def build_sets(model_data, backend_model):
for coord_name, coord_vals in model_data.coords.items():
setattr(
backend_model,
coord_name,
po.Set(initialize=coord_vals.to_index(), ordered=True),
)
def add_buy_sell_price(m):
# Sets
m.com_sell = pyomo.Set(within=m.com,
initialize=commodity_subset(m.com_tuples, 'Sell'),
doc='Commodities that can be sold')
m.com_buy = pyomo.Set(within=m.com,
initialize=commodity_subset(m.com_tuples, 'Buy'),
doc='Commodities that can be purchased')
# Variables
m.e_co_sell = pyomo.Var(
m.tm,
m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of sell commodity source (MW) per timestep')
m.e_co_buy = pyomo.Var(m.tm,
m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of buy commodity source (MW) per timestep')
# Rules
m.res_sell_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_sell_step_rule,
doc='sell commodity output per step <= commodity.maxperstep')
m.res_sell_total = pyomo.Constraint(
m.com_tuples,
rule=res_sell_total_rule,
doc='total sell commodity output <= commodity.max')
m.res_buy_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_buy_step_rule,
doc='buy commodity output per step <= commodity.maxperstep')
m.res_buy_total = pyomo.Constraint(
m.com_tuples,
rule=res_buy_total_rule,
doc='total buy commodity output <= commodity.max')
m.res_sell_buy_symmetry = pyomo.Constraint(
m.pro_input_tuples,
rule=res_sell_buy_symmetry_rule,
doc='power connection capacity must be symmetric in both directions')
return m
def test_invalid(self):
pyomo_model = po.ConcreteModel()
pyomo_model.new_set = po.Set(initialize=["a", "b"])
pyomo_model.new_param = po.Param(
pyomo_model.new_set,
initialize={"a": 1},
mutable=True,
within=po.NonNegativeReals,
)
assert invalid(pyomo_model.new_param["a"]) is False
assert invalid(pyomo_model.new_param["b"]) is True
def create_model(data, dt=1, timesteps=None, dual=False):
"""Create a pyomo ConcreteModel urbs object from given input data.
Args:
data: a dict of 6 DataFrames with the keys 'commodity', 'process',
'transmission', 'storage', 'demand' and 'supim'.
dt: timestep duration in hours (default: 1)
timesteps: optional list of timesteps, default: demand timeseries
dual: set True to add dual variables to model (slower); default: False
Returns:
a pyomo ConcreteModel object
"""
# Optional
if not timesteps:
timesteps = data['demand'].index.tolist()
m = pyomo_model_prep(data, timesteps) # preparing pyomo model
m.name = 'urbs'
m.created = datetime.now().strftime('%Y%m%dT%H%M')
m._data = data
# Parameters
# weight = length of year (hours) / length of simulation (hours)
# weight scales costs and emissions from length of simulation to a full
# year, making comparisons among cost types (invest is annualized, fixed
# costs are annual by default, variable costs are scaled by weight) and
# among different simulation durations meaningful.
m.weight = pyomo.Param(
initialize=float(8760) / (len(m.timesteps) * dt),
doc='Pre-factor for variable costs and emissions for an annual result')
# dt = spacing between timesteps. Required for storage equation that
# converts between energy (storage content, e_sto_con) and power (all other
# quantities that start with "e_")
m.dt = pyomo.Param(initialize=dt,
doc='Time step duration (in hours), default: 1')
# Sets
# ====
# Syntax: m.{name} = Set({domain}, initialize={values})
# where name: set name
# domain: set domain for tuple sets, a cartesian set product
# values: set values, a list or array of element tuples
# generate ordered time step sets
m.t = pyomo.Set(initialize=m.timesteps,
ordered=True,
doc='Set of timesteps')
# modelled (i.e. excluding init time step for storage) time steps
m.tm = pyomo.Set(within=m.t,
initialize=m.timesteps[1:],
ordered=True,
doc='Set of modelled timesteps')
# modelled Demand Side Management time steps (downshift):
# downshift effective in tt to compensate for upshift in t
m.tt = pyomo.Set(within=m.t,
initialize=m.timesteps[1:],
ordered=True,
doc='Set of additional DSM time steps')
# site (e.g. north, middle, south...)
m.sit = pyomo.Set(
initialize=m.commodity.index.get_level_values('Site').unique(),
doc='Set of sites')
