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import distopf as opf
Run a power flow on the IEEE 13 bus system with no control variables.¶
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case = opf.DistOPFCase(data_path="ieee13")
case.run_pf() # run power flow
case.plot_network()
Run a power flow on the IEEE 123 bus system with no control variables.¶
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case = opf.DistOPFCase(data_path="ieee123")
case.run_pf() # run power flow
case.plot_network()
Run using OpenDSS Model¶
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case = opf.DistOPFCase(data_path=opf.CASES_DIR/"dss"/"ieee123_dss"/"Run_IEEE123Bus.DSS")
case.run_pf()
case.plot_network()
Run a power flow on the IEEE 123 bus system with DERs on 30 buses.¶
Give the DERs 10x power and use curtailment minimization to bring the voltages in bounds.
In [5]:
case = opf.DistOPFCase(data_path="ieee123_30der", control_variable="P", objective_function="curtail_min", gen_mult=10)
case.run_pf()
case.plot_network(v_max=1.05, v_min=0.95)
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case.plot_voltages()
Use the DERs to minimize power losses by injecting reactive power.¶
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case.run()
case.plot_network()
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case.plot_voltages()
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case.plot_decision_variables()
Using plot_network¶
(latitude and longitude data required in bus_data.csv)
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case.plot_network(show_phases="a", show_reactive_power=True)
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case.plot_network(show_phases="b", show_reactive_power=False)
Run using lower level API¶
In [12]:
case = opf.DistOPFCase(data_path="ieee123_30der", gen_mult=10, control_variable="P")
model = opf.LinDistModel(
branch_data=case.branch_data,
bus_data=case.bus_data,
gen_data=case.gen_data,
cap_data=case.cap_data,
reg_data=case.reg_data
)
# Solve model using provided objective function
# result = opf.lp_solve(model, np.zeros(model.n_x))
result = opf.cvxpy_solve(model, opf.cp_obj_curtail)
print(result.fun)
v = model.get_voltages(result.x)
s = model.get_apparent_power_flows(result.x)
p_gens = model.get_p_gens(result.x)
q_gens = model.get_q_gens(result.x)
opf.plot_network(model, v, s, p_gen=p_gens, q_gen=q_gens).show()
opf.plot_voltages(v).show()
opf.plot_power_flows(s).show()
0.48013883015073655
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