Merge remote-tracking branch 'origin/master' into dev_omegalpes_jf

README.md

@@ -5,6 +5,9 @@ OMEGAlpes Lib for linear energy systems modelling
 
 **OMEGAlpes** aims to be an energy systems modelling tool for linear optimisation (LP, MILP).
 
+A **web interface** is available at https://mhi-srv.g2elab.grenoble-inp.fr/OMEGAlpes-web-front-end/ to generate scripts
+
+
 We are happy that you will use or develop the OMEGAlpes library. 
 
 It is an **Open Source** project located on GitLab at https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes
@@ -28,6 +31,16 @@ Some examples and article case studies are avalaible as Notebooks at:
 A scientific article presenting OMEGAlpes with a detailed example is available here:
 https://hal.archives-ouvertes.fr/hal-02285954v1
 
+**Notebooks :**
+Mostly all examples and article case studies are associated to a Notebook.
+They can be found in the folder notebooks at : 
+    https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes_examples
+
+**OMEGAlpes-web interface :**
+An web interface is available at https://mhi-srv.g2elab.grenoble-inp.fr/OMEGAlpes-web-front-end/ 
+This interface enable you to generate scripts more easily using the graphical representation detailed 
+in https://omegalpes.readthedocs.io/en/latest/OMEGAlpes_grah_representation.html
+
 
 OMEGAlpes' Community
 ====================

docs/index.rst

@@ -10,7 +10,8 @@ It aims to be an energy systems modelling tool for linear optimisation (LP, MILP
 It is currently based on the LP modeler PuLP.
 
 It is an Open Source project located on GitLab at `OMEGAlpes Gitlab`_
-
+An associated web interface is avalable at `OMEGAlpes interface`_ to help
+generate scripts
 
 Contents
 --------
@@ -53,3 +54,4 @@ in the framework of the "Investissements d’avenir” program (ANR-15-IDEX-02)
 .. _CDP Eco-SESA: https://ecosesa.univ-grenoble-alpes.fr/
 .. _OMEGAlpes Gitlab: https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/omegalpes
 .. _OMEGAlpes Examples Documentation: https://omegalpes_examples.readthedocs.io/
+.. _OMEGAlpes interface: https://mhi-srv.g2elab.grenoble-inp.fr/OMEGAlpes-web-front-end/
\ No newline at end of file

docs/new_functionalities/v0.3.2.rst

@@ -8,16 +8,18 @@ Bug fixed
   *general.optimisation.model*, as well as requirements.txt, setup.py and
   associated docs.
 
-
 New functionalities
 -------------------
 
-Main Folder
+Energy
 +++++++++++
 
-Category
+Units
 ********
-
+- e_max and e_min parameters now add technical constraints (set_e_min and set_e_max) when set to a value.
+- e_tot upper bound is now set as the p_max value times the number of hours over the studied time period. The lower bound is either set to 0, or minus the upper bound if the unit is a storage unit.
+- the *add* methods put in place with parameters are now *_add* methods.
+- set_e_max constraint changed to set_e_max_period (respectively e_min)
 
 
 Deprecated

omegalpes/energy/units/consumption_units.py

@@ -62,19 +62,14 @@ class ConsumptionUnit(EnergyUnit):
     """
     **Description**
 
-        Simple Consumption unit
+        Simple Consumption unit. The parameters and attributes are described
+        in EnergyUnit parent class. Here, consumption_cost is the cost
+        associated to the energy consumption of the unit (€/kWh).
 
-    **Attributes**
-
-         * p : instantaneous power demand (kW)
-         * p_max : maximal instantaneous power demand (kW)
-         * p_min : minimal instantaneous power demand (kW)
-         * energy_type : type of energy ('Electrical', 'Heat', ...)
-         * consumption_cost : cost associated to the energy consumption
      """
 
-    def __init__(self, time, name, p=None, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, co2_out=None, starting_cost=None,
+    def __init__(self, time, name, p=None, p_min=1e-5, p_max=1e+5, e_min=None,
+                 e_max=None, co2_out=None, starting_cost=None,
                  consumption_cost=None, min_time_on=None, min_time_off=None,
                  max_ramp_up=None, max_ramp_down=None, availability_hours=None,
                  energy_type=None, verbose=True):
@@ -90,26 +85,32 @@ class ConsumptionUnit(EnergyUnit):
                             verbose=verbose)
 