# commodity (e.g. solar, wind, coal...)
m.com = pyomo.Set(
initialize=m.commodity.index.get_level_values('Commodity').unique(),
doc='Set of commodities')
# commodity type (i.e. SupIm, Demand, Stock, Env)
m.com_type = pyomo.Set(
initialize=m.commodity.index.get_level_values('Type').unique(),
doc='Set of commodity types')
# process (e.g. Wind turbine, Gas plant, Photovoltaics...)
m.pro = pyomo.Set(
initialize=m.process.index.get_level_values('Process').unique(),
doc='Set of conversion processes')
# tranmission (e.g. hvac, hvdc, pipeline...)
m.tra = pyomo.Set(initialize=m.transmission.index.get_level_values(
'Transmission').unique(),
doc='Set of transmission technologies')
# storage (e.g. hydrogen, pump storage)
m.sto = pyomo.Set(
initialize=m.storage.index.get_level_values('Storage').unique(),
doc='Set of storage technologies')
# cost_type
m.cost_type = pyomo.Set(initialize=[
'Invest', 'Fixed', 'Variable', 'Fuel', 'Revenue', 'Purchase',
'Environmental'
],
doc='Set of cost types (hard-coded)')
# tuple sets
m.com_tuples = pyomo.Set(
within=m.sit * m.com * m.com_type,
initialize=m.commodity.index,
doc='Combinations of defined commodities, e.g. (Mid,Elec,Demand)')
m.pro_tuples = pyomo.Set(
within=m.sit * m.pro,
initialize=m.process.index,
doc='Combinations of possible processes, e.g. (North,Coal plant)')
m.tra_tuples = pyomo.Set(
within=m.sit * m.sit * m.tra * m.com,
initialize=m.transmission.index,
doc='Combinations of possible transmissions, e.g. '
'(South,Mid,hvac,Elec)')
m.sto_tuples = pyomo.Set(
within=m.sit * m.sto * m.com,
initialize=m.storage.index,
doc='Combinations of possible storage by site, e.g. (Mid,Bat,Elec)')
m.dsm_site_tuples = pyomo.Set(
within=m.sit * m.com,
initialize=m.dsm.index,
doc='Combinations of possible dsm by site, e.g. (Mid, Elec)')
m.dsm_down_tuples = pyomo.Set(
within=m.tm * m.tm * m.sit * m.com,
initialize=[(t, tt, site, commodity)
for (t, tt, site, commodity) in dsm_down_time_tuples(
m.timesteps[1:], m.dsm_site_tuples, m)],
doc='Combinations of possible dsm_down combinations, e.g. '
'(5001,5003,Mid,Elec)')
# commodity type subsets
m.com_supim = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'SupIm'),
doc='Commodities that have intermittent (timeseries) input')
m.com_stock = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Stock'),
doc='Commodities that can be purchased at some site(s)')
m.com_sell = pyomo.Set(within=m.com,
initialize=commodity_subset(m.com_tuples, 'Sell'),
doc='Commodities that can be sold')
m.com_buy = pyomo.Set(within=m.com,
initialize=commodity_subset(m.com_tuples, 'Buy'),
doc='Commodities that can be purchased')
m.com_demand = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Demand'),
doc='Commodities that have a demand (implies timeseries)')
m.com_env = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Env'),
doc='Commodities that (might) have a maximum creation limit')
# process tuples for area rule
m.pro_area_tuples = pyomo.Set(
within=m.sit * m.pro,
initialize=m.proc_area.index,
doc='Processes and Sites with area Restriction')
# process input/output
m.pro_input_tuples = pyomo.Set(
within=m.sit * m.pro * m.com,
initialize=[(site, process, commodity)
for (site, process) in m.pro_tuples
for (pro, commodity) in m.r_in.index if process == pro],
doc='Commodities consumed by process by site, e.g. (Mid,PV,Solar)')
m.pro_output_tuples = pyomo.Set(
within=m.sit * m.pro * m.com,
initialize=[(site, process, commodity)
for (site, process) in m.pro_tuples
for (pro, commodity) in m.r_out.index if process == pro],
doc='Commodities produced by process by site, e.g. (Mid,PV,Elec)')
# process tuples for maximum gradient feature
m.pro_maxgrad_tuples = pyomo.Set(
within=m.sit * m.pro,
initialize=[(sit, pro) for (sit, pro) in m.pro_tuples
if m.process.loc[sit, pro]['max-grad'] < 1.0 / dt],
