How to Use

• How to Use
• • Install
  • • Starting Julia
    • Adding TulipaEnergyModel
    • (Optional) Running automatic tests
  • Running a Scenario
  • • Input
    • Writing the output to CSV
  • Exploring infeasibility
  • Speed improvements in the model creation
  • Finding an input parameter
  • Storage specific setups
  • • Seasonal and non-seasonal storage
    • The energy storage investment method
    • Control simultaneous charging and discharging
  • Setting up unit commitment constraints
  • Setting up ramping constraints
  • Setting up a maximum or minimum outgoing energy limit
  • • Example: Setting Energy Limits
  • Defining a group of assets
  • • Setting up a maximum or minimum investment limit for a group
    • Example: Group of Assets
  • Setting up multi-year investments
  • • Filling the input data
    • Economic representation

Install

To use Tulipa, you first need to install the opensource Julia programming language.

Then consider installing a user-friendly code editor, such as VSCode. Otherwise you will be running from the terminal/command prompt.

Starting Julia

Choose one:

• In VSCode: Press CTRL+Shift+P and press Enter to start a Julia REPL.
• In the terminal: Type julia and press Enter

Adding TulipaEnergyModel

In Julia:

• Enter package mode (press "]")

pkg> add TulipaEnergyModel

• Return to Julia mode (backspace)

julia> using TulipaEnergyModel

(Optional) Running automatic tests

It is nice to check that tests are passing to make sure your environment is working, this takes a minute or two.

• Enter package mode (press "]")

pkg> test TulipaEnergyModel

All tests should pass.

Running a Scenario

To run a scenario, use the function:

• run_scenario(connection)
• run_scenario(connection; output_folder)

The connection should have been created and the data loaded into it using TulipaIO. See the tutorials for a complete guide on how to achieve this. The output_folder is optional if the user wants to export the output.

Input

Currently, we only accept input from CSV files that follow the Schemas.

You can also check the test/inputs folder for examples of different predefined energy systems and features. Moreover, Tulipa's Offshore Bidding Zone Case Study can be found in https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy. It shows how to start from user-friendly files and transform the data into the input files in the Schemas through different functions.

Writing the output to CSV

To save the solution to CSV files, you can use export_solution_to_csv_files. See the tutorials for an example showcasing this function.

Exploring infeasibility

If your model is infeasible, you can try exploring the infeasibility with JuMP.compute_conflict! and JuMP.copy_conflict.

Use energy_problem.model for the model argument. For instance:

if energy_problem.termination_status == INFEASIBLE
 compute_conflict!(energy_problem.model)
 iis_model, reference_map = copy_conflict(energy_problem.model)
 print(iis_model)
end

Speed improvements in the model creation

If you want to speed-up model creation, consider disabling the naming of variables and constraints. Of course, removing the names will make debugging difficult (or impossible) - so enable/disable naming as needed for your analysis.

# Disable names while using run_scenario
run_scenario(connection; enable_names = false)

# OR while using create_model!
create_model!(energy_problem; enable_names = false)

For more information, see the JuMP documentation.

Finding an input parameter

Storage specific setups

Seasonal and non-seasonal storage

Section Storage Modeling explains the main concepts for modeling seasonal and non-seasonal storage in TulipaEnergyModel.jl. To define if an asset is one type or the other then consider the following:

• Seasonal storage: When the storage capacity of an asset is greater than the total length of representative periods, we recommend using the inter-temporal constraints. To apply these constraints, you must set the input parameter is_seasonal to true.
• Non-seasonal storage: When the storage capacity of an asset is lower than the total length of representative periods, we recommend using the intra-temporal constraints. To apply these constraints, you must set the input parameter is_seasonal to false.

The energy storage investment method

Energy storage assets have a unique characteristic wherein the investment is based not solely on the capacity to charge and discharge, but also on the energy capacity. Some storage asset types have a fixed duration for a given capacity, which means that there is a predefined ratio between energy and power. For instance, a battery of 10MW/unit and 4h duration implies that the energy capacity is 40MWh. Conversely, other storage asset types don't have a fixed ratio between the investment of capacity and storage capacity. Therefore, the energy capacity can be optimized independently of the capacity investment, such as hydrogen storage in salt caverns. To define if an energy asset is one type or the other then consider the following parameters:

• Investment energy method: To use this method, set the parameter storage_method_energy to true. In addition, it is necessary to define:

  • investment_cost_storage_energy: To establish the cost of investing in the storage capacity (e.g., kEUR/MWh/unit).
  • fixed_cost_storage_energy: To establish the fixed cost of energy storage capacity (e.g., kEUR/MWh/unit).
  • investment_limit_storage_energy: To define the potential of the energy capacity investment (e.g., MWh). Missing values mean that there is no limit.
  • investment_integer_storage_energy: To determine whether the investment variables of storage capacity are integers of continuous.

