diff --git a/docs/_static/opti_results.csv b/docs/_static/opti_results.csv
index ac280506..d995de84 100644
--- a/docs/_static/opti_results.csv
+++ b/docs/_static/opti_results.csv
@@ -5,6 +5,8 @@ attribute,type,unit,description,status
 **length**,float,m,Length of pipe (see  :ref:`pipes of network<edges_csv>`),Input
 **hp_type**,object,n/a,Label of pipe which got selected from :ref:`network/pipes.csv <invest_pipes>`,Result
 **capacity**,float,kW,Installed pipe capacity,Result
+**flow**,float,kW,Flow through the pipe. For new pipes *capacity = flow + losses*. For existing pipes *capacity >= flow + losses*,Result
 **direction**,float,-1/0/1,Flow direction of pipe: 1 if direction corresponds to the *from_node*/*to_node* notation. -1: opposite direction. 0: no investment. This works only if the setting option *bidirectional_pipes* is set *False*.,Result
 **costs**,float,Eur,Total cost of pipe element.,Result
 **losses**,float,kW,Total losses of pipe element.,Result
+**existing**,bool,True/False,"*True* if pipe is existing, *False* if pipe is new. Attribute is only included if the optimization setting *return_existing* is *True*.",Result
diff --git a/docs/_static/opti_settings.csv b/docs/_static/opti_settings.csv
index 126eacf5..bee69cb9 100644
--- a/docs/_static/opti_settings.csv
+++ b/docs/_static/opti_settings.csv
@@ -12,3 +12,5 @@ attribute,type,default,description
 **dump_name**,str,dump.oemof,Name of dump file
 **print_logging_info**,bool,*False*,There are still some helpful print statements.
 **write_lp_file**,bool,*False*,Option of writing lp-file. The lp-file is stored in 'User/.oemof/lp_files/DHNx.lp'
+**allow_nonoptimal**,bool,*False*,"*False*: If no optimal solution is found, an error will be raised. *True*: If no optimal solution is found, there will be a warning."
+**return_existing**,bool,*False*,"Return existing pipes in the resulting network, but only those that receive a flow. Only applies if *existing=1* was used for any input pipe segments."
diff --git a/docs/whatsnew/v0-1-0.rst b/docs/whatsnew/v0-1-0.rst
index 16ef8150..51651e13 100644
--- a/docs/whatsnew/v0-1-0.rst
+++ b/docs/whatsnew/v0-1-0.rst
@@ -16,6 +16,29 @@ API changes
   We reccomend using PandaPipes (typically) or TESPy
   (e.g. if phase changes need to be considered) instead.
 
+* Renamed argument ``welding`` of ``process_geometry()`` to ``simplify``
+  to reflect the extended functionality (not only merging line segments and
+  loose ends, but also removing unnecessary detours)
+
+* The result ``network.results.optimization['components']['pipes']`` of
+  the optimization now contains the following additional column:
+
+  * ``flow``: Thermal power flow through the pipe.
+    For new pipes ``capacity = flow + losses``.
+    For existing pipes ``capacity >= flow + losses``.
+
+* ``network.optimize_investment()`` accepts new arguments:
+
+  * ``return_existing``: If ``True``, this will return existing pipes in the
+    resulting network, but only those that receive a flow.
+    Only applies if ``existing=1`` was used for any input pipe segments.
+    Default is ``False``, in which case the previous behaviour
+    is preserved, where existing pipe segments are not included in the
+    resulting network (despite being used in the optimization).
+  * ``allow_nonoptimal``: ``False`` (default): If no optimal solution is
+    found, an error will be raised. ``True``: If no optimal solution is
+    found, there will be a warning.
+
 
 New features
 ^^^^^^^^^^^^^^^^^^^^
@@ -54,6 +77,7 @@ Other changes
 
 * Updated build system to pyproject.toml
 * Improved simplifying pre-processing
+* Fix several issues related to the workflow with existing pipes
 
 
 Contributors
diff --git a/src/dhnx/gistools/connect_points.py b/src/dhnx/gistools/connect_points.py
index aeddcd1e..3bb9e742 100644
--- a/src/dhnx/gistools/connect_points.py
+++ b/src/dhnx/gistools/connect_points.py
@@ -29,6 +29,7 @@
     print("Need to install shapely to process geometry.")
 
 import logging
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -550,6 +551,7 @@ def process_geometry(
     n_conn=1,
     n_conn_prod=1,
     simplify=True,
+    **kwargs,
 ):
     """
     This function connects the consumers and producers to the line network,
@@ -595,10 +597,11 @@ def process_geometry(
         the nearest line segments in the street network. This allows the
         placement of the connection lines to be part of the optimization
         process. The default is 1.
-    welding : bool, optional
-        Weld continuous line segments together and cut loose ends. This
-        can improve the performance of the optimization, as it decreases
-        the total number of line elements. Default is True.
+    simplify : bool, optional
+        Merge continuous line segments together, cut loose ends and remove
+        unnecessary detours. This can improve the performance of the
+        optimization, as it decreases the total number of line elements
+        and branches. Default is True.
 
     Returns
     -------
@@ -613,6 +616,14 @@ def process_geometry(
             "supported. Use 'reset_index=True'."
         )
 
