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Merge branch 'develop' into develop
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Florian Schaefer authored Oct 7, 2019
2 parents 3e8c0eb + 97ce25a commit 0f0795b
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18 changes: 10 additions & 8 deletions pandapower/test/api/test_toolbox.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,8 @@ def test_opf_task():
net = pp.create_empty_network()
pp.create_buses(net, 6, [10, 10, 10, 0.4, 7, 7],
min_vm_pu=[0.9, 0.9, 0.88, 0.9, np.nan, np.nan])
pp.create_ext_grid(net, 0, max_q_mvar=80, min_p_mw=0)
idx_ext_grid = 1
pp.create_ext_grid(net, 0, max_q_mvar=80, min_p_mw=0, index=idx_ext_grid)
pp.create_gen(net, 1, 10, min_q_mvar=-50, max_q_mvar=-10, min_p_mw=0, max_p_mw=60)
pp.create_gen(net, 2, 8)
pp.create_gen(net, 3, 5)
Expand All @@ -41,6 +42,7 @@ def test_opf_task():
assert df.shape[0]
if "gen" in key:
assert df.shape[0] > 1
assert out1["flexibilities"]["Pext_grid"].loc[0, "index"] == [1]
assert np.isnan(out1["flexibilities"]["Pext_grid"].loc[0, "max"])
assert out1["flexibilities"]["Pext_grid"].loc[0, "min"] == 0
assert np.isnan(out1["flexibilities"]["Qext_grid"].loc[0, "min"])
Expand All @@ -61,7 +63,7 @@ def test_opf_task():
assert pp.dataframes_equal(out3["flexibilities"][key], out1["flexibilities"][key])

# check costs
pp.create_poly_cost(net, 0, "ext_grid", 2)
pp.create_poly_cost(net, idx_ext_grid, "ext_grid", 2)
pp.create_poly_cost(net, 1, "gen", 1.7)
pp.create_poly_cost(net, 0, "dcline", 2, type="q")
pp.create_pwl_cost(net, 2, "gen", [[-1e9, 1, 3.1], [1, 1e9, 0.5]], power_type="q")
Expand Down Expand Up @@ -241,28 +243,28 @@ def test_continuos_bus_numbering():

def test_reindex_elements():
net = nw.example_simple()

new_sw_idx = np.random.randint(0, 1000, size=net.switch.shape[0])
pp.reindex_elements(net, "switch", new_sw_idx)
assert np.allclose(net.switch.index.values, new_sw_idx)

previous_idx = new_sw_idx[:3]
new_sw_idx = [2, 3, 4]
pp.reindex_elements(net, "switch", new_sw_idx, previous_idx)
assert np.allclose(net.switch.index.values[:3], new_sw_idx)

pp.reindex_elements(net, "line", [77, 22], [2, 0])
assert np.allclose(net.line.index.values, [22, 1, 77, 3])
assert np.allclose(net.switch.element.iloc[[4, 5]], [77, 77])

old_idx = copy.deepcopy(net.bus.index.values)
pp.reindex_elements(net, "bus", old_idx+2)
assert np.allclose(net.bus.index.values, old_idx+2)

pp.reindex_elements(net, "bus", [400, 600], [4, 6])
assert 400 in net.bus.index
assert 600 in net.bus.index



def test_continuous_element_numbering():
Expand Down
116 changes: 62 additions & 54 deletions pandapower/toolbox.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -107,25 +107,27 @@ def _determine_flexibilities_dict(net, data, delta_pq, **kwargs):
"min_q_to_mvar", "max_q_to_mvar"]}[power_type]

# determine indices of controllable elements, continue if no controllable element exists
if "controllable" in net[elm].columns:
if elm in ["ext_grid", "dcline"]:
controllables = net[elm].index
elif "controllable" in net[elm].columns:
controllables = net[elm].index[net[elm].controllable]
if not len(controllables):
continue
elif controllable_default and net[elm].shape[0]:
controllables = net[elm].index
else:
continue
if not len(controllables):
continue

