Welcome to oemof’s documentation!

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oemof.solph

A model generator for energy system modelling and optimisation (LP/MILP)

Introduction

The oemof.solph package is part of the Open energy modelling framework (oemof). This an organisational framework to bundle tools for energy (system) modelling. oemof-solph is a model generator for energy system modelling and optimisation.

The oemof.solph package is very often called just oemof as it was part of the oemof meta package. Now you need to install oemof.solph separately, but everything else is still the same. Since v0.4.0. it is not possible to install just oemof, use pip install oemof.solph instead.

Everybody is welcome to use and/or develop oemof.solph. Read our contribution section.

Contribution is already possible on a low level by simply fixing typos in oemof’s documentation or rephrasing sections which are unclear. If you want to support us that way please fork the oemof repository to your own github account and make changes as described in the github guidelines: https://guides.github.com/activities/hello-world/

If you have questions regarding the use of oemof you can visit the forum at openmod-initiative.org and open a new thread if your questions hasn’t been already answered.

Keep in touch! - You can become a watcher at our github site, but this will bring you quite a few mails and might be more interesting for developers. If you just want to get the latest news, like when is the next oemof meeting, you can follow our news-blog at oemof.org.

Documentation

The oemof.solph documentation is powered by readthedocs. Use the project site of oemof.solph to choose the version of the documentation. Go to the download page to download different versions and formats (pdf, html, epub) of the documentation.

Installation

If you have a working Python3 environment, use pypi to install the latest oemof version. Python >= 3.6 is recommended. Lower versions may work but are not tested.

pip install oemof.solph

If you want to use the latest features, you might want to install the developer version. The developer version is not recommended for productive use:

pip install https://github.com/oemof/oemof-solph/archive/dev.zip

For running an oemof-solph optimisation model, you need to install a solver. Following you will find guidelines for the installation process for different operation systems.

Installing a solver

There are various commercial and open-source solvers that can be used with oemof. There are two common OpenSource solvers available (CBC, GLPK), while oemof recommends CBC (Coin-or branch and cut). But sometimes its worth comparing the results of different solvers. Other commercial solvers like Gurobi or Cplex can be used as well. Have a look at the pyomo docs to learn about which solvers are supported.

Check the solver installation by executing the test_installation example below (section Installation test).

Linux

To install the solvers have a look at the package repository of your Linux distribution or search for precompiled packages. GLPK and CBC ares available at Debian, Feodora, Ubuntu and others.

Windows

1. Download CBC (64 or 32 bit)
2. Download GLPK (64/32 bit)
3. Unpack CBC/GLPK to any folder (e.g. C:/Users/Somebody/my_programs)
4. Add the path of the executable files of both solvers to the PATH variable using this tutorial
5. Restart Windows

Check the solver installation by executing the test_installation example (see the Installation test section).

Mac OSX

Please follow the installation instructions on the respective homepages for details.

CBC-solver: https://projects.coin-or.org/Cbc

GLPK-solver: http://arnab-deka.com/posts/2010/02/installing-glpk-on-a-mac/

If you install the CBC solver via brew (highly recommended), it should work without additional configuration.

Installation test

Test the installation and the installed solver by running the installation test in your virtual environment:

oemof_installation_test

If the installation was successful, you will receive something like this:

*********
Solver installed with oemof:
glpk: working
cplex: not working
cbc: working
gurobi: not working
*********
oemof.solph successfully installed.

as an output.

Contributing

A warm welcome to all who want to join the developers and contribute to oemof.solph.

Information on the details and how to approach us can be found in the oemof documentation .

Citing

For explicitly citing solph, you might want to refer to DOI:10.1016/j.simpa.2020.100028, which gives an overview over the capabilities of solph. The core ideas of oemof as a whole are described in DOI:10.1016/j.esr.2018.07.001 (preprint at arXiv:1808.0807). To allow citing specific versions, we use the zenodo project to get a DOI for each version.

Examples

The linkage of specific modules of the various packages is called an application (app) and depicts for example a concrete energy system model. You can find a large variety of helpful examples in oemof’s example repository on github to download or clone. The examples show optimisations of different energy systems and are supposed to help new users to understand the framework’s structure. There is some elaboration on the examples in the respective repository. The repository has sections for each major release.

You are welcome to contribute your own examples via a pull request or by sending us an e-mail (see here for contact information).

License

Copyright (c) 2019 oemof developer group

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

User’s guide

Solph is an oemof-package, designed to create and solve linear or mixed-integer linear optimization problems. The package is based on pyomo. To create an energy system model generic and specific components are available. To get started with solph, checkout the examples in the Examples section.

This User’s guide provides a user-friendly introduction into oemof-solph, which includes small examples and nice illustrations. However, the functionality of oemof-solph go beyond the content of this User’s guide section. So, if you want to know all details of a certain component or a function, please go the API Reference. There, you will find a detailed and complete description of all oemof-solph modules.

How can I use solph?

To use solph you have to install oemof and at least one solver (see Installation), which can be used together with pyomo (e.g. CBC, GLPK, Gurobi, Cplex). See the pyomo installation guide for all supported solver. You can test it by executing one of the existing examples (see Examples, or directly oemof’s example repository). Be aware that the examples require the CBC solver but you can change the solver name in the example files to your solver.

Once the example work you are close to your first energy model.

Handling of Warnings

The solph library is designed to be as generic as possible to make it possible to use it in different use cases. This concept makes it difficult to raise Error or Warnings because sometimes untypical combinations of parameters are allowed even though they might be wrong in over 99% of the use cases.

Therefore, a SuspiciousUsageWarning was introduced. This warning will warn you if you do something untypical. If you are sure that you know what you are doing you can switch the warning off.

See the debugging module of oemof-tools for more information.

Set up an energy system

In most cases an EnergySystem object is defined when we start to build up an energy system model. The EnergySystem object will be the main container for the model.

To define an EnergySystem we need a Datetime index to define the time range and increment of our model. An easy way to this is to use the pandas time_range function. The following code example defines the year 2011 in hourly steps. See pandas date_range guide for more information.

import pandas as pd
my_index = pd.date_range('1/1/2011', periods=8760, freq='H')

This index can be used to define the EnergySystem:

import oemof.solph as solph
my_energysystem = solph.EnergySystem(timeindex=my_index)

Now you can start to add the components of the network.

Add components to the energy system

After defining an instance of the EnergySystem class you have to add all nodes you define in the following to your EnergySystem.

Basically, there are two types of nodes - components and buses. Every Component has to be connected with one or more buses. The connection between a component and a bus is the flow.

All solph components can be used to set up an energy system model but you should read the documentation of each component to learn about usage and restrictions. For example it is not possible to combine every component with every flow. Furthermore, you can add your own components in your application (see below) but we would be pleased to integrate them into solph if they are of general interest (see Feature requests and feedback).

An example of a simple energy system shows the usage of the nodes for real world representations:

The figure shows a simple energy system using the four basic network classes and the Bus class. If you remove the transmission line (transport 1 and transport 2) you get two systems but they are still one energy system in terms of solph and will be optimised at once.

There are different ways to add components to an energy system. The following line adds a bus object to the energy system defined above.

my_energysystem.add(solph.Bus())

It is also possible to assign the bus to a variable and add it afterwards. In that case it is easy to add as many objects as you like.

my_bus1 = solph.Bus()
my_bus2 = solph.Bus()
my_energysystem.add(my_bus1, my_bus2)

Therefore it is also possible to add lists or dictionaries with components but you have to dissolve them.

# add a list
my_energysystem.add(*my_list)

# add a dictionary
my_energysystem.add(*my_dictionary.values())

Bus

All flows into and out of a bus are balanced. Therefore an instance of the Bus class represents a grid or network without losses. To define an instance of a Bus only a unique label is necessary. If you do not set a label a random label is used but this makes it difficult to get the results later on.

To make it easier to connect the bus to a component you can optionally assign a variable for later use.

solph.Bus(label='natural_gas')
electricity_bus = solph.Bus(label='electricity')

Note

See the Bus class for all parameters and the mathematical background.

Flow

The flow class has to be used to connect. An instance of the Flow class is normally used in combination with the definition of a component. A Flow can be limited by upper and lower bounds (constant or time-dependent) or by summarised limits. For all parameters see the API documentation of the Flow class or the examples of the nodes below. A basic flow can be defined without any parameter.

solph.Flow()

Oemof has different types of flows but you should be aware that you cannot connect every flow type with every component.

Note

See the Flow class for all parameters and the mathematical background.

Components

Components are divided in three categories. Basic components (solph.network), additional components (solph.components) and custom components (solph.custom). The custom section was created to lower the entry barrier for new components. Be aware that these components are in an experimental state. Let us know if you have used and tested these components. This is the first step to move them to the components section.

See Solph components for a list of all components.

Optimise your energy system

The typical optimisation of an energy system in solph is the dispatch optimisation, which means that the use of the sources is optimised to satisfy the demand at least costs. Therefore, variable cost can be defined for all components. The cost for gas should be defined in the gas source while the variable costs of the gas power plant are caused by operating material. You can deviate from this scheme but you should keep it consistent to make it understandable for others.

Costs do not have to be monetary costs but could be emissions or other variable units.

Furthermore, it is possible to optimise the capacity of different components (see Using the investment mode).

# set up a simple least cost optimisation
om = solph.Model(my_energysystem)

# solve the energy model using the CBC solver
om.solve(solver='cbc', solve_kwargs={'tee': True})

If you want to analyse the lp-file to see all equations and bounds you can write the file to you disc. In that case you should reduce the timesteps to 3. This will increase the readability of the file.

# set up a simple least cost optimisation
om = solph.Model(my_energysystem)

# write the lp file for debugging or other reasons
om.write('path/my_model.lp', io_options={'symbolic_solver_labels': True})

Analysing your results

If you want to analyse your results, you should first dump your EnergySystem instance, otherwise you have to run the simulation again.

my_energysystem.results = processing.results(om)
my_energysystem.dump('my_path', 'my_dump.oemof')

If you need the meta results of the solver you can do the following:

my_energysystem.results['main'] = processing.results(om)
my_energysystem.results['meta'] = processing.meta_results(om)
my_energysystem.dump('my_path', 'my_dump.oemof')

To restore the dump you can simply create an EnergySystem instance and restore your dump into it.

import oemof.solph as solph
my_energysystem = solph.EnergySystem()
my_energysystem.restore('my_path', 'my_dump.oemof')
results = my_energysystem.results

# If you use meta results do the following instead of the previous line.
results = my_energysystem.results['main']
meta = my_energysystem.results['meta']

If you call dump/restore without any parameters, the dump will be stored as ‘es_dump.oemof’ into the ‘.oemof/dumps/’ folder created in your HOME directory.

See Handling Results to learn how to process, plot and analyse the results.

Solph components

• Sink (basic)
• Source (basic)
• Transformer (basic)
• ExtractionTurbineCHP (component)
• GenericCHP (component)
• Link (custom)
• GenericStorage (component)
• ElectricalLine (custom)
• GenericCAES (custom)
• SinkDSM (custom)

Sink (basic)

A sink is normally used to define the demand within an energy model but it can also be used to detect excesses.

The example shows the electricity demand of the electricity_bus defined above. The ‘my_demand_series’ should be sequence of normalised valueswhile the ‘nominal_value’ is the maximum demand the normalised sequence is multiplied with. Giving ‘my_demand_series’ as parameter ‘fix’ means that the demand cannot be changed by the solver.

solph.Sink(label='electricity_demand', inputs={electricity_bus: solph.Flow(
    fix=my_demand_series, nominal_value=nominal_demand)})

In contrast to the demand sink the excess sink has normally less restrictions but is open to take the whole excess.

solph.Sink(label='electricity_excess', inputs={electricity_bus: solph.Flow()})

Note

The Sink class is only a plug and provides no additional constraints or variables.

Source (basic)

A source can represent a pv-system, a wind power plant, an import of natural gas or a slack variable to avoid creating an in-feasible model.