+    if "welding" in kwargs:
+        warnings.warn(
+            "Argument 'welding' is deprecated and now overwrites 'simplify'. "
+            "Please use 'simplify' instead.",
+            FutureWarning,
+        )
+        simplify = kwargs["welding"]
+
     # Copies of the original polygons are needed for method 'boundary'
     consumers_poly = go.check_crs(consumers, crs=projected_crs).copy()
     producers_poly = go.check_crs(producers, crs=projected_crs).copy()
diff --git a/src/dhnx/gistools/geometry_operations.py b/src/dhnx/gistools/geometry_operations.py
index 62163f21..aaf8a437 100644
--- a/src/dhnx/gistools/geometry_operations.py
+++ b/src/dhnx/gistools/geometry_operations.py
@@ -147,7 +147,7 @@ def match_multipoint(point_wkt):
             [wkt.loads(x) for x in lines["b1_wkt"] if not match_multipoint(x)]
         )
         gdf_errors = gpd.GeoDataFrame(geometry=errors, crs=lines.crs)
-        ax = lines.plot()
+        ax = lines.plot(color='blue')
         gdf_errors.plot(ax=ax, color="red", label="Point(s) causing error")
         plt.legend()
         plt.show()
diff --git a/src/dhnx/optimization/add_components.py b/src/dhnx/optimization/add_components.py
index ae4b0d0d..f028ac22 100644
--- a/src/dhnx/optimization/add_components.py
+++ b/src/dhnx/optimization/add_components.py
@@ -457,11 +457,10 @@ def add_heatpipes(it, labels, bidirectional, length, b_in, b_out, nodes):
         # definition of tag3 of label -> type of pipe
         labels["l_3"] = t["label_3"]
 
-        epc_p = t["capex_pipes"] * length
-        epc_fix = t["fix_costs"] * length
-
         # Heatpipe with binary variable
         nc = bool(t["nonconvex"])
+        epc_p = t["capex_pipes"] * length
+        epc_fix = t["fix_costs"] * length if nc else 0
 
         # bidirectional heatpipelines yes or no
         flow_bi_args = {"minimum": -1} if bidirectional else {}
diff --git a/src/dhnx/optimization/dhs_nodes.py b/src/dhnx/optimization/dhs_nodes.py
index 670ac81a..19773912 100644
--- a/src/dhnx/optimization/dhs_nodes.py
+++ b/src/dhnx/optimization/dhs_nodes.py
@@ -11,7 +11,6 @@
 SPDX-License-Identifier: MIT
 """
 
-
 import oemof.solph as solph
 
 import dhnx.optimization.add_components as ac
@@ -75,7 +74,7 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
         d_labels["l_1"] = "infrastructure"
         d_labels["l_2"] = "heat"
 
-        if q["existing"]:
+        if q["existing"] == 1:
 
             # terminate the first label
             l_1_in = "infrastructure"
@@ -127,7 +126,6 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
                 )
 
             elif (typ_from == "forks") and (typ_to == "forks"):
-
                 start = q["from_node"]
                 end = q["to_node"]
                 b_in = busd[(l_1_in, d_labels["l_2"], "bus", start)]
@@ -137,6 +135,18 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
                     pipe_data, d_labels, gd, q, b_in, b_out, nodes
                 )
 
+                if not gd["bidirectional_pipes"]:
+                    # the heatpipes from fork to fork need to be created in
+                    # both directions, in case of bidirectional == False
+                    start = q["to_node"]
+                    end = q["from_node"]
+                    b_in = busd[(l_1_in, d_labels["l_2"], "bus", start)]
+                    b_out = busd[(l_1_out, d_labels["l_2"], "bus", end)]
+                    d_labels["l_4"] = start + "-" + end
+                    nodes = ac.add_heatpipes_exist(
+                        pipe_data, d_labels, gd, q, b_in, b_out, nodes
+                    )
+
             else:
                 raise ValueError("Something wrong!")
 
@@ -208,14 +218,11 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
             elif (q["from_node"].split("-")[0] == "forks") and (
                 q["to_node"].split("-")[0] == "forks"
             ):
-
-                b_in = busd[
-                    (d_labels["l_1"], d_labels["l_2"], "bus", q["from_node"])
-                ]
-                b_out = busd[
-                    (d_labels["l_1"], d_labels["l_2"], "bus", q["to_node"])
-                ]
-                d_labels["l_4"] = q["from_node"] + "-" + q["to_node"]
+                start = q["from_node"]
+                end = q["to_node"]
+                b_in = busd[(d_labels["l_1"], d_labels["l_2"], "bus", start)]
+                b_out = busd[(d_labels["l_1"], d_labels["l_2"], "bus", end)]
+                d_labels["l_4"] = start + "-" + end
 
                 nodes = ac.add_heatpipes(
                     pipe_data,
@@ -229,19 +236,16 @@ def add_nodes_dhs(opti_network, gd, nodes, busd):
 
                 if not gd["bidirectional_pipes"]:
                     # the heatpipes from fork to fork need to be created in
-                    # both directions in this case bidiretional = False
+                    # both directions, in case of bidirectional == False
+                    start = q["to_node"]
+                    end = q["from_node"]
                     b_in = busd[
-                        (d_labels["l_1"], d_labels["l_2"], "bus", q["to_node"])
+                        (d_labels["l_1"], d_labels["l_2"], "bus", start)
                     ]
                     b_out = busd[
-                        (
-                            d_labels["l_1"],
-                            d_labels["l_2"],
-                            "bus",
-                            q["from_node"],
-                        )
+                        (d_labels["l_1"], d_labels["l_2"], "bus", end)
                     ]
-                    d_labels["l_4"] = q["to_node"] + "-" + q["from_node"]
+                    d_labels["l_4"] = start + "-" + end
 
                     nodes = ac.add_heatpipes(
                         pipe_data,
diff --git a/src/dhnx/optimization/oemof_heatpipe.py b/src/dhnx/optimization/oemof_heatpipe.py
index 2159bbba..129ae4c2 100644
--- a/src/dhnx/optimization/oemof_heatpipe.py
+++ b/src/dhnx/optimization/oemof_heatpipe.py
@@ -131,7 +131,7 @@ def __init__(
                     "the results. Hopefully, you know what you are doing."
                 )
         else:
-            self._set_nominal_value()
+            self._set_nominal_capacity()
 
     def _check_flows_invest(self):
         # for flow in self.inputs.values():
@@ -148,15 +148,15 @@ def _set_flows(self):
         for flow in self.inputs.values():
             flow.investment = Investment()
 