# consider delta_pq
if len(constraints) >= 2 and pd.Series(constraints[:2]).isin(net[elm].columns).all():
controllables = _find_idx_without_numerical_difference(
net[elm], constraints[0], constraints[1], delta_pq, idx=controllables,
equal_nan=True)
equal_nan=False)
if elm == "dcline" and power_type == "Q" and len(controllables) and \
pd.Series(constraints[2:4]).isin(net[elm].columns).all():
controllables = _find_idx_without_numerical_difference(
net[elm], constraints[2], constraints[3], delta_pq, idx=controllables,
equal_nan=True)
equal_nan=False)

# add missing constraint columns
for col_to_add in set(constraints) - set(net[elm].columns):
Expand All @@ -139,7 +141,7 @@ def _determine_flexibilities_dict(net, data, delta_pq, **kwargs):

def _find_idx_without_numerical_difference(df, column1, column2, delta, idx=None, equal_nan=False):
"""
Returns indices which have bigger numerical difference than delta.
Returns indices where comlumn1 and column2 have a numerical difference bigger than delta.
INPUT:
**df** (DataFrame)
Expand All @@ -155,8 +157,8 @@ def _find_idx_without_numerical_difference(df, column1, column2, delta, idx=None
OPTIONAL:
**idx** (iterable, None) - list of indices which should be considered only
**equal_nan** (bool, False) - if True, indices are included where at least value in
df[column1] or df[column2] is NaN
**equal_nan** (bool, False) - if False, indices are included where at least one value in
df[column1] and df[column2] is NaN
OUTPUT:
**index** (pandas.Index) - index within idx where df[column1] and df[column2] deviates by
Expand All @@ -170,9 +172,9 @@ def _find_idx_without_numerical_difference(df, column1, column2, delta, idx=None
df.loc[idx_without_null, column2]).abs().values <= delta]

if equal_nan:
return idx.difference(idx_no_delta)
else:
return idx_without_null.difference(idx_no_delta)
else:
return idx.difference(idx_no_delta)


def _determine_network_constraints_dict(net, data, **kwargs):
Expand Down Expand Up @@ -209,7 +211,9 @@ def _determine_network_constraints_dict(net, data, **kwargs):

def _determine_costs_dict(net, opf_task_overview):
"""
Determines which flexibilities do not have costs in the net.
Determines which flexibilities do not have costs in the net. Each element is considered as one,
i.e. if ext_grid 0, for instance, is flexible in both, P and Q, and has one cost entry for P,
it is not considered as 'flexibilities_without_costs'.
INPUT:
**net** - panpdapower net
Expand All @@ -228,8 +232,8 @@ def _determine_costs_dict(net, opf_task_overview):
for flex_element in flex_elements:

# determine keys of opf_task_overview["flexibilities"] ending with flex_element
keys = pd.Series(list(opf_task_overview["flexibilities"].keys()))
keys = keys.loc[keys.str.endswith(flex_element)]
keys = [power_type + flex_element for power_type in ["P", "Q"] if (
power_type + flex_element) in opf_task_overview["flexibilities"].keys()]

# determine indices of all flexibles
idx_without_cost = set()
Expand Down Expand Up @@ -279,8 +283,8 @@ def _cluster_same_floats(df, subset=None, **kwargs):
cluster_df[subset] = df.loc[uniq, subset].values

if sum(uniq) == df.shape[0]: # fast return if df has no duplicates
for i1 in range(df.shape[0]):
cluster_df.at[i1, "index"] = [i1]
for i1, idx in enumerate(df.index):
cluster_df.at[i1, "index"] = [idx]
else: # determine index clusters
i2 = 0
for i1, uni in enumerate(uniq):
Expand Down Expand Up @@ -326,44 +330,48 @@ def _log_opf_task_overview(opf_task_overview):
else:
assert isinstance(data, dict)
heading_logged = False
all_keys = sorted(data.keys())
elms = ["".join(c for c in key if not c.isupper()) for key in all_keys]
elms = sorted(set(elms))
for elm in elms:
for key in all_keys:
if elm not in key:
keys, elms = _get_keys_and_elements_from_opf_task_dict(data)
for key, elm in zip(keys, elms):
assert elm in key
df = data[key]

if dict_key in ["flexibilities", "network_constraints"]:
if not df.shape[0]:
continue
df = data[key]

if dict_key in ["flexibilities", "network_constraints"]:
if not df.shape[0]:
continue
if not heading_logged:
s += "\n\n%s:" % dict_key
heading_logged = True