doc='Processes with maximum gradient smaller than timestep length')
# process tuples for partial feature
m.pro_partial_tuples = pyomo.Set(within=m.sit * m.pro,
initialize=[
(site, process)
for (site, process) in m.pro_tuples
for (pro,
_) in m.r_in_min_fraction.index
if process == pro
],
doc='Processes with partial input')
m.pro_partial_input_tuples = pyomo.Set(
within=m.sit * m.pro * m.com,
initialize=[(site, process, commodity)
for (site, process) in m.pro_partial_tuples
for (pro, commodity) in m.r_in_min_fraction.index
if process == pro],
doc='Commodities with partial input ratio, e.g. (Mid,Coal PP,Coal)')
m.pro_partial_output_tuples = pyomo.Set(
within=m.sit * m.pro * m.com,
initialize=[(site, process, commodity)
for (site, process) in m.pro_partial_tuples
for (pro, commodity) in m.r_out_min_fraction.index
if process == pro],
doc='Commodities with partial input ratio, e.g. (Mid,Coal PP,CO2)')
# process tuples for time variable efficiency
m.pro_timevar_output_tuples = pyomo.Set(
within=m.sit * m.pro * m.com,
initialize=[(site, process, commodity)
for (site, process) in m.eff_factor.columns.values
for (pro, commodity) in m.r_out.index if process == pro],
doc='Outputs of processes with time dependent efficiency')
# Variables
# costs
m.costs = pyomo.Var(m.cost_type,
within=pyomo.Reals,
doc='Costs by type (EUR/a)')
# commodity
m.e_co_stock = pyomo.Var(
m.tm,
m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of stock commodity source (MW) per timestep')
m.e_co_sell = pyomo.Var(
m.tm,
m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of sell commodity source (MW) per timestep')
m.e_co_buy = pyomo.Var(m.tm,
m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of buy commodity source (MW) per timestep')
# process
m.cap_pro = pyomo.Var(m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Total process capacity (MW)')
m.cap_pro_new = pyomo.Var(m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='New process capacity (MW)')
m.tau_pro = pyomo.Var(m.t,
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow (MW) through process')
m.e_pro_in = pyomo.Var(
m.tm,
m.pro_tuples,
m.com,
within=pyomo.NonNegativeReals,
doc='Power flow of commodity into process (MW) per timestep')
m.e_pro_out = pyomo.Var(m.tm,
m.pro_tuples,
m.com,
within=pyomo.NonNegativeReals,
doc='Power flow out of process (MW) per timestep')
# transmission
m.cap_tra = pyomo.Var(m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Total transmission capacity (MW)')
m.cap_tra_new = pyomo.Var(m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='New transmission capacity (MW)')
m.e_tra_in = pyomo.Var(
m.tm,
m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into transmission line (MW) per timestep')
m.e_tra_out = pyomo.Var(
m.tm,
m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of transmission line (MW) per timestep')
# storage
m.cap_sto_c = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Total storage size (MWh)')
m.cap_sto_c_new = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage size (MWh)')
m.cap_sto_p = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Total storage power (MW)')
m.cap_sto_p_new = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage power (MW)')
m.e_sto_in = pyomo.Var(m.tm,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into storage (MW) per timestep')
m.e_sto_out = pyomo.Var(m.tm,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of storage (MW) per timestep')
m.e_sto_con = pyomo.Var(m.t,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Energy content of storage (MWh) in timestep')
# demand side management
m.dsm_up = pyomo.Var(m.tm,
m.dsm_site_tuples,
within=pyomo.NonNegativeReals,
doc='DSM upshift')
m.dsm_down = pyomo.Var(m.dsm_down_tuples,
within=pyomo.NonNegativeReals,
doc='DSM downshift')