While a wind power plant will have an hourly feed-in depending on the weather conditions the natural_gas import might be restricted by maximum value (nominal_value) and an annual limit (summed_max). As we do have to pay for imported gas we should set variable costs. Comparable to the demand series an fix is used to define a fixed the normalised output of a wind power plant. Alternatively, you might use max to allow for easy curtailment. The nominal_value sets the installed capacity.

solph.Source(
    label='import_natural_gas',
    outputs={my_energysystem.groups['natural_gas']: solph.Flow(
        nominal_value=1000, summed_max=1000000, variable_costs=50)})

solph.Source(label='wind', outputs={electricity_bus: solph.Flow(
    fix=wind_power_feedin_series, nominal_value=1000000)})

Note

The Source class is only a plug and provides no additional constraints or variables.

Transformer (basic)

An instance of the Transformer class can represent a node with multiple input and output flows such as a power plant, a transport line or any kind of a transforming process as electrolysis, a cooling device or a heat pump. The efficiency has to be constant within one time step to get a linear transformation. You can define a different efficiency for every time step (e.g. the thermal powerplant efficiency according to the ambient temperature) but this series has to be predefined and cannot be changed within the optimisation.

A condensing power plant can be defined by a transformer with one input (fuel) and one output (electricity).

b_gas = solph.Bus(label='natural_gas')
b_el = solph.Bus(label='electricity')

solph.Transformer(
    label="pp_gas",
    inputs={bgas: solph.Flow()},
    outputs={b_el: solph.Flow(nominal_value=10e10)},
    conversion_factors={electricity_bus: 0.58})

A CHP power plant would be defined in the same manner but with two outputs:

b_gas = solph.Bus(label='natural_gas')
b_el = solph.Bus(label='electricity')
b_th = solph.Bus(label='heat')

solph.Transformer(
    label='pp_chp',
    inputs={b_gas: Flow()},
    outputs={b_el: Flow(nominal_value=30),
             b_th: Flow(nominal_value=40)},
    conversion_factors={b_el: 0.3, b_th: 0.4})

A CHP power plant with 70% coal and 30% natural gas can be defined with two inputs and two outputs:

b_gas = solph.Bus(label='natural_gas')
b_coal = solph.Bus(label='hard_coal')
b_el = solph.Bus(label='electricity')
b_th = solph.Bus(label='heat')

solph.Transformer(
    label='pp_chp',
    inputs={b_gas: Flow(), b_coal: Flow()},
    outputs={b_el: Flow(nominal_value=30),
             b_th: Flow(nominal_value=40)},
    conversion_factors={b_el: 0.3, b_th: 0.4,
                        b_coal: 0.7, b_gas: 0.3})

A heat pump would be defined in the same manner. New buses are defined to make the code cleaner:

b_el = solph.Bus(label='electricity')
b_th_low = solph.Bus(label='low_temp_heat')
b_th_high = solph.Bus(label='high_temp_heat')

# The cop (coefficient of performance) of the heat pump can be defined as
# a scalar or a sequence.
cop = 3

solph.Transformer(
    label='heat_pump',
    inputs={b_el: Flow(), b_th_low: Flow()},
    outputs={b_th_high: Flow()},
    conversion_factors={b_el: 1/cop,
                        b_th_low: (cop-1)/cop})

If the low-temperature reservoir is nearly infinite (ambient air heat pump) the low temperature bus is not needed and, therefore, a Transformer with one input is sufficient.

Note

See the Transformer class for all parameters and the mathematical background.

ExtractionTurbineCHP (component)

The ExtractionTurbineCHP inherits from the Transformer (basic) class. Like the name indicates, the application example for the component is a flexible combined heat and power (chp) plant. Of course, an instance of this class can represent also another component with one input and two output flows and a flexible ratio between these flows, with the following constraints:

where is defined as:

where the first equation is the result of the relation between the input flow and the two output flows, the second equation stems from how the two output flows relate to each other, and the symbols used are defined as follows (with Variables (V) and Parameters (P)):

symbol	attribute	type	explanation
	flow[i, n, t]	V	fuel input flow
	flow[n, main_output, t]	V	electric power
	flow[n, tapped_output, t]	V	thermal output
	main_flow_loss_index[n, t]	P	power loss index
	conversion_factor_full_condensation[n, t]	P	electric efficiency without heat extraction
	conversion_factors[main_output][n, t]	P	electric efficiency with max heat extraction
	conversion_factors[tapped_output][n, t]	P	thermal efficiency with maximal heat extraction

These constraints are applied in addition to those of a standard Transformer. The constraints limit the range of the possible operation points, like the following picture shows. For a certain flow of fuel, there is a line of operation points, whose slope is defined by the power loss factor (in some contexts also referred to as ). The second constraint limits the decrease of electrical power and incorporates the backpressure coefficient .

For now, ExtractionTurbineCHP instances must have one input and two output flows. The class allows the definition of a different efficiency for every time step that can be passed as a series of parameters that are fixed before the optimisation. In contrast to the Transformer, a main flow and a tapped flow is defined. For the main flow you can define a separate conversion factor that applies when the second flow is zero (`conversion_factor_full_condensation`).

solph.ExtractionTurbineCHP(
    label='variable_chp_gas',
    inputs={b_gas: solph.Flow(nominal_value=10e10)},
    outputs={b_el: solph.Flow(), b_th: solph.Flow()},
    conversion_factors={b_el: 0.3, b_th: 0.5},
    conversion_factor_full_condensation={b_el: 0.5})

The key of the parameter ‘conversion_factor_full_condensation’ defines which of the two flows is the main flow. In the example above, the flow to the Bus ‘b_el’ is the main flow and the flow to the Bus ‘b_th’ is the tapped flow. The following plot shows how the variable chp (right) schedules it’s electrical and thermal power production in contrast to a fixed chp (left). The plot is the output of an example in the example repository.

Note

See the ExtractionTurbineCHP class for all parameters and the mathematical background.

GenericCHP (component)

With the GenericCHP class it is possible to model different types of CHP plants (combined cycle extraction turbines, back pressure turbines and motoric CHP), which use different ranges of operation, as shown in the figure below.

Combined cycle extraction turbines: The minimal and maximal electric power without district heating (red dots in the figure) define maximum load and minimum load of the plant. Beta defines electrical power loss through heat extraction. The minimal thermal condenser load to cooling water and the share of flue gas losses at maximal heat extraction determine the right boundary of the operation range.

solph.components.GenericCHP(
    label='combined_cycle_extraction_turbine',
    fuel_input={bgas: solph.Flow(
        H_L_FG_share_max=[0.19 for p in range(0, periods)])},
    electrical_output={bel: solph.Flow(
        P_max_woDH=[200 for p in range(0, periods)],
        P_min_woDH=[80 for p in range(0, periods)],
        Eta_el_max_woDH=[0.53 for p in range(0, periods)],
        Eta_el_min_woDH=[0.43 for p in range(0, periods)])},
    heat_output={bth: solph.Flow(
        Q_CW_min=[30 for p in range(0, periods)])},
    Beta=[0.19 for p in range(0, periods)],
    back_pressure=False)

For modeling a back pressure CHP, the attribute back_pressure has to be set to True. The ratio of power and heat production in a back pressure plant is fixed, therefore the operation range is just a line (see figure). Again, the P_min_woDH and P_max_woDH, the efficiencies at these points and the share of flue gas losses at maximal heat extraction have to be specified. In this case “without district heating” is not to be taken literally since an operation without heat production is not possible. It is advised to set Beta to zero, so the minimal and maximal electric power without district heating are the same as in the operation point (see figure). The minimal thermal condenser load to cooling water has to be zero, because there is no condenser besides the district heating unit.

solph.components.GenericCHP(
    label='back_pressure_turbine',
    fuel_input={bgas: solph.Flow(
        H_L_FG_share_max=[0.19 for p in range(0, periods)])},
    electrical_output={bel: solph.Flow(
        P_max_woDH=[200 for p in range(0, periods)],
        P_min_woDH=[80 for p in range(0, periods)],
        Eta_el_max_woDH=[0.53 for p in range(0, periods)],
        Eta_el_min_woDH=[0.43 for p in range(0, periods)])},
    heat_output={bth: solph.Flow(
        Q_CW_min=[0 for p in range(0, periods)])},
    Beta=[0 for p in range(0, periods)],
    back_pressure=True)

A motoric chp has no condenser, so Q_CW_min is zero. Electrical power does not depend on the amount of heat used so Beta is zero. The minimal and maximal electric power (without district heating) and the efficiencies at these points are needed, whereas the use of electrical power without using thermal energy is not possible. With Beta=0 there is no difference between these points and the electrical output in the operation range. As a consequence of the functionality of a motoric CHP, share of flue gas losses at maximal heat extraction but also at minimal heat extraction have to be specified.

solph.components.GenericCHP(
    label='motoric_chp',
    fuel_input={bgas: solph.Flow(
        H_L_FG_share_max=[0.18 for p in range(0, periods)],
        H_L_FG_share_min=[0.41 for p in range(0, periods)])},
    electrical_output={bel: solph.Flow(
        P_max_woDH=[200 for p in range(0, periods)],
        P_min_woDH=[100 for p in range(0, periods)],
        Eta_el_max_woDH=[0.44 for p in range(0, periods)],
        Eta_el_min_woDH=[0.40 for p in range(0, periods)])},
    heat_output={bth: solph.Flow(
        Q_CW_min=[0 for p in range(0, periods)])},
    Beta=[0 for p in range(0, periods)],
    back_pressure=False)

Modeling different types of plants means telling the component to use different constraints. Constraint 1 to 9 are active in all three cases. Constraint 10 depends on the attribute back_pressure. If true, the constraint is an equality, if not it is a less or equal. Constraint 11 is only needed for modeling motoric CHP which is done by setting the attribute H_L_FG_share_min.

where depends on the CHP being back pressure or not.

The coefficients and can be determined given the efficiencies maximal/minimal load:

If is given, e.g. for a motoric CHP:

The symbols used are defined as follows (with Variables (V) and Parameters (P)):

math. symbol	attribute	type	explanation
	H_F[n,t]	V	input of enthalpy through fuel input
	P[n,t]	V	provided electric power
	P_woDH[n,t]	V	electric power without district heating
	P_min_woDH[n,t]	P	min. electric power without district heating
	P_max_woDH[n,t]	P	max. electric power without district heating
	Q[n,t]	V	provided heat
	Q_CW_min[n,t]	P	minimal therm. condenser load to cooling water
	H_L_FG_min[n,t]	V	flue gas enthalpy loss at min heat extraction
	H_L_FG_max[n,t]	V	flue gas enthalpy loss at max heat extraction
	H_L_FG_share_min[n,t]	P	share of flue gas loss at min heat extraction
	H_L_FG_share_max[n,t]	P	share of flue gas loss at max heat extraction
	Y[n,t]	V	status variable on/off
	n.alphas[0][n,t]	P	coefficient describing efficiency
	n.alphas[1][n,t]	P	coefficient describing efficiency
	Beta[n,t]	P	power loss index
	Eta_el_min_woDH[n,t]	P	el. eff. at min. fuel flow w/o distr. heating
	Eta_el_max_woDH[n,t]	P	el. eff. at max. fuel flow w/o distr. heating

Note

See the GenericCHP class for all parameters and the mathematical background.

