add limit columns and docs to merge_same_bus_generation_plants()

pandapower/test/api/test_toolbox.py

@@ -1359,9 +1359,17 @@ def test_merge_same_bus_generation_plants():
 
     # --- test with case24_ieee_rts
     net = nw.case24_ieee_rts()
+
+    # manipulate net for different functionality checks
+    # 1) q_mvar should be summed which is only possible if no gen or ext_grid has the same bus
     net.gen.drop(net.gen.index[net.gen.bus == 22], inplace=True)
     net.sgen["q_mvar"] = np.arange(net.sgen.shape[0])
+    # 2) remove limit columns or values to check whether merge_same_bus_generation_plants() can
+    # handle that
+    del net.sgen["max_q_mvar"]
+    net.sgen.min_p_mw.at[1] = np.nan
 
+    # prepare expatation values
     dupl_buses = [0,  1,  6, 12, 14, 21, 22]
     n_plants = sum([net[elm].bus.isin(dupl_buses).sum() for elm in gen_elms])
     assert n_plants > len(dupl_buses)  # check that in net are plants with same buses
@@ -1379,6 +1387,12 @@ def test_merge_same_bus_generation_plants():
     assert n_plants == expected_no_of_plants
     assert np.isclose(net.ext_grid.p_disp_mw.at[0], 95.1*2)  # correct value sum (p_disp)
     assert np.isclose(net.gen.p_mw.at[0], 10*2 + 76*2)  # correct value sum (p_mw)
+    assert np.isclose(net.gen.min_p_mw.at[0], 16*2 + 15.2)  # correct value sum (min_p_mw) (
+    # 1x 15.2 has been removed above)
+    assert np.isclose(net.gen.max_p_mw.at[0], 20*2 + 76*2)  # correct value sum (max_p_mw)
+    assert np.isclose(net.gen.min_q_mvar.at[8], -10 - 16*5)  # correct value sum (min_q_mvar)
+    assert np.isclose(net.gen.max_q_mvar.at[8], 16)  # correct value sum (max_q_mvar) (
+    # the sgen max_q_mvar column has been removed above)
     idx_sgen22 = net.sgen.index[net.sgen.bus == 22]
     assert len(idx_sgen22) == 1
     assert np.isclose(net.sgen.q_mvar.at[idx_sgen22[0]], 20 + 21)  # correct value sum (q_mvar)

pandapower/toolbox.py

@@ -2928,12 +2928,27 @@ def merge_parallel_line(net, idx):
     return net
 
 
-def merge_same_bus_generation_plants(net, gen_elms=["ext_grid", "gen", "sgen"], error=True,
-                                     add_info=True):
+def merge_same_bus_generation_plants(net, add_info=True, error=True,
+                                     gen_elms=["ext_grid", "gen", "sgen"]):
     """
     Merge generation plants connected to the same buses so that a maximum of one generation plants
     per node remains.
-    Attention: gen_elms should always be given in order of slack, PV and PQ elements.
+
+    ATTENTION:
+        * gen_elms should always be given in order of slack (1.), PV (2.) and PQ (3.) elements.
+
+    INPUT:
+        **net** - pandapower net
+
+    OPTIONAL:
+        **add_info** (bool, True) - If True, the column 'includes_other_plants' is added to the
+        elements dataframes. This column informs about which lines are the result of a merge of
+        generation plants.
+
+        **error** (bool, True) - If True, raises an Error, if vm_pu values differ with same buses.
+
+        **gen_elms** (list, ["ext_grid", "gen", "sgen"]) - list of elements to be merged by same
+        buses. Should be in order of slack (1.), PV (2.) and PQ (3.) elements.
     """
     if add_info:
         for elm in gen_elms:
@@ -2943,7 +2958,8 @@ def merge_same_bus_generation_plants(net, gen_elms=["ext_grid", "gen", "sgen"],
                 net[elm]["includes_other_plants"] = False
 
     # --- construct gen_df with all relevant plants data
-    cols = pd.Index(["bus", "vm_pu", "p_mw", "q_mvar"])
+    limit_cols = ["min_p_mw", "max_p_mw", "min_q_mvar", "max_q_mvar"]
+    cols = pd.Index(["bus", "vm_pu", "p_mw", "q_mvar"]+limit_cols)
     cols_dict = {elm: cols.intersection(net[elm].columns) for elm in gen_elms}
     gen_df = pd.concat([net[elm][cols_dict[elm]] for elm in gen_elms])
     gen_df["elm_type"] = np.repeat(gen_elms, [net[elm].shape[0] for elm in gen_elms])
@@ -2973,6 +2989,13 @@ def merge_same_bus_generation_plants(net, gen_elms=["ext_grid", "gen", "sgen"],
         if "q_mvar" in net[uniq_et].columns:
             net[uniq_et].at[uniq_idx, "q_mvar"] = gen_df.loc[idxs, "q_mvar"].sum()
 
+        # sum limits
+        for col in limit_cols:
+            if col in gen_df.columns and not gen_df.loc[idxs, col].isnull().all():
+                if col not in net[uniq_et].columns:
+                    net[uniq_et][col] = np.nan
+                net[uniq_et].at[uniq_idx, col] = gen_df.loc[idxs, col].sum()
+
         # drop duplicated elements
         for elm in gen_df["elm_type"].loc[idxs[1:]].unique():
             dupl_idx_elm = gen_df.loc[gen_df.index.isin(idxs[1:]) &