• Fixed energy-to-power ratio method: To use this method, set the parameter storage_method_energy to false. In addition, it is necessary to define the parameter energy_to_power_ratio to establish the predefined duration of the storage asset or ratio between energy and power. Note that all the investment costs should be allocated in the parameter investment_cost.

In addition, the parameter capacity_storage_energy defines the energy per unit of storage capacity invested in (e.g., MWh/unit).

For more details on the constraints that apply when selecting one method or the other, please visit the mathematical formulation section.

Control simultaneous charging and discharging

Depending on the configuration of the energy storage assets, it may or may not be possible to charge and discharge them simultaneously. For instance, a single battery cannot charge and discharge at the same time, but some pumped hydro storage technologies have separate components for charging (pump) and discharging (turbine) that can function independently, allowing them to charge and discharge simultaneously. To account for these differences, the model provides users with three options for the use_binary_storage_method parameter:

• binary: the model adds a binary variable to prevent charging and discharging simultaneously.
• relaxed_binary: the model adds a binary variable that allows values between 0 and 1, reducing the likelihood of charging and discharging simultaneously. This option uses a tighter set of constraints close to the convex hull of the full formulation, resulting in fewer instances of simultaneous charging and discharging in the results.
• If no value is set, i.e., missing value, the storage asset can charge and discharge simultaneously.

For more details on the constraints that apply when selecting this method, please visit the mathematical formulation section.

Setting up unit commitment constraints

The unit commitment constraints are only applied to producer and conversion assets. The unit_commitment parameter must be set to true to include the constraints. Additionally, the following parameters should be set in that same file:

• unit_commitment_method: It determines which unit commitment method to use. The current version of the code only includes the basic version. Future versions will add more detailed constraints as additional options.
• units_on_cost: Objective function coefficient on units_on variable. (e.g., no-load cost or idling cost in kEUR/h/unit)
• unit_commitment_integer: It determines whether the unit commitment variables are considered as integer or not (true or false)
• min_operating_point: Minimum operating point or minimum stable generation level defined as a portion of the capacity of asset (p.u.)

For more details on the constraints that apply when selecting this method, please visit the mathematical formulation section.

Setting up ramping constraints

The ramping constraints are only applied to producer and conversion assets. The ramping parameter must be set to true to include the constraints. Additionally, the following parameters should be set in that same file:

• max_ramp_up: Maximum ramping up rate as a portion of the capacity of asset (p.u./h)
• max_ramp_down:Maximum ramping down rate as a portion of the capacity of asset (p.u./h)

For more details on the constraints that apply when selecting this method, please visit the mathematical formulation section.

Setting up a maximum or minimum outgoing energy limit

For the model to add constraints for a maximum or minimum energy limit for an asset throughout the model's timeframe (e.g., a year), we need to establish a couple of parameters:

• is_seasonal = true. This parameter enables the model to use the inter-temporal constraints.
• max_energy_timeframe_partition $\neq$ missing or min_energy_timeframe_partition $\neq$ missing. This value represents the peak energy that will be then multiplied by the profile for each period in the timeframe.

• (optional) profile_type and profile_name in the timeframe files. If there is no profile defined, then by default it is 1.0 p.u. for all periods in the timeframe.
• (optional) define a period partition in timeframe partition files. If there is no partition defined, then by default the constraint is created for each period in the timeframe, otherwise, it will consider the partition definition in the file.

Example: Setting Energy Limits

Let's assume we have a year divided into 365 days because we are using days as periods in the representatives from TulipaClustering.jl. Also, we define the max_energy_timeframe_partition = 10 MWh, meaning the peak energy we want to have is 10MWh for each period or period partition. So depending on the optional information, we can have:

Defining a group of assets

A group of assets refers to a set of assets that share certain constraints. For example, the investments of a group of assets may be capped at a maximum value, which represents the potential of a specific area that is restricted in terms of the maximum allowable MW due to limitations on building licenses.

In order to define the groups in the model, the following steps are necessary:

1. Create a group file by defining the name property and its parameters in the group_asset table (or CSV file).
2. Assign assets to the group by setting the name in the group parameter/column of the asset file.

Groups are useful to represent several common constraints, the following group constraints are available.

Setting up a maximum or minimum investment limit for a group

The mathematical formulation of the maximum and minimum investment limit for group constraints is available here.

