Data Specification

This section explains the structure in which data sets must be supplied in order for HydrOGEnMod.jl to generate the underlying model data. In total, there must exist 8 subfolders that each contain several data sheets on specific parts of the hydrogen value chain in the form of csv files.

Any provided dataset must be structured in the following way:

datafile
├── conversion
│   ├── Capacities.csv
│   ├── Conversion_Data.csv
│   └── Repurposing.csv
├── demand
│   └── Reference_Data.csv
├── general_data
│   ├── Commodities.csv
│   ├── Demand_Blocks.csv
│   ├── Input_Blocks.csv
│   ├── Inputs.csv
│   ├── Market_Power.csv
│   ├── Nodes.csv
│   ├── Origins.csv
│   ├── Temporal_Preferences.csv
│   ├── Timesteps.csv
│   └── Years.csv
├── inputs
│   ├── Availability_Factors.csv
│   ├── Capacity_Expansion_Limits.csv
│   ├── Cost_Parameters.csv
│   ├── Exogenous_Capacities.csv
│   ├── Investment_Cost_Parameters.csv
│   └── Lifetimes.csv
├── production
│   ├── Capacity_Expansion_Limits.csv
│   ├── Exogenous_Capacities.csv
│   └── Technology.csv
├── storage
│   ├── Capacities.csv
│   ├── Losses.csv
│   ├── Repurposing_Data.csv
│   └── Technologies.csv
├── trade
│   ├── Sanctions.csv
│   └── Specific_Market_Power.csv
└── transport
    ├── Commodity_Data.csv
    ├── Exogenous_Pipelines.csv
    ├── Pipeline_Adjacency.csv
    ├── Pipeline_Distances.csv
    ├── Repurposing_Data.csv
    ├── Shipping_Adjacency.csv
    └── Shipping_Distances.csv

The following table gives an overview of their contents.

Conversion Data

Demand Data

Inverse demand functions are assumed to follow a block-wise linear functional form:

\[ \tilde{P}^{T \rightarrow D}_{ncbmy}(\sum_{o \in \mathcal{O}} \sum_{t \in \mathcal{T}}q^{T \rightarrow D}_{tncbomy}) = \alpha^{D}_{ncbmy} + \beta^{D}_{ncbmy} (\sum_{o \in \mathcal{O}} \sum_{t \in \mathcal{T}}q^{T \rightarrow D}_{tncbomy}) \]

The parameters $\alpha^{D}_{ncbmy}, \beta^{D}_{ncbmy}$ from the mathematical formulation are subsequently defined around a reference point with an assumed price elasticity. This implies, that inverse demand curves are uniquely defined by supplying a set of reference quantity and price, as well as a given price elasticity. These data are used in the demand data set instead of directly computed $\alpha^{D}_{ncbmy}, \beta^{D}_{ncbmy}$. For ease of notation, the following equation will be used subsequently to abbreviate the total quantity consumed of commodity $c$ in block $b$ during time step $m$ of year $y$ in node $n$:

\[ Q_{ncbmy} = \sum_{o \in \mathcal{O}} \sum_{t \in \mathcal{T}}q^{T \rightarrow D}_{tncbomy} \]

The partial derivative of the inverse demand with respect to total supply quantity is:

\[ \frac{\partial\tilde{P}^{T \rightarrow D}_{ncbmy}(Q_{ncbmy})}{\partial Q_{ncbmy} } = \beta^{D}_{ncbmy}\]

The price elasticity at a reference point defined by a reference price $\bar{P}_{ncbmy}$ and a reference quantity $\bar{Q}_{ncbmy}$ can be stated as:

\[ \epsilon^{\tilde{P}}_{ncbmy} = \frac{\partial Q_{ncbmy}}{\partial \tilde{P}^{T \rightarrow D}_{ncbmy} (Q_{ncbmy})} \frac{ \bar{P}_{ncbmy}} {\bar{Q}_{ncbmy}}\]

Rearranging yields:

\[ \frac{\partial \tilde{P}^{T \rightarrow D}_{ncbmy} (Q_{ncbmy})}{\partial Q_{ncbmy}} = \frac{ \bar{P}_{ncbmy}} {\bar{Q}_{ncbmy}} \frac{1}{\epsilon^{\tilde{P}}_{ncbmy} }\]

Which in turn yields:

\[ \beta^{D}_{ncbmy} = \frac{ \bar{P}_{ncbmy}} {\bar{Q}_{ncbmy}} \frac{1}{\epsilon^{\tilde{P}}_{ncbmy} }\]

Inserting this into the inverse demand function, we get:

\[ \alpha^{D}_{ncbmy} = \bar{P}_{ncbmy} - \frac{ \bar{P}_{ncbmy}}{\epsilon^{\tilde{P}}_{ncbmy} }\]

General Data

This dataset provides an overarching structure. Most notably, it determines which options from the datasets are actually included into the model data.

Input Data

Production Data

Storage Data

Trade Data

Transport Data

Commodities are split into shipping-based and pipeline-based. For piped commodities, an explicit investment into specific routes must be made, while shipping-investment is assumed to be flexible, i.e. only distance dependent charges apply.