     # OBJECTIVES#
-    def minimize_consumption(self, weight=1):
+    def minimize_consumption(self, weight=1, pareto=False):
         """
         :param weight: Weight coefficient for the objective
+        :param pareto: if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
         """
-        self.minimize_energy(weight=weight)
+        self.minimize_energy(weight=weight, pareto=pareto)
         self.min_energy.name = 'min_consumption'
 
-    def maximize_consumption(self, weight=1):
+    def maximize_consumption(self, weight=1, pareto=False):
         """
         :param weight: Weight coefficient for the objective
+        :param pareto: if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
         """
-        self.minimize_energy(weight=-1 * weight)
+        self.minimize_energy(weight=-1 * weight, pareto=pareto)
         self.min_energy.name = 'max_consumption'
 
-    def minimize_consumption_cost(self, weight=1):
+    def minimize_consumption_cost(self, weight=1, pareto=False):
         """
 
         :param weight: Weight coefficient for the objective
+        :param pareto: if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
         """
-        self.minimize_operating_cost(weight)
+        self.minimize_operating_cost(weight, pareto=pareto)
         self.min_operating_cost.name = 'min_consumption_cost'
 
 
@@ -154,8 +155,8 @@ class VariableConsumptionUnit(VariableEnergyUnit, ConsumptionUnit):
 
     """
 
-    def __init__(self, time, name, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, co2_out=None, starting_cost=None,
+    def __init__(self, time, name, p_min=1e-5, p_max=1e+5, e_min=None,
+                 e_max=None, co2_out=None, starting_cost=None,
                  operating_cost=None, min_time_on=None, min_time_off=None,
                  max_ramp_up=None, max_ramp_down=None, energy_type=None,
                  verbose=True, no_warn=True):
@@ -186,8 +187,9 @@ class SeveralConsumptionUnit(VariableConsumptionUnit, SeveralEnergyUnit):
 
     """
 
-    def __init__(self, time, name, fixed_cons, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, nb_unit_min=0, nb_unit_max=None, co2_out=None,
+    def __init__(self, time, name, fixed_cons, p_min=1e-5, p_max=1e+5,
+                 e_min=None,
+                 e_max=None, nb_unit_min=0, nb_unit_max=None, co2_out=None,
                  starting_cost=None, operating_cost=None, max_ramp_up=None,
                  max_ramp_down=None, energy_type=None,
                  verbose=True, no_warn=True):
@@ -225,8 +227,9 @@ class SeveralImaginaryConsumptionUnit(VariableConsumptionUnit,
 
     """
 
-    def __init__(self, time, name, fixed_cons, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, nb_unit_min=0, nb_unit_max=None, co2_out=None,
+    def __init__(self, time, name, fixed_cons, p_min=1e-5, p_max=1e+5,
+                 e_min=None,
+                 e_max=None, nb_unit_min=0, nb_unit_max=None, co2_out=None,
                  starting_cost=None, operating_cost=None, max_ramp_up=None,
                  max_ramp_down=None, energy_type=None,
                  verbose=True, no_warn=True):

omegalpes/energy/units/production_units.py

@@ -69,21 +69,13 @@ class ProductionUnit(EnergyUnit):
     """
     **Description**
 
-        Simple Production unit
+        Simple Production unit. The parameters and attributes are described
+        in EnergyUnit parent class.
 
-    **Attributes**
-
-     * co2_out: outside co2 emissions
-     * starting_cost: the starting cost
-     * operating_cost: the operating cost
-     * min_time_on : the minimal operating time
-     * min_time_off : the minimal non-operating time
-     * max_ramp_up : the  maximal increasing ramp
-     * max_ramp_down ; the maximal decreasing ramp
     """
 
-    def __init__(self, time, name, p=None, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, co2_out=None, starting_cost=None,
+    def __init__(self, time, name, p=None, p_min=1e-5, p_max=1e+5, e_min=None,
+                 e_max=None, co2_out=None, starting_cost=None,
                  operating_cost=None, min_time_on=None, min_time_off=None,
                  max_ramp_up=None, max_ramp_down=None, availability_hours=None,
                  energy_type=None, verbose=True, no_warn=True):
@@ -99,20 +91,24 @@ class ProductionUnit(EnergyUnit):
                             energy_type=energy_type,
                             verbose=verbose, no_warn=no_warn)
 
