Dynamic Optimization with pyomo.DAE
The pyomo.DAE modeling extension [PyomoDAE] allows users to incorporate systems of differential algebraic equations (DAE)s in a Pyomo model. The modeling components in this extension are able to represent ordinary or partial differential equations. The differential equations do not have to be written in a particular format and the components are flexible enough to represent higherorder derivatives or mixed partial derivatives. Pyomo.DAE also includes model transformations which use simultaneous discretization approaches to transform a DAE model into an algebraic model. Finally, pyomo.DAE includes utilities for simulating DAE models and initializing dynamic optimization problems.
Modeling Components
Pyomo.DAE introduces three new modeling components to Pyomo:
pyomo.dae.ContinuousSet 
Represents a bounded continuous domain 
pyomo.dae.DerivativeVar 
Represents derivatives in a model and defines how a Var is differentiated 
pyomo.dae.Integral 
Represents an integral over a continuous domain 
As will be shown later, differential equations can be declared using
using these new modeling components along with the standard Pyomo
Var
and
Constraint
components.
ContinuousSet
This component is used to define continuous bounded domains (for example
‘spatial’ or ‘time’ domains). It is similar to a Pyomo
Set
component and can be used to index things
like variables and constraints. Any number of
ContinuousSets
can be used to index a
component and components can be indexed by both
Sets
and
ContinuousSets
in arbitrary order.
In the current implementation, models with
ContinuousSet
components may not be solved
until every ContinuousSet
has been
discretized. Minimally, a ContinuousSet
must be initialized with two numeric values representing the upper and lower
bounds of the continuous domain. A user may also specify additional points in
the domain to be used as finite element points in the discretization.

class
pyomo.dae.
ContinuousSet
(*args, **kwds)[source] Represents a bounded continuous domain
Minimally, this set must contain two numeric values defining the bounds of a continuous range. Discrete points of interest may be added to the continuous set. A continuous set is one dimensional and may only contain numerical values.
Parameters:  initialize (list) – Default discretization points to be included
 bounds (tuple) – The bounding points for the continuous domain. The bounds will
be included as discrete points in the
ContinuousSet
and will be used to bound the points added to theContinuousSet
through the ‘initialize’ argument, a data file, or the add() method

_changed
This keeps track of whether or not the ContinuousSet was changed during discretization. If the user specifies all of the needed discretization points before the discretization then there is no need to go back through the model and reconstruct things indexed by the
ContinuousSet
Type: boolean

_fe
This is a sorted list of the finite element points in the
ContinuousSet
. i.e. this list contains all the discrete points in theContinuousSet
that are not collocation points. Points that are both finite element points and collocation points will be included in this list.Type: list

_discretization_info
This is a dictionary which contains information on the discretization transformation which has been applied to the
ContinuousSet
.Type: dict

construct
(values=None)[source] Constructs a
ContinuousSet
component

find_nearest_index
(target, tolerance=None)[source] Returns the index of the nearest point in the
ContinuousSet
.If a tolerance is specified, the index will only be returned if the distance between the target and the closest point is less than or equal to that tolerance. If there is a tie for closest point, the index on the left is returned.
Parameters:  target (float) –
 tolerance (float or None) –
Returns: Return type: float or None

get_changed
()[source] Returns flag indicating if the
ContinuousSet
was changed during discretizationReturns “True” if additional points were added to the
ContinuousSet
while applying a discretization schemeReturns: Return type: boolean

get_discretization_info
()[source] Returns a dict with information on the discretization scheme that has been applied to the
ContinuousSet
.Returns: Return type: dict

get_finite_elements
()[source] Returns the finite element points
If the
ContinuousSet
has been discretizaed using a collocation scheme, this method will return a list of the finite element discretization points but not the collocation points within each finite element. If theContinuousSet
has not been discretized or a finite difference discretization was used, this method returns a list of all the discretization points in theContinuousSet
.Returns: Return type: list of floats