GenericStorage (component)

A component to model a storage with its basic characteristics. The GenericStorage is designed for one input and one output. The nominal_storage_capacity of the storage signifies the storage capacity. You can either set it to the net capacity or to the gross capacity and limit it using the min/max attribute. To limit the input and output flows, you can define the nominal_value in the Flow objects. Furthermore, an efficiency for loading, unloading and a loss rate can be defined.

solph.GenericStorage(
    label='storage',
    inputs={b_el: solph.Flow(nominal_value=9, variable_costs=10)},
    outputs={b_el: solph.Flow(nominal_value=25, variable_costs=10)},
    loss_rate=0.001, nominal_storage_capacity=50,
    inflow_conversion_factor=0.98, outflow_conversion_factor=0.8)

For initialising the state of charge before the first time step (time step zero) the parameter initial_storage_level (default value: None) can be set by a numeric value as fraction of the storage capacity. Additionally the parameter balanced (default value: True) sets the relation of the state of charge of time step zero and the last time step. If balanced=True, the state of charge in the last time step is equal to initial value in time step zero. Use balanced=False with caution as energy might be added to or taken from the energy system due to different states of charge in time step zero and the last time step. Generally, with these two parameters four configurations are possible, which might result in different solutions of the same optimization model:

• initial_storage_level=None, balanced=True (default setting): The state of charge in time step zero is a result of the optimization. The state of charge of the last time step is equal to time step zero. Thus, the storage is not violating the energy conservation by adding or taking energy from the system due to different states of charge at the beginning and at the end of the optimization period.
• initial_storage_level=0.5, balanced=True: The state of charge in time step zero is fixed to 0.5 (50 % charged). The state of charge in the last time step is also constrained by 0.5 due to the coupling parameter balanced set to True.
• initial_storage_level=None, balanced=False: Both, the state of charge in time step zero and the last time step are a result of the optimization and not coupled.
• initial_storage_level=0.5, balanced=False: The state of charge in time step zero is constrained by a given value. The state of charge of the last time step is a result of the optimization.

The following code block shows an example of the storage parametrization for the second configuration:

solph.GenericStorage(
    label='storage',
    inputs={b_el: solph.Flow(nominal_value=9, variable_costs=10)},
    outputs={b_el: solph.Flow(nominal_value=25, variable_costs=10)},
    loss_rate=0.001, nominal_storage_capacity=50,
    initial_storage_level=0.5, balanced=True,
    inflow_conversion_factor=0.98, outflow_conversion_factor=0.8)

If you want to view the temporal course of the state of charge of your storage after the optimisation, you need to check the storage_content in the results:

from oemof.solph import processing, views
results = processing.results(om)
column_name = (('your_storage_label', 'None'), 'storage_content')
SC = views.node(results, 'your_storage_label')['sequences'][column_name]

The storage_content is the absolute value of the current stored energy. By calling:

views.node(results, 'your_storage_label')['scalars']

you get the results of the scalar values of your storage, e.g. the initial storage content before time step zero (init_content).

For more information see the definition of the GenericStorage class or check the example repository of oemof.

Using an investment object with the GenericStorage component

Based on the GenericStorage object the GenericInvestmentStorageBlock adds two main investment possibilities.

• Invest into the flow parameters e.g. a turbine or a pump
• Invest into capacity of the storage e.g. a basin or a battery cell

Investment in this context refers to the value of the variable for the ‘nominal_value’ (installed capacity) in the investment mode.

As an addition to other flow-investments, the storage class implements the possibility to couple or decouple the flows with the capacity of the storage. Three parameters are responsible for connecting the flows and the capacity of the storage:

• ‘ invest_relation_input_capacity ‘ fixes the input flow investment to the capacity investment. A ratio of ‘1’ means that the storage can be filled within one time-period.
• ‘ invest_relation_output_capacity ‘ fixes the output flow investment to the capacity investment. A ratio of ‘1’ means that the storage can be emptied within one period.
• ‘ invest_relation_input_output ‘ fixes the input flow investment to the output flow investment. For values <1, the input will be smaller and for values >1 the input flow will be larger.

You should not set all 3 parameters at the same time, since it will lead to overdetermination.

The following example pictures a Pumped Hydroelectric Energy Storage (PHES). Both flows and the storage itself (representing: pump, turbine, basin) are free in their investment. You can set the parameters to None or delete them as None is the default value.

solph.GenericStorage(
    label='PHES',
    inputs={b_el: solph.Flow(investment= solph.Investment(ep_costs=500))},
    outputs={b_el: solph.Flow(investment= solph.Investment(ep_costs=500)},
    loss_rate=0.001,
    inflow_conversion_factor=0.98, outflow_conversion_factor=0.8),
    investment = solph.Investment(ep_costs=40))

The following example describes a battery with flows coupled to the capacity of the storage.

solph.GenericStorage(
    label='battery',
    inputs={b_el: solph.Flow()},
    outputs={b_el: solph.Flow()},
    loss_rate=0.001,
    inflow_conversion_factor=0.98,
     outflow_conversion_factor=0.8,
    invest_relation_input_capacity = 1/6,
    invest_relation_output_capacity = 1/6,
    investment = solph.Investment(ep_costs=400))

Note

See the GenericStorage class for all parameters and the mathematical background.

OffsetTransformer (component)

The OffsetTransformer object makes it possible to create a Transformer with different efficiencies in part load condition. For this object it is necessary to define the inflow as a nonconvex flow and to set a minimum load. The following example illustrates how to define an OffsetTransformer for given information for the output:

eta_min = 0.5       # efficiency at minimal operation point
eta_max = 0.8       # efficiency at nominal operation point
P_out_min = 20      # absolute minimal output power
P_out_max = 100     # absolute nominal output power

# calculate limits of input power flow
P_in_min = P_out_min / eta_min
P_in_max = P_out_max / eta_max

# calculate coefficients of input-output line equation
c1 = (P_out_max-P_out_min)/(P_in_max-P_in_min)
c0 = P_out_max - c1*P_in_max

# define OffsetTransformer
solph.custom.OffsetTransformer(
    label='boiler',
    inputs={bfuel: solph.Flow(
        nominal_value=P_in_max,
        max=1,
        min=P_in_min/P_in_max,
        nonconvex=solph.NonConvex())},
    outputs={bth: solph.Flow()},
    coefficients = [c0, c1])

This example represents a boiler, which is supplied by fuel and generates heat. It is assumed that the nominal thermal power of the boiler (output power) is 100 (kW) and the efficiency at nominal power is 80 %. The boiler cannot operate under 20 % of nominal power, in this case 20 (kW) and the efficiency at that part load is 50 %. Note that the nonconvex flow has to be defined for the input flow. By using the OffsetTransformer a linear relation of in- and output power with a power dependent efficiency is generated. The following figures illustrate the relations:

Now, it becomes clear, why this object has been named OffsetTransformer. The linear equation of in- and outflow does not hit the origin, but is offset. By multiplying the Offset with the binary status variable of the nonconvex flow, the origin (0, 0) becomes part of the solution space and the boiler is allowed to switch off:

Variables (V) and Parameters (P)

symbol	attribute	type	explanation
	flow[n, o, t]	V	Power of output
	flow[i, n, t]	V	Power of input
	status[i, n, t]	V	binary status variable of nonconvex input flow
	coefficients[1][n, t]	P	linear coefficient 1 (slope)
	coefficients[0][n, t]	P	linear coefficient 0 (y-intersection)

The following figures shows the efficiency dependent on the output power, which results in a nonlinear relation:

The parameters and can be given by scalars or by series in order to define a different efficiency equation for every timestep.

Note

See the OffsetTransformer class for all parameters and the mathematical background.

ElectricalLine (custom)

Electrical line.

Note

See the ElectricalLine class for all parameters and the mathematical background.

GenericCAES (custom)

Compressed Air Energy Storage (CAES). The following constraints describe the CAES:

Table: Symbols and attribute names of variables and parameters

Variables (V) and Parameters (P)

symbol	attribute	type	explanation
	cmp_st[n,t]	V	Status of compression
	cmp_p[n,t]	V	Compression power
	cmp_p_max[n,t]	V	Max. compression power
	cmp_q_out_sum[n,t]	V	Summed heat flow in compression
	cmp_q_waste[n,t]	V	Waste heat flow from compression
	exp_st[n,t]	V	Status of expansion (binary)
	exp_p[n,t]	V	Expansion power
	exp_p_max[n,t]	V	Max. expansion power
	exp_q_in_sum[n,t]	V	Summed heat flow in expansion
	exp_q_fuel_in[n,t]	V	Heat (external) flow into expansion
	exp_q_add_in[n,t]	V	Additional heat flow into expansion
	cav_level[n,t]	V	Filling level if CAE
	cav_e_in[n,t]	V	Exergy flow into CAS
	cav_e_out[n,t]	V	Exergy flow from CAS
	tes_level[n,t]	V	Filling level of Thermal Energy Storage (TES)
	tes_e_in[n,t]	V	Heat flow into TES
	tes_e_out[n,t]	V	Heat flow from TES
	cmp_p_max_b[n,t]	P	Specific y-intersection
	cmp_q_out_b[n,t]	P	Specific y-intersection
	exp_p_max_b[n,t]	P	Specific y-intersection
	exp_q_in_b[n,t]	P	Specific y-intersection
	cav_e_in_b[n,t]	P	Specific y-intersection
	cav_e_out_b[n,t]	P	Specific y-intersection
	cmp_p_max_m[n,t]	P	Specific slope
	cmp_q_out_m[n,t]	P	Specific slope
	exp_p_max_m[n,t]	P	Specific slope
	exp_q_in_m[n,t]	P	Specific slope
	cav_e_in_m[n,t]	P	Specific slope
	cav_e_out_m[n,t]	P	Specific slope
	cmp_p_min[n,t]	P	Min. compression power
	cmp_q_tes_share[n,t]	P	Ratio between waste heat flow and heat flow into TES
	exp_q_tes_share[n,t]	P	Ratio between external heat flow into expansion and heat flows from TES and additional source
	m.timeincrement[n,t]	P	Time interval length
	tes_level_max[n,t]	P	Max. filling level of TES
	cav_level_max[n,t]	P	Max. filling level of TES
	cav_eta_tmp[n,t]	P	Temporal efficiency (loss factor to take intertemporal losses into account)
	flow[list(n.electrical_input.keys())[0], n, t]	P	Electr. power input into compression
	flow[n, list(n.electrical_output.keys())[0], t]	P	Electr. power output of expansion
	flow[list(n.fuel_input.keys())[0], n, t]	P	Heat input (external) into Expansion

Note

See the GenericCAES class for all parameters and the mathematical background.

Link (custom)

Link.

Note

See the Link class for all parameters and the mathematical background.

SinkDSM (custom)

SinkDSM can used to represent flexibility in a demand time series. It can represent both, load shifting or load shedding. For load shifting, elasticity of the demand is described by upper (capacity_up) and lower (capacity_down) bounds where within the demand is allowed to vary. Upwards shifted demand is then balanced with downwards shifted demand. For load shedding, shedding capability is described by capacity_down. It both, load shifting and load shedding are allowed, capacity_down limits the sum of both downshift categories.

SinkDSM provides three approaches how the Demand-Side Management (DSM) flexibility is represented in constraints It can be used for both, dispatch and investments modeling.

• “DLR”: Implementation of the DSM modeling approach from by Gils (2015): `Balancing of Intermittent Renewable Power Generation by Demand Response and Thermal Energy Storage, Stuttgart, <http://dx.doi.org/10.18419/opus-6888>,`_, Details: SinkDSMDLRBlock and SinkDSMDLRInvestmentBlock
• “DIW”: Implementation of the DSM modeling approach by Zerrahn & Schill (2015): On the representation of demand-side management in power system models, in: Energy (84), pp. 840-845, 10.1016/j.energy.2015.03.037. Details: SinkDSMDIWBlock and SinkDSMDIWInvestmentBlock
• “oemof”: Is a fairly simple approach. Within a defined windows of time steps, demand can be shifted within the defined bounds of elasticity. The window sequentially moves forwards. Details: SinkDSMOemofBlock and SinkDSMOemofInvestmentBlock

Cost can be associated to either demand up shifts or demand down shifts or both.

