def create_rsv_acopf_model(model_data,
include_feasibility_slack=False,
pw_cost_model='delta'):
model, md = _create_base_power_ac_model(
model_data,
include_feasibility_slack=include_feasibility_slack,
pw_cost_model=pw_cost_model)
bus_attrs = md.attributes(element_type='bus')
branch_attrs = md.attributes(element_type='branch')
bus_pairs = zip_items(branch_attrs['from_bus'], branch_attrs['to_bus'])
unique_bus_pairs = list(
OrderedDict((val, None) for idx, val in bus_pairs.items()).keys())
# declare the rectangular voltages
neg_v_max = map_items(op.neg, bus_attrs['v_max'])
vr_init = {
k: bus_attrs['vm'][k] * pe.cos(radians(bus_attrs['va'][k]))
for k in bus_attrs['vm']
}
libbus.declare_var_vr(model,
bus_attrs['names'],
initialize=vr_init,
bounds=zip_items(neg_v_max, bus_attrs['v_max']))
vj_init = {
k: bus_attrs['vm'][k] * pe.sin(radians(bus_attrs['va'][k]))
for k in bus_attrs['vm']
}
libbus.declare_var_vj(model,
bus_attrs['names'],
initialize=vj_init,
bounds=zip_items(neg_v_max, bus_attrs['v_max']))
# fix the reference bus
ref_bus = md.data['system']['reference_bus']
ref_angle = md.data['system']['reference_bus_angle']
if ref_angle != 0.0:
libbus.declare_eq_ref_bus_nonzero(model, ref_angle, ref_bus)
else:
model.vj[ref_bus].fix(0.0)
model.vr[ref_bus].setlb(bus_attrs['v_min'][ref_bus])
# relate c, s, and vmsq to vm and va
libbus.declare_eq_vmsq(model=model,
index_set=bus_attrs['names'],
coordinate_type=CoordinateType.RECTANGULAR)
libbranch.declare_eq_c(model=model,
index_set=unique_bus_pairs,
coordinate_type=CoordinateType.RECTANGULAR)
libbranch.declare_eq_s(model=model,
index_set=unique_bus_pairs,
coordinate_type=CoordinateType.RECTANGULAR)
return model, md
def create_riv_acopf_model(model_data, include_feasibility_slack=False):
md = model_data.clone_in_service()
tx_utils.scale_ModelData_to_pu(md, inplace=True)
gens = dict(md.elements(element_type='generator'))
buses = dict(md.elements(element_type='bus'))
branches = dict(md.elements(element_type='branch'))
loads = dict(md.elements(element_type='load'))
shunts = dict(md.elements(element_type='shunt'))
gen_attrs = md.attributes(element_type='generator')
bus_attrs = md.attributes(element_type='bus')
branch_attrs = md.attributes(element_type='branch')
load_attrs = md.attributes(element_type='load')
shunt_attrs = md.attributes(element_type='shunt')
inlet_branches_by_bus, outlet_branches_by_bus = \
tx_utils.inlet_outlet_branches_by_bus(branches, buses)
gens_by_bus = tx_utils.gens_by_bus(buses, gens)
model = pe.ConcreteModel()
### declare (and fix) the loads at the buses
bus_p_loads, bus_q_loads = tx_utils.dict_of_bus_loads(buses, loads)
libbus.declare_var_pl(model, bus_attrs['names'], initialize=bus_p_loads)
libbus.declare_var_ql(model, bus_attrs['names'], initialize=bus_q_loads)
model.pl.fix()
model.ql.fix()
### declare the fixed shunts at the buses
bus_bs_fixed_shunts, bus_gs_fixed_shunts = tx_utils.dict_of_bus_fixed_shunts(
buses, shunts)
### declare the rectangular voltages
neg_v_max = map_items(op.neg, bus_attrs['v_max'])
vr_init = {
k: bus_attrs['vm'][k] * pe.cos(bus_attrs['va'][k])
for k in bus_attrs['vm']
}
libbus.declare_var_vr(model,
bus_attrs['names'],
initialize=vr_init,
bounds=zip_items(neg_v_max, bus_attrs['v_max']))
vj_init = {
k: bus_attrs['vm'][k] * pe.sin(bus_attrs['va'][k])