-    def minimize_production(self, weight=1):
+    def minimize_production(self, weight=1, pareto=False):
         """
 
         :param weight: Weight coefficient for the objective
+        :param pareto: if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
         """
-        self.minimize_energy(weight=weight)
+        self.minimize_energy(weight=weight, pareto=pareto)
         self.min_energy.name = 'min_production'
 
-    def maximize_production(self, weight=1):
+    def maximize_production(self, weight=1, pareto=False):
         """
 
         :param weight: Weight coefficient for the objective
+        :param pareto: if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
         """
-        self.minimize_energy(weight=-1 * weight)
+        self.minimize_energy(weight=-1 * weight, pareto=pareto)
         self.min_energy.name = 'max_production'
 
 
@@ -157,8 +153,8 @@ class VariableProductionUnit(VariableEnergyUnit, ProductionUnit):
 
     """
 
-    def __init__(self, time, name, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, co2_out=None, starting_cost=None,
+    def __init__(self, time, name, p_min=1e-5, p_max=1e+5, e_min=None,
+                 e_max=None, co2_out=None, starting_cost=None,
                  operating_cost=None, min_time_on=None, min_time_off=None,
                  max_ramp_up=None, max_ramp_down=None, energy_type=None,
                  verbose=True, no_warn=True):
@@ -191,8 +187,9 @@ class SeveralProductionUnit(VariableProductionUnit, SeveralEnergyUnit):
 
     """
 
-    def __init__(self, time, name, fixed_prod, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, nb_unit_min=0, nb_unit_max=None, co2_out=None,
+    def __init__(self, time, name, fixed_prod, p_min=1e-5, p_max=1e+5,
+                 e_min=None,
+                 e_max=None, nb_unit_min=0, nb_unit_max=None, co2_out=None,
                  starting_cost=None, operating_cost=None, max_ramp_up=None,
                  max_ramp_down=None, energy_type=None,
                  verbose=True, no_warn=True):
@@ -229,8 +226,9 @@ class SeveralImaginaryProductionUnit(VariableProductionUnit, SeveralEnergyUnit):
 
     """
 
-    def __init__(self, time, name, fixed_prod, p_min=1e-5, p_max=1e+5, e_min=0,
-                 e_max=1e6, nb_unit_min=0, nb_unit_max=None, co2_out=None,
+    def __init__(self, time, name, fixed_prod, p_min=1e-5, p_max=1e+5,
+                 e_min=None,
+                 e_max=None, nb_unit_min=0, nb_unit_max=None, co2_out=None,
                  starting_cost=None, operating_cost=None, max_ramp_up=None,
                  max_ramp_down=None, energy_type=None,
                  verbose=True, no_warn=True):

omegalpes/energy/units/storage_units.py

@@ -93,7 +93,7 @@ class StorageUnit(VariableEnergyUnit):
                  pd_min=1e-5, pd_max=1e+5, capacity=None, e_0=None,
                  e_f=None, soc_min=0, soc_max=1, eff_c=1, eff_d=1,
                  self_disch=0, self_disch_t=0, ef_is_e0=False, cycles=None,
-                 energy_type=None, e_min=-1e+6, e_max=1e+6):
+                 energy_type=None, e_min=None, e_max=None):
         """
         :param time: TimeUnit describing the studied time period
         :param name: name of the storage unit
@@ -353,10 +353,12 @@ class StorageUnit(VariableEnergyUnit):
                                 'hours between cycling (e[t] = e[t+cycles/dt]')
 