This small example of PV, grid and SinkDSM shows how to use the component

# Create some data
pv_day = [(-(1 / 6 * x ** 2) + 6) / 6 for x in range(-6, 7)]
pv_ts = [0] * 6 + pv_day + [0] * 6
data_dict = {"demand_el": [3] * len(pv_ts),
             "pv": pv_ts,
             "Cap_up": [0.5] * len(pv_ts),
             "Cap_do": [0.5] * len(pv_ts)}
data = pd.DataFrame.from_dict(data_dict)

# Do timestamp stuff
datetimeindex = pd.date_range(start='1/1/2013', periods=len(data.index), freq='H')
data['timestamp'] = datetimeindex
data.set_index('timestamp', inplace=True)

# Create Energy System
es = solph.EnergySystem(timeindex=datetimeindex)
Node.registry = es

# Create bus representing electricity grid
b_elec = solph.Bus(label='Electricity bus')

# Create a back supply
grid = solph.Source(label='Grid',
                    outputs={
                        b_elec: solph.Flow(
                            nominal_value=10000,
                            variable_costs=50)}
                    )

# PV supply from time series
s_wind = solph.Source(label='wind',
                      outputs={
                          b_elec: solph.Flow(
                              fix=data['pv'],
                              nominal_value=3.5)}
                      )

# Create DSM Sink
demand_dsm = solph.custom.SinkDSM(label="DSM",
                                  inputs={b_elec: solph.Flow()},
                                  demand=data['demand_el'],
                                  capacity_up=data["Cap_up"],
                                  capacity_down=data["Cap_do"],
                                  delay_time=6,
                                  max_demand=1,
                                  max_capacity_up=1,
                                  max_capacity_down=1,
                                  approach="DIW",
                                  cost_dsm_down=5)

Yielding the following results

Note

• Keyword argument method from v0.4.1 has been renamed to approach in v0.4.2

and methods have been renamed.

• The parameters demand, capacity_up and capacity_down have been normalized to allow

investments modeling. To retreive the original dispatch behaviour from v0.4.1, set max_demand=1, max_capacity_up=1, max_capacity_down=1.

• This component is a candidate component. It’s implemented as a custom

component for users that like to use and test the component at early stage. Please report issues to improve the component.

• See the SinkDSM class for all parameters and the mathematical

background.

Using the investment mode

As described in Optimise your energy system the typical way to optimise an energy system is the dispatch optimisation based on marginal costs. Solph also provides a combined dispatch and investment optimisation. Based on investment costs you can compare the usage of existing components against building up new capacity. The annual savings by building up new capacity must therefore compensate the annuity of the investment costs (the time period does not have to be one year but depends on your Datetime index).

See the API of the Investment class to see all possible parameters.

Basically an instance of the investment class can be added to a Flow or a Storage. All parameters that usually refer to the nominal_value/capacity will now refer to the investment variables and existing capacity. It is also possible to set a maximum limit for the capacity that can be build. If existing capacity is considered for a component with investment mode enabled, the ep_costs still apply only to the newly built capacity.

The investment object can be used in Flows and some components. See the Solph components section for detailed information of each component.

For example if you want to find out what would be the optimal capacity of a wind power plant to decrease the costs of an existing energy system, you can define this model and add an investment source. The wind_power_time_series has to be a normalised feed-in time series of you wind power plant. The maximum value might be caused by limited space for wind turbines.

solph.Source(label='new_wind_pp', outputs={electricity: solph.Flow(
    fix=wind_power_time_series,
    investment=solph.Investment(ep_costs=epc, maximum=50000))})

Let’s slightly alter the case and consider for already existing wind power capacity of 20,000 kW. We’re still expecting the total wind power capacity, thus we allow for 30,000 kW of new installations and formulate as follows.

solph.Source(label='new_wind_pp', outputs={electricity: solph.Flow(
    fix=wind_power_time_series,
        investment=solph.Investment(ep_costs=epc,
                                    maximum=30000,
                                    existing=20000))})

The periodical costs (ep_costs) are typically calculated as follows:

capex = 1000  # investment cost
lifetime = 20  # life expectancy
wacc = 0.05  # weighted average of capital cost
epc = capex * (wacc * (1 + wacc) ** lifetime) / ((1 + wacc) ** lifetime - 1)

This also implemented in annuity(). The code above would look like this:

from oemof.tools import economics
epc = economics.annuity(1000, 20, 0.05)

So far, the investment costs and the installed capacity are mathematically a line through origin. But what if there is a minimum threshold for doing an investment, e.g. you cannot buy gas turbines lower than a certain nominal power, or, the marginal costs of bigger plants decrease. Therefore, you can use the parameter nonconvex and offset of the investment class. Both, work with investment in flows and storages. Here is an example of an transformer:

trafo = solph.Transformer(
    label='transformer_nonconvex',
    inputs={bus_0: solph.Flow()},
    outputs={bus_1: solph.Flow(
        investment=solph.Investment(
            ep_costs=4,
            maximum=100,
            minimum=20,
            nonconvex=True,
            offset=400))},
    conversion_factors={bus_1: 0.9})

In this examples, it is assumed, that independent of the size of the transformer, there are always fix investment costs of 400 (€). The minimum investment size is 20 (kW) and the costs per installed unit are 4 (€/kW). With this option, you could theoretically approximate every cost function you want. But be aware that for every nonconvex investment flow or storage you are using, an additional binary variable is created. This might boost your computing time into the limitless.

The following figures illustrates the use of the nonconvex investment flow. Here, is the offset value and is the ep_costs value:

In case of a convex investment (which is the default setting nonconvex=Flase), the minimum attribute leads to a forced investment, whereas in the nonconvex case, the investment can become zero as well.

The calculation of the specific costs per kilowatt installed capacity results in the following relation for convex and nonconvex investments:

See InvestmentFlow and GenericInvestmentStorageBlock for all the mathematical background, like variables and constraints, which are used.

Note

At the moment the investment class is not compatible with the MIP classes NonConvex.

Mixed Integer (Linear) Problems

Solph also allows you to model components with respect to more technical details such as a minimal power production. Therefore, the class NonConvex exists in the options module. Note that the usage of this class is currently not compatible with the Investment class.

If you want to use the functionality of the options-module, the only thing you have to do is to invoke a class instance inside your Flow() - declaration:

b_gas = solph.Bus(label='natural_gas')
b_el = solph.Bus(label='electricity')
b_th = solph.Bus(label='heat')

solph.Transformer(
    label='pp_chp',
    inputs={b_gas: Flow()},
    outputs={b_el: Flow(nominal_value=30,
                        min=0.5,
                        nonconvex=NonConvex()),
             b_th: Flow(nominal_value=40)},
    conversion_factors={b_el: 0.3, b_th: 0.4})

The NonConvex() object of the electrical output of the created LinearTransformer will create a ‘status’ variable for the flow. This will be used to model for example minimal/maximal power production constraints if the attributes min/max of the flow are set. It will also be used to include start up constraints and costs if corresponding attributes of the class are provided. For more information see the API of the NonConvex class and its corresponding block class NonConvex.

Note

The usage of this class can sometimes be tricky as there are many interdenpendencies. So check out the examples and do not hesitate to ask the developers if your model does not work as expected.

Adding additional constraints

You can add additional constraints to your Model. See flexible_modelling in the example repository to learn how to do it.

Some predefined additional constraints can be found in the constraints module.

• Emission limit for the model -> emission_limit()
• Generic integral limit (general form of emission limit) -> generic_integral_limit()
• Coupling of two variables e.g. investment variables) with a factor -> equate_variables()
• Overall investment limit -> investment_limit()
• Generic investment limit -> additional_investment_flow_limit()
• Limit active flow count -> limit_active_flow_count()
• Limit active flow count by keyword -> limit_active_flow_count_by_keyword()

The Grouping module (Sets)

To construct constraints, variables and objective expressions inside the blocks and the models modules, so called groups are used. Consequently, certain constraints are created for all elements of a specific group. Thus, mathematically the groups depict sets of elements inside the model.

The grouping is handled by the solph grouping module groupings which is based on the oemof core groupings functionality. You do not need to understand how the underlying functionality works. Instead, checkout how the solph grouping module is used to create groups.

The simplest form is a function that looks at every node of the energy system and returns a key for the group depending e.g. on node attributes:

 def constraint_grouping(node):
     if isinstance(node, Bus) and node.balanced:
         return blocks.Bus
     if isinstance(node, Transformer):
         return blocks.Transformer
GROUPINGS = [constraint_grouping]

This function can be passed in a list to groupings of oemof.solph.network.EnergySystem. So that we end up with two groups, one with all Transformers and one with all Buses that are balanced. These groups are simply stored in a dictionary. There are some advanced functionalities to group two connected nodes with their connecting flow and others (see for example: FlowsWithNodes).

Using the Excel (csv) reader

Alternatively to a manual creation of energy system component objects as describe above, can also be created from a excel sheet (libreoffice, gnumeric…).

The idea is to create different sheets within one spreadsheet file for different components. Afterwards you can loop over the rows with the attributes in the columns. The name of the columns may differ from the name of the attribute. You may even create two sheets for the GenericStorage class with attributes such as C-rate for batteries or capacity of turbine for a PHES.

Once you have create your specific excel reader you can lower the entry barrier for other users. It is some sort of a GUI in form of platform independent spreadsheet software and to make data and models exchangeable in one archive.

See oemof’s example repository for an excel reader example.

Handling Results

The main purpose of the processing module is to collect and organise results. The views module will provide some typical representations of the results. Plots are not part of solph, because plots are highly individual. However, the provided pandas.DataFrames are a good start for plots. Some basic functions for plotting of optimisation results can be found in the separate repository oemof_visio.

The processing.results function gives back the results as a python dictionary holding pandas Series for scalar values and pandas DataFrames for all nodes and flows between them. This way we can make use of the full power of the pandas package available to process the results.

See the pandas documentation to learn how to visualise, read or write or how to access parts of the DataFrame to process them.

The results chapter consists of three parts:

The first step is the processing of the results (Collecting results) This is followed by basic examples of the general analysis of the results (General approach) and finally the use of functionality already included in solph for providing a quick access to your results (Easy access). Especially for larger energy systems the general approach will help you to write your own results processing functions.

Collecting results

Collecting results can be done with the help of the processing module. A solved model is needed:

[...]
model.solve(solver=solver)
results = solph.processing.results(model)

The scalars and sequences describe nodes (with keys like (node, None)) and flows between nodes (with keys like (node_1, node_2)). You can directly extract the data in the dictionary by using these keys, where “node” is the name of the object you want to address. Processing the results is the prerequisite for the examples in the following sections.

General approach

As stated above, after processing you will get a dictionary with all result data. If you want to access your results directly via labels, you can continue with Easy access. For a systematic analysis list comprehensions are the easiest way of filtering and analysing your results.

The keys of the results dictionary are tuples containing two nodes. Since flows have a starting node and an ending node, you get a list of all flows by filtering the results using the following expression:

flows = [x for x in results.keys() if x[1] is not None]

On the same way you can get a list of all nodes by applying:

nodes = [x for x in results.keys() if x[1] is None]

Probably you will just get storages as nodes, if you have some in your energy system. Note, that just nodes containing decision variables are listed, e.g. a Source or a Transformer object does not have decision variables. These are in the flows from or to the nodes.

All items within the results dictionary are dictionaries and have two items with ‘scalars’ and ‘sequences’ as keys:

for flow in flows:
    print(flow)
    print(results[flow]['scalars'])
    print(results[flow]['sequences'])

There many options of filtering the flows and nodes as you prefer. The following will give you all flows which are outputs of transformer:

flows_from_transformer = [x for x in flows if isinstance(
    x[0], solph.Transformer)]

You can filter your flows, if the label of in- or output contains a given string, e.g.:

flows_to_elec = [x for x in results.keys() if 'elec' in x[1].label]

Getting all labels of the starting node of your investment flows:

flows_invest = [x[0].label for x in flows if hasattr(
    results[x]['scalars'], 'invest')]

Easy access

The solph package provides some functions which will help you to access your results directly via labels, which is helpful especially for small energy systems. So, if you want to address objects by their label, you can convert the results dictionary such that the keys are changed to strings given by the labels:

views.convert_keys_to_strings(results)
print(results[('wind', 'bus_electricity')]['sequences']

Another option is to access data belonging to a grouping by the name of the grouping (note also this section on groupings. Given the label of an object, e.g. ‘wind’ you can access the grouping by its label and use this to extract data from the results dictionary.

node_wind = energysystem.groups['wind']
print(results[(node_wind, bus_electricity)])

However, in many situations it might be convenient to use the views module to collect information on a specific node. You can request all data related to a specific node by using either the node’s variable name or its label:

data_wind = solph.views.node(results, 'wind')

A function for collecting and printing meta results, i.e. information on the objective function, the problem and the solver, is provided as well:

meta_results = solph.processing.meta_results(om)
pp.pprint(meta_results)

API Reference

oemof.solph package

Submodules

oemof.solph.blocks module

Creating sets, variables, constraints and parts of the objective function for the specified groups.

oemof.solph.components module

This module is designed to hold components with their classes and associated individual constraints (blocks) and groupings. Therefore this module holds the class definition and the block directly located by each other.

oemof.solph.constraints module

Additional constraints to be used in an oemof energy model.

oemof.solph.console_scripts module

This module can be used to check the installation.

