def define_mga_objective(n):
components, pattern, sense = n.mga_obj
if isinstance(components, str):
components = [components]
terms = []
for c in components:
variables = get_var(n, c,
nominal_attrs[c]).filter(regex=to_regex(pattern))
if c in ["Link", "Line"] and pattern in ["", "LN|LK", "LK|LN"]:
coeffs = sense * n.df(c).loc[variables.index, "length"]
else:
coeffs = sense
terms.append(linexpr((coeffs, variables)))
joint_terms = pd.concat(terms)
write_objective(n, joint_terms)
# print objective to console
print(joint_terms)
def mga_objective(network, snapshots, direction, options):
mga_variables = options['mga_variables']
expr_list = []
for i, variable in enumerate(mga_variables):
if variable == 'transmission':
expr_list.append(
linexpr((direction[i], get_var(network, 'Link',
'p_nom'))).sum())
if variable == 'co2_emission':
expr_list.append(
linexpr((direction[i], get_var(
network, 'Generator', 'p').filter(network.generators.index[
network.generators.type == 'ocgt']))).sum().sum())
elif variable == 'H2' or variable == 'battery':
expr_list.append(
linexpr((direction[i], get_var(
network, 'StorageUnit',
'p_nom').filter(network.storage_units.index[
network.storage_units.carrier == variable]))).sum())
else:
expr_list.append(
linexpr(
(direction[i],
get_var(network, 'Generator',
'p_nom').filter(network.generators.index[
network.generators.type == variable]))).sum())
mga_obj = join_exprs(np.array(expr_list))
write_objective(network, mga_obj)
def add_mga_objective(net, sense='min'):
# Minimize transmission capacity
link_p_nom = get_var(net, 'Link', 'p_nom')
sense = 1 if sense == 'min' else -1
link_capacity_expr = linexpr((sense, link_p_nom)).sum()
write_objective(net, link_capacity_expr)
def add_mga_objective(net: pypsa.Network, mga_type: str):
if mga_type == 'link':
# Minimize transmission capacity (in TWkm)
p_nom = get_var(net, 'Link', 'p_nom')[net.components_to_minimize]
capacity_expr = linexpr(
(net.links.length[net.components_to_minimize], p_nom)).sum()
write_objective(net, capacity_expr)
elif mga_type == 'storage':
# Minimize storage energy/power capacity
p_nom = get_var(net, 'StorageUnit',
'p_nom')[net.components_to_minimize]
capacity_expr = linexpr((1, p_nom)).sum()
write_objective(net, capacity_expr)
elif mga_type == 'generator-cap':
# Minimize generators power capacity
p_nom = get_var(net, 'Generator', 'p_nom')[net.components_to_minimize]
capacity_expr = linexpr((1, p_nom)).sum()
write_objective(net, capacity_expr)
elif mga_type == 'generator-power':
# Minimize generators power generation
p = get_var(net, 'Generator', 'p')[net.components_to_minimize]
capacity_expr = linexpr((1, p)).sum().sum()
write_objective(net, capacity_expr)
def add_to_objective(n, snapshots):
"""
adds to the pypsa objective function the costs for the Netzbooster,
these consists of:
(a) costs for the capacities of the Netzbooster
(b) costs for compensation dispatch (e.g. paid to DSM consumers, for storage)
i => bus
t => snapshot
k => outage
"""
logger.info("define objective")
n.determine_network_topology()
sub = n.sub_networks.at["0", "obj"]
sub.calculate_PTDF()
sub.calculate_BODF()
buses = sub.buses_i()
lines = sub.lines_i()
outages = [l for l in lines if "outage" in l]
coefficient1 = 18100
coefficient2 = 10
# (a) costs for Netzbooster capacities
# sum_i ( c_pos * P(i)_pos + c_neg * P(i)_neg)
# add noise to the netzbooster capacitiy costs to avoid numerical troubles
coefficient1_noise = np.random.normal(coefficient1, .01, get_var(n, "Bus", "P_pos").shape)
coefficient1_noise2 = np.random.normal(coefficient1, .01, get_var(n, "Bus", "P_pos").shape)
netzbooster_cap_cost = linexpr(
(coefficient1_noise, get_var(n, "Bus", "P_pos")),
(coefficient1_noise2, get_var(n, "Bus", "P_neg"))
).sum()
write_objective(n, netzbooster_cap_cost)
# (b) costs for compensation dispatch (paid to DSM consumers/running costs for storage)
# sum_(i, t, k) ( o_pos * p(i, t, k)_pos + o_neg * p(i, t, k)_pos)
# add noise to the marginal costs to avoid numerical troubles
coefficient2_noise = np.random.normal(coefficient2, .00001, get_var(n, "Bus", "p_pos").shape)
coefficient2_noise2 = np.random.normal(coefficient2, .00001, get_var(n, "Bus", "p_pos").shape)
compensate_p_cost = linexpr(
(coefficient2_noise, get_var(n, "Bus", "p_pos").loc[snapshots]),
(coefficient2_noise2, get_var(n, "Bus", "p_neg").loc[snapshots]),
).sum().sum()
write_objective(n, compensate_p_cost)
def add_to_objective(n, snapshots):
"""
adds to the pypsa objective function the costs for the Netzbooster,
these consists of:
(a) costs for the capacities of the Netzbooster
(b) costs for compensation dispatch (e.g. paid to DSM consumers, for storage)
i => bus
t => snapshot
k => outage
"""
logger.info("define objective")
n.determine_network_topology()
sub = n.sub_networks.at["0", "obj"]
sub.calculate_PTDF()
sub.calculate_BODF()
buses = sub.buses_i()
lines = sub.lines_i()
outages = [l for l in lines if "outage" in l]
coefficient1 = float(snakemake.wildcards.flex_cost) #18000
coefficient2 = 1 #float(snakemake.wildcards.flex_cost)*0.001 #1
# (a) costs for Netzbooster capacities
# sum_i ( c_pos * P(i)_pos + c_neg * P(i)_neg)
netzbooster_cap_cost = linexpr(
(coefficient1, get_var(n, "Bus", "P_pos").loc[buses]),
(coefficient1, get_var(n, "Bus", "P_neg").loc[buses])).sum()
write_objective(n, netzbooster_cap_cost)
# (b) costs for compensation dispatch (paid to DSM consumers/running costs for storage)
# sum_(i, t, k) ( o_pos * p(i, t, k)_pos + o_neg * p(i, t, k)_pos)
compensate_p_cost = linexpr(
(coefficient2, get_var(n, "Bus", "p_pos").loc[snapshots, buses]),
(coefficient2, get_var(n, "Bus", "p_neg").loc[snapshots, buses]),
).sum().sum()
write_objective(n, compensate_p_cost)
def add_to_objective(n, snapshots):
logger.info("define objective")
n.determine_network_topology()
sub = n.sub_networks.at["0", "obj"]
sub.calculate_PTDF()
sub.calculate_BODF()
buses = sub.buses_i()
lines = sub.lines_i()
outages = [l for l in lines if "outage" in l]
coefficient1 = 1
coefficient2 = 0.0001
terms = linexpr(
(coefficient1, get_var(n, "Bus", "P_pos").loc[buses]),
(coefficient1, get_var(n, "Bus", "P_neg").loc[buses]),
(coefficient2, get_var(n, "Bus", "p_pos").loc[buses].sum().sum()))
write_objective(n, terms)