     # OBJECTIVES
-    def minimize_capacity(self, weight=1):
+    def minimize_capacity(self, weight=1, pareto=False):
 
         """
         :param weight: Weight coefficient for the objective
+        :param pareto: if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
         """
         def_capacity = DefinitionDynamicConstraint(
             exp_t='{0}_e[t] <= {0}_capacity'
@@ -367,6 +369,7 @@ class StorageUnit(VariableEnergyUnit):
         min_capacity = Objective(name='min_capacity',
                                  exp='{0}_capacity'.format(self.name),
                                  weight=weight,
+                                 pareto=pareto,
                                  parent=self)
 
         setattr(self, 'def_capacity', def_capacity)
@@ -395,8 +398,8 @@ class ThermoclineStorage(StorageUnit):
     def __init__(self, time, name, pc_min=1e-5, pc_max=1e+5,
                  pd_min=1e-5, pd_max=1e+5,
                  capacity=None, e_0=None, e_f=None, soc_min=0,
-                 soc_max=1, eff_c=1, eff_d=1, self_disch=0, e_min=-1e6,
-                 e_max=1e6, Tcycl=120, ef_is_e0=False):
+                 soc_max=1, eff_c=1, eff_d=1, self_disch=0, e_min=None,
+                 e_max=None, Tcycl=120, ef_is_e0=False):
         """
         :param time: TimeUnit describing the studied time period
         :param name: name of the storage unit

omegalpes/general/optimisation/core.py

@@ -85,6 +85,7 @@ class OptObject:
         self._technical_constraints_list = []  # only for technical constraints
         self._actor_constraints_list = []  # only for actor's constraints
         self._objectives_list = []
+        self._pareto_objectives_list = []
 
         if verbose:
             print(("Creating the {0}.".format(name)))

omegalpes/general/optimisation/elements.py

@@ -820,12 +820,19 @@ class Objective:
      **Attributes**
 
       - name (str) :
+      - exp (str) :
       - description (str) :
       - active (bool) :
-      - exp (str) :
       - weight (float) : weighted factor of the objective
       - parent (unit)
       - unit (str) : unit of the cost expression
+      - pareto (str) : if True, OMEGAlpes calculates a pareto front based on
+            this objective (two objectives needed)
+
+    **Methods**
+
+    - _add_objectives_list()
+    - _add_pareto_objectives_list()
 
      .. note::
         Make sure that all the modifications on Objectives are made before
@@ -835,7 +842,7 @@ class Objective:
      """
 
     def __init__(self, exp, name='OBJ0', description='', active=True, weight=1,
-                 unit='s.u.', parent=None):
+                 unit='s.u.', pareto=False, parent=None):
         self.name = name
         self.exp = exp
         self.description = description
@@ -843,9 +850,17 @@ class Objective:
         self.weight = weight
         self.parent = parent
         self.unit = unit
-        self._add_objective_list()
+        self.pareto = pareto
+        if pareto:
+            self._add_pareto_objectives_list()
+        else:
+            self._add_objectives_list()
 
-    def _add_objective_list(self):
+    def _add_objectives_list(self):
+        """
+        Add the objectives in the _objectives_list of its parent (normally an
+        energy_unit or an actor)
+        """
         if self.parent:
             if self not in self.parent._objectives_list:
                 self.parent._objectives_list.append(self)
@@ -853,3 +868,12 @@ class Objective:
             Warning("the objective {} should have a parent otherwise "
                     "the objective won't be added to the parent "
                     "_objectives_list".format(self.name))
+
+    def _add_pareto_objectives_list(self):
+        """
+        Add the objectives for the pareto front in the
+        _pareto_objectives_list of its parent (normally an
+        energy_unit or an actor)
+        """
+        if self not in self.parent._pareto_objectives_list:
+            self.parent._pareto_objectives_list.append(self)

omegalpes/general/optimisation/model.py

@@ -24,7 +24,10 @@ LP or MILP based on the package PuLP (LpProblem)**
 import os
 import warnings
 import glob
+import collections
 import time as pytime
+import numpy as np
+import copy
 
 from pulp import LpProblem, LpStatus, LpVariable, lpSum
 from pulp.apis import LpSolver
@@ -50,16 +53,19 @@ class OptimisationModel(LpProblem):
 
     def __init__(self, time, name='optimisation_model'):
         """
-        :param name: Name of your optimisation model (str)
+        :param time:
+        :param name:
         """
         LpProblem.__init__(self, name)
         self.verbose = 1
         self.noOverlap = False
         self.time = time
+        self.quantities = collections.OrderedDict()
         self._model_units_list = []
         self._model_quantities_list = []
         self._model_constraints_list = []
         self._model_objectives_list = []
+        self._model_pareto_objectives_list = []
 
     def add_nodes(self, *nodes):
         """
@@ -229,6 +235,10 @@ class OptimisationModel(LpProblem):
                                q_lb=lb, q_ub=ub, q_opt=opt,
                                parent=quantity.parent)
 