This is not an illustrated example.

SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de> SPDX-FileCopyrightText: jnnr

SPDX-License-Identifier: MIT

oemof.solph.console_scripts.check_oemof_installation(silent=False)

oemof.solph.custom module

This module is designed to hold in-development components with their classes and associated individual constraints (blocks) and groupings.

Requirements for documentation and unit tests are relaxed, so code within this module might not have production quality.

oemof.solph.groupings module

Groupings needed on an energy system for it to work with solph.

If you want to use solph on an energy system, you need to create it with these groupings specified like this:

from oemof.network import EnergySystem import solph

energy_system = EnergySystem(groupings=solph.GROUPINGS)

SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de> SPDX-FileCopyrightText: Simon Hilpert SPDX-FileCopyrightText: Cord Kaldemeyer SPDX-FileCopyrightText: Stephan Günther

SPDX-License-Identifier: MIT

oemof.solph.groupings.constraint_grouping(node, fallback=<function <lambda>>)

Grouping function for constraints.

This function can be passed in a list to groupings of oemof.solph.network.EnergySystem.

Parameters:

• node (Node <oemof.network.Node) – The node for which the figure out a constraint group.
• fallback (callable, optional) – A function of one argument. If node doesn’t have a constraint_group attribute, this is used to group the node instead. Defaults to not group the node at all.

oemof.solph.helpers module

This is a collection of helper functions which work on their own and can be used by various classes. If there are too many helper-functions, they will be sorted in different modules.

SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de> SPDX-FileCopyrightText: Caroline Möller SPDX-FileCopyrightText: henhuy SPDX-FileCopyrightText: gplssm SPDX-FileCopyrightText: Stephan Günther SPDX-FileCopyrightText: elisapap

SPDX-License-Identifier: MIT

oemof.solph.helpers.calculate_timeincrement(timeindex, fill_value=None)

Calculates timeincrement for timeindex

Parameters:

• timeindex (pd.DatetimeIndex) – timeindex of energysystem
• fill_value (numerical) – timeincrement for first timestep in hours

oemof.solph.helpers.extend_basic_path(subfolder)

Returns a path based on the basic oemof path and creates it if necessary. The subfolder is the name of the path extension.

oemof.solph.helpers.flatten(d, parent_key='', sep='_')

Flatten dictionary by compressing keys.

See: https://stackoverflow.com/questions/6027558/

flatten-nested-python-dictionaries-compressing-keys

d : dictionary sep : separator for flattening keys

Returns:dict

oemof.solph.helpers.get_basic_path()

Returns the basic oemof path and creates it if necessary. The basic path is the ‘.oemof’ folder in the $HOME directory.

oemof.solph.models module

Solph Optimization Models.

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SPDX-License-Identifier: MIT

class oemof.solph.models.BaseModel(energysystem, **kwargs)

Bases: pyomo.core.base.PyomoModel.ConcreteModel

The BaseModel for other solph-models (Model, MultiPeriodModel, etc.)

Parameters:

• energysystem (EnergySystem object) – Object that holds the nodes of an oemof energy system graph
• constraint_groups (list (optional)) – Solph looks for these groups in the given energy system and uses them to create the constraints of the optimization problem. Defaults to Model.CONSTRAINTS
• objective_weighting (array like (optional)) – Weights used for temporal objective function expressions. If nothing is passed timeincrement will be used which is calculated from the freq length of the energy system timeindex .
• auto_construct (boolean) – If this value is true, the set, variables, constraints, etc. are added, automatically when instantiating the model. For sequential model building process set this value to False and use methods _add_parent_block_sets, _add_parent_block_variables, _add_blocks, _add_objective
• Attributes
• ———–
• timeincrement (sequence) – Time increments.
• flows (dict) – Flows of the model.
• name (str) – Name of the model.
• es (solph.EnergySystem) – Energy system of the model.
• meta (pyomo.opt.results.results_.SolverResults or None) – Solver results.
• dual (… or None)
• rc (… or None)

CONSTRAINT_GROUPS = []

receive_duals()

Method sets solver suffix to extract information about dual variables from solver. Shadow prices (duals) and reduced costs (rc) are set as attributes of the model.

relax_problem()

Relaxes integer variables to reals of optimization model self.

results()

Returns a nested dictionary of the results of this optimization

solve(solver='cbc', solver_io='lp', **kwargs)

Takes care of communication with solver to solve the model.

Parameters:

• solver (string) – solver to be used e.g. “glpk”,”gurobi”,”cplex”
• solver_io (string) – pyomo solver interface file format: “lp”,”python”,”nl”, etc.
• **kwargs (keyword arguments) – Possible keys can be set see below:

Other Parameters:  

• solve_kwargs (dict) – Other arguments for the pyomo.opt.SolverFactory.solve() method Example : {“tee”:True}
• cmdline_options (dict) – Dictionary with command line options for solver e.g. {“mipgap”:”0.01”} results in “–mipgap 0.01” {“interior”:” “} results in “–interior” Gurobi solver takes numeric parameter values such as {“method”: 2}

class oemof.solph.models.Model(energysystem, **kwargs)

Bases: oemof.solph.models.BaseModel

An energy system model for operational and investment optimization.

Parameters:

• energysystem (EnergySystem object) – Object that holds the nodes of an oemof energy system graph
• constraint_groups (list) – Solph looks for these groups in the given energy system and uses them to create the constraints of the optimization problem. Defaults to Model.CONSTRAINTS
• **The following basic sets are created**
• NODES – A set with all nodes of the given energy system.
• TIMESTEPS – A set with all timesteps of the given time horizon.
• FLOWS – A 2 dimensional set with all flows. Index: (source, target)
• **The following basic variables are created**
• flow – Flow from source to target indexed by FLOWS, TIMESTEPS. Note: Bounds of this variable are set depending on attributes of the corresponding flow object.

CONSTRAINT_GROUPS = [<class 'oemof.solph.blocks.bus.Bus'>, <class 'oemof.solph.blocks.transformer.Transformer'>, <class 'oemof.solph.blocks.investment_flow.InvestmentFlow'>, <class 'oemof.solph.blocks.flow.Flow'>, <class 'oemof.solph.blocks.non_convex_flow.NonConvexFlow'>]

oemof.solph.network module

Classes used to model energy supply systems within solph.

Classes are derived from oemof core network classes and adapted for specific optimization tasks. An energy system is modelled as a graph/network of nodes with very specific constraints on which types of nodes are allowed to be connected.

oemof.solph.options module

Optional classes to be added to a network class.

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SPDX-License-Identifier: MIT

class oemof.solph.options.Investment(maximum=inf, minimum=0, ep_costs=0, existing=0, nonconvex=False, offset=0, **kwargs)

Bases: object

Parameters:

• maximum (float, or ) – Maximum of the additional invested capacity
• minimum (float, or ) – Minimum of the additional invested capacity. If nonconvex is True, minimum defines the threshold for the invested capacity.
• ep_costs (float, ) – Equivalent periodical costs for the investment per flow capacity.
• existing (float, or ) – Existing / installed capacity. The invested capacity is added on top of this value. Not applicable if nonconvex is set to True.
• nonconvex (bool) – If True, a binary variable for the status of the investment is created. This enables additional fix investment costs (offset) independent of the invested flow capacity. Therefore, use the offset parameter.
• offset (float, ) – Additional fix investment costs. Only applicable if nonconvex is set to True.

For the variables, constraints and parts of the objective function, which are created, see oemof.solph.blocks.InvestmentFlow and oemof.solph.components.GenericInvestmentStorageBlock.

class oemof.solph.options.NonConvex(**kwargs)

Bases: object

Parameters:

• startup_costs (numeric (iterable or scalar)) – Costs associated with a start of the flow (representing a unit).

• shutdown_costs (numeric (iterable or scalar)) – Costs associated with the shutdown of the flow (representing a unit).

• activity_costs (numeric (iterable or scalar)) – Costs associated with the active operation of the flow, independently from the actual output.

• minimum_uptime (numeric (1 or positive integer)) – Minimum time that a flow must be greater then its minimum flow after startup. Be aware that minimum up and downtimes can contradict each other and may lead to infeasible problems.

• minimum_downtime (numeric (1 or positive integer)) – Minimum time a flow is forced to zero after shutting down. Be aware that minimum up and downtimes can contradict each other and may to infeasible problems.

• maximum_startups (numeric (0 or positive integer)) – Maximum number of start-ups.

• maximum_shutdowns (numeric (0 or positive integer)) – Maximum number of shutdowns.

• initial_status (numeric (0 or 1)) – Integer value indicating the status of the flow in the first time step (0 = off, 1 = on). For minimum up and downtimes, the initial status is set for the respective values in the edge regions e.g. if a minimum uptime of four timesteps is defined, the initial status is fixed for the four first and last timesteps of the optimization period. If both, up and downtimes are defined, the initial status is set for the maximum of both e.g. for six timesteps if a minimum downtime of six timesteps is defined in addition to a four timestep minimum uptime.

• positive_gradient (dict, default: {‘ub’: None, ‘costs’: 0}) –

  A dictionary containing the following two keys:

  • ‘ub’: numeric (iterable, scalar or None), the normed upper bound on the positive difference (flow[t-1] < flow[t]) of two consecutive flow values.
  • ‘costs`: numeric (scalar or None), the gradient cost per unit.

• negative_gradient (dict, default: {‘ub’: None, ‘costs’: 0}) –

  A dictionary containing the following two keys:

  • ‘ub’: numeric (iterable, scalar or None), the normed upper bound on the negative difference (flow[t-1] > flow[t]) of two consecutive flow values.
  • ‘costs`: numeric (scalar or None), the gradient cost per unit.

max_up_down

Compute or return the _max_up_down attribute.

oemof.solph.plumbing module

Plumbing stuff.

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SPDX-License-Identifier: MIT

oemof.solph.plumbing.sequence(iterable_or_scalar)

Tests if an object is iterable (except string) or scalar and returns a the original sequence if object is an iterable and a ‘emulated’ sequence object of class _Sequence if object is a scalar or string.

Parameters:iterable_or_scalar (iterable or None or int or float)

Examples

>>> sequence([1,2])
[1, 2]

>>> x = sequence(10)
>>> x[0]
10

>>> x[10]
10
>>> print(x)
[10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]

oemof.solph.processing module

Modules for providing a convenient data structure for solph results.

Information about the possible usage is provided within the examples.

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SPDX-License-Identifier: MIT

oemof.solph.processing.convert_keys_to_strings(result, keep_none_type=False)

Convert the dictionary keys to strings.

All (tuple) keys of the result object e.g. results[(pp1, bus1)] are converted into strings that represent the object labels e.g. results[(‘pp1’,’bus1’)].

oemof.solph.processing.create_dataframe(om)

Create a result dataframe with all optimization data.

Results from Pyomo are written into pandas DataFrame where separate columns are created for the variable index e.g. for tuples of the flows and components or the timesteps.

oemof.solph.processing.get_timestep(x)

Get the timestep from oemof tuples.

The timestep from tuples (n, n, int), (n, n), (n, int) and (n,) is fetched as the last element. For time-independent data (scalars) zero ist returned.

oemof.solph.processing.get_tuple(x)

Get oemof tuple within iterable or create it.

Tuples from Pyomo are of type (n, n, int), (n, n) and (n, int). For single nodes n a tuple with one object (n,) is created.

oemof.solph.processing.meta_results(om, undefined=False)

Fetch some meta data from the Solver. Feel free to add more keys.

Valid keys of the resulting dictionary are: ‘objective’, ‘problem’, ‘solver’.

om : oemof.solph.Model

A solved Model.

undefined : bool

By default (False) only defined keys can be found in the dictionary. Set to True to get also the undefined keys.

