Changing OPF cost definition:

The sign was confusing and maximization and minimization could not be clearly distinguished. Tutorial and further tests are coming.

pandapower/converter/pypower/from_ppc.py

@@ -32,7 +32,7 @@ def _create_costs(net, ppc, gen_lookup, type, idx):
                          idx)
         pp.create_piecewise_linear_cost(net, gen_lookup.element.at[idx],
                                         gen_lookup.element_type.at[idx],
-                                        ppc['gencost'][idx, 4:], type)
+                                        - ppc['gencost'][idx, 4:], type)
     elif ppc['gencost'][idx, 0] == 2:
         if len(ppc['gencost'][idx, 4:]) == ppc['gencost'][idx, 3]:
             n = len(ppc['gencost'][idx, 4:])
@@ -41,7 +41,7 @@ def _create_costs(net, ppc, gen_lookup, type, idx):
             logger.error("In gencost line %s, the number n does not fit to the number of values" %
                          idx)
         pp.create_polynomial_cost(net, gen_lookup.element.at[idx], gen_lookup.element_type.at[idx],
-                                  values, type)
+                                  - values, type)
     else:
         logger.info("Cost mode of gencost line %s is unknown." % idx)
 

pandapower/opf/make_objective.py

@@ -8,7 +8,7 @@
 # and Energy System Technology (IEE), Kassel. All rights reserved.
 
 
-from numpy import zeros, array, concatenate, power
+from numpy import zeros, array, concatenate, power, ndarray
 from pandapower.idx_cost import MODEL, NCOST, COST
 
 
@@ -133,7 +133,7 @@ def _make_objective(ppci, net):
                                                     n_piece_lin_coefficients+1:2] = - f * 1e3
                                 else:
                                     ppci["gencost"][elements, COST+1:COST+n_piece_lin_coefficients +
-                                                    1:2] = f * 1e3 * sign_corr
+                                                    1:2] = - f * 1e3 * sign_corr
 
                                 ppci["gencost"][elements, NCOST] = n_coefficients / 2
                                 ppci["gencost"][elements, MODEL] = 1
@@ -172,7 +172,7 @@ def _make_objective(ppci, net):
                             c = costs.loc[(costs.element_type == el) &
                                           (costs.element.isin(el_is))].c.reset_index(drop=True)
 
-                            if len(c) > 0:
+                            if len(c) > 0 and isinstance(idx, ndarray):
                                 c = concatenate(c)
                                 n_c = c.shape[1]
                                 c = c * power(1e3, array(range(n_c))[::-1])
@@ -188,7 +188,7 @@ def _make_objective(ppci, net):
                                 if el in ["load", "dcline"]:
                                     ppci["gencost"][elements, COST:(COST + n_c):] = - c
                                 else:
-                                    ppci["gencost"][elements, -n_c:n_gencost] = c * sign_corr
+                                    ppci["gencost"][elements, -n_c:n_gencost] = - c * sign_corr
 
                                 ppci["gencost"][elements, NCOST] = n_coefficients
                                 ppci["gencost"][elements, MODEL] = 2

pandapower/test/opf/test_3point_pwl.py

@@ -20,7 +20,7 @@ def test_3point_pwl():
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
                                    max_loading_percent=100 * 690)
     pp.create_piecewise_linear_cost(net, 0, "sgen", np.array(
-        [[-100, 1], [0, 0], [100, 1], ]), type="q")
+        [[-100, -1], [0, 0], [100, -1], ]), type="q")
 
     # creating a pwl cost function that actually is realistic: The absolute value of the reactive power has costs.
 
@@ -33,14 +33,14 @@ def test_3point_pwl():
 
     # the first sgen is only representing the positive segment of the function:
     net.piecewise_linear_cost.p.at[0] = np.array([[0, 1]])
-    net.piecewise_linear_cost.f.at[0] = np.array([[0, 1]])
+    net.piecewise_linear_cost.f.at[0] = np.array([[0, -1]])
     net.sgen.min_q_kvar.at[0] = 0
 
     # what we can do instead is modelling a second sgen on the same bus representing the negative segment of the function:
     pp.create_sgen(net, 1, p_kw=0, q_kvar=0, controllable=True, max_p_kw=0.01, min_p_kw=-0.01, max_q_kvar=0,
                    min_q_kvar=-10)
     pp.create_piecewise_linear_cost(net, 1, "sgen", np.array(
-        [[-100, 100], [0, 0], ]), type="q")
+        [[-100, -100], [0, 0], ]), type="q")
 