-    # set nominal values of in- and outflow equal in case of
+    # set nominal capacities of in- and outflow equal in case of
     # invest_group = False
-    def _set_nominal_value(self):
+    def _set_nominal_capacity(self):
         i = list(self.inputs.keys())[0]
         o = list(self.outputs.keys())[0]
-        if self.outputs[o].nominal_value is not None:
-            self.inputs[i].nominal_value = self.outputs[o].nominal_value
-        elif self.inputs[i].nominal_value is not None:
-            self.outputs[o].nominal_value = self.inputs[i].nominal_value
+        if self.outputs[o].nominal_capacity is not None:
+            self.inputs[i].nominal_capacity = self.outputs[o].nominal_capacity
+        elif self.inputs[i].nominal_capacity is not None:
+            self.outputs[o].nominal_capacity = self.inputs[i].nominal_capacity
 
     def constraint_group(self):
         if self._invest_group is True:
@@ -191,8 +191,8 @@ class HeatPipelineBlock(ScalarBlock):  # pylint: disable=too-many-ancestors
         ":math:`\dot{Q}_{in}(t)`", ":py:obj:`flow[i, n, t]`", "V", "Heat input"
         ":math:`\dot{Q}_{loss}(t)`", ":py:obj:`heat_loss[n, t]`", "P", "Heat
         loss of heat pipeline"
-        ":math:`\dot{Q}_{nominal}`", ":py:obj:`flows[n, o].nominal_value`", "
-        P", "Nominal capacity of heating pipeline"
+        ":math:`\dot{Q}_{nominal}`", ":py:obj:`flows[n, o].nominal_capacity`
+        ", "P", "Nominal capacity of heating pipeline"
         ":math:`f_{loss}(t)`", ":py:obj:`heat_loss_factor`", "P", "Specific
         heat loss factor for pipeline"
         ":math:`l`", ":py:obj:`length`", "P", "Length of heating pipeline"
@@ -242,13 +242,13 @@ def _create(self, group=None):
 
         def _heat_loss_rule_fix(block, n, t):
             """Rule definition for the heat loss depending on the nominal
-            capacity for fix fix heat loss.
+            capacity for fix heat loss.
             """
             o = list(n.outputs.keys())[0]
 
             expr = 0
             expr += -block.heat_loss[n, t]
-            expr += n.heat_loss_factor[t] * m.flows[n, o].nominal_value
+            expr += n.heat_loss_factor[t] * m.flows[n, o].nominal_capacity
             expr += n.heat_loss_factor_fix[t]
             return expr == 0
 
@@ -265,7 +265,7 @@ def _heat_loss_rule_on_off(block, n, t):
             expr = 0
             expr += -block.heat_loss[n, t]
             expr += (
-                n.heat_loss_factor[t] * m.flows[n, o].nominal_value
+                n.heat_loss_factor[t] * m.flows[n, o].nominal_capacity
                 + n.heat_loss_factor_fix[t]
             ) * m.NonConvexFlowBlock.status[n, o, t]
             return expr == 0
@@ -325,8 +325,8 @@ class HeatPipelineInvestBlock(
         ":math:`\dot{Q}_{in}(t)`", ":py:obj:`flow[i, n, t]`", "V", "Heat input"
         ":math:`\dot{Q}_{loss}(t)`", ":py:obj:`heat_loss[n, t]`", "V", "Heat
         loss of heat pipeline"
-        ":math:`\dot{Q}_{nominal}`", ":py:obj:`flows[n, o].nominal_value`", "
-        V", "Nominal capacity of heating pipeline"
+        ":math:`\dot{Q}_{nominal}`", ":py:obj:`flows[n, o].nominal_capacity`
+        ", "V", "Nominal capacity of heating pipeline"
         ":math:`f_{loss}(t)`", ":py:obj:`heat_loss_factor`", "P", "Specific
         heat loss factor for pipeline"
         ":math:`l`", ":py:obj:`length`", "P", "Length of heating pipeline"
diff --git a/src/dhnx/optimization/optimization_models.py b/src/dhnx/optimization/optimization_models.py
index dcd6b1bc..0849a76b 100644
--- a/src/dhnx/optimization/optimization_models.py
+++ b/src/dhnx/optimization/optimization_models.py
@@ -362,6 +362,12 @@ def check_existing(self):
         # check whether there the 'existing' attribute is present at the pipes
         if "existing" not in self.thermal_network.components["pipes"].columns:
             self.thermal_network.components["pipes"]["existing"] = 0
+        else:  # If any pipes are existing, set undefined to non-existing
+            self.thermal_network.components["pipes"] = (
+                self.thermal_network.components["pipes"].fillna(
+                    {"existing": 0}
+                )
+            )
 
         # create pipes attribute hp_type, if not in the table so far
         if "hp_type" not in list(
@@ -503,7 +509,7 @@ def solve(self):
     def get_results_edges(self):
         """Postprocessing of the investment results of the pipes."""
 
-        def get_invest_val(lab):
+        def get_result_val(lab, attr):
 
             res = self.es.results["main"]
 
@@ -518,46 +524,22 @@ def get_invest_val(lab):
                 logger.info("Multiple IDs!")
 
             try:
-                invest = res[outflow[0]]["scalars"]["invest"]
+                val = res[outflow[0]]["scalars"][attr]
             except (KeyError, IndexError):
                 try:
-                    # that's in case of a one timestep optimisation due to
-                    # an oemof bug in outputlib
-                    invest = res[outflow[0]]["sequences"]["invest"].iloc[0]
+                    # When dhnx is solving an actual time series,
+                    # each time step may have a unique flow value.
+                    # We want the largest value in absolute terms, while
+                    # preserving the sign, which can indicate direction
+                    vals = res[outflow[0]]["sequences"][attr]
+                    val = vals.loc[vals.abs().idxmax()]
                 except (KeyError, IndexError):
                     # this is in case there is no bi-directional heatpipe,
                     # e.g. at forks-consumers, producers-forks
-                    invest = 0
+                    val = 0
 