for k in bus_attrs['vm']
}
libbus.declare_var_vj(model,
bus_attrs['names'],
initialize=vj_init,
bounds=zip_items(neg_v_max, bus_attrs['v_max']))
### include the feasibility slack for the bus balances
p_rhs_kwargs = {}
q_rhs_kwargs = {}
if include_feasibility_slack:
p_marginal_slack_penalty, q_marginal_slack_penalty = _validate_and_extract_slack_penalties(
md)
p_rhs_kwargs, q_rhs_kwargs, penalty_expr = _include_feasibility_slack(
model, bus_attrs, gen_attrs, bus_p_loads, bus_q_loads,
p_marginal_slack_penalty, q_marginal_slack_penalty)
### fix the reference bus
ref_bus = md.data['system']['reference_bus']
ref_angle = md.data['system']['reference_bus_angle']
if ref_angle != 0.0:
libbus.declare_eq_ref_bus_nonzero(model, ref_angle, ref_bus)
else:
model.vj[ref_bus].fix(0.0)
model.vr[ref_bus].setlb(0.0)
### declare the generator real and reactive power
pg_init = {
k: (gen_attrs['p_min'][k] + gen_attrs['p_max'][k]) / 2.0
for k in gen_attrs['pg']
}
libgen.declare_var_pg(model,
gen_attrs['names'],
initialize=pg_init,
bounds=zip_items(gen_attrs['p_min'],
gen_attrs['p_max']))
qg_init = {
k: (gen_attrs['q_min'][k] + gen_attrs['q_max'][k]) / 2.0
for k in gen_attrs['qg']
}
libgen.declare_var_qg(model,
gen_attrs['names'],
initialize=qg_init,
bounds=zip_items(gen_attrs['q_min'],
gen_attrs['q_max']))
### declare the current flows in the branches
branch_currents = tx_utils.dict_of_branch_currents(branches, buses)
s_max = {k: branches[k]['rating_long_term'] for k in branches.keys()}
if_bounds = dict()
it_bounds = dict()
ifr_init = dict()
ifj_init = dict()
itr_init = dict()
itj_init = dict()
for branch_name, branch in branches.items():
from_bus = branch['from_bus']
to_bus = branch['to_bus']
y_matrix = tx_calc.calculate_y_matrix_from_branch(branch)
ifr_init[branch_name] = tx_calc.calculate_ifr(vr_init[from_bus],
vj_init[from_bus],
vr_init[to_bus],
vj_init[to_bus],
y_matrix)
ifj_init[branch_name] = tx_calc.calculate_ifj(vr_init[from_bus],
vj_init[from_bus],
vr_init[to_bus],
vj_init[to_bus],
y_matrix)
itr_init[branch_name] = tx_calc.calculate_itr(vr_init[from_bus],
vj_init[from_bus],
vr_init[to_bus],
vj_init[to_bus],
y_matrix)
itj_init[branch_name] = tx_calc.calculate_itj(vr_init[from_bus],
vj_init[from_bus],
vr_init[to_bus],
vj_init[to_bus],
y_matrix)
if s_max[branch_name] is None:
if_bounds[branch_name] = (None, None)
it_bounds[branch_name] = (None, None)
else:
if_max = s_max[branch_name] / buses[branches[branch_name]
['from_bus']]['v_min']
it_max = s_max[branch_name] / buses[branches[branch_name]
['to_bus']]['v_min']
if_bounds[branch_name] = (-if_max, if_max)
it_bounds[branch_name] = (-it_max, it_max)
libbranch.declare_var_ifr(model=model,
index_set=branch_attrs['names'],
initialize=ifr_init,
bounds=if_bounds)
libbranch.declare_var_ifj(model=model,
index_set=branch_attrs['names'],
initialize=ifj_init,
bounds=if_bounds)
libbranch.declare_var_itr(model=model,
index_set=branch_attrs['names'],
initialize=itr_init,
bounds=it_bounds)
libbranch.declare_var_itj(model=model,
index_set=branch_attrs['names'],
initialize=itj_init,
bounds=it_bounds)
ir_init = dict()
ij_init = dict()
for bus_name, bus in buses.items():
ir_expr = sum([
ifr_init[branch_name]
for branch_name in outlet_branches_by_bus[bus_name]
])
ir_expr += sum([
itr_init[branch_name]