get_lower_element_boundary
(point)[source] Returns the first finite element point that is less than or equal to ‘point’
Parameters: point (float) – Returns: Return type: float
The following code snippet shows examples of declaring a
ContinuousSet
component on a
concrete Pyomo model:
Required imports
>>> from pyomo.environ import *
>>> from pyomo.dae import *
>>> model = ConcreteModel()
Declaration by providing bounds
>>> model.t = ContinuousSet(bounds=(0,5))
Declaration by initializing with desired discretization points
>>> model.x = ContinuousSet(initialize=[0,1,2,5])
Note
A ContinuousSet
may not be
constructed unless at least two numeric points are provided to bound the
continuous domain.
The following code snippet shows an example of declaring a
ContinuousSet
component on an
abstract Pyomo model using the example data file.
set t := 0 0.5 2.25 3.75 5;
Required imports
>>> from pyomo.environ import *
>>> from pyomo.dae import *
>>> model = AbstractModel()
The ContinuousSet below will be initialized using the points
in the data file when a model instance is created.
>>> model.t = ContinuousSet()
Note
If a separate data file is used to initialize a
ContinuousSet
, it is done using
the ‘set’ command and not ‘continuousset’
Note
Most valid ways to declare and initialize a
Set
can be used to
declare and initialize a ContinuousSet
.
See the documentation for Set
for additional
options.
Warning
Be careful using a ContinuousSet
as an implicit index in an expression,
i.e. sum(m.v[i] for i in m.myContinuousSet)
. The expression will
be generated using the discretization points contained in the
ContinuousSet
at the time the
expression was constructed and will not be updated if additional
points are added to the set during discretization.
Note
ContinuousSet
components are
always ordered (sorted) therefore the first()
and last()
Set
methods can be used to access the lower
and upper boundaries of the
ContinuousSet
respectively
DerivativeVar

class
pyomo.dae.
DerivativeVar
(*args, **kwds)[source] Represents derivatives in a model and defines how a
Var
is differentiatedThe
DerivativeVar
component is used to declare a derivative of aVar
. The constructor accepts a single positional argument which is theVar
that’s being differentiated. AVar
may only be differentiated with respect to aContinuousSet
that it is indexed by. The indexing sets of aDerivativeVar
are identical to those of theVar
it is differentiating.Parameters:  sVar (
pyomo.environ.Var
) – The variable being differentiated  wrt (
pyomo.dae.ContinuousSet
or tuple) – Equivalent to withrespectto keyword argument. TheContinuousSet
that the derivative is being taken with respect to. Higher order derivatives are represented by including theContinuousSet
multiple times in the tuple sent to this keyword. i.e.wrt=(m.t, m.t)
would be the second order derivative with respect tom.t

get_continuousset_list
()[source] Return the a list of
ContinuousSet
components the derivative is being taken with respect to.Returns: Return type: list

get_derivative_expression
()[source] Returns the current discretization expression for this derivative or creates an access function to its
Var
the first time this method is called. The expression gets built up as the discretization transformations are sequentially applied to eachContinuousSet
in the model.

is_fully_discretized
()[source] Check to see if all the
ContinuousSets
this derivative is taken with respect to have been discretized.Returns: Return type: boolean