             # the rounding is performed due to numerical issues
-            return round(invest, 6)
-
-        def get_invest_status(lab):
-
-            res = self.es.results["main"]
-
-            outflow = [
-                x
-                for x in res.keys()
-                if x[1] is not None
-                if lab == str(x[0].label)
-            ]
-
-            try:
-                invest_status = res[outflow[0]]["scalars"]["invest_status"]
-            except (KeyError, IndexError):
-                try:
-                    # that's in case of a one timestep optimisation due to
-                    # an oemof bug in outputlib
-                    invest_status = res[outflow[0]]["sequences"][
-                        "invest_status"
-                    ].iloc[0]
-                except (KeyError, IndexError):
-                    # this is in case there is no bi-directional heatpipe,
-                    # e.g. at forks-consumers, producers-forks
-                    invest_status = 0
-
-            return invest_status
+            return round(val, 6)
 
         def get_hp_results(p):
 
@@ -567,33 +549,88 @@ def get_hp_results(p):
             # maybe slow approach with lambda function
             df[hp_lab + "." + "dir-1"] = df["from_node"] + "-" + df["to_node"]
             df[hp_lab + "." + "size-1"] = df[hp_lab + "." + "dir-1"].apply(
-                lambda x: get_invest_val(label_base + x)
+                lambda x: get_result_val(label_base + x, attr="invest")
+            )
+            df[hp_lab + "." + "flow-1"] = df[hp_lab + "." + "dir-1"].apply(
+                lambda x: get_result_val(label_base + x, attr="flow")
             )
+            df[hp_lab + "." + "status_nominal-1"] = df[
+                hp_lab + "." + "dir-1"
+            ].apply(
+                lambda x: get_result_val(label_base + x, attr="status_nominal")
+            )
+
             df[hp_lab + "." + "dir-2"] = df["to_node"] + "-" + df["from_node"]
             df[hp_lab + "." + "size-2"] = df[hp_lab + "." + "dir-2"].apply(
-                lambda x: get_invest_val(label_base + x)
+                lambda x: get_result_val(label_base + x, attr="invest")
+            )
+            df[hp_lab + "." + "flow-2"] = df[hp_lab + "." + "dir-2"].apply(
+                lambda x: get_result_val(label_base + x, attr="flow")
+            )
+            df[hp_lab + "." + "status_nominal-2"] = df[
+                hp_lab + "." + "dir-2"
+            ].apply(
+                lambda x: get_result_val(label_base + x, attr="status_nominal")
             )
 
             df[hp_lab + "." + "size"] = df[
                 [hp_lab + "." + "size-1", hp_lab + "." + "size-2"]
             ].max(axis=1)
+            df[hp_lab + "." + "status_nominal"] = df[
+                [
+                    hp_lab + "." + "status_nominal-1",
+                    hp_lab + "." + "status_nominal-2",
+                ]
+            ].max(axis=1)
 
-            # get direction of pipes
+            # Store the flow direction for new and existing pipes.
+            # Since there is no investment for existing pipes, 'size' cannot
+            # be used and the 'flow' attribute is used instead.
+            # When 'bidirectional_pipes' is True, "flow-1" was observed to
+            # become negative, which then indicated a reverse flow direction.
+            # This happened e.g.
+            # - for existing pipes
+            # - for new pipes in a network with multiple producers
+            # When 'bidirectional_pipes' is True, the concept of a flow
+            # direction only makes limited sense (e.g. when seperate time
+            # steps have different flow directions). Nevertheless we can
+            # save the direction which uses the larger flow and can therefore
+            # be called a 'primary' direction.
+            # When 'bidirectional_pipes' is False, the logic is well defined:
+            #   flow-1 > flow-2 --> dir = 1
+            #   flow-2 > flow-1 --> dir = -1
             for r, c in df.iterrows():
-                if c[hp_lab + "." + "size-1"] > c[hp_lab + "." + "size-2"]:
+                # Get flow direction
+                if c[hp_lab + "." + "flow-1"] > c[hp_lab + "." + "flow-2"]:
                     df.at[r, hp_lab + ".direction"] = 1
-                elif c[hp_lab + "." + "size-1"] < c[hp_lab + "." + "size-2"]:
+                elif c[hp_lab + "." + "flow-1"] < c[hp_lab + "." + "flow-2"]:
+                    # Is True if both values are positive, and also
+                    # if flow-1 < 0  (in case 'bidirectional_pipes' is True)
                     df.at[r, hp_lab + ".direction"] = -1
                 else:
                     df.at[r, hp_lab + ".direction"] = 0
 
+                # Get largest absolute 'flow' value
+                if abs(c[hp_lab + "." + "flow-1"]) > abs(
+                    c[hp_lab + "." + "flow-2"]
+                ):
+                    df.at[r, hp_lab + ".flow"] = df.at[r, hp_lab + ".flow-1"]
+                else:
+                    df.at[r, hp_lab + ".flow"] = df.at[r, hp_lab + ".flow-2"]
+                # Since direction was saved, sign of flow value can be removed
+                df.at[r, hp_lab + ".flow"] = abs(df.at[r, hp_lab + ".flow"])
+
             if p["nonconvex"]:
                 df[hp_lab + "." + "status-1"] = df[
                     hp_lab + "." + "dir-1"
-                ].apply(lambda x: get_invest_status(label_base + x))
+                ].apply(
+                    lambda x: get_result_val(label_base + x, "invest_status")
+                )
                 df[hp_lab + "." + "status-2"] = df[
                     hp_lab + "." + "dir-2"
-                ].apply(lambda x: get_invest_status(label_base + x))
+                ].apply(
+                    lambda x: get_result_val(label_base + x, "invest_status")
+                )
                 df[hp_lab + "." + "status"] = df[
                     [hp_lab + "." + "status-1", hp_lab + "." + "status-2"]
                 ].max(axis=1)
@@ -606,7 +643,7 @@ def get_hp_results(p):
                     ):
                         logger.warning(
                             "Investment status of pipe id {} is 1"
-                            " for both dircetions!"
+                            " for both directions!"
                             " This is not allowed!".format(r)
                         )
                     if (
@@ -679,8 +716,10 @@ def check_invest_label(hp_type, edge_id):
                     )
 
             df["hp_type"] = None
+            # capacity invest for new pipes / given capacity for existing pipes
             df["capacity"] = float(0)
-            df["direction"] = 0
+            df["flow"] = float(0)  # actual thermal power flow
+            df["direction"] = 0  # flow direction
             df["status"] = float(0)
 
             for ahp in active_hp:
@@ -695,6 +734,15 @@ def check_invest_label(hp_type, edge_id):
                         if ahp + ".status" in c.index:
                             df.at[r, "status"] = c[ahp + ".status"]
 
