diff --git a/src/oemof/tabular/datapackage/writing.py b/src/oemof/tabular/datapackage/writing.py
new file mode 100644
index 00000000..45aeada0
--- /dev/null
+++ b/src/oemof/tabular/datapackage/writing.py
@@ -0,0 +1,186 @@
+import pandas as pd
+from oemof.solph.helpers import flatten
+
+exclude_attrs = []
+exclude_none = True
+
+
+def move_undetected_scalars(com):
+    # copied from oemof.solph.processing
+    for ckey, value in list(com["sequences"].items()):
+        if isinstance(value, str):
+            com["scalars"][ckey] = value
+            del com["sequences"][ckey]
+            continue
+        try:
+            _ = (e for e in value)
+        except TypeError:
+            com["scalars"][ckey] = value
+            del com["sequences"][ckey]
+        else:
+            try:
+                if not value.default_changed:
+                    com["scalars"][ckey] = value.default
+                    del com["sequences"][ckey]
+            except AttributeError:
+                pass
+
+
+def remove_nones(com):
+    # copied from oemof.solph.processing
+    for ckey, value in list(com["scalars"].items()):
+        if value is None:
+            del com["scalars"][ckey]
+    for ckey, value in list(com["sequences"].items()):
+        if len(value) == 0 or value[0] is None:
+            del com["sequences"][ckey]
+
+
+def detect_scalars_and_sequences(com):
+    # copied from oemof.solph.processing
+    com_data = {"scalars": {}, "sequences": {}}
+
+    default_exclusions = [
+        "__",
+        "_",
+        "registry",
+        "inputs",
+        "outputs",
+        "Label",
+        "input",
+        "output",
+        "constraint_group",
+    ]
+    # Must be tuple in order to work with `str.startswith()`:
+    exclusions = tuple(default_exclusions + exclude_attrs)
+    attrs = [
+        i
+        for i in dir(com)
+        if not (callable(getattr(com, i)) or i.startswith(exclusions))
+    ]
+
+    for a in attrs:
+        attr_value = getattr(com, a)
+
+        # Iterate trough investment and add scalars and sequences with
+        # "investment" prefix to component data:
+        if attr_value.__class__.__name__ == "Investment":
+            invest_data = detect_scalars_and_sequences(attr_value)
+            com_data["scalars"].update(
+                {
+                    "investment_" + str(k): v
+                    for k, v in invest_data["scalars"].items()
+                }
+            )
+            com_data["sequences"].update(
+                {
+                    "investment_" + str(k): v
+                    for k, v in invest_data["sequences"].items()
+                }
+            )
+            continue
+
+        if isinstance(attr_value, str):
+            com_data["scalars"][a] = attr_value
+            continue
+
+        # If the label is a tuple it is iterable, therefore it should be
+        # converted to a string. Otherwise, it will be a sequence.
+        if a == "label":
+            attr_value = str(attr_value)
+
+        # check if attribute is iterable
+        # see: https://stackoverflow.com/questions/1952464/
+        # in-python-how-do-i-determine-if-an-object-is-iterable
+        try:
+            _ = (e for e in attr_value)
+            com_data["sequences"][a] = attr_value
+        except TypeError:
+            com_data["scalars"][a] = attr_value
+
+    com_data["sequences"] = flatten(com_data["sequences"])
+    move_undetected_scalars(com_data)
+    if exclude_none:
+        remove_nones(com_data)
+
+    com_data = {
+        "scalars": pd.Series(com_data["scalars"]),
+        "sequences": pd.DataFrame(com_data["sequences"]),
+    }
+    return com_data
+
+
+def get_inputs_outputs(component, attr):
+    if hasattr(component, attr):
+        return getattr(component, attr)
+    else:
+        return {}
+
+
+def get_connected_busses(component):
+    inputs = list(get_inputs_outputs(component, "inputs"))
+    outputs = list(get_inputs_outputs(component, "outputs"))
+
+    def formatting(lst, name):
+        return {
+            (f"bus_{n}", name, None): str(bus) for n, bus in enumerate(lst)
+        }
+
+    connected_busses = formatting(inputs, "inputs")
+    connected_busses.update(formatting(outputs, "outputs"))
+
+    connected_busses = pd.Series(connected_busses, name="var_value")
+    return connected_busses
+
+
+def serialize_energysystem(energysystem, path):
+    from pathlib import Path
+
+    import oemof.network
+
+    components = [
+        node
+        for node in energysystem.nodes
+        if isinstance(node, oemof.network.Component)
+    ]
+    types = {}
+
+    # group components by type and number of inputs/outputs
+    for component in components:
+        typ = type(component).__name__
+
+        # find out number of inputs and outputs
+        n_inputs = len(get_inputs_outputs(component, "inputs"))
+        n_outputs = len(get_inputs_outputs(component, "outputs"))
+
+        key = (typ, n_inputs, n_outputs)
+
+        if typ not in types:
+            types[key] = []
+
+        component_data = detect_scalars_and_sequences(component)
+
+        component_data = component_data["scalars"]
+        # set index
+        index = pd.DataFrame(component_data.index, columns=["0"])
+        index["1"] = None
+        index["2"] = None
+        index = pd.MultiIndex.from_frame(index)
+        component_data.index = index
+        component_data.name = "var_value"
+
+        busses = get_connected_busses(component)
+
+        data = pd.concat([component_data, busses], axis=0)
+
+        types[key].append(data)
+
+        # TODO: flow_data = get_flow_data(component)
+
+        # TODO: Handle sequences with foreign keys
+
+    # save data to destination
+    for typ, data in types.items():
+        filepath = Path(path) / f"{typ}.csv"
+        df = pd.DataFrame(data)
+        df.to_csv(filepath)