for branch_name in inlet_branches_by_bus[bus_name]
])
ij_expr = sum([
ifj_init[branch_name]
for branch_name in outlet_branches_by_bus[bus_name]
])
ij_expr += sum([
itj_init[branch_name]
for branch_name in inlet_branches_by_bus[bus_name]
])
if bus_gs_fixed_shunts[bus_name] != 0.0:
ir_expr += bus_gs_fixed_shunts[bus_name] * vr_init[bus_name]
ij_expr += bus_gs_fixed_shunts[bus_name] * vj_init[bus_name]
if bus_bs_fixed_shunts[bus_name] != 0.0:
ir_expr += bus_bs_fixed_shunts[bus_name] * vj_init[bus_name]
ij_expr += bus_bs_fixed_shunts[bus_name] * vr_init[bus_name]
ir_init[bus_name] = ir_expr
ij_init[bus_name] = ij_expr
# TODO: Implement better bounds (?) for these aggregated variables -- note, these are unbounded in old Egret
libbus.declare_var_ir_aggregation_at_bus(model=model,
index_set=bus_attrs['names'],
initialize=ir_init,
bounds=(None, None))
libbus.declare_var_ij_aggregation_at_bus(model=model,
index_set=bus_attrs['names'],
initialize=ij_init,
bounds=(None, None))
### declare the branch current flow constraints
libbranch.declare_eq_branch_current(model=model,
index_set=branch_attrs['names'],
branches=branches)
### declare the ir/ij_aggregation constraints
libbus.declare_eq_i_aggregation_at_bus(
model=model,
index_set=bus_attrs['names'],
bus_bs_fixed_shunts=bus_bs_fixed_shunts,
bus_gs_fixed_shunts=bus_gs_fixed_shunts,
inlet_branches_by_bus=inlet_branches_by_bus,
outlet_branches_by_bus=outlet_branches_by_bus)
### declare the pq balances
libbus.declare_eq_p_balance_with_i_aggregation(
model=model,
index_set=bus_attrs['names'],
bus_p_loads=bus_p_loads,
gens_by_bus=gens_by_bus,
**p_rhs_kwargs)
libbus.declare_eq_q_balance_with_i_aggregation(
model=model,
index_set=bus_attrs['names'],
bus_q_loads=bus_q_loads,
gens_by_bus=gens_by_bus,
**q_rhs_kwargs)
### declare the thermal limits
libbranch.declare_ineq_s_branch_thermal_limit(
model=model,
index_set=branch_attrs['names'],
branches=branches,
s_thermal_limits=s_max,
flow_type=FlowType.CURRENT)
### declare the voltage min and max inequalities
libbus.declare_ineq_vm_bus_lbub(model=model,
index_set=bus_attrs['names'],
buses=buses,
coordinate_type=CoordinateType.RECTANGULAR)
### declare angle difference limits on interconnected buses
libbranch.declare_ineq_angle_diff_branch_lbub(
model=model,
index_set=branch_attrs['names'],
branches=branches,
coordinate_type=CoordinateType.RECTANGULAR)
### declare the generator cost objective
libgen.declare_expression_pgqg_operating_cost(model=model,
index_set=gen_attrs['names'],
p_costs=gen_attrs['p_cost'],
q_costs=gen_attrs.get(
'q_cost', None))
obj_expr = sum(model.pg_operating_cost[gen_name]
for gen_name in model.pg_operating_cost)
if include_feasibility_slack:
obj_expr += penalty_expr
if hasattr(model, 'qg_operating_cost'):
obj_expr += sum(model.qg_operating_cost[gen_name]
for gen_name in model.qg_operating_cost)
model.obj = pe.Objective(expr=obj_expr)
return model, md
def create_rsv_acopf_model(model_data, include_feasibility_slack=False):
md = model_data.clone_in_service()
tx_utils.scale_ModelData_to_pu(md, inplace=True)
gens = dict(md.elements(element_type='generator'))
buses = dict(md.elements(element_type='bus'))
branches = dict(md.elements(element_type='branch'))
loads = dict(md.elements(element_type='load'))