set_derivative_expression
(expr)[source] Sets``_expr``, an expression representing the discretization equations linking the
DerivativeVar
to its stateVar
 sVar (
The code snippet below shows examples of declaring
DerivativeVar
components on a
Pyomo model. In each case, the variable being differentiated is supplied
as the only positional argument and the type of derivative is specified
using the ‘wrt’ (or the more verbose ‘withrespectto’) keyword
argument. Any keyword argument that is valid for a Pyomo
Var
component may also be specified.
Required imports
>>> from pyomo.environ import *
>>> from pyomo.dae import *
>>> model = ConcreteModel()
>>> model.s = Set(initialize=['a','b'])
>>> model.t = ContinuousSet(bounds=(0,5))
>>> model.l = ContinuousSet(bounds=(10,10))
>>> model.x = Var(model.t)
>>> model.y = Var(model.s,model.t)
>>> model.z = Var(model.t,model.l)
Declare the first derivative of model.x with respect to model.t
>>> model.dxdt = DerivativeVar(model.x, withrespectto=model.t)
Declare the second derivative of model.y with respect to model.t
Note that this DerivativeVar will be indexed by both model.s and model.t
>>> model.dydt2 = DerivativeVar(model.y, wrt=(model.t,model.t))
Declare the partial derivative of model.z with respect to model.l
Note that this DerivativeVar will be indexed by both model.t and model.l
>>> model.dzdl = DerivativeVar(model.z, wrt=(model.l), initialize=0)
Declare the mixed second order partial derivative of model.z with respect
to model.t and model.l and set bounds
>>> model.dz2 = DerivativeVar(model.z, wrt=(model.t, model.l), bounds=(10, 10))
Note
The ‘initialize’ keyword argument will initialize the value of a
derivative and is not the same as specifying an initial
condition. Initial or boundary conditions should be specified using a
Constraint
or
ConstraintList
or
by fixing the value of a Var
at a boundary
point.
Declaring Differential Equations
A differential equations is declared as a standard Pyomo
Constraint
and is not required to have
any particular form. The following code snippet shows how one might declare
an ordinary or partial differential equation.
Required imports
>>> from pyomo.environ import *
>>> from pyomo.dae import *
>>> model = ConcreteModel()
>>> model.s = Set(initialize=['a', 'b'])
>>> model.t = ContinuousSet(bounds=(0, 5))
>>> model.l = ContinuousSet(bounds=(10, 10))
>>> model.x = Var(model.s, model.t)
>>> model.y = Var(model.t, model.l)
>>> model.dxdt = DerivativeVar(model.x, wrt=model.t)
>>> model.dydt = DerivativeVar(model.y, wrt=model.t)
>>> model.dydl2 = DerivativeVar(model.y, wrt=(model.l, model.l))
An ordinary differential equation
>>> def _ode_rule(m, s, t):
... if t == 0:
... return Constraint.Skip
... return m.dxdt[s, t] == m.x[s, t]**2
>>> model.ode = Constraint(model.s, model.t, rule=_ode_rule)
A partial differential equation
>>> def _pde_rule(m, t, l):
... if t == 0 or l == m.l.first() or l == m.l.last():
... return Constraint.Skip
... return m.dydt[t, l] == m.dydl2[t, l]
>>> model.pde = Constraint(model.t, model.l, rule=_pde_rule)
By default, a Constraint
declared over a
ContinuousSet
will be applied at every
discretization point contained in the set. Often a modeler does not want to
enforce a differential equation at one or both boundaries of a continuous
domain. This may be addressed explicitly in the
Constraint
declaration using
Constraint.Skip
as shown above. Alternatively, the desired constraints can
be deactivated just before the model is sent to a solver as shown below.
>>> def _ode_rule(m, s, t):
... return m.dxdt[s, t] == m.x[s, t]**2
>>> model.ode = Constraint(model.s, model.t, rule=_ode_rule)
>>> def _pde_rule(m, t, l):
... return m.dydt[t, l] == m.dydl2[t, l]
>>> model.pde = Constraint(model.t, model.l, rule=_pde_rule)
Declare other model components and apply a discretization transformation
...
Deactivate the differential equations at certain boundary points
>>> for con in model.ode[:, model.t.first()]:
... con.deactivate()
>>> for con in model.pde[0, :]:
... con.deactivate()
>>> for con in model.pde[:, model.l.first()]:
... con.deactivate()
>>> for con in model.pde[:, model.l.last()]:
... con.deactivate()
Solve the model
...
Note
If you intend to use the pyomo.DAE
Simulator
on your model then you
must use constraint deactivation instead of constraint
skipping in the differential equation rule.
Declaring Integrals
Warning
The Integral
component is still under
development and considered a prototype. It currently includes only basic
functionality for simple integrals. We welcome feedback on the interface
and functionality but we do not recommend using it on general
models. Instead, integrals should be reformulated as differential
equations.

class
pyomo.dae.
Integral
(*args, **kwds)[source] Represents an integral over a continuous domain
The
Integral
component can be used to represent an integral taken over the entire domain of aContinuousSet
. Once everyContinuousSet
in a model has been discretized, any integrals in the model will be converted to algebraic equations using the trapezoid rule. Future development will include more sophisticated numerical integration methods.Parameters:  *args – Every indexing set needed to evaluate the integral expression
 wrt (
ContinuousSet
) – The continuous domain over which the integral is being taken  rule (function) – Function returning the expression being integrated