     # runOPF
     pp.runopp(net, verbose=False)
@@ -55,5 +55,5 @@ def test_3point_pwl():
 
 
 if __name__ == "__main__":
-    pytest.main(["test_3point_pwl.py", "-xs"])
+    pytest.main(['-s', __file__])
     # test_cost_piecewise_linear_eg_q()

pandapower/test/opf/test_basic.py

@@ -86,7 +86,7 @@ def test_simplest_voltage():
     pp.create_line_from_parameters(net, 0, 1, 50, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
                                    max_loading_percent=100)
-    pp.create_polynomial_cost(net, 0, "gen", np.array([-100, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([100, 0]))
     # run OPF
     for init in ["pf", "flat"]:
         pp.runopp(net, verbose=False, init=init)
@@ -158,9 +158,9 @@ def test_simplest_dispatch():
     pp.create_bus(net, max_vm_pu=vm_max, min_vm_pu=vm_min, vn_kv=.4)
     pp.create_gen(net, 1, p_kw=-100, controllable=True, max_p_kw=-5, min_p_kw=-150, max_q_kvar=50,
                   min_q_kvar=-50)
-    pp.create_polynomial_cost(net, 0, "gen", np.array([-100, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([100, 0]))
     pp.create_ext_grid(net, 0)
-    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([-101, 0]))
+    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([101, 0]))
     pp.create_load(net, 1, p_kw=20, controllable=False)
     pp.create_line_from_parameters(net, 0, 1, 50, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
@@ -203,7 +203,7 @@ def test_opf_gen_voltage():
                                           in_service=True, index=None, max_loading_percent=200)
     pp.create_gen(net, 3, p_kw=-10, controllable=True, max_p_kw=0, min_p_kw=-25, max_q_kvar=500,
                   min_q_kvar=-500)
-    pp.create_polynomial_cost(net, 0, "gen", np.array([-10, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([10, 0]))
     pp.create_ext_grid(net, 0)
     pp.create_line_from_parameters(net, 1, 2, 1, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
@@ -249,7 +249,7 @@ def test_opf_sgen_voltage():
                                           in_service=True, index=None, max_loading_percent=1000000)
     pp.create_sgen(net, 3, p_kw=-10, controllable=True, max_p_kw=-5, min_p_kw=-15, max_q_kvar=25,
                    min_q_kvar=-25)
-    pp.create_polynomial_cost(net, 0, "sgen", np.array([-100, 0]))
+    pp.create_polynomial_cost(net, 0, "sgen", np.array([100, 0]))
     pp.create_ext_grid(net, 0)
     pp.create_line_from_parameters(net, 1, 2, 1, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
@@ -296,9 +296,9 @@ def test_opf_gen_loading():
                                           in_service=True, index=None, max_loading_percent=145)
     pp.create_gen(net, 3, p_kw=-10, controllable=True, max_p_kw=-5, min_p_kw=-15, max_q_kvar=50,
                   min_q_kvar=-50)
-    pp.create_polynomial_cost(net, 0, "gen", np.array([10, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([-10, 0]))
     pp.create_ext_grid(net, 0)
-    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([-.1, 0]))
+    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([.1, 0]))
     pp.create_line_from_parameters(net, 1, 2, 1, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
                                    max_loading_percent=max_line_loading)
@@ -347,9 +347,9 @@ def test_opf_sgen_loading():
                                           max_loading_percent=max_trafo_loading)
     pp.create_sgen(net, 3, p_kw=-10, controllable=True, max_p_kw=-5, min_p_kw=-15, max_q_kvar=25,
                    min_q_kvar=-25)
-    pp.create_polynomial_cost(net, 0, "sgen", np.array([10, 0]))
+    pp.create_polynomial_cost(net, 0, "sgen", np.array([-10, 0]))
     pp.create_ext_grid(net, 0)
-    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([-.1, 0]))
+    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([.1, 0]))
     pp.create_line_from_parameters(net, 1, 2, 1, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876,
                                    max_loading_percent=max_line_loading)
@@ -392,7 +392,7 @@ def test_unconstrained_line():
     pp.create_load(net, 1, p_kw=20, controllable=False)
     pp.create_line_from_parameters(net, 0, 1, 50, name="line2", r_ohm_per_km=0.876,
                                    c_nf_per_km=260.0, max_i_ka=0.123, x_ohm_per_km=0.1159876)
-    pp.create_polynomial_cost(net, 0, "gen", np.array([-1, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([1, 0]))
     # run OPF
     for init in ["pf", "flat"]:
         pp.runopp(net, verbose=False, init=init)
@@ -430,7 +430,7 @@ def test_trafo3w_loading():
 
