Reference - Data Formats¶

As rivus expects its input variables from different sources, this section shall help you to get along with the parameters.

Input sources and their data-flow until the main variables. (Vertex, Edge, Data)

Non Spatial¶

You can retrieve non-spatial data from a spreadsheet or from a database. As the spreadsheet was is the standard data input format, we will discuss that in the following section. You can find examples (templates) in the /data/haag15/ or /data/mnl/ data folders.

This summary of the columns shall ease your understanding of those input variables. (Extracted from the tool-tip info in the haag15 project.)

Note

If a cell does not have meaningful value in the record, that should be marked with the =N/A formula. Not empty cell.
Names of the sheets and columns must be preserved, as they are hard-coded into the parser.
Do not forget to check the unit of your inputs!
Do not leave ‘zombie’ record (e.g. a process without listed commodity).

Commodity¶

cost-inv-fix: Fixed investment costs [€/m] Capacity-independent investment costs for pipe/cable to transmit that commodity.
cost-inv-var: Variable invest costs [€/kW/m] Capacity-dependent investment costs for transmission capacity of a commodity from one vertex to another.
cost-fix: Variable fixed costs [€/kW/m] Capacity-dependent fixed costs for maintaining transmission capacity.
cost-var: Purchase costs [€/kWh] Cost for buying that commodity at source vertices, if any exist in the vertex_shapefile.
loss-fix: Fixed loss fraction [kW/m] Powerflow-independent loss of energy per meter of transmission length through the network. The fixed loss is calculated by (length * loss-fix).
loss-var: Variable power loss [1/kW/m] Relative loss term, dependent on input power flow through a “pipe”: Ingoing power flow per edge is multiplied by (1 - length * loss-var)
cap-max: Maximum capacity [kW] Maximum possible transmission capacity per edge.
allowed-max: Maximum allowed generation Limits the net amount of generation of this commodity (e.g. CO2). Note that processes that consume a commodity (e.g. CCS) can reduce the net amount.

Process¶

cost-inv-fix

Fixed investment costs [€]

Up-front investment for building a plant, independent of size. Has value zero mainly for small-scale technologies.

cost-inv-var

Specific investment costs [€/kW]

Size-dependent part for building a plant.

cost-fix

Specific fixed costs [€/kW]

Size-dependent part for maintaining a plant.

cost-var

Variable costs [€/kWh]

Operational costs to produce one unit of output, excluding fuel costs. Has value zero e.g. for PV or wind turbines (or if no sources are available).

cap-min

Minimum capacity [kW]

Smallest size a plant is typically available in. Has value zero for domestic technologies.

cap-max

Maximum capacity [kW]

Biggest capacity a plant typically is available in.

Process-Commodity¶

ratio: Input/output ratio

Flows in and out of processes, relative to 1 unit of throughput. For CO2, unit is kg/kWh (for example)

Time¶

weight: Time step weight [hours]

Length of time step in hours. Sum of all weights == 8760
Elec: Scaling factor Elec [1]

Relative scaling factor of demand ‘Elec’ per time step. Interpret like y-values of a normalised annual load duration curve.
Heat: Scaling factor Heat [1]

Relative scaling factor of demand ‘Heat’ per time step. Interpret like y-values of a normalised annual load duration curve.

Area-Demand¶

peak: Building peak demand [kW/m²]

Peak demand of building type (must be present in building_shapefile) normalised to building area. Annual demand is encoded in time step weights on sheet Time.

Vertex¶

The examples are given with the help of the Gridder sub-package, but the that depicts very well what you should see in a shapefile’s attribute list. (Excluding the special geometry column of course.)

You can also check it out quickly:

vdf, edf = create_square_grid()
extend_edge_data(edf)
vert_init_commodities(vdf, ['Elec', 'Heat', 'Gas'],[('Elec', 0, 5000), ])
print(vdf.head())

Should give you:

                                      geometry  Vertex  Elec  Heat  Gas
                  POINT (11.66842 48.26739)       0  5000     0    0
POINT (11.66976700131334 48.26738999211108)       1     0     0    0
         POINT (11.66842 48.26828931656865)       2     0     0    0
POINT (11.66976702494603 48.26828930867949)       3     0     0    0

Edge¶

The examples are given with the help of the Gridder sub-package, but the that depicts very well what you should see in a shapefile’s attribute list. (Excluding the special geometry column of course.)

Vertex1	Vertex2	Edge	geometry	Area Type 1. - e.g.: residential	Area Type 2. - e.g.: industrial	Area Type 3. - e.g.: other
Reference to one end	Reference to other end	Zero-based numbering	Shapely.LineString	Sum of area of type 1 along this edge	same	same

You can also check it out quickly:

vdf, edf = create_square_grid()
extend_edge_data(edf)
vert_init_commodities(vdf, ['Elec', 'Heat', 'Gas'])
print(edf.head())

Should give you:

                                            geometry  Edge  Vertex1  Vertex2  \
LINESTRING (11.66842 48.26739, 11.669767001313...     0        0        1
LINESTRING (11.66842 48.26828931656865, 11.669...     1        2        3
LINESTRING (11.66842 48.26739, 11.66842 48.268...     2        0        2
LINESTRING (11.66976700131334 48.2673899921110...     3        1        3

   residential
       1000
       1000
       1000
       1000