Returns:dict

oemof.solph.processing.parameter_as_dict(system, exclude_none=True)

Create a result dictionary containing node parameters.

Results are written into a dictionary of pandas objects where a Series holds all scalar values and a dataframe all sequences for nodes and flows. The dictionary is keyed by flows (n, n) and nodes (n, None), e.g. parameter[(n, n)][‘sequences’] or parameter[(n, n)][‘scalars’].

Parameters:

• system (energy_system.EnergySystem) – A populated energy system.
• exclude_none (bool) – If True, all scalars and sequences containing None values are excluded

Returns:

dict (Parameters for all nodes and flows)

oemof.solph.processing.remove_timestep(x)

Remove the timestep from oemof tuples.

The timestep is removed from tuples of type (n, n, int) and (n, int).

oemof.solph.processing.results(om)

Create a result dictionary from the result DataFrame.

Results from Pyomo are written into a dictionary of pandas objects where a Series holds all scalar values and a dataframe all sequences for nodes and flows. The dictionary is keyed by the nodes e.g. results[idx][‘scalars’] and flows e.g. results[n, n][‘sequences’].

oemof.solph.views module

Modules for providing convenient views for solph results.

Information about the possible usage is provided within the examples.

SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de> SPDX-FileCopyrightText: Simon Hilpert SPDX-FileCopyrightText: Cord Kaldemeyer SPDX-FileCopyrightText: Stephan Günther SPDX-FileCopyrightText: henhuy

SPDX-License-Identifier: MIT

class oemof.solph.views.NodeOption

Bases: str, enum.Enum

An enumeration.

All = 'all'

HasInputs = 'has_inputs'

HasOnlyInputs = 'has_only_inputs'

HasOnlyOutputs = 'has_only_outputs'

HasOutputs = 'has_outputs'

oemof.solph.views.convert_to_multiindex(group, index_names=None, droplevel=None)

Convert dict to pandas DataFrame with multiindex

Parameters:

• group (dict) – Sequences of the oemof.solph.Model.results dictionary
• index_names (arraylike) – Array with names of the MultiIndex
• droplevel (arraylike) – List containing levels to be dropped from the dataframe

oemof.solph.views.filter_nodes(results, option=<NodeOption.All: 'all'>, exclude_busses=False)

Get set of nodes from results-dict for given node option.

This function filters nodes from results for special needs. At the moment, the following options are available:

• NodeOption.All: ‘all’: Returns all nodes

• NodeOption.HasOutputs: ‘has_outputs’:

  Returns nodes with an output flow (eg. Transformer, Source)

• NodeOption.HasInputs: ‘has_inputs’:

  Returns nodes with an input flow (eg. Transformer, Sink)

• NodeOption.HasOnlyOutputs: ‘has_only_outputs’:

  Returns nodes having only output flows (eg. Source)

• NodeOption.HasOnlyInputs: ‘has_only_inputs’:

  Returns nodes having only input flows (eg. Sink)

Additionally, busses can be excluded by setting exclude_busses to True.

Parameters:

• results (dict)
• option (NodeOption)
• exclude_busses (bool) – If set, all bus nodes are excluded from the resulting node set.

Returns:

set – A set of Nodes.

oemof.solph.views.get_node_by_name(results, *names)

Searches results for nodes

Names are looked up in nodes from results and either returned single node (in case only one name is given) or as list of nodes. If name is not found, None is returned.

oemof.solph.views.net_storage_flow(results, node_type)

Calculates the net storage flow for storage models that have one input edge and one output edge both with flows within the domain of non-negative reals.

Parameters:

• results (dict) – A result dictionary from a solved oemof.solph.Model object
• node_type (oemof.solph class) – Specifies the type for which (storage) type net flows are calculated

Returns:

• pandas.DataFrame object with multiindex colums. Names of levels of columns
• are (from, to, net_flow.)

Examples

import oemof.solph as solph from oemof.outputlib import views

# solve oemof solph model ‘m’ # Then collect node weights views.net_storage_flow(m.results(), node_type=solph.GenericStorage)

oemof.solph.views.node(results, node, multiindex=False, keep_none_type=False)

Obtain results for a single node e.g. a Bus or Component.

Either a node or its label string can be passed. Results are written into a dictionary which is keyed by ‘scalars’ and ‘sequences’ holding respective data in a pandas Series and DataFrame.

oemof.solph.views.node_input_by_type(results, node_type, droplevel=None)

Gets all inputs for all nodes of the type node_type and returns a dataframe.

Parameters:

• results (dict) – A result dictionary from a solved oemof.solph.Model object
• node_type (oemof.solph class) – Specifies the type of the node for that inputs are selected
• droplevel (list)

Notes

from oemof import solph from oemof.outputlib import views

# solve oemof solph model ‘m’ # Then collect node weights views.node_input_by_type(m.results(), node_type=solph.Sink)

oemof.solph.views.node_output_by_type(results, node_type, droplevel=None)

Gets all outputs for all nodes of the type node_type and returns a dataframe.

Parameters:

• results (dict) – A result dictionary from a solved oemof.solph.Model object
• node_type (oemof.solph class) – Specifies the type of the node for that outputs are selected
• droplevel (list)

Notes

import oemof.solph as solph from oemof.outputlib import views

# solve oemof solph model ‘m’ # Then collect node weights views.node_output_by_type(m.results(), node_type=solph.Transformer)

oemof.solph.views.node_weight_by_type(results, node_type)

Extracts node weights (if exist) of all components of the specified node_type.

Node weight are endogenous optimzation variables associated with the node and not the edge between two node, foxample the variable representing the storage level.

Parameters:

• results (dict) – A result dictionary from a solved oemof.solph.Model object
• node_type (oemof.solph class) – Specifies the type for which node weights should be collected

Example

from oemof.outputlib import views

# solve oemof model ‘m’ # Then collect node weights views.node_weight_by_type(m.results(), node_type=solph.GenericStorage)

Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

Bug reports

When reporting a bug please include:

• Your operating system name and version.
• Any details about your local setup that might be helpful in troubleshooting.
• Detailed steps to reproduce the bug.

Documentation improvements

oemof-solph could always use more documentation, whether as part of the official oemof-solph docs, in docstrings, or even on the web in blog posts, articles, and such.

Feature requests and feedback

The best way to send feedback is to file an issue at https://github.com/oemof/oemof-solph/issues.

If you are proposing a feature:

• Explain in detail how it would work.
• Keep the scope as narrow as possible, to make it easier to implement.
• Remember that this is a volunteer-driven project, and that code contributions are welcome :)

Development

To set up oemof-solph for local development:

1. Fork oemof-solph (look for the “Fork” button).

2. Clone your fork locally:

   git clone git@github.com:oemof/oemof-solph.git
   

3. Create a branch for local development:

   git checkout -b name-of-your-bugfix-or-feature
   

   Now you can make your changes locally.

4. When you’re done making changes run all the checks and docs builder with tox one command:

   tox
   

5. Commit your changes and push your branch to GitHub:

   git add .
   git commit -m "Your detailed description of your changes."
   git push origin name-of-your-bugfix-or-feature
   

6. Submit a pull request through the GitHub website.

Pull Request Guidelines

If you need some code review or feedback while you’re developing the code just make the pull request.

For merging, you should:

1. Include passing tests (run tox) [1].
2. Update documentation when there’s new API, functionality etc.
3. Add a note to CHANGELOG.rst about the changes.
4. Add yourself to AUTHORS.rst.

[1]	If you don’t have all the necessary python versions available locally you can rely on Travis - it will run the tests for each change you add in the pull request. It will be slower though …

Tests

To run the all tests run:

tox

Note, to combine the coverage data from all the tox environments run:

Windows	set PYTEST_ADDOPTS=--cov-append tox
Other	PYTEST_ADDOPTS=--cov-append tox

Tips

To run a subset of tests:

tox -e envname -- pytest -k test_myfeature

To run all the test environments in parallel (you need to pip install detox):

detox

Authors

–alphabetic order–

(see full list on github)

• Birgit Schachler
• Brian Michael Lancien
• Caroline Möller
• Caterina Köhl
• Clemens Wingenbach
• Cord Kaldemeyer
• Daniel Rank
• Elisa Gaudchau
• Elisa Papadis
• Fabian Büllesbach
• Francesco Witte
• Guido Plessmann
• Hendrik Hyskens
• Jakob Wolf
• Jann Launer
• Jens-Olaf Delfs
• Johannes Kochems
• Johannes Röder
• Jonathan Amme
• Julian Endres
• Lluis Millet
• Martin Soethe
• Patrik Schönfeldt
• Simon Hilpert
• Stephan Günther
• Uwe Krien

Changelog

These are new features and improvements of note in each release

v0.4.2 (May, 11, 2021)

• Exclude Pyomo version 5.7.3, because this version causes an unusual high computing time to create a model.

v0.4.1 (June 24, 2020)

Bug fixes

• Fixed incompatibility with recent Pyomo release (5.7)

Known issues

• Results of one-time-step optimisation counterintuitive

  If an optimisation with one time-step is performed, at the processing of the results, the scalars of the results is stored in the dict of the sequences. (See Issue #693)

Contributors

• Uwe Krien
• Patrik Schönfeldt

v0.4.0 (June 6, 2020)

API changes

• New package name

  For installation via pypi use pip install oemof.solph.

• Change the import of oemof-solph due to unbundling oemof solph

  The import statements have changed, for example from outputlib.views import processing –> from oemof.solph import processing. There are further changes for the modules views, helpers, economics, logger, network.

• Rename GenericStorage attributes

  The attribute capacity of the GenericStorage describing the current absolute stored energy/material/water etc. has been renamed to storage_content. In the GenericStorageBlock and GenericInvestmentStorageBlock, the attribute init_cap has been renamed init_content. This change is intended to avoid confusion with nominal_storage_capacity or capacity in terms of installed capacity.

• Rename the flow attribute ``actual_value`` to ``fix`` and remove ``fixed``

New features

• Allows having a non equidistant timeindex

  By adding the calculate_timeincrement function to tools/helpers.py a non equidistant timeincrement can be calculated. The EnergySystem will now be defined by the timeindex and the calculated timeincrement.

• Allows non-convex investments for flows and storages.

  With this feature, fix investment costs, which do not dependent on the nominal capacity, can be considered.

• Add user warnings for debugging.

  A UserWarning is raised for untypical uses even though this kind of usage is valid if you really know what you are doing. This will help users to debug their code but can be turned of for experienced users.

• Add fixed losses to GenericStorage

  ~oemof.solph.GenericStorage can now have fixed_losses, that are independent from storage content.

New components/constraints

• Allows a generic limit for attribute weighted investment flows

  InvestmentFlow, which share other limited resources (e.g. space), can be considered.

• Allow to limit count of concurrently active flows in group of flows

  Flows have to be NonConvex, the limit can be an upper or lower one.

• New constraint shared_limit

  Shared limit allows to restrict the weighted sum of arbitrary variables to a corridor. This can be used, e.g. to model shared space is used to store wood pallets and logs with their respective energy density.

Documentation

• Restructure and clean-up documentation due to the unbundling
• Improved documentation of ExtractionTurbineCHP

Known issues

• Results of one-time-step optimisation counterintuitive

  If an optimisation with one time-step is performed, at the processing of the results, the scalars of the results is stored in the dict of the sequences. (See Issue #693)

Testing

• Use tox for testing

  Now, pep8 tests and build of documentation are tested.

• Skip github link checks when testing locally

Other changes

• Redefine loss_rate of GenericStorage

  The loss_rate of ~oemof.solph.components.GenericStorage is now defined per time increment.

• Change parameters’ data type in the docstrings

  The parameters’ data type is changed from numeric (sequence or scalar) to numeric (iterable or scalar) (Issue #673).

• Add python 3.8 support, remove python 3.5 support

Contributors

• Caterina Köhl
• Jonathan Amme
• Uwe Krien
• Johannes Röder
• Jann Launer
• Daniel Rank
• Patrik Schönfeldt
• Stephan Günther

v0.3.2 (November 29, 2019)

New features

• Allow generic limits for integral over weighted flows. (This is a generalised version of <solph.constraints.emission_limit>.)
• Allow time-dependent weights for integrated weighted limit.