 def test_dcopf_poly(simple_opf_test_net):
     net = simple_opf_test_net
-    pp.create_polynomial_cost(net, 0, "gen", np.array([-100, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([100, 0]))
     # run OPF
     pp.rundcopp(net, verbose=False)
 
@@ -444,7 +444,7 @@ def test_dcopf_poly(simple_opf_test_net):
 
 def test_opf_poly(simple_opf_test_net):
     net = simple_opf_test_net
-    pp.create_polynomial_cost(net, 0, "gen", np.array([-100, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([100, 0]))
     # run OPF
     for init in ["pf", "flat"]:
         pp.runopp(net, verbose=False, init=init)
@@ -462,7 +462,7 @@ def test_opf_pwl(simple_opf_test_net):
     # create net
     net = simple_opf_test_net
     # pp.create_polynomial_cost(net, 0, "gen", np.array([-100, 0]))
-    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-200, 20000], [-100, 10000], [0, 0]]))
+    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-200, -20000], [-100, -10000], [0, 0]]))
     # run OPF
     for init in ["pf", "flat"]:
         pp.runopp(net, verbose=False, init=init)
@@ -481,7 +481,7 @@ def test_dcopf_pwl(simple_opf_test_net):
     # create net
     net = simple_opf_test_net
     # pp.create_polynomial_cost(net, 0, "gen", np.array([-100, 0]))
-    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-200, 20000], [-100, 10000], [0, 0]]))
+    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-200, -20000], [-100, -10000], [0, 0]]))
     # run OPF
     pp.rundcopp(net, verbose=False)
     assert net["OPF_converged"]
@@ -522,8 +522,8 @@ def test_opf_varying_max_line_loading():
                    min_q_kvar=-25)
     pp.create_sgen(net, 2, p_kw=-100, controllable=True, max_p_kw=-5, min_p_kw=-150, max_q_kvar=25,
                    min_q_kvar=-25)
-    pp.create_polynomial_cost(net, 0, "sgen", np.array([-10, 0]))
-    pp.create_polynomial_cost(net, 1, "sgen", np.array([-10, 0]))
+    pp.create_polynomial_cost(net, 0, "sgen", np.array([10, 0]))
+    pp.create_polynomial_cost(net, 1, "sgen", np.array([10, 0]))
     pp.create_ext_grid(net, 0)
     pp.create_polynomial_cost(net, 0, "ext_grid", np.array([-.1, 0]))
     pp.create_line_from_parameters(net, 1, 2, 1, name="line1", r_ohm_per_km=0.876,
@@ -585,13 +585,13 @@ def test_storage_opf():
 
 
     # costs
-    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([0, 3, 0]))
+    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([0, -3, 0]))
     #pp.create_polynomial_cost(net, 0, "load", np.array([0, -1, 0]))
     # TODO - Gitlab Issue #27 - OPF error wenn Kosten für non-controllable PQ-Element zugewiesen
     #   vgl. make_objective.py, Z.47ff.
-    pp.create_polynomial_cost(net, 0, "sgen", np.array([0, 2, 0]))
-    pp.create_polynomial_cost(net, 0, "storage", np.array([0, 1, 0]))
-    pp.create_polynomial_cost(net, 1, "sgen", np.array([0, 1, 0]))
+    pp.create_polynomial_cost(net, 0, "sgen", np.array([0, -2, 0]))
+    pp.create_polynomial_cost(net, 0, "storage", np.array([0, -1, 0]))
+    pp.create_polynomial_cost(net, 1, "sgen", np.array([0, -1, 0]))
     pp.create_polynomial_cost(net, 1, "load", np.array([0, -3, 0]))
 
