general package¶

Please, have a look to the following general modules

Elements module
Core module
Time module
Model module
Input Data module
Output Data module
Plots module
Maths module

Firstly, the main modules to build an energy optimisation model are presented :

Elements module ¶

This module includes the optimization elements (quantities, constraints and objectives) formulated in LP or MILP

Quantity : related to the decision variable or parameter

Constraint : related to the optimization problem constraints

Objective : related to the objective function

class omegalpes.general.optimisation.elements.Constraint(exp, name='CST0', description='', active=True, parent=None)[source]¶

Bases: object

Description

Class that defines a constraint object

Attributes

name: name of the constraint

description: a description of the constraint

active: False = non-active constraint; True = active constraint

exp: (str) : expression of the constraint

parent: (unit) : this constraint belongs to this unit

Note

Make sure that all the modifications on Constraints are made before adding the unit to the Model (OptimisationModel.addUnit()).

class omegalpes.general.optimisation.elements.DynamicConstraint(exp_t, t_range='for t in time.I', name='DCST0', description='dynamic constraint', active=True, parent=None)[source]¶

Bases: omegalpes.general.optimisation.elements.Constraint

Description

Defining a constraint depending on the time. NB : Mandatory for PuLP

class omegalpes.general.optimisation.elements.ExtDynConstraint(exp_t, t_range='for t in time.I', name='EDCST0', active=True, description='Non-physical and dynamic constraint', parent=None)[source]¶

Bases: omegalpes.general.optimisation.elements.DynamicConstraint, omegalpes.general.optimisation.elements.ExternalConstraint

Description

Defining a constraint both external and dynamic (see: DynamicConstraint, ExternalConstraint)

class omegalpes.general.optimisation.elements.ExternalConstraint(exp, name='ExCST0', description='', active=True, parent=None)[source]¶

Bases: omegalpes.general.optimisation.elements.Constraint

Description

Defining a special type of constraint: the external constraint

This constraint does not translate a physical constraint

This constraint defines an external constraint, which could be relaxed

deactivate_constraint()[source]¶: An external constraint can be deactivated : - To compare scenarios - To try a less constrained problem

class omegalpes.general.optimisation.elements.HourlyDynamicConstraint(exp_t, time, init_h: int = 0, final_h: int = 24, name='HDCST0', description='hourly dynamic constraint', active=True, parent=None)[source]¶

Bases: omegalpes.general.optimisation.elements.DynamicConstraint

Description

Class that defines an dynamic contraint for a time range

Ex: Constraint applying between 7am and 10pm

ex_cst = HourlyDynamicConstraint(exp_t, time, init_h=7, final_h=22, name=’ex_cst’)

Attributes

name (str) : name of the constraint

exp_t (str) : expression of the constraint

init_h (int) : hour of beginning of the constraint [0-23]

final_h (int) : hour of end of the constraint [1-24]

description (str) : description of the constraint

active (bool) : defines if the constraint is active or not

parent (OptObject) : parent of the constraint

class omegalpes.general.optimisation.elements.Objective(exp, name='OBJ0', description='', active=True, weight=1, unit='s.u.', parent=None)[source]¶

Bases: object

Description

Class that defines an optimisation objective

Attributes

name (str) :

description (str) :

active (bool) :

exp (str) :

weight (float) : weighted factor of the objective

parent (unit)

unit (str) : unit of the cost expression

Note

Make sure that all the modifications on Objectives are made before adding the unit to the OptimisationModel, otherwise, it won’t be taken into account

class omegalpes.general.optimisation.elements.Quantity(name='var0', opt=True, unit='s.u', vlen=None, value=None, description='', vtype='Continuous', lb=None, ub=None, parent=None)[source]¶

Bases: object

Description

Class that defines what is a quantity. A quantity can wether be a decision variable or a parameter, depending on the opt parameter

Attributes

name (str) : the name of the quantity

description (str) : a description of the meaning of the quantity

vtype (PuLP) : the variable type, depending on PuLP

LpBinary (binary variable)

LpInteger (integer variable)

LpContinuous (continuous variable)

vlen (int) : size of the variable

unit (str) : unit of the quantity

opt (binary)

True: this is an optimization variable

False: this is a constant - a parameter

value (float, list, dict) : value (unused if opt=True)

ub, lb : upper and lower bounds

parent (OptObject) : the quantity belongs to this unit

Note

Make sure that all the modifications on Quantity are made before adding the unit to the Model

Core module ¶

This module define the main Core object on which the energy units and actors will be based

class omegalpes.general.optimisation.core.OptObject(name='U0', description='Optimization object', verbose=True)[source]¶

Bases: object

Description

OptObject class is used as an “abstract class”, i.e. it defines some general attributes and methods but doesn’t contain variable, constraint nor objective. In the OMEGAlpes package, all the subsystem models are represented by a unit. A model is then generated adding OptObject to it. Variable, objective and constraints declarations are usually done using the __init__ method of the OptObject class.

