Simplify optimisation input

dhnx/network.py

@@ -314,15 +314,43 @@ def reproject(self, crs):
     def optimize_operation(self):
         self.results.operation = optimize_operation(self)
 
-    def optimize_investment(self, invest_options, **kwargs):
+    def optimize_investment(
+            self, pipeline_invest_options,
+            additional_invest_options=None, **kwargs):
+        """
+
+        Parameters
+        ----------
+        pipeline_invest_options : pandas.DataFrame
+            Table with the investment options for the district heating
+            pipelines.
+            The table requires the columns:
+            - "":
+            - "":
+            - "":
+            - "":
+        additional_invest_options : dict
+            Optional dictionary with pandas.DataFrame with additional
+            oemof-solph components for the consumers and the producers.
+        kwargs
+
+        Returns
+        -------
+
+        """
 
         oemof_opti_model = setup_optimise_investment(
-            self, invest_options, **kwargs
+            self,
+            pipeline_invest_options,
+            additional_invest_options=additional_invest_options,
+            **kwargs
         )
 
         self.results.optimization = solve_optimisation_investment(
             oemof_opti_model
         )
 
+        return self.results.optimization
+
     def simulate(self, *args, **kwargs):
         self.results.simulation = simulate(self, *args, **kwargs)

dhnx/optimization/dhs_nodes.py

@@ -54,6 +54,7 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
     d_labels['l_2'] = 'heat'
     d_labels['l_3'] = 'bus'
 
+    # add a solph.Bus to every fork
     for n, _ in opti_network.thermal_network.components['forks'].iterrows():
         d_labels['l_4'] = 'forks-' + str(n)
         d_labels['l_1'] = 'infrastructure'
@@ -66,7 +67,7 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
     # add heatpipes for all lines
     for p, q in opti_network.thermal_network.components['pipes'].iterrows():
 
-        pipe_data = opti_network.invest_options['network']['pipes']
+        pipe_data = opti_network.pipelines_invest_options
 
         d_labels['l_1'] = 'infrastructure'
         d_labels['l_2'] = 'heat'

dhnx/optimization/optimization_models.py

@@ -53,6 +53,8 @@ class OemofInvestOptimizationModel(InvestOptimizationModel):
     ----------
     settings : dict
         Dictionary holding the optimisation settings. See .
+    pipelines_invest_options : pandas.DataFrame
+        Table with the investment options for DHS pipelines.
     invest_options : dict
         Dictionary holding the investment options for the district heating system.
     nodes : list
@@ -83,10 +85,12 @@ class OemofInvestOptimizationModel(InvestOptimizationModel):
         Calls *check_input()*, *complete_exist_data()*, *get_pipe_data()*, and *setup_oemof_es()*.
 
     """
-    def __init__(self, thermal_network, settings, investment_options):
+    def __init__(self, thermal_network, pipeline_invest_options,
+                 settings, additional_invest_options):
 
         self.settings = settings
-        self.invest_options = investment_options
+        self.pipelines_invest_options = pipeline_invest_options
+        self.invest_options = additional_invest_options
         self.nodes = []  # list of all nodes
         self.buses = {}  # dict of all buses
         self.es = solph.EnergySystem()
@@ -222,8 +226,8 @@ def clean_df(df):
         for k, v in self.thermal_network.components.items():
             self.thermal_network.components[k] = clean_df(v)
 
-        pipes = self.invest_options['network']['pipes']
-        self.invest_options['network']['pipes'] = clean_df(pipes)
+        pipes = self.pipelines_invest_options
+        self.pipelines_invest_options = clean_df(pipes)
 
         for node_typ in ['consumers', 'producers']:
             for k, v in self.invest_options[node_typ].items():
@@ -307,7 +311,7 @@ def check_existing(self):
             self.thermal_network.components['pipes']['hp_type'] = None
 
         edges = self.thermal_network.components['pipes']
-        pipe_types = self.invest_options['network']['pipes']
+        pipe_types = self.pipelines_invest_options
 
         hp_list = list({x for x in edges['hp_type'].tolist()
                         if isinstance(x, str)})
@@ -631,7 +635,7 @@ def recalc_costs_losses():
         df = df[['from_node', 'to_node', 'length']].copy()
 
         # putting the results of the investments in heatpipes to the pipes:
-        df_hp = self.invest_options['network']['pipes']
+        df_hp = self.pipelines_invest_options
 
         # list of active heat pipes
         active_hp = list(df_hp['label_3'].values)
@@ -664,7 +668,8 @@ def optimize_operation(thermal_network):
 
 
 def setup_optimise_investment(
-        thermal_network, invest_options, heat_demand='scalar', num_ts=1,
+        thermal_network, pipeline_invest_options,
+        additional_invest_options=None, heat_demand='scalar', num_ts=1,
         time_res=1, start_date='1/1/2018', frequence='H', solver='cbc',
         solve_kw=None, solver_cmdline_options=None, simultaneity=1,
         bidirectional_pipes=False, dump_path=None, dump_name='dump.oemof',
@@ -676,7 +681,9 @@ def setup_optimise_investment(
     ----------
     thermal_network : ThermalNetwork
         See the ThermalNetwork class.
-    invest_options : dict
+    pipeline_invest_options : pandas.DataFrame
+        Table with the investment options for the DHS pipelines.
+    additional_invest_options : dict
         Dictionary holding the investment options for the district heating system.
     heat_demand : str
         'scalar': Peak heat load is used as heat consumers’ heat demand.
@@ -738,7 +745,12 @@ def setup_optimise_investment(
         'write_lp_file': write_lp_file,
     }
 
-    model = OemofInvestOptimizationModel(thermal_network, settings, invest_options)
+    model = OemofInvestOptimizationModel(
+        thermal_network,
+        pipeline_invest_options,
+        settings,
+        additional_invest_options,
+    )
 
     return model
 

examples/optimisation/minimal_network/minimal_network.py

@@ -1,17 +1,37 @@
 import matplotlib.pyplot as plt
+import pandas as pd
 import dhnx
 
 
 # Initialize thermal network
 network = dhnx.network.ThermalNetwork()
 network = network.from_csv_folder('twn_data')
 
+# DHS pipeline invest data
+df_pipes = pd.DataFrame(
+    {
+        "label_3": "your-pipe-type-label",
+        "active": 1,
+        "nonconvex": 1,
+        "l_factor": 0.000002,
+        "l_factor_fix": 0.001,
+        "cap_max": 10000,
+        "cap_min": 25,
+        "capex_pipes": 5,
+        "fix_costs": 200,
+    }, index=[0],
+)
+
 # Load investment parameter
 invest_opt = dhnx.input_output.load_invest_options('invest_data')
 
 # Execute investment optimization
-network.optimize_investment(invest_options=invest_opt,
-                            write_lp_file=True)
+network.optimize_investment(
+    pipeline_invest_options=df_pipes,
+    additional_invest_options=invest_opt,
+    write_lp_file=True,
+    print_logging_info=True,
+)
 
 # ####### Postprocessing and Plotting ###########
 # Draw network

examples/optimisation/optimisation_tutorial/dhnx_optimisation_tutorial.ipynb

@@ -0,0 +1,49 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Tutorial"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}