New components

• Custom component: ~oemof.solph.custom.SinkDSM. Demand Side Management component that allows to represent flexibile demand. How the component is used is shown in SinkDSM (custom).

Documentation

• Revision of the outputlib documentation.

Other changes

• The license hase been changed from GPLv3 to the MIT license
• The BaseModel has been revised (test, docstring, warnings, internal naming) (PR #605)
• Style revision to meet pep8 and other pep rules.

Contributors

• Guido Plessmann
• Johannes Röder
• Julian Endres
• Patrik Schönfeldt
• Uwe Krien

v0.3.1 (June 11, 2019)

Other changes

• The API of the GenericStorage changed. Due to the open structure of solph the old parameters are still accepted. Therefore users may not notice that the default value is used instead of the given value after an update from v0.2.x to v0.3.x. With this version an error is raised. We work on a structure to avoid such problems in the future.

Contributors

• Patrik Schönfeldt
• Stephan Günther
• Uwe Krien

v0.3.0 (June 5, 2019)

API changes

• The param_results function does not exist anymore. It has been renamed to parameter_as_dict (Issue #537).
• The storage API has been revised. Please check the API documentation for all details.
• The OffsetTransformer is now a regular oemof.solph component. It has been tested and the documentation has been improved. So it has been move from custom to components. Use oemof.solph.components.OffsetTransformer ( Issue #522).

New features

• Now it is possible to model just one time step. This is important for time step based models and all other models with an outer loop (Issue #519).
• The storage can be used unbalanced now, which means that the level at the end could be different to the level at the beginning of the modeled time period. See the storage documentation for more details.
• NonConvexFlow <oemof.solph.blocks.NonConvexFlow> can now have activity_costs, maximum_startups, and maximum_shutdowns. This helps, to model e.g. terms of maintannace contracts for small CHP plants.
• Namedtuples and tuples as labels work now without problems. This makes it much easier to find objects and results in large energy systems (Issue #507).
• Groups are now fully lazy. This means that groups are only computed when they are accessed. Previously, whenever nodes where added to an energy system, groups where computed for all but the most recently added node. This node was then only grouped upon addition of another node or upon access of the groups property.
• There is now an explicit Edge <oemof.network.Edge> class. This means that an energy system now consists of Buses <oemof.network.Bus>, Components <oemof.network.Component> and Edges <oemof.network.Edge>. For implementation reasons, Edges <oemof.network.Edge> are still Nodes <oemof.network.Node>. If you know a bit of graph theory and this seems strange to you, think of these Edges <oemof.network.Edge> as classical graph theoretic edges, reified as nodes with an in- and outdegree of one.
• Energy systems <oemof.energy_system.EnergySystem> now support blinker signals. The first supported signal gets emitted, whenever a node <oemof.network.node> is added <oemof.energy_system.EnergySystem.add> to an energy system <oemof.energy_system.EnergySystem>. (blinker)

Documentation

• The template for docstrings with equations (docstring of block classes) has been improved.
• A lot of improvements on the documentation

Bug fixes

• The timeincrement attribute of the model is not set to one anymore. In earlier versions the timeincrement was set to one by default. This was a problem if a wrong time index was passed. In that case the timeincrement was set to one without a warning. Now an error is raised if no timeincrement or valid time index is found (Issue #549).

Testing

• Automatic test coverage control was implemented. Now a PR will not be accepted if it decreases the test coverage.
• Test coverage was increased to over 90%. A badge was added to the oemof github page that shows the actual test coverage.
• Test coverage on the groupings <oemof.groupings> and network <oemof.network> modules has significantly increased. These modules where historically very weakly tested and are now approaching 90% and 95% respectively with more tests being planned.

Contributors

(alphabetical order)

• ajimenezUCLA
• FranziPl
• Johannes Röder
• Jakob Wolf
• Jann Launer
• Lluis Millet
• Patrik Schönfeldt
• Simon Hilpert
• Stephan Günther
• Uwe Krien

v0.2.3 (November 21, 2018)

Bug fixes

• Some functions did not work with tuples as labels. It has been fixed for the ExtractionTurbineCHP, the graph module and the parameter_as_dict function. (Issue #507)

Contributors

• Simon Hilpert
• Stephan Günther
• Uwe Krien

v0.2.2 (July 1, 2018)

API changes

• The storage API has been revised, even though it is still possible to use the old API. In that case a warning is raised (Issue #491).
• The newly introduced parm_results are not results and therefore renamed to parameter_as_dict. The old name is still valid but raises a warning.

New features

• We added a new attribute existing to the solph.options.Investement class. It will now be possible to run investment optimization based on already installed capacity of a component. Take a look on Using the investment mode for usage of this option. (Issue #489).
• Investement variables for the capacity and the flows are now decoupled to enable more flexibility. It is possible to couple the flows to the capacity, the flows to itself or to not couple anything. The newly added attributes invest_relation_input_output, invest_relation_input_capacity and invest_relation_output_capacity replace the existing attributes nominal_input_capacity_ratio and nominal_input_capacity_ratio for the investment mode. In case of the dispatch mode one should use the nominal_value of the Flow classes. The attributes nominal_input_capacity_ratio and nominal_input_capacity_ratio will be removed in v0.3.0. Please adapt your application to avoid problems in the future (Issue #480).
• We now have experimental support for deserializing an energy system from a tabular data package. Since we have to extend the datapackage format a bit, the specification is not yet finalized and documentation as well as tests range from sparse to nonexistent. But anyone who wishes to help with the code is welcome to check it out in the datapackage <oemof.tools.datapackage> module.

New components

Documentation

• The documentation of the storage storage component has been improved.
• The documentation of the Extraction Turbine has been improved.

Known issues

• It is not possible to model one time step with oemof.solph. You have to model at least two timesteps (Issue #306). Please leave a comment if this bug affects you.

Bug fixes

• Fix file extension check to dump a graph correctly as .graphml-file
• The full constraint set of the ExtractionTurbineCHP class was only build for one object. If more than one object was present the input/output constraint was missing. This lead to wrong results.
• In the solph constraints module the emission constraint did not include the timeincrement from the model which has now be fixed.
• The parameter_as_dict (former: param_results) do work with the views functions now (Issue #494).

Testing

• The test coverage has been increased (>80%). oemof has experimental areas to test new functions. These functions are marked as experimental and will not be tested. Therefore the real coverage is even higher.

Other changes

• Subclasses of Node <oemof.network.Node> are no longer optimized using __slots__. The abstract parent class still defines __slots__ <oemof.network.Node.__slots__> so that custom subclasses still have the option of using it.

Contributors

• Fabian Büllesbach
• Guido Plessmann
• Simon Hilpert
• Stephan Günther
• Uwe Krien

v0.2.1 (March 19, 2018)

API changes

• The function create_nx_graph only takes an energysystem as argument, not a solph model. As it is not a major release you can still pass a Model but you should adapt your application as soon as possible. (Issue #439)

New features

• It is now possible determine minimum up and downtimes for nonconvex flows. Check the oemof_examples repository for an exemplary usage.
• Startup and shutdown costs can now be defined time-dependent.
• The graph module has been revised. (Issue #439)
  • You can now store a graph to disc as .graphml file to open it in yEd with labels.
  • You can add weight to edges.
  • Labels are attached to the nodes.
• Two functions get_node_by_name and filter_nodes have been added that allow to get specified nodes or nodes of one kind from the results dictionary. (Issue #426)
• A new function param_results() collects all parameters of nodes and flows in a dictionary similar to the results dictionary. (Issue #445)
• In outputlib.views.node(), an option for multiindex dataframe has been added.

Documentation

• Some small fixes and corrected typos.

Known issues

• It is not possible to model one time step with oemof.solph. You have to model at least two timesteps (Issue #306). Please leave a comment if this bug affects you.

Bug fixes

• Shutdown costs for nonconvex flows are now accounted within the objective which was not the case before due to a naming error.
• Console script oemof_test_installation has been fixed. (Issue #452)
• Adapt solph to API change in the Pyomo package.
• Deserializing a Node <oemof.network.Node> leads to an object which was no longer serializable. This is fixed now. Node <oemof.network.Node> instances should be able to be dumped and restored an arbitraty amount of times.
• Adding timesteps to index of constraint for component el-line fixes an issue with pyomo.

Testing

• New console script test_oemof has been added (experimental). (Issue #453)

Other changes

• Internal change: Unnecessary list extensions while creating a model are avoided, which leads to a decrease in runtime. (Issue #438)
• The negative/positive gradient attributes are dictionaries. In the constructor they moved from sequences to a new dictionaries argument. (Issue #437)
• License agreement was adapted according to the reuse project (Issue #442)
• Code of conduct was added. (Issue #440)
• Version of required packages is now limited to the most actual version (Issue #464)

Contributors

• Cord Kaldemeyer
• Jann Launer
• Simon Hilpert
• Stephan Günther
• Uwe Krien

v0.2.0 (January 12, 2018)

API changes

• The NodesFromCSV has been removed from the code base. An alternative excel (spreadsheet) reader is provided in the newly created excel example in the oemof_examples repository.
• LinearTransformer and LinearN1Transformer classes are now combined within one Transformer class. The new class has n inputs and n outputs. Please note that the definition of the conversion factors (for N1) has changed. See the new docstring of ~oemof.solph.network.Transformer class to avoid errors (Issue #351).
• The oemof.solph.network.Storage class has been renamed and moved to oemof.solph.components.GenericStorage.
• As the example section has been moved to a new repository the oemof_example command was removed. Use oemof_installation_test to check your installation and the installed solvers.
• OperationalModel has been renamed to Model. The es parameter was renamed to energysystem parameter.
• Nodes <oemof.network.Node> are no longer automatically added to the most recently created energy system <oemof.energy_system.EnergySystem>. You can still restore the old automatic registration by manually assigning an energy system <oemof.energy_system.EnergySystem> to Node.registry <oemof.network.Node.registry>. On the other hand you can still explicitly add <oemof.energy_system.EnergySystem.add> nodes <oemof.network.Node> to an energy system <oemof.energy_system.EnergySystem>. This option has been made a bit more feature rich by the way, but see below for more on this. Also check the oemof_examples repository for more information on the usage.
• The fixed_costs attribute of the nodes <oemof.solph.network.Flow> has been removed. See (Issue #407) for more information.
• The classes DataFramePlot <outputlib.DataFramePlot> and ResultsDataFrame <outputlib.ResultsDataFrame> have been removed due to the redesign of the outputlib module.

New features

• A new oemof_examples repository with some new examples was created.
• A new outputlib module has been created to provide a convenient data structure for optimization results and enable quick analyses. All decision variables of a Node are now collected automatically which enables an easier development of custom components. See the revised Handling Results documentation for more details or have a look at the oemof_examples repository for information on the usage. Keep your eyes open, some new functions will come soon that make the processing of the results easier. See the actual pull request or issues for detailed information.
• The transformer class can now be used with n inputs and n outputs ( ~oemof.solph.network.Transformer)
• A new module with useful additional constraints were created with these constraints global emission or investment limits can be set. Furthermore it is possible to connect investment variables. Please add your own additional constraints or let us know what is needed in the future.
• A module to create a networkx graph from your energy system or your optimisation model was added. You can use networkx to plot and analyse graphs. See the graph module in the documentation of oemof-network for more information.
• It’s now possible to modify a node’s <oemof.network.Node> inputs <oemof.network.Node.inputs> and outputs <oemof.network.Node.outputs> by inserting and removing nodes <oemof.network.Node> to and from the correspoding dictionaries. Outputs <oemof.network.Node.outputs> where already working previously, but due to an implementation quirk, inputs <oemof.network.Node.inputs> did not behave as expected. This is now fixed.
• One can now explicitly add <oemof.energy_system.EnergySystem.add> nodes <oemof.network.Node> to an energy system <oemof.energy_system.EnergySystem> in bulk using * and ** syntax. For the latter case, the values <dict.values> of the dictionary passed in will be added.
• New components can now be added to the custom.py module. Components in this module are indicated as in a testing state. Use them with care. This lowers the entry barriers to test new components within the solph structure and find other testers.