Attributes

name

description

_quantities_list : list of the quanitities of the OptObject (active or not)

_constraints_list : list of the constraints of the OptObject(active or not)

_external_constraints_list : list of the constraints of the OptObject (active or not)

_objectives_list : list of the objectives of the OptObject (active or not)

Methods

__str__: defines the

__repr__: defines the unit with its name

get_constraints_list

get_constraints_name_list

get_external_constraints_list

get_external_constraints_name_list

get_objectives_list

get_objectives_name_list

get_quantities_list

get_quantities_name_list

deactivate_optobject_external_constraints

Note

The OptObject class shouldn’t be instantiated in a python script, except if you want to create your own model from the beginning. In this case, one should consider creating a new class NewModel(OptObject).

deactivate_optobject_external_constraints(ext_cst_name_list=None)[source]¶

Enable to remove an external constraint linked with an OptObject

Parameters:	ext_cst_name_list – list of external constraint that would be removed

get_constraints_list()[source]¶: Get the constraints associated with the unit as a dictionary shape [‘constraint_name’ , constraint]

get_constraints_name_list()[source]¶: Get the names of the constraints associated with the unit

get_external_constraints_list()[source]¶: Get the external constraints associated with the unit as a dictionary shape [‘constraint_name’ , constraint]

get_external_constraints_name_list()[source]¶: Get the names of the external constraints associated with the unit

get_objectives_list()[source]¶: Get objectives associated with the unit as a dictionary shape [‘objective_name’ , objective]

get_objectives_name_list()[source]¶: Get the names of the objectives associated with the unit

get_quantities_list()[source]¶: Get the quantities associated with the unit as a dictionary shape [‘quantity_name’ , quantity]

get_quantities_name_list()[source]¶: Get the names of the quantities associated with the unit

Time module ¶

This module creates the Time object

class omegalpes.general.time.TimeUnit(start='01/01/2018', end=None, periods=None, dt=1)[source]¶

Bases: object

Description

Class defining the studied time period.

Attributes

DATES : dated list of simulation steps

DT : delta t between values in hours (int or float), i.e. 1/6 will be 10 minutes.

LEN : number of simulation steps (length of DATES)

I : index of time ([0 : LEN])

get_date_for_index(index)[source]¶

Getting a date for a given index

Parameters:	index – int value for the index of the wanted dated, between 0 and LEN (it must be in the studied period)

get_days¶

Getting days for the studied period

Return all_days:
	list of days of the studied period

get_index_for_date(date='YYYY-MM-DD HH:MM:SS')[source]¶

Getting the index associated with a date

Parameters:	date – date the index of is wanted. Format YYYY-MM-DD HH:MM:SS, must be within the studied period and consistent with the timestep value

get_index_for_date_range(starting_date='YYYY-MM-DD HH:MM:SS', end=None, periods=None)[source]¶

Getting a list of index for a date range

Return index_list:
Parameters:	starting_date – starting date of the wanted index end – ending date of the wanted index periods – number of periods from the starting_date of the wanted index
	list of indexes for the given dates

get_non_working_dates(month_range=range(0, 12), hour_range=range(0, 24), country='France')[source]¶

get_non_working_days(country='France')[source]¶

get_working_dates(month_range=range(0, 12), hour_range=range(0, 24), country='France')[source]¶

get_working_days(country='France')[source]¶

print_studied_period()[source]¶

omegalpes.general.time.convert_european_format(date)[source]¶

Converting a date with an european format DD/MM/YYYY into a datetime format YYYY-MM-DD or return

Parameters:	date – date in european format
Returns:	date in format datetime

Model module ¶

This module enables to fill the optimization model and formulate it in LP or MILP based on the package PuLP (LpProblem)

class omegalpes.general.optimisation.model.OptimisationModel(time, name='optimisation_model')[source]¶

Bases: pulp.pulp.LpProblem

Description

This class includes the optimization model formulated in LP or MILP based on the package PuLP (LpProblem)

add_nodes(*nodes)[source]¶

Add nodes and all connected units to the model Check that the time is the same for the model and all the units

Parameters:	nodes – EnergyNode

add_nodes_and_actors(*nodes_or_actors)[source]¶

Add nodes, actors and all connected units to the model Check that the time is the same for the model and all the units

Parameters:	nodes_or_actors – EnergyNode or Actor type

get_model_constraints_list()[source]¶: Gets constraints of the model

get_model_constraints_name_list()[source]¶: Gets the names of the constraints of the model

get_model_objectives_list()[source]¶: Gets objectives of the model

get_model_objectives_name_list()[source]¶: Gets the names of the objectives of the model

get_model_quantities_list()[source]¶: Gets quantities of the model

get_model_quantities_name_list()[source]¶: Gets the names of the quantities of the model

solve_and_update(solver: pulp.solvers.LpSolver = None) → None[source]¶

Solves the optimization model and updates all variables values.