get_continuousset
()[source] Return the
ContinuousSet
the integral is being taken over
Declaring an Integral
component is similar to
declaring an Expression
component. A
simple example is shown below:
>>> model = ConcreteModel()
>>> model.time = ContinuousSet(bounds=(0,10))
>>> model.X = Var(model.time)
>>> model.scale = Param(initialize=1E3)
>>> def _intX(m,t):
... return m.X[t]
>>> model.intX = Integral(model.time,wrt=model.time,rule=_intX)
>>> def _obj(m):
... return m.scale*m.intX
>>> model.obj = Objective(rule=_obj)
Notice that the positional arguments supplied to the
Integral
declaration must include all indices
needed to evaluate the integral expression. The integral expression is defined
in a function and supplied to the ‘rule’ keyword argument. Finally, a user must
specify a ContinuousSet
that the integral
is being evaluated over. This is done using the ‘wrt’ keyword argument.
Note
The ContinuousSet
specified using the
‘wrt’ keyword argument must be explicitly specified as one of the indexing
sets (meaning it must be supplied as a positional argument). This is to
ensure consistency in the ordering and dimension of the indexing sets
After an Integral
has been declared, it can be
used just like a Pyomo Expression
component and can be included in constraints or the objective function as shown
above.
If an Integral
is specified with multiple
positional arguments, i.e. multiple indexing sets, the final component will be
indexed by all of those sets except for the
ContinuousSet
that the integral was
taken over. In other words, the
ContinuousSet
specified with the
‘wrt’ keyword argument is removed from the indexing sets of the
Integral
even though it must be specified as a
positional argument. This should become more clear with the following example
showing a double integral over the
ContinuousSet
components model.t1
and
model.t2
. In addition, the expression is also indexed by the
Set
model.s
. The mathematical representation
and implementation in Pyomo are shown below:
>>> model = ConcreteModel()
>>> model.t1 = ContinuousSet(bounds=(0, 10))
>>> model.t2 = ContinuousSet(bounds=(1, 1))
>>> model.s = Set(initialize=['A', 'B', 'C'])
>>> model.X = Var(model.t1, model.t2, model.s)
>>> def _intX1(m, t1, t2, s):
... return m.X[t1, t2, s]
>>> model.intX1 = Integral(model.t1, model.t2, model.s, wrt=model.t1,
... rule=_intX1)
>>> def _intX2(m, t2, s):
... return m.intX1[t2, s]
>>> model.intX2 = Integral(model.t2, model.s, wrt=model.t2, rule=_intX2)
>>> def _obj(m):
... return sum(m.intX2[k] for k in m.s)
>>> model.obj = Objective(rule=_obj)
Discretization Transformations
Before a Pyomo model with DerivativeVar
or Integral
components can be sent to a
solver it must first be sent through a discretization transformation. These
transformations approximate any derivatives or integrals in the model by
using a numerical method. The numerical methods currently included in pyomo.DAE
discretize the continuous domains in the problem and introduce equality
constraints which approximate the derivatives and integrals at the
discretization points. Two families of discretization schemes have been
implemented in pyomo.DAE, Finite Difference and Collocation. These schemes are
described in more detail below.
Note
The schemes described here are for derivatives only. All integrals will be transformed using the trapezoid rule.
The user must write a Python script in order to use these discretizations, they have not been tested on the pyomo command line. Example scripts are shown below for each of the discretization schemes. The transformations are applied to Pyomo model objects which can be further manipulated before being sent to a solver. Examples of this are also shown below.
Finite Difference Transformation
This transformation includes implementations of several finite difference methods. For example, the Backward Difference method (also called Implicit or Backward Euler) has been implemented. The discretization equations for this method are shown below:
where \(h\) is the step size between discretization points or the size of
each finite element. These equations are generated automatically as
Constraints
when the backward
difference method is applied to a Pyomo model.
There are several discretization options available to a