# Equation declarations
# equation bodies are defined in separate functions, referred to here by
# their name in the "rule" keyword.
# commodity
m.res_vertex = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_vertex_rule,
doc='storage + transmission + process + source + buy - sell == demand')
m.res_stock_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_stock_step_rule,
doc='stock commodity input per step <= commodity.maxperstep')
m.res_stock_total = pyomo.Constraint(
m.com_tuples,
rule=res_stock_total_rule,
doc='total stock commodity input <= commodity.max')
m.res_sell_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_sell_step_rule,
doc='sell commodity output per step <= commodity.maxperstep')
m.res_sell_total = pyomo.Constraint(
m.com_tuples,
rule=res_sell_total_rule,
doc='total sell commodity output <= commodity.max')
m.res_buy_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_buy_step_rule,
doc='buy commodity output per step <= commodity.maxperstep')
m.res_buy_total = pyomo.Constraint(
m.com_tuples,
rule=res_buy_total_rule,
doc='total buy commodity output <= commodity.max')
m.res_env_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_env_step_rule,
doc='environmental output per step <= commodity.maxperstep')
m.res_env_total = pyomo.Constraint(
m.com_tuples,
rule=res_env_total_rule,
doc='total environmental commodity output <= commodity.max')
# process
m.def_process_capacity = pyomo.Constraint(
m.pro_tuples,
rule=def_process_capacity_rule,
doc='total process capacity = inst-cap + new capacity')
m.def_process_input = pyomo.Constraint(
m.tm,
m.pro_input_tuples - m.pro_partial_input_tuples,
rule=def_process_input_rule,
doc='process input = process throughput * input ratio')
m.def_process_output = pyomo.Constraint(
m.tm, (m.pro_output_tuples - m.pro_partial_output_tuples -
m.pro_timevar_output_tuples),
rule=def_process_output_rule,
doc='process output = process throughput * output ratio')
m.def_intermittent_supply = pyomo.Constraint(
m.tm,
m.pro_input_tuples,
rule=def_intermittent_supply_rule,
doc='process output = process capacity * supim timeseries')
m.res_process_throughput_by_capacity = pyomo.Constraint(
m.tm,
m.pro_tuples,
rule=res_process_throughput_by_capacity_rule,
doc='process throughput <= total process capacity')
m.res_process_maxgrad_lower = pyomo.Constraint(
m.tm,
m.pro_maxgrad_tuples,
rule=res_process_maxgrad_lower_rule,
doc='throughput may not decrease faster than maximal gradient')
m.res_process_maxgrad_upper = pyomo.Constraint(
m.tm,
m.pro_maxgrad_tuples,
rule=res_process_maxgrad_upper_rule,
doc='throughput may not increase faster than maximal gradient')
m.res_process_capacity = pyomo.Constraint(
m.pro_tuples,
rule=res_process_capacity_rule,
doc='process.cap-lo <= total process capacity <= process.cap-up')
m.res_area = pyomo.Constraint(
m.sit,
rule=res_area_rule,
doc='used process area <= total process area')
m.res_sell_buy_symmetry = pyomo.Constraint(
m.pro_input_tuples,
rule=res_sell_buy_symmetry_rule,
doc='power connection capacity must be symmetric in both directions')
m.res_throughput_by_capacity_min = pyomo.Constraint(
m.tm,
m.pro_partial_tuples,
rule=res_throughput_by_capacity_min_rule,
doc='cap_pro * min-fraction <= tau_pro')
m.def_partial_process_input = pyomo.Constraint(
m.tm,
m.pro_partial_input_tuples,
rule=def_partial_process_input_rule,
doc='e_pro_in = '
' cap_pro * min_fraction * (r - R) / (1 - min_fraction)'
' + tau_pro * (R - min_fraction * r) / (1 - min_fraction)')
m.def_partial_process_output = pyomo.Constraint(
m.tm, (m.pro_partial_output_tuples -
(m.pro_partial_output_tuples & m.pro_timevar_output_tuples)),
rule=def_partial_process_output_rule,
doc='e_pro_out = '
' cap_pro * min_fraction * (r - R) / (1 - min_fraction)'
' + tau_pro * (R - min_fraction * r) / (1 - min_fraction)')
m.def_process_timevar_output = pyomo.Constraint(
m.tm, (m.pro_timevar_output_tuples -
(m.pro_partial_output_tuples & m.pro_timevar_output_tuples)),
