def get_power_flow_interface_expr_ptdf(model,
interface_name,
PTDF,
rel_ptdf_tol=None,
abs_ptdf_tol=None):
"""
Create a pyomo power flow expression from PTDF matrix for an interface
"""
if rel_ptdf_tol is None:
rel_ptdf_tol = 0.
if abs_ptdf_tol is None:
abs_ptdf_tol = 0.
const = PTDF.get_interface_const(interface_name)
max_coef = PTDF.get_interface_ptdf_abs_max(interface_name)
ptdf_tol = max(abs_ptdf_tol, rel_ptdf_tol * max_coef)
m_p_nw = model.p_nw
## if model.p_nw is Var, we can use LinearExpression
## to build these dense constraints much faster
if isinstance(m_p_nw, pe.Var):
coef_list = list()
var_list = list()
for bus_name, coef in PTDF.get_interface_ptdf_iterator(interface_name):
if abs(coef) >= ptdf_tol:
coef_list.append(coef)
var_list.append(m_p_nw[bus_name])
if interface_name in model.pfi_slack_neg:
coef_list.append(1)
var_list.append(model.pfi_slack_neg[interface_name])
if interface_name in model.pfi_slack_pos:
coef_list.append(-1)
var_list.append(model.pfi_slack_pos[interface_name])
lin_expr_list = [const] + coef_list + var_list
expr = LinearExpression(lin_expr_list)
else:
expr = quicksum(
(coef * m_p_nw[bus_name]
for bus_name, coef in PTDF.get_interface_ptdf_iterator(
interface_name) if abs(coef) >= ptdf_tol),
start=const,
linear=True)
if interface_name in model.pfi_slack_neg:
expr += model.pfi_slack_neg[interface_name]
if interface_name in model.pfi_slack_pos:
expr -= model.pfi_slack_pos[interface_name]
return expr
def get_branch_loss_expr_ptdf_approx(model,
branch_name,
PTDF,
rel_ptdf_tol=None,
abs_ptdf_tol=None):
"""
Create a pyomo power flow loss expression from PTDF matrix
"""
if rel_ptdf_tol is None:
rel_ptdf_tol = 0.
if abs_ptdf_tol is None:
abs_ptdf_tol = 0.
const = PTDF.get_branch_losses_phase_shift(branch_name)
const += PTDF.get_branch_ldf_c(branch_name)
const += PTDF.get_branch_phi_losses_adj(branch_name)
max_coef = PTDF.get_branch_ldf_abs_max(branch_name)
ptdf_tol = max(abs_ptdf_tol, rel_ptdf_tol * max_coef)
## NOTE: It would be easy to hold on to the 'ptdf' dictionary here,
## if we wanted to
m_p_nw = model.p_nw
## if model.p_nw is Var, we can use LinearExpression
## to build these dense constraints much faster
if isinstance(m_p_nw, pe.Var):
coef_list = list()
var_list = list()
for bus_name, coef in PTDF.get_branch_ldf_iterator(branch_name):
if abs(coef) >= ptdf_tol:
coef_list.append(coef)
var_list.append(m_p_nw[bus_name])
lin_expr_list = [const] + coef_list + var_list
expr = LinearExpression(lin_expr_list)
else:
expr = quicksum(
(coef * m_p_nw[bus_name]
for bus_name, coef in PTDF.get_branch_ldf_iterator(branch_name)
if abs(coef) >= ptdf_tol),
start=const,
linear=True)
return expr
def create_optimization_model(self, config):
self.logger.info('Creating optimization model.')
# Consider using context managers
# limit_sell = False
# GET COMPONENTS FROM SYSTEM
if self.system.has_battery:
battery = self.system.get_battery_object()
if self.system.has_external_grid:
supply = self.system.get_external_grid_object()
# STARTING MODEL
m = pk.block()