     # test storage generator behaviour
@@ -633,7 +633,7 @@ def test_storage_opf():
     net["storage"].min_q_kvar.iloc[0] = -25
     # gencost for storages: positive costs in pandapower per definition
     # --> storage gencosts are similar to sgen gencosts (make_objective.py, l.128ff. and l.185ff.)
-    net["polynomial_cost"].c.iloc[2] = - net["polynomial_cost"].c.iloc[4]
+    net["polynomial_cost"].c.iloc[2] = net["polynomial_cost"].c.iloc[4]
     net["sgen"].in_service.iloc[1] = False
     net["load"].in_service.iloc[1] = False 
 
@@ -710,57 +710,57 @@ def test_no_controllables(simple_opf_test_net):
     net.gen.controllable = False
     # pp.runopp(net)
     # net.gen = net.gen.drop(index=0)
-    pp.create_polynomial_cost(net, 0, "gen", np.array([0, 2, 0]))
-    pp.create_polynomial_cost(net, 0, "load", np.array([0, -1, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([0, -2, 0]))
+    pp.create_polynomial_cost(net, 0, "load", np.array([0, 1, 0]))
     pp.runopp(net)
 
-def test_controllables_default():
-    """ Testing sgens/gens/loads with no defined controllable parameter  """
-    # boundaries
-    vm_max = 1.1
-    #todo
-    vm_min = 0.9
-    max_line_loading_percent = 100
-
-    # create network
-    net = pp.create_empty_network()
-    b1 = pp.create_bus(net, vn_kv=0.4, max_vm_pu=vm_max, min_vm_pu=vm_min)
-    b2 = pp.create_bus(net, vn_kv=0.4, max_vm_pu=vm_max, min_vm_pu=vm_min)
-
-    pp.create_line(net, b1, b2, length_km=5, std_type="NAYY 4x50 SE",
-                   max_loading_percent=max_line_loading_percent)
-
-    # test elements static
-    pp.create_ext_grid(net, b2)
-    pp.create_load(net, b1, p_kw=7.5, max_p_kw=10, min_p_kw=0,
-                   max_q_kvar=2.5, min_q_kvar=-2.5)
-    net.load["controllable"] = False
-    # load should default to controllable =False
-    # pp.create_sgen(net, b1, p_kw=-25, max_p_kw=-10, min_p_kw=-25,
-    #                max_q_kvar=25, min_q_kvar=-25)
-    # # sgen should default to controllable =True
-    # pp.create_gen(net, b1, p_kw=-25, max_p_kw=-10, min_p_kw=-25,
-    #                max_q_kvar=25, min_q_kvar=-25)
-    # # gen should default to controllable =True
-
-    # costs
-    pp.create_polynomial_cost(net, 0, "ext_grid", np.array([0, 3, 0]))
-    pp.create_polynomial_cost(net, 0, "load", np.array([0, -1, 0]))
+# def test_controllables_default():
+#     """ Testing sgens/gens/loads with no defined controllable parameter  """
+#     # boundaries
+#     vm_max = 1.1
+#     #todo
+#     vm_min = 0.9
+#     max_line_loading_percent = 100
+#9
+#     # create network
+#     net = pp.create_empty_network()
+#     b1 = pp.create_bus(net, vn_kv=0.4, max_vm_pu=vm_max, min_vm_pu=vm_min)
+#     b2 = pp.create_bus(net, vn_kv=0.4, max_vm_pu=vm_max, min_vm_pu=vm_min)
+#
+#     pp.create_line(net, b1, b2, length_km=5, std_type="NAYY 4x50 SE",
+#                    max_loading_percent=max_line_loading_percent)
+#
+#     # test elements static
+#     pp.create_ext_grid(net, b2)
+#     pp.create_load(net, b1, p_kw=7.5, max_p_kw=10, min_p_kw=0,
+#                    max_q_kvar=2.5, min_q_kvar=-2.5)
+#     net.load["controllable"] = False
+#     # load should default to controllable =False
+#     # pp.create_sgen(net, b1, p_kw=-25, max_p_kw=-10, min_p_kw=-25,
+#     #                max_q_kvar=25, min_q_kvar=-25)
+#     # # sgen should default to controllable =True
+#     # pp.create_gen(net, b1, p_kw=-25, max_p_kw=-10, min_p_kw=-25,
+#     #                max_q_kvar=25, min_q_kvar=-25)
+#     # # gen should default to controllable =True
+#
+#     # costs
+#     pp.create_polynomial_cost(net, 0, "ext_grid", np.array([0,-3, 0]))
+#     pp.create_polynomial_cost(net, 0, "load", np.array([0, 1, 0]))
     # pp.create_polynomial_cost(net, 0, "sgen", np.array([0, 2, 0]))
     # pp.create_polynomial_cost(net, 0, "gen", np.array([0, 2, 0]))
 