shunts = dict(md.elements(element_type='shunt'))
gen_attrs = md.attributes(element_type='generator')
bus_attrs = md.attributes(element_type='bus')
branch_attrs = md.attributes(element_type='branch')
load_attrs = md.attributes(element_type='load')
shunt_attrs = md.attributes(element_type='shunt')
inlet_branches_by_bus, outlet_branches_by_bus = \
tx_utils.inlet_outlet_branches_by_bus(branches, buses)
gens_by_bus = tx_utils.gens_by_bus(buses, gens)
model = pe.ConcreteModel()
### declare (and fix) the loads at the buses
bus_p_loads, bus_q_loads = tx_utils.dict_of_bus_loads(buses, loads)
libbus.declare_var_pl(model, bus_attrs['names'], initialize=bus_p_loads)
libbus.declare_var_ql(model, bus_attrs['names'], initialize=bus_q_loads)
model.pl.fix()
model.ql.fix()
### declare the fixed shunts at the buses
bus_bs_fixed_shunts, bus_gs_fixed_shunts = tx_utils.dict_of_bus_fixed_shunts(
buses, shunts)
### declare the rectangular voltages
neg_v_max = map_items(op.neg, bus_attrs['v_max'])
vr_init = {
k: bus_attrs['vm'][k] * pe.cos(bus_attrs['va'][k])
for k in bus_attrs['vm']
}
libbus.declare_var_vr(model,
bus_attrs['names'],
initialize=vr_init,
bounds=zip_items(neg_v_max, bus_attrs['v_max']))
vj_init = {
k: bus_attrs['vm'][k] * pe.sin(bus_attrs['va'][k])
for k in bus_attrs['vm']
}
libbus.declare_var_vj(model,
bus_attrs['names'],
initialize=vj_init,
bounds=zip_items(neg_v_max, bus_attrs['v_max']))
libbus.declare_expr_vmsq(model=model,
index_set=bus_attrs['names'],
coordinate_type=CoordinateType.RECTANGULAR)
### include the feasibility slack for the bus balances
p_rhs_kwargs = {}
q_rhs_kwargs = {}
if include_feasibility_slack:
p_rhs_kwargs, q_rhs_kwargs, penalty_expr = _include_feasibility_slack(
model, bus_attrs, gen_attrs, bus_p_loads, bus_q_loads)
### fix the reference bus
ref_bus = md.data['system']['reference_bus']
ref_angle = md.data['system']['reference_bus_angle']
if ref_angle != 0.0:
libbus.declare_eq_ref_bus_nonzero(model, ref_angle, ref_bus)
else:
model.vj[ref_bus].fix(0.0)
model.vr[ref_bus].setlb(0.0)
### declare the generator real and reactive power
pg_init = {
k: (gen_attrs['p_min'][k] + gen_attrs['p_max'][k]) / 2.0
for k in gen_attrs['pg']
}
libgen.declare_var_pg(model,
gen_attrs['names'],
initialize=pg_init,
bounds=zip_items(gen_attrs['p_min'],
gen_attrs['p_max']))
qg_init = {
k: (gen_attrs['q_min'][k] + gen_attrs['q_max'][k]) / 2.0
for k in gen_attrs['qg']
}
libgen.declare_var_qg(model,
gen_attrs['names'],
initialize=qg_init,
bounds=zip_items(gen_attrs['q_min'],
gen_attrs['q_max']))
### declare the current flows in the branches
s_max = {k: branches[k]['rating_long_term'] for k in branches.keys()}
s_lbub = dict()
for k in branches.keys():
if s_max[k] is None:
s_lbub[k] = (None, None)
else:
s_lbub[k] = (-s_max[k], s_max[k])
pf_bounds = s_lbub
pt_bounds = s_lbub
qf_bounds = s_lbub
qt_bounds = s_lbub
pf_init = dict()
pt_init = dict()
qf_init = dict()
qt_init = dict()
for branch_name, branch in branches.items():
from_bus = branch['from_bus']
to_bus = branch['to_bus']
y_matrix = tx_calc.calculate_y_matrix_from_branch(branch)
ifr_init = tx_calc.calculate_ifr(vr_init[from_bus], vj_init[from_bus],
vr_init[to_bus], vj_init[to_bus],
y_matrix)
ifj_init = tx_calc.calculate_ifj(vr_init[from_bus], vj_init[from_bus],
vr_init[to_bus], vj_init[to_bus],
y_matrix)