• invest_method = true. This parameter enables the model to use the investment group constraints.
• min_investment_limit $\neq$ missing or max_investment_limit $\neq$ missing. This value represents the limits that will be imposed on the investment that belongs to the group.

Example: Group of Assets

Let's explore how the groups are set up in the test case called Norse. First, let's take a look at the group-asset.csv file:

In the given data, there are two groups: renewables and ccgt. Both groups have the invest_method parameter set to true, indicating that investment group constraints apply to both. For the renewables group, the min_investment_limit parameter is missing, signifying that there is no minimum limit imposed on the group. However, the max_investment_limit parameter is set to 40000 MW, indicating that the total investments of assets in the group must be less than or equal to this value. In contrast, the ccgt group has a missing value in the max_investment_limit parameter, indicating no maximum limit, while the min_investment_limit is set to 10000 MW for the total investments in that group.

Let's now explore which assets are in each group. To do so, we can take a look at the asset.csv file:

Here we can see that the assets Asgard_Solar and Midgard_Wind belong to the renewables group, while the assets Asgard_CCGT and Midgard_CCGT belong to the ccgt group.

Setting up multi-year investments

It is possible to simutaneously model different years, which is especially relevant for modeling multi-year investments. Multi-year investments refer to making investment decisions at different points in time, such that a pathway of investments can be modeled. This is particularly useful when long-term scenarios are modeled, but modeling each year is not practical. Or in a business case, investment decisions are supposed to be made in different years which has an impact on the cash flow.

Filling the input data

In order to set up a model with year information, the following steps are necessary.

Year data

Fill in all the years in year-data.csv file by defining the year property and its parameters. Differentiate milestone years and non-milestone years.

• Milestone years are the years you would like to model, e.g., if you want to model operation and/or investments (it is possibile to not allow investments) in 2030, 2040, and 2050. These 3 years are then milestone years.
• Non-milestone years are the investment years of existing units. For example, you want to consider a existing wind unit that is invested in 2020, then 2020 is a non-milestone year.

Commission year data

Fill in the parameters related to the investment year (commission_year in the data) of the asset, i.e., investment costs and fixed costs.

Assets investment data

Fill in the parameters in the asset file. These parameters are for the assets across all the years, i.e., not dependent on years. Examples are lifetime (both technical_lifetime and economic_lifetime) and capacity of a unit.

You also have to choose a investment_method for the asset, between none, simple, and compact. The below tables shows what happens to the activation of the investment and decommission variable for certain investment methods and the investable parameter.

Consider you only want to model operation without investments, then you would need to set investable_method to none. Neither investment variables and decommission variables are activated. And here the investable_method overrules investable, because the latter does not matter.

For more details on the constraints that apply when selecting these methods, please visit the mathematical formulation section.

Assets and flows information

Fill in the assets and flows information.

• In the year column, fill in all the milestone years. In the commission_year column, fill in the investment years of the existing assets that are still available in this year.
  • If the commission_year is a non-milestone year, then it means the row is for an existing unit. The investable has to be set to false, and you put the existing units in the column initial_units.
  • If the commission_year is a milestone year, then you put the existing units in the column initial_units. Depending on whether you want to model investments or not, you put the investable to either true or false.

Let's explain further using an example. To do so, we can take a look at the asset-both.csv file:

We allow investments of ocgt, ccgt, battery, wind, and solar in 2030.

• ocgt has no existing units.
• ccgt has 1 existing units, invested in 2028, and still available in 2030.
• ccgt has 0.07 existing units, invested in 2020, and still available in 2030. Another 0.02 existing units, invested in 2030.
• wind has 0.07 existing units, invested in 2020, and still available in 2030. Another 0.02 existing units, invested in 2030.
• solar has no existing units.

Profiles information

Fill in relevant profiles in the files. Important to know that you can use different profiles for assets that are invested in different years. You fill in the profile names in assets-profiles.csv for relevant years. In profiles-rep-periods.csv, you relate the profile names with the modeled years.

Let's explain further using an example. To do so, we can take a look at the assets-profiles.csv file:

We have two profiles for wind invested in 2020 and 2030. Imagine these are two wind turbines with different efficiencies due to the year of manufacture. These are reflected in the profiles for the model year 2030, which are defined in the profiles-rep-periods.csv file.

Economic representation

For economic representation, the following parameters need to be set up:

• [optional] discount year and discount rate in the model-parameters-example.toml file: model-wide discount year and rate. By default, the model will use a discount rate of 0, and a discount year of the first milestone year. In other words, the costs will be discounted to the cost of the first milestone year.
• discount_rate: technology-specific discount rates.
• economic_lifetime: used for discounting the costs.

For more details on the formulas for economic representation, please visit the mathematical formulation section.