+            # Add the whole variable as an attribute of Variable
+            # added in the optimization model
+            self.quantities[new_name] = quantity
+
     def _add_quantity(self, q_name, q_val, q_type, q_lb, q_ub, q_opt,
                       parent=None):
         """
@@ -242,16 +252,16 @@ class OptimisationModel(LpProblem):
             lb_cst_exp = q_name + '[t] >= ' + str(q_lb) + '[t]'
             setattr(parent, 'set_lb', DefinitionDynamicConstraint(
                 exp_t=lb_cst_exp,
-                                                        name='set_lb',
-                                                        parent=parent))
+                name='set_lb',
+                parent=parent))
             q_lb = min(q_lb)
 
         if isinstance(q_ub, list):
             ub_cst_exp = q_name + '[t] <= ' + str(q_ub) + '[t]'
             setattr(parent, 'set_ub', DefinitionDynamicConstraint(
                 exp_t=ub_cst_exp,
-                                                        name='set_ub',
-                                                        parent=parent))
+                name='set_ub',
+                parent=parent))
             q_ub = max(q_ub)
 
         # If the values are stored in a dictionary
@@ -350,9 +360,58 @@ class OptimisationModel(LpProblem):
 
             if self.verbose:
                 print('Adding objective : {0}'.format(obj_name))
-            objective += eval(obj_weight + ' * (' + obj_exp + ')')
+
+            if obj.pareto:
+                self._model_pareto_objectives_list.append(obj)
+            else:
+                objective += eval(obj_weight + ' * (' + obj_exp + ')')
         self += objective, "obj_tot"
 
+    def _pareto_and_solve(self, solver=None, pareto_step=0.1):
+        """
+        Solves the optimization model to produce a pareto front.
+
+        """
+        pareto_models = []
+        time = self.time
+        i = 0
+        if len(self._model_pareto_objectives_list) == 2:
+            for alpha in np.arange(0.0, 1.1, pareto_step):
+                i = i + 1
+                print("\n - - - - RUN OPTIMIZATION {0} - - - - ".format(i))
+                pareto_model = None
+                pareto_model = copy.deepcopy(self)
+                pareto_model.objective += \
+                    eval(str(alpha) + ' * (' +
+                         pareto_model._model_pareto_objectives_list[
+                             0].exp + ')')
+                pareto_model.objective += \
+                    eval(str(1 - alpha) + ' * (' +
+                         pareto_model._model_pareto_objectives_list[
+                             1].exp + ')')
+                print('{0} * {1} + {2} * {3}'.format(
+                    alpha, pareto_model._model_pareto_objectives_list[0].exp,
+                    1 - alpha,
+                    pareto_model._model_pareto_objectives_list[1].exp))
+
+                if solver is None:
+                    pareto_model.solve(solver=solver, use_mps=False)
+                else:
+                    pareto_model.solve(solver=solver)
+
+                if LpStatus[pareto_model.status] != 'Optimal':
+                    print('Your problem should have solution before launching'
+                          'the method pareto_solve()')
+                else:
+                    print("\n- - UPDATE RESULTS IN PARETO MODEL {}"
+                          " - - ".format(i))
+                    pareto_model.update_units()
+                    pareto_models.append(copy.deepcopy(pareto_model))
+            self.pareto_models = pareto_models
+        else:
+            print('Your problem should have only 2 pareto objectives and '
+                  'not {}'.format(len(self._model_pareto_objectives_list)))
+
     def update_units(self):
         """
         Updates all units values with optimization results
@@ -390,48 +449,59 @@ class OptimisationModel(LpProblem):
                                 pass
 
     def solve_and_update(self, solver: LpSolver = None,
-                         find_infeasible_cst_set=False) -> None:
+                         find_infeasible_cst_set=False, pareto_step=0.1) -> \
+            None:
         """
         Solves the optimization model and updates all variables values.
 