+                    if c[ahp + ".status_nominal"] > 0:
+                        # Set attributes of existing pipes
+                        df.at[r, "hp_type"] = ahp
+                        df.at[r, "capacity"] = c[ahp + ".status_nominal"]
+                        df.at[r, "direction"] = c[ahp + ".direction"]
+
+                    # Always set flow
+                    df.at[r, "flow"] = c[ahp + ".flow"]
+
         def recalc_costs_losses():
             """
             Calculates the investment costs and thermal losses for each
@@ -723,34 +771,61 @@ def recalc_costs_losses():
                     hp_lab = c["hp_type"]
                     # select row from heatpipe type table
                     hp_p = df_hp[df_hp["label_3"] == hp_lab].squeeze()
-                    if hp_p["nonconvex"] == 1:
-                        df.at[r, "costs"] = c["length"] * (
-                            c["capacity"] * hp_p["capex_pipes"]
-                            + hp_p["fix_costs"] * c["status"]
-                        )
-                        df.at[r, "losses"] = c["length"] * (
-                            c["capacity"] * hp_p["l_factor"]
-                            + hp_p["l_factor_fix"] * c["status"]
-                        )
 
-                    elif hp_p["nonconvex"] == 0:
-                        df.at[r, "costs"] = (
-                            c["length"] * c["capacity"] * hp_p["capex_pipes"]
-                        )
-                        # Note that a constant loss is possible also for convex
+                    if "existing" in c and c["existing"] == 1:
+                        # For existing pipes, only losses need to be calculated
                         df.at[r, "losses"] = c["length"] * (
                             c["capacity"] * hp_p["l_factor"]
                             + hp_p["l_factor_fix"]
                         )
+                    else:
+                        if hp_p["nonconvex"] == 1:
+                            df.at[r, "costs"] = c["length"] * (
+                                c["capacity"] * hp_p["capex_pipes"]
+                                + hp_p["fix_costs"] * c["status"]
+                            )
+                            df.at[r, "losses"] = c["length"] * (
+                                c["capacity"] * hp_p["l_factor"]
+                                + hp_p["l_factor_fix"] * c["status"]
+                            )
+
+                        elif hp_p["nonconvex"] == 0:
+                            df.at[r, "costs"] = (
+                                c["length"]
+                                * c["capacity"]
+                                * hp_p["capex_pipes"]
+                            )
+                            # Note that a constant loss is possible also
+                            # for convex
+                            df.at[r, "losses"] = c["length"] * (
+                                c["capacity"] * hp_p["l_factor"]
+                                + hp_p["l_factor_fix"]
+                            )
 
         # use pipes dataframe as base and add results as new columns to it
         df = self.thermal_network.components["pipes"]
 
-        # only select not existing pipes
-        df = df[df["existing"] == 0].copy()
+        cols_select = ["from_node", "to_node", "length"]
+        cols_return = [
+            "from_node",
+            "to_node",
+            "length",
+            "hp_type",
+            "direction",
+            "capacity",
+            "flow",
+            "losses",
+            "costs",
+        ]
+        if not self.settings.get("return_existing", False):
+            # only select not existing pipes
+            df = df[df["existing"] == 0].copy()
+        else:  # yes, return information about existing pipes
+            cols_select.append("existing")
+            cols_return.append("existing")
 
         # remove input data
-        df = df[["from_node", "to_node", "length"]].copy()
+        df = df[cols_select].copy()
 
         # putting the results of the investments in heatpipes to the pipes:
         df_hp = self.invest_options["network"]["pipes"]
@@ -767,18 +842,7 @@ def recalc_costs_losses():
 
         recalc_costs_losses()
 
-        return df[
-            [
-                "from_node",
-                "to_node",
-                "length",
-                "hp_type",
-                "capacity",
-                "direction",
-                "costs",
-                "losses",
-            ]
-        ]
+        return df[cols_return]
 
 
 def optimize_operation(thermal_network):
@@ -813,6 +877,7 @@ def setup_optimise_investment(
     print_logging_info=False,
     write_lp_file=False,
     allow_nonoptimal=False,
+    return_existing=False,
 ):
     """
     Function for setting up the oemof solph operational Model.
@@ -855,9 +920,12 @@ def setup_optimise_investment(
     write_lp_file : bool
         Linear program file is stored (‘User/.oemof/lp_files/DHNx.lp’).
     allow_nonoptimal : bool
-        False: If no optimal solution is found, an error will be risen.
+        False: If no optimal solution is found, an error will be raised.
         True: If no optimal solution is found, there will be a warning.
         Default is False.
+    return_existing : bool
+        Return existing pipes in the resulting network. Only applies if
+        ``existing=1`` was used for any input pipe segments. Default is False.
     Returns
     -------
     oemof.solph.Model : The oemof.solph.Model is build.
@@ -887,6 +955,7 @@ def setup_optimise_investment(
         "print_logging_info": print_logging_info,
         "write_lp_file": write_lp_file,
         "allow_nonoptimal": allow_nonoptimal,
+        "return_existing": return_existing,
     }
 