New components

• The nodes ElectricalLine <oemof.solph.custom.ElectricalLine> and ElectricalBus <oemof.solph.custom.ElectricalBus> can be used for Linear Optimal Powerflow calculation based on angle formulations. These components have been added to the solph.custom module. Though it should work correctly, it is in a preliminary stage. Please check your results. Feedback is welcome!
• The custom component Link <oemof.solph.custom.Link> can now be used to model a bidirectional connection within one component. Check out the example in the oemof_examples repository.
• The component GenericCHP <oemof.solph.components.GenericCHP> can be used to model different CHP types such as extraction or back-pressure turbines and motoric plants. The component uses a mixed-integer linear formulation and can be adapted to different technical layouts with a high level of detail. Check out the example in the oemof_examples repository.
• The component GenericCAES <oemof.solph.custom.GenericCAES> can be used to model different concepts of compressed air energy storage. Technical concepts such as diabatic or adiabatic layouts can be modelled at a high level of detail. The component uses a mixed-integer linear formulation.
• The custom component GenericOffsetTransformer <oemof.solph.custom.GenericOffsetTransformer> can be used to model components with load ranges such as heat pumps and also uses a mixed-integer linear formulation.

Documentation

• Large parts of the documentation have been proofread and improved since the last developer meeting in Flensburg.
• The solph documentation has got an extra section with all existing components (Solph components).
• The developer documentation has been developed to lower the barriers for new developers. Furthermore, a template for pull request was created.

Known issues

• It is not possible to model one time step with oemof.solph. You have to model at least two timesteps (Issue #306). Please leave a comment if this bug affects you.

Bug fixes

• LP-file tests are now invariant against sign changes in equations, because the equations are now normalized to always have non-negative right hand sides.

Testing

• All known and newly created components are now tested within an independent testing environment which can be found in /tests/.
• Other testing routines have been streamlined and reviewed and example tests have been integrated in the nosetest environment.

Other changes

• The plot functionalities have been removed completely from the outputlib as they are less a necessary part but more an optional tool . Basic plotting examples that show how to quickly create plots from optimization results can now be found in the oemof_examples repository. You can still find the “old” standard plots within the oemof_visio repository as they are now maintained separately.
• A user forum has been created to answer use questions.

Contributors

• Cord Kaldemeyer
• Jens-Olaf Delfs
• Stephan Günther
• Simon Hilpert
• Uwe Krien

v0.1.4 (March 28, 2017)

Bug fixes

• fix examples (issue #298)

Documentation

• Adapt installation guide.

Contributors

• Uwe Krien
• Stephan Günther

v0.1.2 (March 27, 2017)

New features

• Revise examples - clearer naming, cleaner code, all examples work with cbc solver (issue #238, issue #247).
• Add option to choose solver when executing the examples (issue #247).
• Add new transformer class: VariableFractionTransformer (child class of LinearTransformer). This class represents transformers with a variable fraction between its output flows. In contrast to the LinearTransformer by now it is restricted to two output flows.(issue #248)
• Add new transformer class: N1Transformer (counterpart of LinearTransformer). This class allows to have multiple inputflows that are converted into one output flow e.g. heat pumps or mixing-components.
• Allow to set addtional flow attributes inside NodesFromCSV in solph inputlib
• Add economics module to calculate investment annuities (more to come in future versions)
• Add module to store input data in multiple csv files and merge by preprocessing
• Allow to slice all information around busses via a new method of the ResultsDataFrame
• Add the option to save formatted balances around busses as single csv files via a new method of the ResultsDataFrame

Documentation

• Improve the installation guide.

Bug fixes

• Allow conversion factors as a sequence in the CSV reader

Other changes

• Speed up constraint-building process by removing unnecessary method call
• Clean up the code according to pep8 and pylint

Contributors

• Cord Kaldemeyer
• Guido Plessmann
• Uwe Krien
• Simon Hilpert
• Stephan Günther

v0.1.1 (November 2, 2016)

Hot fix release to make examples executable.

Bug fixes

• Fix copy of default logging.ini (issue #235)
• Add matplotlib to requirements to make examples executable after installation (issue #236)

Contributors

• Guido Plessmann
• Uwe Krien

v0.1.0 (November 1, 2016)

The framework provides the basis for a great range of different energy system model types, ranging from LP bottom-up (power and heat) economic dispatch models with optional investment to MILP operational unit commitment models.

With v0.1.0 we refactored oemof (not backward compatible!) to bring the implementation in line with the general concept. Hence, the API of components has changed significantly and we introduced the new ‘Flow’ component. Besides an extensive grouping functionality for automatic creation of constraints based on component input data the documentation has been revised.

We provide examples to show the broad range of possible applications and the frameworks flexibility.

API changes

• The demandlib is no longer part of the oemof package. It has its own package now: (demandlib)

New features

• Solph’s EnergySystem <oemof.solph.network.EnergySystem> now automatically uses solph’s GROUPINGS <oemof.solph.groupings.GROUPINGS> in addition to any user supplied ones. See the API documentation for more information.

• The groupings <oemof.groupings.Grouping> introduced in version 0.0.5 now have more features, more documentation and should generally be pretty usable:

  • They moved to their own module: oemof.groupings and deprecated constructs ensuring compatibility with prior versions have been removed.
  • It’s possible to assign a node to multiple groups from one Grouping <oemof.groupings.Grouping> by returning a list of group keys from key <oemof.groupings.Grouping.key>.
  • If you use a non callable object as the key <oemof.groupings.Grouping.key> parameter to Groupings <oemof.groupings.Grouping>, the constructor will not make an attempt to call them, but use the object directly as a key.
  • There’s now a filter <oemof.groupings.Grouping.filter> parameter, enabling a more concise way of filtering group contents than using value <oemof.groupings.Grouping.value>.

Documentation

• Complete revision of the documentation. We hope it is now more intuitive and easier to understand.

Testing

• Create a structure to use examples as system tests (issue #160)

Bug fixes

• Fix relative path of logger (issue #201)
• More path fixes regarding installation via pip

Other changes

• Travis CI will now check PR’s automatically
• Examples executable from command-line (issue #227)

Contributors

• Stephan Günther
• Simon Hilpert
• Uwe Krien
• Guido Pleßmann
• Cord Kaldemeyer

v0.0.7 (May 4, 2016)

Bug fixes

• Exclude non working pyomo version

v0.0.6 (April 29, 2016)

New features

• It is now possible to choose whether or not the heat load profile generated with the BDEW heat load profile method should only include space heating or space heating and warm water combined. (Issue #130)
• Add possibility to change the order of the columns of a DataFrame subset. This is useful to change the order of stacked plots. (Issue #148)

Documentation

Testing

• Fix constraint tests (Issue #137)

Bug fixes

• Use of wrong columns in generation of SF vector in BDEW heat load profile generation (Issue #129)
• Use of wrong temperature vector in generation of h vector in BDEW heat load profile generation.

Other changes

Contributors

• Uwe Krien
• Stephan Günther
• Simon Hilpert
• Cord Kaldemeyer
• Birgit Schachler

v0.0.5 (April 1, 2016)

New features

• There’s now a flexible transformer <oemof.core.network.entities.components.transformers.TwoInputsOneOutput> with two inputs and one output. (Issue #116)
• You now have the option create special groups of entities in your energy system. The feature is not yet fully implemented, but simple use cases are usable already. (Issue #60)

Documentation

• The documentation of the electrical demand <oemof.demandlib.demand.electrical_demand> class has been cleaned up.
• The API documentation now has its own section so it doesn’t clutter up the main navigation sidebar so much anymore.

Testing

• There’s now a dedicated module/suite testing solph constraints.
• This suite now has proper fixtures (i.e. setup/teardown methods) making them (hopefully) independent of the order in which they are run (which, previously, they where not).

Bug fixes

• Searching for oemof’s configuration directory is now done in a platform independent manner. (Issue #122)
• Weeks no longer have more than seven days. (Issue #126)

Other changes

• Oemof has a new dependency: dill. It enables serialization of less common types and acts as a drop in replacement for pickle.
• Demandlib’s API has been simplified.

Contributors

• Uwe Krien
• Stephan Günther
• Guido Pleßmann

v0.0.4 (March 03, 2016)

New features

• Revise the outputlib according to (issue #54)
• Add postheating device to transport energy between two buses with different temperature levels (issue #97)
• Better integration with pandas

Documentation

• Update developer notes

Testing

• Described testing procedures in developer notes
• New constraint tests for heating buses

Bug fixes

• Use of pyomo fast build
• Broken result-DataFrame in outputlib
• Dumping of EnergySystem

Other changes

• PEP8

Contributors

• Cord Kaldemeyer
• Uwe Krien
• Simon Hilpert
• Stephan Günther
• Clemens Wingenbach
• Elisa Papdis
• Martin Soethe
• Guido Plessmann

v0.0.3 (January 29, 2016)

New features

• Added a class to convert the results dictionary to a multiindex DataFrame (issue #36)
• Added a basic plot library (issue #36)
• Add logging functionalities (issue #28)
• Add entities_from_csv functionality for creating of entities from csv-files
• Add a time-depended upper bound for the output of a component (issue #65)
• Add fast_build functionlity for pyomo models in solph module (issue #68)
• The package is no longer named oemof_base but is now just called oemof.
• The results dictionary stored in the energy system now contains an attribute for the objective function and for objects which have special result attributes, those are now accessible under the object keys, too. (issue #58)

Documentation

• Added the Readme.rst as “Getting started” to the documentation.
• Fixed installation description (issue #38)
• Improved the developer notes.

Testing

• With this release we start implementing nosetests (issue #47
• Tests added to test constraints and the registration process (issue #73).

Bug fixes

• Fix contraints in solph
• Fix pep8

Other changes

Contributors

• Cord Kaldemeyer
• Uwe Krien
• Clemens Wingenbach
• Simon Hilpert
• Stephan Günther

v0.0.2 (December 22, 2015)

New features

• Adding a definition of a default oemof logger (issue #28)
• Revise the EnergySystem class according to the oemof developing meeting (issue #25)
• Add a dump and restore method to the EnergySystem class to dump/restore its attributes (issue #31)
• Functionality for minimum up- and downtime constraints (oemof.solph.linear_mixed_integer_constraints module)
• Add relax option to simulation class for calculation of linear relaxed mixed integer problems
• Instances of EnergySystem <oemof.core.energy_system.EnergySystem> now keep track of Entities <oemof.core.network.Entity> via the entities <oemof.core.energy_system.EnergySystem.entities> attribute. (issue #20)
• There’s now a standard way of working with the results obtained via a call to OptimizationModel#results <oemof.solph.optimization_model.OptimizationModel.results>. See its documentation, the documentation of EnergySystem#optimize <oemof.core.energy_system.EnergySystem.optimize> and finally the discussion at issue #33 for more information.
• New class VariableEfficiencyCHP <oemof.core.network.entities.components.transformers.VariableEfficiencyCHP> to model combined heat and power units with variable electrical efficiency.
• New methods for VariableEfficiencyCHP <oemof.core.network.entities.components.transformers.VariableEfficiencyCHP> inside the solph-module:
• MILP-constraint <oemof.solph.linear_mixed_integer_constraints.add_variable_linear_eta_relation>
• Linear-constraint <oemof.solph.linear_constraints.add_eta_total_chp_relation>

Documentation

• missing docstrings of the core subpackage added (issue #9)
• missing figures of the meta-documentation added
• missing content in developer notes (issue #34)

Testing

Bug fixes

• now the api-docs can be read on readthedocs.org
• a storage automically calculates its maximum output/input if the capacity and the c-rate is given (issue #27)
• Fix error in accessing dual variables in oemof.solph.postprocessing

Other changes

Contributors

• Uwe Krien
• Simon Hilpert
• Cord Kaldemeyer
• Guido Pleßmann
• Stephan Günther

v0.0.1 (November 25, 2015)

First release by the oemof developing group.

Indices and tables

• Index
• Module Index
• Search Page