def test_pypower_case():
#ppopt is a dictionary with the details of the optimization routine to run
ppopt = ppoption(PF_ALG=2)
#choose DC or AC
ppopt["PF_DC"] = True
#ppc is a dictionary with details about the network, including baseMVA, branches and generators
ppc = case()
results,success = runpf(ppc, ppopt)
#store results in a DataFrame for easy access
results_df = {}
#branches
columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(", ")
results_df['branch'] = pd.DataFrame(data=results["branch"],columns=columns)
#buses
columns = ["bus","type","Pd","Qd","Gs","Bs","area","v_mag_pu_set","v_ang_set","v_nom","zone","Vmax","Vmin"]
results_df['bus'] = pd.DataFrame(data=results["bus"],columns=columns,index=results["bus"][:,0])
#generators
columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(", ")
results_df['gen'] = pd.DataFrame(data=results["gen"],columns=columns)
#now compute in PyPSA
network = pypsa.Network()
network.import_from_pypower_ppc(ppc)
network.lpf()
#compare generator dispatch
p_pypsa = network.generators_t.p.loc["now"].values
p_pypower = results_df['gen']["p"].values
np.testing.assert_array_almost_equal(p_pypsa,p_pypower)
#compare branch flows
for item in ["lines","transformers"]:
df = getattr(network,item)
pnl = getattr(network,item + "_t")
for si in ["p0","p1"]:
si_pypsa = getattr(pnl,si).loc["now"].values
si_pypower = results_df['branch'][si][df.original_index].values
np.testing.assert_array_almost_equal(si_pypsa,si_pypower)
def test_pypower_case():
#ppopt is a dictionary with the details of the optimization routine to run
ppopt = ppoption(PF_ALG=2)
#choose DC or AC
ppopt["PF_DC"] = False
#ppc is a dictionary with details about the network, including baseMVA, branches and generators
ppc = case()
results,success = runpf(ppc, ppopt)
#store results in a DataFrame for easy access
results_df = {}
#branches
columns = 'bus0, bus1, r, x, b, rateA, rateB, rateC, ratio, angle, status, angmin, angmax, p0, q0, p1, q1'.split(", ")
results_df['branch'] = pd.DataFrame(data=results["branch"],columns=columns)
#buses
columns = ["bus","type","Pd","Qd","Gs","Bs","area","v_mag_pu","v_ang","v_nom","zone","Vmax","Vmin"]
results_df['bus'] = pd.DataFrame(data=results["bus"],columns=columns,index=results["bus"][:,0])
#generators
columns = "bus, p, q, q_max, q_min, Vg, mBase, status, p_max, p_min, Pc1, Pc2, Qc1min, Qc1max, Qc2min, Qc2max, ramp_agc, ramp_10, ramp_30, ramp_q, apf".split(", ")
results_df['gen'] = pd.DataFrame(data=results["gen"],columns=columns)
#now compute in PyPSA
network = pypsa.Network()
network.import_from_pypower_ppc(ppc)
#PYPOWER uses PI model for transformers, whereas PyPSA defaults to
#T since version 0.8.0
network.transformers.model = "pi"
network.pf()
#compare branch flows
for c in network.iterate_components(pypsa.components.passive_branch_components):
for si in ["p0","p1","q0","q1"]:
si_pypsa = getattr(c.pnl,si).loc["now"].values
si_pypower = results_df['branch'][si][c.df.original_index].values
np.testing.assert_array_almost_equal(si_pypsa,si_pypower)
#compare generator dispatch
for s in ["p","q"]:
s_pypsa = getattr(network.generators_t,s).loc["now"].values
s_pypower = results_df["gen"][s].values
np.testing.assert_array_almost_equal(s_pypsa,s_pypower)
#compare voltages
v_mag_pypsa = network.buses_t.v_mag_pu.loc["now"]
v_mag_pypower = results_df["bus"]["v_mag_pu"]
np.testing.assert_array_almost_equal(v_mag_pypsa,v_mag_pypower)
v_ang_pypsa = network.buses_t.v_ang.loc["now"]
pypower_slack_angle = results_df["bus"]["v_ang"][results_df["bus"]["type"] == 3].values[0]
v_ang_pypower = (results_df["bus"]["v_ang"] - pypower_slack_angle)*np.pi/180.
np.testing.assert_array_almost_equal(v_ang_pypsa,v_ang_pypower)