     model = OemofInvestOptimizationModel(
diff --git a/tests/_files/process_geometry/out/pipes_result_01.geojson b/tests/_files/process_geometry/out/pipes_result_01.geojson
new file mode 100644
index 00000000..3606c060
--- /dev/null
+++ b/tests/_files/process_geometry/out/pipes_result_01.geojson
@@ -0,0 +1,297 @@
+{
+"type": "FeatureCollection",
+"name": "pipes_result",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4647" } },
+"features": [
+{ "type": "Feature", "properties": { "id": "0", "id_full": null, "type": "DL", "from_node": "forks-1", "to_node": "forks-173", "length": 74.06718, "hp_type": "pipe-typ-A", "direction": -1, "capacity": 1391.7066, "flow": 1390.0522, "losses": 1.6544, "costs": 73265.3842, "existing": 0.0 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506654.861689653247595, 6005025.331065262667835 ], [ 32506672.19807019457221, 6004953.321366431191564 ] ] } },
+{ "type": "Feature", "properties": { "id": "1", "id_full": null, "type": "DL", "from_node": "forks-1", "to_node": "forks-185", "length": 43.59186, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 14.33202, "flow": 13.36782, "losses": 0.96419, "costs": 41373.38164, "existing": 0.0 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506654.861689653247595, 6005025.331065262667835 ], [ 32506643.925446726381779, 6005067.528800801374018 ] ] } },
+{ "type": "Feature", "properties": { "id": "2", "id_full": null, "type": "DL", "from_node": "forks-1", "to_node": "forks-147", "length": 38.23573, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 1375.7202, "flow": 1374.8662, "losses": 0.85395, "costs": 37804.04367, "existing": 0.0 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506654.861689653247595, 6005025.331065262667835 ], [ 32506674.726261701434851, 6005018.216621936298907 ], [ 32506691.422387953847647, 6005014.360785491764545 ] ] } },
+{ "type": "Feature", "properties": { "id": "3", "id_full": null, "type": "DL", "from_node": "forks-2", "to_node": "forks-3", "length": 27.69976, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 641.43089, "flow": 640.81546, "losses": 0.61543, "costs": 26795.35304, "existing": 0.0 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506930.132400441914797, 6005038.999399862252176 ], [ 32506957.828444872051477, 6005039.452800406143069 ] ] } },
+{ "type": "Feature", "properties": { "id": "4", "id_full": null, "type": "DL", "from_node": "forks-2", "to_node": "forks-62", "length": 21.231, "hp_type": "pipe-typ-A", "direction": -1, "capacity": 662.9679, "flow": 662.49612, "losses": 0.47178, "costs": 20551.10005, "existing": 0.0 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506930.132400441914797, 6005038.999399862252176 ], [ 32506909.028953850269318, 6005036.675676330924034 ] ] } },
+{ "type": "Feature", "properties": { "id": "5", "id_full": null, "type": "DL", "from_node": "forks-2", "to_node": "forks-115", "length": 36.70572, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 21.06523, "flow": 20.25331, "losses": 0.81192, "costs": 34844.88149, "existing": 0.0 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506930.132400441914797, 6005038.999399862252176 ], [ 32506926.128980439156294, 6005058.943066515028477 ], [ 32506925.048307441174984, 6005061.689670381136239 ], [ 32506923.234117347747087, 6005066.282159684225917 ], [ 32506920.500818144530058, 6005074.304099330678582 ] ] } },
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diff --git a/tests/_files/process_geometry/out/pipes_result_02.geojson b/tests/_files/process_geometry/out/pipes_result_02.geojson
new file mode 100644
index 00000000..7bd9e423
--- /dev/null
+++ b/tests/_files/process_geometry/out/pipes_result_02.geojson
@@ -0,0 +1,295 @@
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+{ "type": "Feature", "properties": { "id": "280", "type": "HL", "id_full": "consumers-138", "existing": 0, "from_node": "forks-235", "to_node": "consumers-138", "length": 11.34121, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 6.85248, "flow": 6.60164, "losses": 0.25084, "costs": 2.26069 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506742.56112327054143, 6004679.077212387695909 ], [ 32506745.835843969136477, 6004668.219065302051604 ] ] } },
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+{ "type": "Feature", "properties": { "id": "282", "type": "HL", "id_full": "consumers-139", "existing": 0, "from_node": "forks-236", "to_node": "consumers-139", "length": 11.54376, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 6.17929, "flow": 5.92397, "losses": 0.25532, "costs": 2.075 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506736.701256807893515, 6004677.30992872081697 ], [ 32506740.034462817013264, 6004666.257859108969569 ] ] } },
+{ "type": "Feature", "properties": { "id": "283", "type": "HL", "id_full": "consumers-140", "existing": 0, "from_node": "forks-237", "to_node": "consumers-140", "length": 11.73362, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 6.28196, "flow": 6.02244, "losses": 0.25952, "costs": 2.14417 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506731.171738620847464, 6004675.642275160178542 ], [ 32506734.559764791280031, 6004664.408435733057559 ] ] } },
+{ "type": "Feature", "properties": { "id": "284", "type": "HL", "id_full": "consumers-141", "existing": 0, "from_node": "forks-239", "to_node": "consumers-141", "length": 6.63056, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 7.89518, "flow": 7.74853, "losses": 0.14665, "costs": 1.52281 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506756.704313155263662, 6004683.342672811821103 ], [ 32506754.789770606905222, 6004689.690812552347779 ] ] } },
+{ "type": "Feature", "properties": { "id": "285", "type": "HL", "id_full": "consumers-142", "existing": 0, "from_node": "forks-240", "to_node": "consumers-142", "length": 11.62047, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 10.98568, "flow": 10.72866, "losses": 0.25702, "costs": 3.7135 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506770.973308652639389, 6004717.554045321419835 ], [ 32506759.586695671081543, 6004715.234476590529084 ] ] } },
+{ "type": "Feature", "properties": { "id": "286", "type": "HL", "id_full": "consumers-144", "existing": 0, "from_node": "forks-242", "to_node": "consumers-144", "length": 5.6321, "hp_type": "pipe-typ-A", "direction": 1, "capacity": 12.19481, "flow": 12.07024, "losses": 0.12457, "costs": 1.99792 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506710.603514529764652, 6004747.567726431414485 ], [ 32506714.053161434829235, 6004752.019746305420995 ] ] } },
+{ "type": "Feature", "properties": { "id": "287", "type": "GL", "id_full": "producers-0", "existing": 0, "from_node": "forks-243", "to_node": "producers-0", "length": 2.27026, "hp_type": "pipe-typ-A", "direction": -1, "capacity": 1459.9917, "flow": 1459.941, "losses": 0.05073, "costs": 96.41816 }, "geometry": { "type": "LineString", "coordinates": [ [ 32506676.925561025738716, 6004933.684903668239713 ], [ 32506679.132756624370813, 6004934.216287404298782 ] ] } }
+]
+}
diff --git a/tests/test_gistools.py b/tests/test_gistools.py
index 554e6a87..22e80b01 100644
--- a/tests/test_gistools.py
+++ b/tests/test_gistools.py
@@ -16,6 +16,7 @@
 import networkx as nx
 import numpy as np
 import pandas as pd
+from geopandas.testing import assert_geodataframe_equal
 from shapely.geometry import LineString
 from shapely.geometry import MultiLineString
 from shapely.geometry import Point
@@ -366,6 +367,7 @@ def test_process_geometry():
     assert tn_input["pipes"].index.name == "id"
 