         :param solver: Optimization solver
         :type solver: LpSolver
+        :param pareto_step: if there are pareto objectives, you can change
+            the step to calculate the pareto front
         """
-        print("\n - - - - - RUN OPTIMIZATION - - - - - ")
-        if solver is None:
-            start_time = pytime.time()
-            self.solve(solver=solver, use_mps=False)
-            print('Resolution duration =', pytime.time() - start_time,
-                  'seconds.')
-        else:
-            self.solve(solver=solver)
+        if not self._model_pareto_objectives_list:
+            print("\n - - - - - RUN OPTIMIZATION - - - - - ")
+            if solver is None:
+                start_time = pytime.time()
+                self.solve(solver=solver, use_mps=False)
+                print('Resolution duration =', pytime.time() - start_time,
+                      'seconds.')
+            else:
+                self.solve(solver=solver)
 
-        if LpStatus[self.status] == 'Optimal':
-            print("\n - - - - - UPDATE RESULTS - - - - - ")
-            self.update_units()
+            if LpStatus[self.status] == 'Optimal':
+                print("\n - - - - - UPDATE RESULTS - - - - - ")
+                self.update_units()
+            else:
+                # warnings.warn("Your optimization failed with status : {
+                # }.".format(
+                #     LpStatus[self.status]))
+                print("\n\n/!\ Your optimization FAILED with status : {} "
+                      "/!\.".format(
+                    LpStatus[self.status]))
+                if LpStatus[self.status] == 'Infeasible':
+                    print(
+                        '\nIf you want to catch the source of infeasibility:\n'
+                        '* Please download LPFICS and use the method '
+                        'find_infeasible_constraint_set(your_model)\n'
+                        'You can also use according to your needs:\n'
+                        '- '
+                        'find_definition_and_actor_infeasible_constraints_set('
+                        'your_model)\n'
+                        '- find_definition_and_technical_'
+                        'infeasible_constraints_set(your_model)')
+                    print(
+                        '* If you are a Gurobi user, you can also refer to '
+                        'the '
+                        'method "compute_gurobi_IIS()" in '
+                        'general\optimation\model.')
+                    if find_infeasible_cst_set:
+                        find_infeasible_cst_set
+                if LpStatus[self.status] == 'Not Solved':
+                    print('You can maybe try with another solver')
         else:
-            # warnings.warn("Your optimization failed with status : {
-            # }.".format(
-            #     LpStatus[self.status]))
-            print("\n\n/!\ Your optimization FAILED with status : {} "
-                  "/!\.".format(
-                LpStatus[self.status]))
-            if LpStatus[self.status] == 'Infeasible':
-                print('\nIf you want to catch the source of infeasibility:\n'
-                      '* Please download LPFICS and use the method '
-                      'find_infeasible_constraint_set(your_model)\n'
-                      'You can also use according to your needs:\n'
-                      '- find_definition_and_actor_infeasible_constraints_set('
-                      'your_model)\n'
-                      '- find_definition_and_technical_'
-                      'infeasible_constraints_set(your_model)')
-                print('* If you are a Gurobi user, you can also refer to the '
-                      'method "compute_gurobi_IIS()" in '
-                      'general\optimation\model.')
-                if find_infeasible_cst_set:
-                    find_infeasible_cst_set
-            if LpStatus[self.status] == 'Not Solved':
-                print('You can maybe try with another solver')
+            self._pareto_and_solve(solver=solver,
+                                   pareto_step=pareto_step)
 
     def get_model_constraints_list(self):
         """ Gets constraints of the model """

omegalpes/general/utils/plots.py

@@ -4,12 +4,14 @@
 """
 **This module includes the following display utils:**
 
-    - plot_node_energetic_flows() : enables one to plot the energy flows through
-      an EnergyNode
+    - 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
+    - plot_pareto2D() : enables one to plot a pareto front based on two
+      quantities
+    - plot_quantity() : enables one to plot easily a Quantity
+    - plot_quantity_bar() : enables one to plot easily a Quantity as a bar
     - sum_quantities_in_quantity() : enables one to to plot several quantities
       in one once the optimisation is done
 
@@ -57,7 +59,8 @@ def plot_node_energetic_flows(node):
     matplotlib.style.use('seaborn')