def test_soc_model(self, case_name):
test_case = os.path.join(current_dir, 'transmission_test_instances',
'pglib-opf-master', '{}.m'.format(case_name))
upper_bound_soln = upper_bounds[case_name]
gap_soln = gaps[case_name]
md = create_ModelData(test_case)
nlp, scaled_md = create_psv_acopf_model(md)
rel, scaled_md = create_soc_relaxation(md)
opt = SolverFactory('ipopt')
opt.options['linear_solver'] = 'mumps'
res = opt.solve(nlp, tee=False)
self.assertTrue(
res.solver.termination_condition == TerminationCondition.optimal)
ub = pe.value(nlp.obj)
res = opt.solve(rel, tee=False)
self.assertTrue(
res.solver.termination_condition == TerminationCondition.optimal)
lb = pe.value(rel.obj)
gap = (ub - lb) / ub * 100
comparison = math.isclose(ub, upper_bound_soln, rel_tol=1e-4)
self.assertTrue(comparison)
comparison = math.isclose(gap, gap_soln, abs_tol=1e-2)
self.assertTrue(comparison)
def _egret_model(md_dict):
# the exact acopf model is hard-wired here:
if not convex_relaxation:
pyomod, mdict = eac.create_riv_acopf_model(
md_dict, include_feasibility_slack=True)
else:
pyomod, mdict = eac_relax.create_soc_relaxation(
md_dict,
include_feasibility_slack=True,
use_linear_relaxation=False)
return pyomod, mdict
def _egret_model(md_dict):
# the exact acopf model is hard-wired here:
md_dict.data['system']['load_mismatch_cost'] = load_mismatch_cost
md_dict.data['system']['q_load_mismatch_cost'] = q_load_mismatch_cost
if not convex_relaxation:
pyomod, mdict = eac.create_riv_acopf_model(
md_dict, include_feasibility_slack=True)
else:
pyomod, mdict = eac_relax.create_soc_relaxation(
md_dict,
include_feasibility_slack=True,
use_linear_relaxation=False)
return pyomod, mdict