itr_init = tx_calc.calculate_itr(vr_init[from_bus], vj_init[from_bus],
vr_init[to_bus], vj_init[to_bus],
y_matrix)
itj_init = tx_calc.calculate_itj(vr_init[from_bus], vj_init[from_bus],
vr_init[to_bus], vj_init[to_bus],
y_matrix)
pf_init[branch_name] = tx_calc.calculate_p(ifr_init, ifj_init,
vr_init[from_bus],
vj_init[from_bus])
pt_init[branch_name] = tx_calc.calculate_p(itr_init, itj_init,
vr_init[to_bus],
vj_init[to_bus])
qf_init[branch_name] = tx_calc.calculate_q(ifr_init, ifj_init,
vr_init[from_bus],
vj_init[from_bus])
qt_init[branch_name] = tx_calc.calculate_q(itr_init, itj_init,
vr_init[to_bus],
vj_init[to_bus])
libbranch.declare_var_pf(model=model,
index_set=branch_attrs['names'],
initialize=pf_init,
bounds=pf_bounds)
libbranch.declare_var_pt(model=model,
index_set=branch_attrs['names'],
initialize=pt_init,
bounds=pt_bounds)
libbranch.declare_var_qf(model=model,
index_set=branch_attrs['names'],
initialize=qf_init,
bounds=qf_bounds)
libbranch.declare_var_qt(model=model,
index_set=branch_attrs['names'],
initialize=qt_init,
bounds=qt_bounds)
### declare the branch power flow constraints
bus_pairs = zip_items(branch_attrs['from_bus'], branch_attrs['to_bus'])
unique_bus_pairs = list(
OrderedDict((val, None) for idx, val in bus_pairs.items()).keys())
libbranch.declare_expr_c(model=model,
index_set=unique_bus_pairs,
coordinate_type=CoordinateType.RECTANGULAR)
libbranch.declare_expr_s(model=model,
index_set=unique_bus_pairs,
coordinate_type=CoordinateType.RECTANGULAR)
libbranch.declare_eq_branch_power(model=model,
index_set=branch_attrs['names'],
branches=branches)
### declare the pq balances
libbus.declare_eq_p_balance(model=model,
index_set=bus_attrs['names'],
bus_p_loads=bus_p_loads,
gens_by_bus=gens_by_bus,
bus_gs_fixed_shunts=bus_gs_fixed_shunts,
inlet_branches_by_bus=inlet_branches_by_bus,
outlet_branches_by_bus=outlet_branches_by_bus,
**p_rhs_kwargs)
libbus.declare_eq_q_balance(model=model,
index_set=bus_attrs['names'],
bus_q_loads=bus_q_loads,
gens_by_bus=gens_by_bus,
bus_bs_fixed_shunts=bus_bs_fixed_shunts,
inlet_branches_by_bus=inlet_branches_by_bus,
outlet_branches_by_bus=outlet_branches_by_bus,
**q_rhs_kwargs)
### declare the thermal limits
libbranch.declare_ineq_s_branch_thermal_limit(
model=model,
index_set=branch_attrs['names'],
branches=branches,
s_thermal_limits=s_max,
flow_type=FlowType.POWER)
### declare the voltage min and max inequalities
libbus.declare_ineq_vm_bus_lbub(model=model,
index_set=bus_attrs['names'],
buses=buses,
coordinate_type=CoordinateType.RECTANGULAR)
### declare angle difference limits on interconnected buses
libbranch.declare_ineq_angle_diff_branch_lbub(
model=model,
index_set=branch_attrs['names'],
branches=branches,
coordinate_type=CoordinateType.RECTANGULAR)
### declare the generator cost objective
libgen.declare_expression_pgqg_operating_cost(model=model,
index_set=gen_attrs['names'],
p_costs=gen_attrs['p_cost'],
q_costs=gen_attrs.get(
'q_cost', None))
obj_expr = sum(model.pg_operating_cost[gen_name]
for gen_name in model.pg_operating_cost)
if include_feasibility_slack:
obj_expr += penalty_expr
if hasattr(model, 'qg_operating_cost'):
obj_expr += sum(model.qg_operating_cost[gen_name]
for gen_name in model.qg_operating_cost)
model.obj = pe.Objective(expr=obj_expr)
return model, md