     tn_input["pipes"] = tn_input["pipes"][sorted(tn_input["pipes"].columns)]
+    tn_input["pipes"] = tn_input["pipes"].replace({None: np.nan})
     tn_input["pipes"] = tn_input["pipes"].round(5)
 
     # Update expected result (after intentional changes)
@@ -376,15 +378,13 @@ def test_process_geometry():
         gpd.read_file(file_pipes)
         .set_index("id")
         .reindex(tn_input["pipes"].columns, axis="columns")
+        .replace({None: np.nan})
     )
 
-    assert (gdf_pipes_test.columns == tn_input["pipes"].columns).all()
-
-    # Compare with tolerance, due to loss of floating point precision
-    for col in tn_input["pipes"].columns:
-        if col == tn_input["pipes"].geometry.name:
-            assert gdf_pipes_test.geom_equals_exact(
-                tn_input["pipes"], tolerance=1e-7
-            ).all()
-        else:
-            assert gdf_pipes_test[[col]].equals(tn_input["pipes"][[col]])
+    assert_geodataframe_equal(
+        tn_input["pipes"],
+        gdf_pipes_test,
+        check_index_type=False,  # dtype changes after reading file
+        check_column_type=False,  # dtype changes after reading file
+        check_less_precise=True,  # Required due to floating point precision
+    )
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
new file mode 100644
index 00000000..bb0b23bb
--- /dev/null
+++ b/tests/test_optimization.py
@@ -0,0 +1,259 @@
+# -*- coding: utf-8 -*-
+"""
+Test the complete investment optimization workflow of DHNx.
+
+This file is part of project oemof (). It's copyrighted
+by the contributors recorded in the version control history of the file,
+available from its original location oemof/oemof/tools/helpers.py
+
+SPDX-License-Identifier: MIT
+"""
+
+import io
+import os
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from geopandas.testing import assert_geodataframe_equal
+
+import dhnx
+
+
+def get_default_dhnx_invest_options():
+    """Generate a dictionary with default investments options for DHNx."""
+    consumers_bus = """
+label_2,active,excess,shortage,shortage costs,excess costs
+heat,1,0,0,999999,9999
+"""
+    consumers_demand = """
+label_2,active,nominal_capacity
+heat,1,1
+"""
+    producers_bus = """
+,label_2,active,excess,shortage,shortage costs,excess costs
+1,heat,1,0,0,9999,9999
+"""
+    producers_source = """
+label_2,active
+heat,1
+"""
+    network = """
+label_3,nonconvex,l_factor,l_factor_fix,cap_min,cap_max,capex_pipes,fix_costs
+pipe-typ-A,1,1.5812551e-07,0.0221164,1,519307.085403,0.02908927,948.69105059
+"""
+
+    invest_options = dict(
+        consumers=dict(
+            bus=pd.read_csv(io.StringIO(consumers_bus)),
+            demand=pd.read_csv(io.StringIO(consumers_demand)),
+        ),
+        producers=dict(
+            bus=pd.read_csv(io.StringIO(producers_bus)),
+            source=pd.read_csv(io.StringIO(producers_source)),
+        ),
+        network=dict(pipes=pd.read_csv(io.StringIO(network))),
+    )
+    return invest_options
+
+
+def test_optimization_example_01():
+    """Test ``optimize_investment()`` function with a simple example.
+
+    It includes pipes with ``existing=1``, which is supposed to cover
+    certain edge cases in the optimization workflow.
+    """
+    base_dir = os.path.join(
+        os.path.dirname(__file__), "_files/process_geometry"
+    )
+    gdf_lines = gpd.read_file(
+        os.path.join(base_dir, "in/lines_input_existing.geojson")
+    )
+    gdf_prod = gpd.read_file(
+        os.path.join(base_dir, "in/producers_polygon.geojson")
+    )
+    gdf_cons = gpd.read_file(
+        os.path.join(base_dir, "in/consumers_polygon.geojson")
+    )
+    file_pipes = os.path.join(base_dir, "out/pipes_result_01.geojson")
+    os.makedirs(os.path.dirname(file_pipes), exist_ok=True)
+
+    tn_input = dhnx.gistools.connect_points.process_geometry(
+        lines=gdf_lines,
+        producers=gdf_prod,
+        consumers=gdf_cons,
+        method="boundary",
+        reset_index=True,
+        simplify=True,
+    )
+
+    # initialize a ThermalNetwork
+    network = dhnx.network.ThermalNetwork()
+
+    # add the pipes, forks, consumer, and producers to the ThermalNetwork
+    for k, v in tn_input.items():
+        network.components[k] = v
+
+    # check if ThermalNetwork is consistent
+    network.is_consistent()
+
+    # load the specification of the oemof-solph components
+    invest_opt = get_default_dhnx_invest_options()
+
+    settings = dict(
+        solve_kw={"tee": False},  # Hide solver output
+        return_existing=True,  # Include existing pipes in results
+    )
+
+    # Perform the investment optimisation
+    network.optimize_investment(invest_options=invest_opt, **settings)
+
+    results_edges = network.results.optimization["components"]["pipes"]
+    gdf_pipes = network.components["pipes"].copy()
+    cols_drop = [c for c in results_edges.columns if c in gdf_pipes]
+    gdf_pipes = gdf_pipes.drop(columns=cols_drop)  # Drop duplicate columns
+    gdf_pipes = gdf_pipes.join(results_edges, rsuffix="_results")
+    gdf_pipes = gdf_pipes[gdf_pipes["capacity"] > 0]  # Keep only DN>0
+
+    gdf_pipes = gdf_pipes.round(5)  # Round for comparison
+
+    # Update expected result (after intentional changes)
+    # gdf_pipes.to_file(file_pipes)