     pp.runopp(net, verbose=True)
     assert net["OPF_converged"]
 
 if __name__ == "__main__":
-    # pytest.main(['-s', __file__])
-    #test_storage_opf()
-    #test_opf_varying_max_line_loading()
+    pytest.main(['-s', __file__])
+    # test_storage_opf()
+    # test_opf_varying_max_line_loading()
      # pytest.main(["test_basic.py", "-s"])
     # test_simplest_dispatch()
     # test_trafo3w_loading()
     # test_trafo3w_loading()
     # test_dcopf_pwl()
-    net = simple_opf_test_net()
-    test_no_controllables(net)
+    # net = simple_opf_test_net()
+    # test_no_controllables(net)
     # test_controllables_default()

pandapower/test/opf/test_costs_mixed.py

@@ -39,17 +39,17 @@ def test_cost_mixed():
                                    max_loading_percent=100 * 690)
 
     # testing some combinations
-    pp.create_polynomial_cost(net, 0, "gen", np.array([0, 1, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([0, -1, 0]))
     pp.runopp(net, verbose=False)
     assert net["OPF_converged"]
     assert net.res_cost == - net.res_gen.p_kw.values
 
-    net.polynomial_cost.c.at[0] = np.array([[1, 0, 0]])
+    net.polynomial_cost.c.at[0] = np.array([[-1, 0, 0]])
     pp.runopp(net, verbose=False)
     assert net["OPF_converged"]
     assert net.res_cost - net.res_gen.p_kw.values**2 < 1e-5
 
-    net.polynomial_cost.c.at[0] = np.array([[1, 0, 1]])
+    net.polynomial_cost.c.at[0] = np.array([[-1, 0, -1]])
     pp.runopp(net, verbose=False)
     assert net["OPF_converged"]
     assert net.res_cost - net.res_gen.p_kw.values**2 - 1 < 1e-5
@@ -58,7 +58,7 @@ def test_cost_mixed():
     pp.runopp(net, verbose=False)
     assert net.res_cost - net.res_gen.p_kw.values ** 2 - 1 < 1e-5
 
-    pp.create_piecewise_linear_cost(net, 0, "load", np.array([[0, 0], [100, 100]]), type="p")
+    pp.create_piecewise_linear_cost(net, 0, "load", np.array([[0, 0], [100, -100]]), type="p")
     pp.runopp(net, verbose=False)
     assert net.res_cost - net.res_gen.p_kw.values ** 2 - 1 - net.res_load.p_kw.values < 1e-5
 
@@ -81,8 +81,8 @@ def test_mixed_p_q_pol():
                                    max_loading_percent=100 * 690)
 
     # testing some combinations
-    pp.create_polynomial_cost(net, 0, "gen", np.array([0, 1, 0]))
-    pp.create_polynomial_cost(net, 0, "gen", np.array([0, 1, 0]), type ="q")
+    pp.create_polynomial_cost(net, 0, "gen", np.array([0, -1, 0]))
+    pp.create_polynomial_cost(net, 0, "gen", np.array([0, -1, 0]), type ="q")
     pp.runopp(net, verbose=False)
     assert net["OPF_converged"]
     assert net.res_cost == - net.res_gen.p_kw.values + net.res_gen.q_kvar.values
@@ -106,8 +106,8 @@ def test_mixed_p_q_pwl():
                                    max_loading_percent=100 * 690)
 
     # testing some combinations
-    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-150, -150],[150, 150]]))
-    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-150, -150],[150, 150]]), type ="q")
+    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-150, 150],[150, -150]]))
+    pp.create_piecewise_linear_cost(net, 0, "gen", np.array([[-150, 150],[150, -150]]), type ="q")
     pp.runopp(net, verbose=False)
     assert net["OPF_converged"]
     assert net.res_cost == - net.res_gen.p_kw.values + net.res_gen.q_kvar.values