rule=def_pro_timevar_output_rule,
doc='e_pro_out = tau_pro * r_out * eff_factor')
m.def_process_partial_timevar_output = pyomo.Constraint(
m.tm,
m.pro_partial_output_tuples & m.pro_timevar_output_tuples,
rule=def_pro_partial_timevar_output_rule,
doc='e_pro_out = tau_pro * r_out * eff_factor')
# transmission
m.def_transmission_capacity = pyomo.Constraint(
m.tra_tuples,
rule=def_transmission_capacity_rule,
doc='total transmission capacity = inst-cap + new capacity')
m.def_transmission_output = pyomo.Constraint(
m.tm,
m.tra_tuples,
rule=def_transmission_output_rule,
doc='transmission output = transmission input * efficiency')
m.res_transmission_input_by_capacity = pyomo.Constraint(
m.tm,
m.tra_tuples,
rule=res_transmission_input_by_capacity_rule,
doc='transmission input <= total transmission capacity')
m.res_transmission_capacity = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_capacity_rule,
doc='transmission.cap-lo <= total transmission capacity <= '
'transmission.cap-up')
m.res_transmission_symmetry = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_symmetry_rule,
doc='total transmission capacity must be symmetric in both directions')
# storage
m.def_storage_state = pyomo.Constraint(
m.tm,
m.sto_tuples,
rule=def_storage_state_rule,
doc='storage[t] = (1 - sd) * storage[t-1] + in * eff_i - out / eff_o')
m.def_storage_power = pyomo.Constraint(
m.sto_tuples,
rule=def_storage_power_rule,
doc='storage power = inst-cap + new power')
m.def_storage_capacity = pyomo.Constraint(
m.sto_tuples,
rule=def_storage_capacity_rule,
doc='storage capacity = inst-cap + new capacity')
m.res_storage_input_by_power = pyomo.Constraint(
m.tm,
m.sto_tuples,
rule=res_storage_input_by_power_rule,
doc='storage input <= storage power')
m.res_storage_output_by_power = pyomo.Constraint(
m.tm,
m.sto_tuples,
rule=res_storage_output_by_power_rule,
doc='storage output <= storage power')
m.res_storage_state_by_capacity = pyomo.Constraint(
m.t,
m.sto_tuples,
rule=res_storage_state_by_capacity_rule,
doc='storage content <= storage capacity')
m.res_storage_power = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_power_rule,
doc='storage.cap-lo-p <= storage power <= storage.cap-up-p')
m.res_storage_capacity = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_capacity_rule,
doc='storage.cap-lo-c <= storage capacity <= storage.cap-up-c')
m.res_initial_and_final_storage_state = pyomo.Constraint(
m.t,
m.sto_tuples,
rule=res_initial_and_final_storage_state_rule,
doc='storage content initial == and final >= storage.init * capacity')
# costs
m.def_costs = pyomo.Constraint(m.cost_type,
rule=def_costs_rule,
doc='main cost function by cost type')
m.obj = pyomo.Objective(rule=obj_rule,
sense=pyomo.minimize,
doc='minimize(cost = sum of all cost types)')
# demand side management
m.def_dsm_variables = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=def_dsm_variables_rule,
doc='DSMup * efficiency factor n == DSMdo (summed)')
m.res_dsm_upward = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_upward_rule,
doc='DSMup <= Cup (threshold capacity of DSMup)')
m.res_dsm_downward = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_downward_rule,
doc='DSMdo (summed) <= Cdo (threshold capacity of DSMdo)')
m.res_dsm_maximum = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_maximum_rule,
doc='DSMup + DSMdo (summed) <= max(Cup,Cdo)')
m.res_dsm_recovery = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_recovery_rule,
doc='DSMup(t, t + recovery time R) <= Cup * delay time L')
m.res_global_co2_limit = pyomo.Constraint(
rule=res_global_co2_limit_rule,
doc='total co2 commodity output <= Global CO2 limit')
if dual:
m.dual = pyomo.Suffix(direction=pyomo.Suffix.IMPORT)
return m
def group_set(group_type):
try:
group = model.config_model.group_fraction[group_type].keys()
except (TypeError, KeyError):
group = []
return po.Set(initialize=group)
def generate_model(model_data):
"""
Generate a Pyomo model.