Parameters:	solver (LpSolver) – Optimization solver

update_units()[source]¶: Updates all units values with optimization results

omegalpes.general.optimisation.model.check_if_unit_could_have_parent(unit)[source]¶

Checks if the unit has an associated parent

Parameters:	unit – unit which parents will be checked

omegalpes.general.optimisation.model.compute_gurobi_IIS(gurobi_exe_path='C:\\gurobi800\\win64\\bin', opt_model=None, MPS_model=None)[source]¶

Identifies the constraints in a .ilp file

Parameters:	gurobi_exe_path – Path to the gurobi solver “gurobi_cl.exe” opt_model – OptimisationModel to whom compute IIS MPS_model – name of the mps model

Then, utils methods are developped and are presented in the following module:

Input Data module ¶

This module includes the following utils for input data management

It contains the following methods:

select_csv_file_between_dates() : select data in a .csv file between two dates

read_enedis_data_csv_file() : select and rearrange the data in a .csv file of Enedis (the French Distribion System Operator company), possibly between two dates

omegalpes.general.utils.input_data.read_enedis_data_csv_file(file_path=None, start=None, end=None)[source]¶

Rearrange the Enedis data in cvs file in oder to have a Dataframe of the following form

DD MM YYYY HH:00 ; a DD MM YYYY HH:30 ; b …

Parameters:	file_path – path of the file to rearrange start – DD MM YYYY HH:00 : first date which should be considered end – DD MM YYYY HH:00 : last date which should be considered
Returns:	df_list: the data as a list

omegalpes.general.utils.input_data.select_csv_file_between_dates(file_path=None, start='DD/MM/YYYY HH:MM', end='DD/MM/YYYY HH:MM', v_cols=[], sep=';')[source]¶

Select data in a .csv file between two dates

Parameters:	file_path – path of the file to rearrange start – DD MM YYYY HH:00 : first date which should be considered end – DD MM YYYY HH:00 : last date which should be considered v_cols – columns which should be considered
Returns:	df: a dataframe considering the dates

Output Data module ¶

This module includes the following utils for output data management

It contains the following methods:

save_energy_flows() : Save the optimisation results in a .csv file

omegalpes.general.utils.output_data.save_energy_flows(*nodes, file_name=None, sep='\t', decimal_sep='.')[source]¶

Save the optimisation results in a .csv file

Parameters:	nodes – list of the nodes from which should be collected the data file_name – name of the file to save the data sep – separator for the data decimal_sep – separator dor the decimals of the data

Plots module ¶

This module includes the following display utils:

plot_node_energetic_flows() : enables one to plot the energy flows through an EnergyNode

plot_energy_mix() : enables one to plot the energy flows connected to a node

plot_quantity() : enables one to plot easily a Quantitiy

plot_quantity_bar() : enables one to plot easily a Quantitiy as a bar

sum_quantities_in_quantity() : enables one to to plot several quantities in one once the optimisation is done

omegalpes.general.utils.plots.plot_energy_mix(node)[source]¶

omegalpes.general.utils.plots.plot_node_energetic_flows(node)[source]¶

Description

This function allows to plot the energy flows through an EnergyNode

The display is realized :

with histograms for production and storage flow

with dashed curve for consumption flow

Parameters:	node – EnergyNode

omegalpes.general.utils.plots.plot_quantity(time, quantity, fig=None, ax=None, color=None, label=None)[source]¶

Description

Function that plots a OMEGALPES.general.optimisation.elements.Quantity

Attributes

quantity is the OMEGALPES.general.optimisation.elements.Quantity

fig could be None, a matplotlib.pyplot.Figure or Axes for multiple plots

Returns

arg1 the matplotlib.pyplot.Figure handle object

arg2 the matplotlib.pyplot.Axes handle object

arg3 the matplotlib.pyplot.Line2D handle object

omegalpes.general.utils.plots.plot_quantity_bar(time, quantity, fig=None, ax=None, color=None, label=None)[source]¶

Description

Function that plots a OMEGALPES.general.optimisation.elements.Quantity as a bar

Attributes

quantity is the OMEGALPES.general.optimisation.elements.Quantity

fig could be None, a matplotlib.pyplot.Figure or Axes for multiple plots

Returns

arg1 the matplotlib.pyplot.Figure handle object

arg2 the matplotlib.pyplot.Axes handle object

arg3 the matplotlib.pyplot.Line2D handle object

omegalpes.general.utils.plots.sum_quantities_in_quantity(quantities_list=[], tot_quantity_name='sum_quantity')[source]¶

Description

Function that creates a new quantity gathering several values of quantities Should be used in order to plot several quantities in one once the optimisation is done

Attributes

quantities : a list of Quantities (OMEGALPES.general.optimisation.elements.Quantity)

tot_quantity_name : string : name of the new quantity

Returns

tot_quantity : the new quantity created and filled

Maths module ¶

This module includes the following maths utils

It contains the following methods:

def_abs_value() : Define easily absolute value for quantity

omegalpes.general.utils.maths.def_abs_value(quantity, q_min, q_max)[source]¶

Parameters:	quantity – Quantity whose absolute value is wanted q_min – Minimal value of the quantity (negative value) q_max – Maximal value of the quantity (positive value)
Returns:	A new Quantity whose values equal absolute values of the initial Quantity