dae.finite_difference
transformation which can be specified as keyword
arguments to the .apply_to()
function of the transformation object. These
keywords are summarized below:
Keyword arguments for applying a finite difference transformation:
 ‘nfe’
 The desired number of finite element points to be included in the discretization. The default value is 10.
 ‘wrt’
 Indicates which
ContinuousSet
the transformation should be applied to. If this keyword argument is not specified then the same scheme will be applied to everyContinuousSet
.  ‘scheme’
 Indicates which finite difference method to apply. Options are ‘BACKWARD’, ‘CENTRAL’, or ‘FORWARD’. The default scheme is the backward difference method.
If the existing number of finite element points in a
ContinuousSet
is less than the desired
number, new discretization points will be added to the set. If a user specifies
a number of finite element points which is less than the number of points
already included in the ContinuousSet
then
the transformation will ignore the specified number and proceed with the larger
set of points. Discretization points will never be removed from a
ContinousSet
during the discretization.
The following code is a Python script applying the backward difference method. The code also shows how to add a constraint to a discretized model.
Discretize model using Backward Difference method
>>> discretizer = TransformationFactory('dae.finite_difference')
>>> discretizer.apply_to(model,nfe=20,wrt=model.time,scheme='BACKWARD')
Add another constraint to discretized model
>>> def _sum_limit(m):
... return sum(m.x1[i] for i in m.time) <= 50
>>> model.con_sum_limit = Constraint(rule=_sum_limit)
Solve discretized model
>>> solver = SolverFactory('ipopt')
>>> results = solver.solve(model)
Collocation Transformation
This transformation uses orthogonal collocation to discretize the differential equations in the model. Currently, two types of collocation have been implemented. They both use Lagrange polynomials with either GaussRadau roots or GaussLegendre roots. For more information on orthogonal collocation and the discretization equations associated with this method please see chapter 10 of the book “Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes” by L.T. Biegler.
The discretization options available to a dae.collocation
transformation
are the same as those described above for the finite difference transformation
with different available schemes and the addition of the ‘ncp’ option.
Additional keyword arguments for collocation discretizations:
 ‘scheme’
 The desired collocation scheme, either ‘LAGRANGERADAU’ or ‘LAGRANGELEGENDRE’. The default is ‘LAGRANGERADAU’.
 ‘ncp’
 The number of collocation points within each finite element. The default value is 3.
Note
If the user’s version of Python has access to the package Numpy then any number of collocation points may be specified, otherwise the maximum number is 10.
Note
Any points that exist in a
ContinuousSet
before discretization
will be used as finite element boundaries and not as collocation points.
The locations of the collocation points cannot be specified by the user,
they must be generated by the transformation.
The following code is a Python script applying collocation with Lagrange polynomials and Radau roots. The code also shows how to add an objective function to a discretized model.
Discretize model using Radau Collocation
>>> discretizer = TransformationFactory('dae.collocation')
>>> discretizer.apply_to(model,nfe=20,ncp=6,scheme='LAGRANGERADAU')
Add objective function after model has been discretized
>>> def obj_rule(m):
... return sum((m.x[i]m.x_ref)**2 for i in m.time)
>>> model.obj = Objective(rule=obj_rule)
Solve discretized model
>>> solver = SolverFactory('ipopt')
>>> results = solver.solve(model)
Restricting Optimal Control Profiles
When solving an optimal control problem a user may want to restrict the
number of degrees of freedom for the control input by forcing, for example,
a piecewise constant profile. Pyomo.DAE provides the
reduce_collocation_points
function to address this usecase. This function
is used in conjunction with the dae.collocation
discretization
transformation to reduce the number of free collocation points within a finite
element for a particular variable.