"""
backend_model = po.ConcreteModel()
# Sets
for coord in list(model_data.coords):
set_data = list(model_data.coords[coord].data)
# Ensure that time steps are pandas.Timestamp objects
if isinstance(set_data[0], np.datetime64):
set_data = pd.to_datetime(set_data)
setattr(backend_model, coord, po.Set(initialize=set_data,
ordered=True))
# "Parameters"
model_data_dict = {
"data": {
k: v.to_series().dropna().replace("inf", np.inf).to_dict()
for k, v in model_data.data_vars.items() if
v.attrs["is_result"] == 0 or v.attrs.get("operate_param", 0) == 1
},
"dims": {
k: v.dims
for k, v in model_data.data_vars.items() if
v.attrs["is_result"] == 0 or v.attrs.get("operate_param", 0) == 1
},
"sets": list(model_data.coords),
"attrs":
{k: v
for k, v in model_data.attrs.items() if k is not "defaults"},
}
# Dims in the dict's keys are ordered as in model_data, which is enforced
# in model_data generation such that timesteps are always last and the
# remainder of dims are in alphabetic order
backend_model.__calliope_model_data = model_data_dict
backend_model.__calliope_defaults = AttrDict.from_yaml_string(
model_data.attrs["defaults"])
backend_model.__calliope_run_config = AttrDict.from_yaml_string(
model_data.attrs["run_config"])
for k, v in model_data_dict["data"].items():
if k in backend_model.__calliope_defaults.keys():
setattr(
backend_model,
k,
po.Param(*[
getattr(backend_model, i)
for i in model_data_dict["dims"][k]
],
initialize=v,
mutable=True,
default=backend_model.__calliope_defaults[k]),
)
# In operate mode, e.g. energy_cap is a parameter, not a decision variable,
# so add those in.
elif (backend_model.__calliope_run_config["mode"] == "operate"
and model_data[k].attrs.get("operate_param") == 1):
setattr(
backend_model,
k,
po.Param(
getattr(backend_model, model_data_dict["dims"][k][0]),
initialize=v,
mutable=True,
),
)
else: # no default value to look up
setattr(
backend_model,
k,
po.Param(*[
getattr(backend_model, i)
for i in model_data_dict["dims"][k]
],
initialize=v,
mutable=True),
)
for option_name, option_val in backend_model.__calliope_run_config[
"objective_options"].items():
if option_name == "cost_class":
objective_cost_class = {
k: v
for k, v in option_val.items() if k in backend_model.costs
}
backend_model.objective_cost_class = po.Param(
backend_model.costs,
initialize=objective_cost_class,
mutable=True)
else:
setattr(backend_model, "objective_" + option_name, option_val)
# Variables
load_function(
"calliope.backend.pyomo.variables.initialize_decision_variables")(
backend_model)
# Constraints
constraints_to_add = [
i.split(".py")[0] for i in os.listdir(constraints.__path__[0])
if not i.startswith("_") and not i.startswith(".")
]
# The list is sorted to ensure that some constraints are added after pyomo
# expressions have been created in other constraint files.
# Ordering is given by the number assigned to the variable ORDER within each
# file (higher number = added later).
try:
constraints_to_add.sort(key=lambda x: load_function(
"calliope.backend.pyomo.constraints." + x + ".ORDER"))
except AttributeError as e:
raise AttributeError(
"{}. This attribute must be set to an integer value based "
"on the order in which the constraints in the file {}.py should be "
"loaded relative to constraints in other constraint files. If order "
"does not matter, set ORDER to a value of 10.".format(
e.args[0], e.args[0].split(".")[-1].split("'")[0]))
logger.info("constraints are loaded in the following order: {}".format(
constraints_to_add))
for c in constraints_to_add:
load_function("calliope.backend.pyomo.constraints." + c +
".load_constraints")(backend_model)
# FIXME: Optional constraints
# optional_constraints = model_data.attrs['constraints']
# if optional_constraints:
# for c in optional_constraints:
# self.add_constraint(load_function(c))
# Objective function
# FIXME re-enable loading custom objectives
# fetch objective function by name, pass through objective options
# if they are present
objective_function = ("calliope.backend.pyomo.objective." +
backend_model.__calliope_run_config["objective"])
load_function(objective_function)(backend_model)
return backend_model
def create_model(data, dt=1, timesteps=None, objective='cost', dual=True):
"""Create a pyomo ConcreteModel urbs object from given input data.
