New test for OPF cost consistency

pandapower/test/opf/test_cost_consistency.py

@@ -0,0 +1,182 @@
+import pandapower as pp
+import pytest
+from numpy import array
+
+@pytest.fixture()
+def base_net():
+    net = pp.create_empty_network()
+    pp.create_bus(net, vn_kv=10)
+    pp.create_bus(net, vn_kv=10)
+    pp.create_ext_grid(net, 0)
+    pp.create_load(net, 1, p_kw=200, controllable=False)
+    pp.create_line_from_parameters(net, 0, 1, 50, name="line", 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 * 690)
+
+    pp.runpp(net)
+    return net
+
+def test_contingency_sgen(base_net):
+
+    net = base_net
+    pp.create_sgen(net, 1, p_kw=-100, q_kvar =0, controllable=True, max_p_kw=-5, min_p_kw=-150, max_q_kvar=50,
+                  min_q_kvar=-50)
+    # pwl costs
+    # maximize the sgen feed in by using a positive cost slope
+    # using a slope of 1
+    #                   |   /
+    #                   |  /
+    #                   | /
+    #                   |/
+    #-------------------------------------------
+    #    p_min_kw      /|
+    #                 / |
+    #                /  |
+
+    pp.create_piecewise_linear_cost(net, 0, "sgen", array([[net.sgen.min_p_kw.at[0], net.sgen.min_p_kw.at[0]], [0, 0]]))
+    pp.runopp(net)
+
+
+    assert abs(net.res_cost - net.res_sgen.p_kw.at[0]) < 1e-5
+    # minimize the sgen feed in by using a positive cost slope
+    # using a slope of 1
+    #               \   |
+    #                \  |
+    #                 \ |
+    #                  \|
+    #-------------------------------------------
+    #    p_min_kw       |\
+    #                   | \
+    #                   |  \
+    net.piecewise_linear_cost.f.at[0] *= -1
+    pp.runopp(net)
+
+    print(net.res_cost)
+    print(net.res_sgen.p_kw)
+
+    assert abs(net.res_cost - net.res_sgen.p_kw.at[0]*-1) < 1e-5
+
+    net.piecewise_linear_cost = net.piecewise_linear_cost.drop(index=0)
+
+    # first using a positive slope as in the case above
+    pp.create_polynomial_cost(net, 0, "sgen", array([1, 0]))
+    pp.runopp(net)
+    assert abs(net.res_cost - net.res_sgen.p_kw.at[0]) < 1e-5
+
+    # negative slope as in the case above
+    net.polynomial_cost.c.at[0] *= -1
+    pp.runopp(net)
+
+    assert abs(net.res_cost - net.res_sgen.p_kw.at[0]*-1) < 1e-5
+
+
+def test_contingency_load(base_net):
+    net = base_net
+    pp.create_load(net, 1, p_kw=-100, q_kvar=0, controllable=True, max_p_kw=150, min_p_kw=5,
+                   max_q_kvar=50,
+                   min_q_kvar=-50)
+    # pwl costs
+    # minimze the load by using a positive cost slope
+    # using a slope of 1
+    #                   |   /
+    #                   |  /
+    #                   | /
+    #                   |/
+    # -------------------------------------------
+    #    p_min_kw      /|
+    #                 / |
+    #                /  |
+
+    pp.create_piecewise_linear_cost(net, 1, "load", array(
+        [[0, 0],[net.load.max_p_kw.at[1], net.load.max_p_kw.at[1]]]))
+    pp.runopp(net)
+
+    assert abs(net.res_cost - net.res_load.p_kw.at[1]) < 1e-5
+    # maximize the load in by using a negative cost slope
+    # using a slope of 1
+    #               \   |
+    #                \  |
+    #                 \ |
+    #                  \|
+    # -------------------------------------------
+    #    p_min_kw       |\
+    #                   | \
+    #                   |  \
+    net.piecewise_linear_cost.f.at[0] *= -1
+    pp.runopp(net)
+
+    print(net.res_cost)
+    print(net.res_load.p_kw)
+
+    assert abs(net.res_cost - net.res_load.p_kw.at[1] * -1) < 1e-5
+
+    # poly costs
+
+    net.piecewise_linear_cost = net.piecewise_linear_cost.drop(index=0)
+
+    # first using a positive slope as in the case above
+    pp.create_polynomial_cost(net, 1, "load", array([1, 0]))
+    pp.runopp(net)
+    assert abs(net.res_cost - net.res_load.p_kw.at[1]) < 1e-5
+
+    # negative slope as in the case above
+    net.polynomial_cost.c.at[0] *= -1
+    pp.runopp(net)
+
+    assert abs(net.res_cost - net.res_load.p_kw.at[1]*-1) < 1e-5
+
+
+def test_contingency_gen(base_net):
+
+    net = base_net
+    pp.create_gen(net, 1, p_kw=-100, vm_pu = 1.05, controllable=True, max_p_kw=-5, min_p_kw=-150, max_q_kvar=50,
+                  min_q_kvar=-50)
+    # pwl costs
+    # maximize the sgen feed in by using a positive cost slope
+    # using a slope of 1
+    #                   |   /
+    #                   |  /
+    #                   | /
+    #                   |/
+    #-------------------------------------------
+    #    p_min_kw      /|
+    #                 / |
+    #                /  |
+
+    pp.create_piecewise_linear_cost(net, 0, "gen", array([[net.gen.min_p_kw.at[0], net.gen.min_p_kw.at[0]], [0, 0]]))
+    pp.runopp(net)
+
+
+    assert abs(net.res_cost - net.res_gen.p_kw.at[0]) < 1e-5
+    # minimize the sgen feed in by using a positive cost slope
+    # using a slope of 1
+    #               \   |
+    #                \  |
+    #                 \ |
+    #                  \|
+    #-------------------------------------------
+    #    p_min_kw       |\
+    #                   | \
+    #                   |  \
+    net.piecewise_linear_cost.f.at[0] *= -1
+    pp.runopp(net)
+
+    assert abs(net.res_cost - net.res_gen.p_kw.at[0]*-1) < 1e-5
+
+    net.piecewise_linear_cost = net.piecewise_linear_cost.drop(index=0)
+
+    # first using a positive slope as in the case above
+    pp.create_polynomial_cost(net, 0, "gen", array([1, 0]))
+    pp.runopp(net)
+    assert abs(net.res_cost - net.res_gen.p_kw.at[0]) < 1e-5
+
+    # negative slope as in the case above
+    net.polynomial_cost.c.at[0] *= -1
+    pp.runopp(net)
+
+    assert abs(net.res_cost - net.res_gen.p_kw.at[0]*-1) < 1e-5
+
+if __name__ == "__main__":
+    # net = base_net()
+    # test_contingency_gen(net)
+    pytest.main(['-s', __file__])