class
pyomo.dae.plugins.colloc.
Collocation_Discretization_Transformation
[source] 
reduce_collocation_points
(instance, var=None, ncp=None, contset=None)[source] This method will add additional constraints to a model to reduce the number of free collocation points (degrees of freedom) for a particular variable.
Parameters:  instance (Pyomo model) – The discretized Pyomo model to add constraints to
 var (
pyomo.environ.Var
) – The Pyomo variable for which the degrees of freedom will be reduced  ncp (int) – The new number of free collocation points for var. Must be less that the number of collocation points used in discretizing the model.
 contset (
pyomo.dae.ContinuousSet
) – TheContinuousSet
that was discretized and for which the var will have a reduced number of degrees of freedom

An example of using this function is shown below:
>>> discretizer = TransformationFactory('dae.collocation')
>>> discretizer.apply_to(model, nfe=10, ncp=6)
>>> model = discretizer.reduce_collocation_points(model,
... var=model.u,
... ncp=1,
... contset=model.time)
In the above example, the reduce_collocation_points
function restricts
the variable model.u
to have only 1 free collocation point per
finite element, thereby enforcing a piecewise constant profile.
Fig. 1 shows the solution profile before and
after appling
the reduce_collocation_points
function.
Applying Multiple Discretization Transformations
Discretizations can be applied independently to each
ContinuousSet
in a model. This allows the
user great flexibility in discretizing their model. For example the same
numerical method can be applied with different resolutions:
>>> discretizer = TransformationFactory('dae.finite_difference')
>>> discretizer.apply_to(model,wrt=model.t1,nfe=10)
>>> discretizer.apply_to(model,wrt=model.t2,nfe=100)
This also allows the user to combine different methods. For example, applying
the forward difference method to one
ContinuousSet
and the central finite
difference method to another
ContinuousSet
:
>>> discretizer = TransformationFactory('dae.finite_difference')
>>> discretizer.apply_to(model,wrt=model.t1,scheme='FORWARD')
>>> discretizer.apply_to(model,wrt=model.t2,scheme='CENTRAL')
In addition, the user may combine finite difference and collocation discretizations. For example:
>>> disc_fe = TransformationFactory('dae.finite_difference')
>>> disc_fe.apply_to(model,wrt=model.t1,nfe=10)
>>> disc_col = TransformationFactory('dae.collocation')
>>> disc_col.apply_to(model,wrt=model.t2,nfe=10,ncp=5)
If the user would like to apply the same discretization to all
ContinuousSet
components in a model, just
specify the discretization once without the ‘wrt’ keyword argument. This will
apply that scheme to all ContinuousSet
components in the model that haven’t already been discretized.
Custom Discretization Schemes
A transformation framework along with certain utility functions has been created so that advanced users may easily implement custom discretization schemes other than those listed above. The transformation framework consists of the following steps:
 Specify Discretization Options
 Discretize the ContinuousSet(s)
 Update Model Components
 Add Discretization Equations
 Return Discretized Model
If a user would like to create a custom finite difference scheme then they only have to worry about step (4) in the framework. The discretization equations for a particular scheme have been isolated from of the rest of the code for implementing the transformation. The function containing these discretization equations can be found at the top of the source code file for the transformation. For example, below is the function for the forward difference method:
def _forward_transform(v,s):
"""
Applies the Forward Difference formula of order O(h) for first derivatives
"""
def _fwd_fun(i):
tmp = sorted(s)
idx = tmp.index(i)
return 1/(tmp[idx+1]tmp[idx])*(v(tmp[idx+1])v(tmp[idx]))
return _fwd_fun
In this function, ‘v’ represents the continuous variable or function that the method is being applied to. ‘s’ represents the set of discrete points in the continuous domain. In order to implement a custom finite difference method, a user would have to copy the above function and just replace the equation next to the first return statement with their method.
After implementing a custom finite difference method using the above function
template, the only other change that must be made is to add the custom method
to the ‘all_schemes’ dictionary in the dae.finite_difference
class.
In the case of a custom collocation method, changes will have to be made in steps (2) and (4) of the transformation framework. In addition to implementing the discretization equations, the user would also have to ensure that the desired collocation points are added to the ContinuousSet being discretized.
Dynamic Model Simulation
The pyomo.dae Simulator class can be used to simulate systems of ODEs and DAEs. It provides an interface to integrators available in other Python packages.
Note
The pyomo.dae Simulator does not include integrators directly. The user must have at least one of the supported Python packages installed in order to use this class.

class
pyomo.dae.
Simulator
(m, package='scipy')[source] Simulator objects allow a user to simulate a dynamic model formulated using pyomo.dae.
Parameters:  m (Pyomo Model) – The Pyomo model to be simulated should be passed as the first argument
 package (string) – The Python simulator package to use. Currently ‘scipy’ and ‘casadi’ are the only supported packages

get_variable_order
(vartype=None)[source] This function returns the ordered list of differential variable names. The order corresponds to the order being sent to the integrator function. Knowing the order allows users to provide initial conditions for the differential equations using a list or map the profiles returned by the simulate function to the Pyomo variables.
Parameters: vartype (string or None) – Optional argument for specifying the type of variables to return the order for. The default behavior is to return the order of the differential variables. ‘timevarying’ will return the order of all the timedependent algebraic variables identified in the model. ‘algebraic’ will return the order of algebraic variables used in the most recent call to the simulate function. ‘input’ will return the order of the timedependent algebraic variables that were treated as inputs in the most recent call to the simulate function. Returns: Return type: list

initialize_model
()[source] This function will initialize the model using the profile obtained from simulating the dynamic model.