# Track the default attributes of the model to be aware of which the user adds.
default_attributes = set(m.__dict__.keys())
default_attributes.add('default_attributes')
# SETS
m.periods = range(config['periods'])
m.E_set = []
# VARIABLES
m.E = pk.variable_dict()
if self.system.has_stochastic_generators and not self.system.has_external_grid:
m.E_set.append('stochastic')
m.E['stochastic'] = pk.variable_list()
for t in m.periods:
m.E['stochastic'].append(
pk.variable(domain_type=pk.RealSet,
lb=0,
ub=self.system.stochastic_electrical_gen[t]))
if self.system.has_external_grid:
m.E_set.append('buy')
m.E_set.append('sell')
m.E['buy'] = pk.variable_list()
for _ in m.periods:
m.E['buy'].append(pk.variable(domain=pk.NonNegativeReals))
m.E['sell'] = pk.variable_list()
for _ in m.periods:
m.E['sell'].append(pk.variable(domain=pk.NonNegativeReals))
m.y_grid = pk.variable_list()
for _ in m.periods:
m.y_grid.append(
pk.variable(domain_type=pk.IntegerSet, lb=0, ub=1))
if self.system.has_battery:
m.E_set.append('batt_chrg')
m.E_set.append('batt_dis')
m.E['batt_chrg'] = pk.variable_list()
for _ in m.periods:
# The upper bound are impose in the constraints below
m.E['batt_chrg'].append(
pk.variable(domain_type=pk.RealSet, lb=0))
m.E['batt_dis'] = pk.variable_list()
for _ in m.periods:
m.E['batt_dis'].append(
pk.variable(domain_type=pk.RealSet, lb=0))
m.y_bat = pk.variable_list()
for _ in m.periods:
m.y_bat.append(
pk.variable(domain_type=pk.IntegerSet, lb=0, ub=1))
m.soc = pk.variable_list()
for _ in m.periods:
m.soc.append(
pk.variable(domain_type=pk.RealSet,
lb=battery.soc_lb,
ub=battery.soc_ub))
# Extra soc variable for the last value of soc that should be >= soc_l
m.soc.append(
pk.variable(domain_type=pk.RealSet,
lb=battery.soc_l,
ub=battery.soc_ub))
# PARAMETERS
if self.system.has_external_grid:
m.prices = {
'buy': supply.electricity_purchase_prices.copy(),
'sell': supply.electricity_selling_prices.copy(),
}
# OBJECTIVE FUNCTION
obj_exp = 0
obj_sense = pk.minimize
if self.system.has_external_grid:
obj_exp = quicksum((m.E['buy'][t] * m.prices['buy'][t] for t in m.periods), linear=True) \
- quicksum((m.E['sell'][t] * m.prices['sell'][t] for t in m.periods), linear=True)
m.obj = pk.objective(obj_exp, sense=obj_sense)
# CONSTRAINTS
# Grid constraints
# if limit_sell:
# m.c_limit_sell = pk.constraint(
# lb=0, body=system['selling_ratio']
# * sum(m.E['buy'][t] + system['E_pv'][t] for t in m.periods)
# - sum(m.E['sell'][t] for t in m.periods))
grid_m = 1e5
m.cl_y_buy = pk.constraint_list()
for t in m.periods:
m.cl_y_buy.append(
pk.constraint(body=m.y_grid[t] * grid_m - m.E['buy'][t], lb=0))
m.cl_y_sell = pk.constraint_list()
for t in m.periods:
m.cl_y_sell.append(
pk.constraint(body=(1 - m.y_grid[t]) * grid_m - m.E['sell'][t],
lb=0))
# Balance constraints
energy_balance_exp = [0 for _ in m.periods]
if self.system.has_fix_loads:
for t in m.periods:
energy_balance_exp[t] = -1 * self.system.fix_electrical_load[t]
if self.system.has_external_grid:
for t in m.periods:
energy_balance_exp[
t] = energy_balance_exp[t] + m.E['buy'][t] - m.E['sell'][t]
if self.system.has_battery:
for t in m.periods:
energy_balance_exp[t] = energy_balance_exp[t] + m.E[
'batt_dis'][t] - m.E['batt_chrg'][t]
if self.system.has_stochastic_generators and not self.system.has_external_grid:
for t in m.periods:
energy_balance_exp[
t] = energy_balance_exp[t] + m.E['stochastic'][t]
else:
for t in m.periods:
energy_balance_exp[t] = energy_balance_exp[
t] + self.system.stochastic_electrical_gen[t]
m.cl_balance = pk.constraint_list()
for t in m.periods:
m.cl_balance.append(
pk.constraint(body=energy_balance_exp[t], rhs=0))
# Battery constraints and restrictions
if self.system.has_battery:
m.soc[0].fix(battery.soc_0)
m.cl_soc = pk.constraint_list()
for t in m.periods:
m.cl_soc.append(
pk.constraint(
body=battery.batt_C * (m.soc[t + 1] - m.soc[t]) +
1 / battery.batt_dis_per * m.E['batt_dis'][t] -
battery.batt_chrg_per * m.E['batt_chrg'][t],
rhs=0))
m.cl_y_char = pk.constraint_list()
for t in m.periods:
m.cl_y_char.append(
pk.constraint(body=m.y_bat[t] * battery.batt_chrg_speed -
m.E['batt_chrg'][t],
lb=0))
m.cl_y_dis = pk.constraint_list()
for t in m.periods:
m.cl_y_char.append(
pk.constraint(
body=(1 - m.y_bat[t]) * battery.batt_dis_speed -
m.E['batt_dis'][t],
lb=0))
# FINISHING
# Determine the user defined attributes (written in this source code) by subtracting the defaults one.
all_attributes = set(m.__dict__.keys())
m.user_defined_attributes = list(all_attributes - default_attributes)
self.optimization_model = m
return m