# --- logging information
if df.shape[0] > 1:
len_idx = len(list(chain(*df["index"])))
s += "\n %ix %s" % (len_idx, key)
else:
if not len(set(df.columns).symmetric_difference({"index", "min", "max"})):
s += "\n %g <= all %s <= %g" % (
df.loc[0, "min"], key, df.loc[0, "max"])
else:
s += "\n all %s with these constraints:" % key
for col in set(df.columns) - {"index"}:
s += " %s=%g" % (col, df.loc[0, col])
elif dict_key == "flexibilities_without_costs":
if not heading_logged:
s += "\n\n%s:" % dict_key
heading_logged = True
s += "\n%ix %s" % (len(df), key)
if not heading_logged:
s += "\n\n%s:" % dict_key
heading_logged = True

# --- logging information
len_idx = len(list(chain(*df["index"])))
if df.shape[0] > 1:
s += "\n %ix %s" % (len_idx, key)
else:
raise NotImplementedError("Key %s is unknown to this code." % dict_key)
if not len(set(df.columns).symmetric_difference({"index", "min", "max"})):
s += "\n %g <= %ix %s (all) <= %g" % (
df.loc[0, "min"], len_idx, key, df.loc[0, "max"])
else:
s += "\n %ix %s (all) with these constraints:" % (len_idx, key)
for col in set(df.columns) - {"index"}:
s += " %s=%g" % (col, df.loc[0, col])
elif dict_key == "flexibilities_without_costs":
if not heading_logged:
s += "\n\n%s:" % dict_key
heading_logged = True
s += "\n%ix %s" % (len(df), key)
else:
raise NotImplementedError("Key %s is unknown to this code." % dict_key)
logger.info(s + "\n")


def _get_keys_and_elements_from_opf_task_dict(dict_):
keys = list(dict_.keys())
elms = ["".join(c for c in key if not c.isupper()) for key in keys]
keys = list(np.array(keys)[np.argsort(elms)])
elms = sorted(elms)
return keys, elms


def switch_info(net, sidx): # pragma: no cover
"""
Prints what buses and elements are connected by a certain switch.
Expand Down Expand Up @@ -691,12 +699,12 @@ def reindex_elements(net, element, new_indices, old_indices=None):
return
assert len(new_indices) == len(old_indices)
lookup = dict(zip(old_indices, new_indices))

if element == "bus":
partial = len(new_indices) < net[element].shape[0]
reindex_buses(net, lookup, partial_lookup=partial)
return

# --- reindex
net[element]["index"] = net[element].index
net[element].loc[old_indices, "index"] = get_indices(old_indices, lookup)
Expand All @@ -708,15 +716,15 @@ def reindex_elements(net, element, new_indices, old_indices=None):
(net.measurement.element.isin(old_indices))]
if len(affected):
net.measurement.loc[affected.index, "element"] = get_indices(affected.element, lookup)

# --- adapt switch link
if element in ["line", "trafo"]:
affected = net.switch[(net.switch.et == element[0]) &
(net.switch.element.isin(old_indices))]
if len(affected):
net.switch.loc[affected.index, "element"] = get_indices(affected.element, lookup)


# --- adapt line_geodata index
if element == "line" and "line_geodata" in net and net["line_geodata"].shape[0]:
net["line_geodata"]["index"] = net["line_geodata"].index
Expand All @@ -737,7 +745,7 @@ def create_continuous_elements_index(net, start=0, add_df_to_reindex=set()):
**add_df_to_reindex** - by default all useful pandapower elements for power flow will be
selected. Customized DataFrames can also be considered here.
OUTPUT:
**net** - pandapower network with odered and continuous indices
Expand Down

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