simulate
(numpoints=None, tstep=None, integrator=None, varying_inputs=None, initcon=None, integrator_options=None)[source] Simulate the model. Integratorspecific options may be specified as keyword arguments and will be passed on to the integrator.
Parameters:  numpoints (int) – The number of points for the profiles returned by the simulator. Default is 100
 tstep (int or float) – The time step to use in the profiles returned by the simulator. This is not the time step used internally by the integrators. This is an optional parameter that may be specified in place of ‘numpoints’.
 integrator (string) – The string name of the integrator to use for simulation. The default is ‘lsoda’ when using Scipy and ‘idas’ when using CasADi
 varying_inputs (
pyomo.environ.Suffix
) – ASuffix
object containing the piecewise constant profiles to be used for certain timevarying algebraic variables.  initcon (list of floats) – The initial conditions for the the differential variables. This is an optional argument. If not specified then the simulator will use the current value of the differential variables at the lower bound of the ContinuousSet for the initial condition.
 integrator_options (dict) – Dictionary containing options that should be passed to the integrator. See the documentation for a specific integrator for a list of valid options.
Returns: The first return value is a 1D array of time points corresponding to the second return value which is a 2D array of the profiles for the simulated differential and algebraic variables.
Return type: numpy array, numpy array
Note
Any keyword options supported by the integrator may be specified as keyword options to the simulate function and will be passed to the integrator.
Supported Simulator Packages
The Simulator currently includes interfaces to SciPy and CasADi. ODE simulation is supported in both packages however, DAE simulation is only supported by CasADi. A list of available integrators for each package is given below. Please refer to the SciPy and CasADi documentation directly for the most uptodate information about these packages and for more information about the various integrators and options.
 SciPy Integrators:
 ‘vode’ : Realvalued Variablecoefficient ODE solver, options for nonstiff and stiff systems
 ‘zvode’ : Complexvalues Variablecoefficient ODE solver, options for nonstiff and stiff systems
 ‘lsoda’ : Realvalues Variablecoefficient ODE solver, automatic switching of algorithms for nonstiff or stiff systems
 ‘dopri5’ : Explicit rungekutta method of order (4)5 ODE solver
 ‘dop853’ : Explicit rungekutta method of order 8(5,3) ODE solver
 CasADi Integrators:
 ‘cvodes’ : CVodes from the Sundials suite, solver for stiff or nonstiff ODE systems
 ‘idas’ : IDAS from the Sundials suite, DAE solver
 ‘collocation’ : Fixedstep implicit rungekutta method, ODE/DAE solver
 ‘rk’ : Fixedstep explicit rungekutta method, ODE solver
Using the Simulator
We now show how to use the Simulator to simulate the following system of ODEs:
We begin by formulating the model using pyomo.DAE
>>> m = ConcreteModel()
>>> m.t = ContinuousSet(bounds=(0.0, 10.0))
>>> m.b = Param(initialize=0.25)
>>> m.c = Param(initialize=5.0)
>>> m.omega = Var(m.t)
>>> m.theta = Var(m.t)
>>> m.domegadt = DerivativeVar(m.omega, wrt=m.t)
>>> m.dthetadt = DerivativeVar(m.theta, wrt=m.t)
Setting the initial conditions
>>> m.omega[0].fix(0.0)
>>> m.theta[0].fix(3.14  0.1)
>>> def _diffeq1(m, t):
... return m.domegadt[t] == m.b * m.omega[t]  m.c * sin(m.theta[t])
>>> m.diffeq1 = Constraint(m.t, rule=_diffeq1)
>>> def _diffeq2(m, t):
... return m.dthetadt[t] == m.omega[t]
>>> m.diffeq2 = Constraint(m.t, rule=_diffeq2)
Notice that the initial conditions are set by fixing the values of
m.omega
and m.theta
at t=0 instead of being specified as extra
equality constraints. Also notice that the differential equations are
specified without using Constraint.Skip
to skip enforcement at t=0. The
Simulator cannot simulate any constraints that contain ifstatements in
their construction rules.
