This part of the guide shows first an example how to insert objects and their parameter data. Then it shows what other objects, relationships and parameter data needs to be added for a very basic model. Lastly, the model instance is run.

This section explains the process of creating a SpineOpt.jl model from scratch in order to give you an understanding of the underlying principles of the data structure, etc. If you simply want to try something out quickly to see results, check out the Example Models section. Furthermore, if you're in a hurry, the Archetypes section provides you with some pre-made templates for the different parts of a SpineOpt.jl model to get you started quickly.

## Creating a SpineOpt model instance

• First, open the database editor by double-clicking the Input DB.
• Right click on model in the Object tree.
• Then, add a model object by writing a name to the object name field. You can use e.g. instance.
• Click ok.
• The model object in SpineOpt is an abstraction that represents the model itself. Every SpineOpt database needs to have at least one model object.
• The model object holds general information about the optimization. The whole range of functionalities is explained in Advanced Concepts chapter - in here a minimal set of parameters is used.

## Add parameter values to the model instance

• Select the model object instance from the object tree.
• Go to the Object parameter value tab.
• Every parameter value belongs to a specific alternative. This allows to hold multiple values for the same parameter of a particular object. The alternative values are used to create scenarios. Choose, Base for all parameter values (Base is required in Spine Toolbox - all other alternatives can be chosen freely).
• Then define a model_start time and a model_end time.
• Double-click on the empty row under parameter_name and select model_start.
• A None should appear in value column.
• To asign a start date value, right-click on None and open the editor (cannot be entered directly, since the datatype needs to be changed).
• The parameter type of model_start is of type Datetime.
• Set the value to e.g. 2019-01-01T00:00:00.
• Proceed accordingly for the model_end.

## Add other necessary objects and parameter data for the objects.

• Add all objects and their parameter data by replicating what has been done in the picture below. Do it the same way as explained above with the following caveats.
• Whilst most object names can be freely defined by the user, there is one object name in the example below that needs to be written exactly since it is used internally by SpineOpt: unit_flow.
• The parameter_name can be selected from a drop down menu.
• The date time and time series parameter data can be added by using right-click to access the Edit... dialog. When creating the time series, use the fixed resolution with Start time of the model run and with 1h resolution. Then only values need to be entered (or copy pasted) and time stamps come automatically.
• Parameter balance_type needs to have value balance_type_none in the gas node, since it allows the node to create energy (natural gas) against a price and therefore the energy balance is not maintained.

## Define temporal and stochastic structures

• To specify the temporal structure for SpineOpt, you need to define temporal_block objects. Think of a temporal_block as a distinctive way of 'slicing' time across the model horizon.
• To link the temporal structure to the spatial structure, you need to specify node__temporal_block relationships, establishing which temporal__block applies to each node. This relationship is added by right-clicking the node__temporal_block in the relationship tree and then using the add relationships... dialog. Double clicking on an empty cell gives you the list of valid objects. The relationship name is automatically formed, but you can change it if that is desirable.
• To keep things simple at this point, let's just define one temporal_block for our model and apply it to all nodes. We add the object hourly_temporal_block of type temporal_block following the same procedure as before and establish node__temporal_block relationships between node_gas and hourly_temporal_block, and electricity_node and hourly_temporal_block.
• In practical terms, the above means that there energy flows over gas_node and electricity_node for each 'time-slice' comprised in hourly_temporal_block.
• Similarly with the stochastic structure, each node is assigned a deterministic stochastic_structure.

## Define the spatial structure

• To specify the spatial structure for SpineOpt, you will need to use the node, unit, and connection objects added before.
• Nodes can be understood as spatial aggregators. In combination with units and connections, they form the energy network.
• Units in SpineOpt represent any kind of conversion process. As one example, a unit can represent a power plant that converts the flow of a commodity fuel into an electricity and/or heat flow.
• Connections on the other hand describe the transport of goods from one location to another. Electricity lines and gas pipelines are examples of such connections. This example does not use connections.
• The database should have an object gas_turbine for the unit object class and objects node_gas and node_elec for the node object class.
• Next, define how the unit and the nodes interact with each other: create a unit__from_node relationship between gas_turbine and node_gas, and unit__to_node relationships between gas_turbine and node_elec.
• In practical terms, the above means that there is an energy flow going from node_gas into node_elec, through the gas_turbine.

## Add remaining relationships and parameter data for the relationships.

• Similar to adding the objects and their parameter data, add the relationships and their parameter data based on the picture below.
• The capacity of the gas_turbine has to be sufficient to meet the highest demand for electricity, otherwise the model will be infeasible (it is possible to set penalty values, but they are not included in this example).
• The parameter fix_ratio_in_out_unit_flow forces the ratio between an input and output flow to be a constant. This is one way to establish an efficiency for a conversion process.

## Run the model

• Select SpineOpt
• Press Execute selection.

## If it fails

• Double-check that the data is correct
• Try to see what the problem might be
• Ask help from the discussion forum

## Explore the results

• Double-clicking the Results database.

## Create and run scenarios and build the model further

• Create a new alternative
• Add parameter data for the new alternative
• Connect alternatives under a scenario. Toolbox modifies Base data with the data from the alternatives in the same scenario.
• Execute multiple scenarios in parallel. First run in a new Julia instance will need to compile SpineOpt taking some time.