+
+    # Load expected result (and fix column order)
+    gdf_pipes_test = (
+        gpd.read_file(file_pipes)
+        .set_index("id")
+        .reindex(gdf_pipes.columns, axis="columns")
+        .replace({None: np.nan})
+    )
+
+    assert_geodataframe_equal(
+        gdf_pipes,
+        gdf_pipes_test,
+        check_dtype=False,  # dtype changes after reading file
+        check_index_type=False,  # dtype changes after reading file
+        check_column_type=False,  # dtype changes after reading file
+        check_less_precise=True,  # Required due to floating point precision
+    )
+
+
+def test_optimization_example_02():
+    """Test ``optimize_investment()`` function with a simple example.
+
+    Differences to test_optimization_example_01:
+
+    - All pipes are new pipes (existing=0)
+    - An artificial time series for consumer demand is used to
+      increase code coverage
+    - nonconvex=0 is chosen for pipe costs. This is purely to increase
+      code coverage and not a recommmended setting
+    - Argument 'welding' is used instead of 'simplify', just to trigger
+      FutureWarning
+    - Optimization settings 'bidirectional_pipes', and
+      'allow_nonoptimal' are changed to increase code coverage
+    """
+    base_dir = os.path.join(
+        os.path.dirname(__file__), "_files/process_geometry"
+    )
+    gdf_lines = gpd.read_file(
+        os.path.join(base_dir, "in/lines_input_existing.geojson")
+    )
+    gdf_prod = gpd.read_file(
+        os.path.join(base_dir, "in/producers_polygon.geojson")
+    )
+    gdf_cons = gpd.read_file(
+        os.path.join(base_dir, "in/consumers_polygon.geojson")
+    )
+    file_pipes = os.path.join(base_dir, "out/pipes_result_02.geojson")
+    os.makedirs(os.path.dirname(file_pipes), exist_ok=True)
+
+    # Remove columns related to existing pipes (make all pipes new)
+    gdf_lines = gdf_lines.drop(columns=["existing", "hp_type", "capacity"])
+
+    # Create artificial time series for consumers
+    df_timeseries = pd.concat(
+        [
+            gdf_cons["P_heat_max"],  # Time step 1
+            gdf_cons["P_heat_max"].mul(0.5),  # Time step 2
+            gdf_cons["P_heat_max"].mul(0.1),  # Time step 3
+        ],
+        axis="columns",
+    ).T
+    # Rename row index
+    df_timeseries.index = range(len(df_timeseries))
+    # Enforce string column names, because dhnx expects them
+    df_timeseries.columns = df_timeseries.columns.astype(str)
+
+    tn_input = dhnx.gistools.connect_points.process_geometry(
+        lines=gdf_lines,
+        producers=gdf_prod,
+        consumers=gdf_cons,
+        method="boundary",
+        reset_index=True,
+        welding=True,  # Instead of 'simplify', just to trigger FutureWarning
+    )
+
+    # initialize a ThermalNetwork
+    network = dhnx.network.ThermalNetwork()
+
+    # add the pipes, forks, consumer, and producers to the ThermalNetwork
+    for k, v in tn_input.items():
+        network.components[k] = v
+
+    network.sequences["consumers"]["heat_flow"] = df_timeseries
+
+    # check if ThermalNetwork is consistent
+    network.is_consistent()
+
+    # load the specification of the oemof-solph components
+    invest_opt = get_default_dhnx_invest_options()
+
+    # Change to nonconvex=0 (purely to increase code coverage of test)
+    invest_opt["network"]["pipes"]["nonconvex"] = 0
+    invest_opt["network"]["pipes"].drop(columns=["fix_costs"], inplace=True)
+
+    settings = dict(
+        solve_kw={"tee": False},  # Hide solver output
+        solver_cmdline_options={
+            "seconds": 30,  # maximum runtime for cbc solver
+        },
+        return_existing=False,
+        bidirectional_pipes=True,  # Just to increase code coverage
+        allow_nonoptimal=True,
+        heat_demand="series",  # Setting required for time series
+        num_ts=len(df_timeseries),  # Setting required for time series
+        frequence="h",  # Setting required for time series
+    )
+
+    # Perform the investment optimisation
+    network.optimize_investment(invest_options=invest_opt, **settings)
+
+    results_edges = network.results.optimization["components"]["pipes"]
+    gdf_pipes = network.components["pipes"].copy()
+    cols_drop = [c for c in results_edges.columns if c in gdf_pipes]
+    gdf_pipes = gdf_pipes.drop(columns=cols_drop)  # Drop duplicate columns
+    gdf_pipes = gdf_pipes.join(results_edges, rsuffix="_results")
+    gdf_pipes = gdf_pipes[gdf_pipes["capacity"] > 0]  # Keep only DN>0
+
+    gdf_pipes = gdf_pipes.round(5)  # Round for comparison
+
+    # Update expected result (after intentional changes)
+    # gdf_pipes.to_file(file_pipes)
+
+    # Load expected result (and fix column order)
+    gdf_pipes_test = (
+        gpd.read_file(file_pipes)
+        .set_index("id")
+        .reindex(gdf_pipes.columns, axis="columns")
+        .replace({None: np.nan})
+    )
+
+    assert_geodataframe_equal(
+        gdf_pipes,
+        gdf_pipes_test,
+        check_dtype=False,  # dtype changes after reading file
+        check_index_type=False,  # dtype changes after reading file
+        check_column_type=False,  # dtype changes after reading file
+        check_less_precise=True,  # Required due to floating point precision
+    )