

### Consider roofs orientation

```{literalinclude} ../../scripts/examples/5a_PV_orientation.py
:language: python
```

### Include photovoltaics on facades

```{literalinclude} ../../scripts/examples/5b_PV_facades.py
:language: python
```

## 6. Mobility

### Add mobility layer

The Layer *Mobility* differs slightly from the other Layers in REHO as this energy carrier is expressed in passenger-kilometers ($\text{pkm}$) rather than $\text{kWh}$.
The mobility demand is represented through an hourly passenger-kilometer ($\text{pkm}$) profile for each typical day, similarly to the other end-use demand profiles.
The transport units represented in the model include EVs, ICEs, bikes, electric bikes and public transport. The model can optimize between the different transport modes. However, it is for now recommended to constrain the modal split, as the optimization based on cost does not reflect typical usage of the different transport modes. The [FSO reports](https://www.are.admin.ch/are/fr/home/mobilite/bases-et-donnees/mrmt.html) can be used to find suitable modal split data.

```{literalinclude} ../../scripts/examples/6a_Mobility_sector.py
:language: python
```

### Co-optimization

Multiple districts can be optimized together in order to calculate EV charging exchanges between districts.
This feature can be used to conduct analyses on EV fleets at the city scale.
Example 6b demonstrates how to use this feature step by step. Only one district is optimized with external charging option available. The optimized district is also parameterized with a load on EV charger representing incoming EVs from other districts.

## 7. Interperiod storage

Investigate interperiod storage units in a building facing grid constraints (e.g. a building with limited import or export capacity).

### Hybrid biomethane/CO2 storage

```{literalinclude} ../../scripts/examples/7a_rSOC_IP.py
```

### Hydrogen production and export

```{literalinclude} ../../scripts/examples/7b_rSOC_H2_export.py
```

### District-scale rSOC with IP storage

```{literalinclude} ../../scripts/examples/7c_district_IP.py
```

## 8. Actors model

Investigate the boundaries of stakeholders in a district energy system and how they influence the optimal design of the system.

### Including building upfront cost

Landlords may consider the upfront cost of building by charging a fixed rent from tenants. Tenants' expenses thus include both the energy cost and the fixed rent.

```{literalinclude} ../../scripts/examples/8a_Actors_problem.py
:language: python
```

### Considering only rent increase

Due to regulations, landlords may increase the rent after renovating the building. In this case, tenants' expenses are limited by the pre-renovation expenses to avoid unacceptable rent increases.

```{literalinclude} ../../scripts/examples/8b_Actors_problem_rent_increase.py
:language: python
```