To simulate the model you must first create a Simulator object. Building this object prepares the Pyomo model for simulation with a particular Python package and performs several checks on the model to ensure compatibility with the Simulator. Be sure to read through the list of limitations at the end of this section to understand the types of models supported by the Simulator.
>>> sim = Simulator(m, package='scipy')
After creating a Simulator object, the model can be simulated by calling the
simulate function. Please see the API documentation for the
Simulator
for more information about the
valid keyword arguments for this function.
>>> tsim, profiles = sim.simulate(numpoints=100, integrator='vode')
The simulate
function returns numpy arrays containing time points and
the corresponding values for the dynamic variable profiles.
 Simulator Limitations:
 Differential equations must be firstorder and separable
 Model can only contain a single ContinuousSet
 Can’t simulate constraints with ifstatements in the construction rules
 Need to provide initial conditions for dynamic states by setting the value or using fix()
Specifying TimeVaring Inputs
The Simulator
supports simulation of a system
of ODE’s or DAE’s with timevarying parameters or control inputs. Timevarying
inputs can be specified using a Pyomo Suffix
. We currently only support
piecewise constant profiles. For more complex inputs defined by a continuous
function of time we recommend adding an algebraic variable and constraint to
your model.
The profile for a timevarying input should be specified
using a Python dictionary where the keys correspond to the switching times
and the values correspond to the value of the input at a time point. A
Suffix
is then used to associate this dictionary with the appropriate
Var
or Param
and pass the information to the
Simulator
. The code snippet below shows an
example.
>>> m = ConcreteModel()
>>> m.t = ContinuousSet(bounds=(0.0, 20.0))
Timevarying inputs
>>> m.b = Var(m.t)
>>> m.c = Param(m.t, default=5.0)
>>> m.omega = Var(m.t)
>>> m.theta = Var(m.t)
>>> m.domegadt = DerivativeVar(m.omega, wrt=m.t)
>>> m.dthetadt = DerivativeVar(m.theta, wrt=m.t)
Setting the initial conditions
>>> m.omega[0] = 0.0
>>> m.theta[0] = 3.14  0.1
>>> def _diffeq1(m, t):
... return m.domegadt[t] == m.b[t] * m.omega[t]  \
... m.c[t] * sin(m.theta[t])
>>> m.diffeq1 = Constraint(m.t, rule=_diffeq1)
>>> def _diffeq2(m, t):
... return m.dthetadt[t] == m.omega[t]
>>> m.diffeq2 = Constraint(m.t, rule=_diffeq2)
Specifying the piecewise constant inputs
>>> b_profile = {0: 0.25, 15: 0.025}
>>> c_profile = {0: 5.0, 7: 50}
Declaring a Pyomo Suffix to pass the timevarying inputs to the Simulator
>>> m.var_input = Suffix(direction=Suffix.LOCAL)
>>> m.var_input[m.b] = b_profile
>>> m.var_input[m.c] = c_profile
Simulate the model using scipy
>>> sim = Simulator(m, package='scipy')
>>> tsim, profiles = sim.simulate(numpoints=100,
... integrator='vode',
... varying_inputs=m.var_input)
Note
The Simulator does not support multiindexed inputs (i.e. if m.b
in
the above example was indexed by another set besides m.t
)
Dynamic Model Initialization
Providing a good initial guess is an important factor in solving dynamic optimization problems. There are several model initialization tools under development in pyomo.DAE to help users initialize their models. These tools will be documented here as they become available.
From Simulation
The Simulator
includes a function for
initializing discretized dynamic optimization models using the profiles
returned from the simulator. An example using this function is shown below
Simulate the model using scipy
>>> sim = Simulator(m, package='scipy')
>>> tsim, profiles = sim.simulate(numpoints=100, integrator='vode',
... varying_inputs=m.var_input)
Discretize the model using Orthogonal Collocation
>>> discretizer = TransformationFactory('dae.collocation')
>>> discretizer.apply_to(m, nfe=10, ncp=3)
Initialize the discretized model using the simulator profiles
>>> sim.initialize_model()
Note
A model must be simulated before it can be initialized using this function