Args:
- data: a dict of up to 12
- dt: timestep duration in hours (default: 1)
- timesteps: optional list of timesteps, default: demand timeseries
- objective: Either "cost" or "CO2" for choice of objective function,
default: "cost"
- dual: set True to add dual variables to model output
(marginally slower), default: True
Returns:
a pyomo ConcreteModel object
"""
# Optional
if not timesteps:
timesteps = data['demand'].index.tolist()
m = pyomo_model_prep(data, timesteps) # preparing pyomo model
m.name = 'urbs'
m.created = datetime.now().strftime('%Y%m%dT%H%M')
m._data = data
# Parameters
# weight = length of year (hours) / length of simulation (hours)
# weight scales costs and emissions from length of simulation to a full
# year, making comparisons among cost types (invest is annualized, fixed
# costs are annual by default, variable costs are scaled by weight) and
# among different simulation durations meaningful.
m.weight = pyomo.Param(
initialize=float(8760) / (len(m.timesteps) * dt),
doc='Pre-factor for variable costs and emissions for an annual result')
# dt = spacing between timesteps. Required for storage equation that
# converts between energy (storage content, e_sto_con) and power (all other
# quantities that start with "e_")
m.dt = pyomo.Param(initialize=dt,
doc='Time step duration (in hours), default: 1')
# import objective function information
m.obj = pyomo.Param(
initialize=objective,
doc='Specification of minimized quantity, default: "cost"')
# Sets
# ====
# Syntax: m.{name} = Set({domain}, initialize={values})
# where name: set name
# domain: set domain for tuple sets, a cartesian set product
# values: set values, a list or array of element tuples
# generate ordered time step sets
m.t = pyomo.Set(initialize=m.timesteps,
ordered=True,
doc='Set of timesteps')
# modelled (i.e. excluding init time step for storage) time steps
m.tm = pyomo.Set(within=m.t,
initialize=m.timesteps[1:],
ordered=True,
doc='Set of modelled timesteps')
# support timeframes (e.g. 2020, 2030...)
indexlist = set()
for key in m.commodity_dict["price"]:
indexlist.add(tuple(key)[0])
m.stf = pyomo.Set(initialize=indexlist,
doc='Set of modeled support timeframes (e.g. years)')
# site (e.g. north, middle, south...)
indexlist = set()
for key in m.commodity_dict["price"]:
indexlist.add(tuple(key)[1])
m.sit = pyomo.Set(initialize=indexlist, doc='Set of sites')
# commodity (e.g. solar, wind, coal...)
indexlist = set()
for key in m.commodity_dict["price"]:
indexlist.add(tuple(key)[2])
m.com = pyomo.Set(initialize=indexlist, doc='Set of commodities')
# commodity type (i.e. SupIm, Demand, Stock, Env)
indexlist = set()
for key in m.commodity_dict["price"]:
indexlist.add(tuple(key)[3])
m.com_type = pyomo.Set(initialize=indexlist, doc='Set of commodity types')
# process (e.g. Wind turbine, Gas plant, Photovoltaics...)
indexlist = set()
for key in m.process_dict["inv-cost"]:
indexlist.add(tuple(key)[2])
m.pro = pyomo.Set(initialize=indexlist, doc='Set of conversion processes')
# cost_type
m.cost_type = pyomo.Set(initialize=m.cost_type_list,
doc='Set of cost types (hard-coded)')
# tuple sets
m.sit_tuples = pyomo.Set(
within=m.stf * m.sit,
initialize=tuple(m.site_dict["area"].keys()),
doc='Combinations of support timeframes and sites')
m.com_tuples = pyomo.Set(
within=m.stf * m.sit * m.com * m.com_type,
initialize=tuple(m.commodity_dict["price"].keys()),
doc='Combinations of defined commodities, e.g. (2018,Mid,Elec,Demand)')
m.pro_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro,
initialize=tuple(m.process_dict["inv-cost"].keys()),
doc='Combinations of possible processes, e.g. (2018,North,Coal plant)')
m.com_stock = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Stock'),
doc='Commodities that can be purchased at some site(s)')
if m.mode['int']:
# tuples for operational status of technologies
m.operational_pro_tuples = pyomo.Set(
within=m.sit * m.pro * m.stf * m.stf,
initialize=[(sit, pro, stf, stf_later)
for (sit, pro, stf,
stf_later) in op_pro_tuples(m.pro_tuples, m)],
doc='Processes that are still operational through stf_later'
'(and the relevant years following), if built in stf'
'in stf.')
# tuples for rest lifetime of installed capacities of technologies
m.inst_pro_tuples = pyomo.Set(
within=m.sit * m.pro * m.stf,
initialize=[(sit, pro, stf)
for (sit, pro, stf) in inst_pro_tuples(m)],
doc='Installed processes that are still operational through stf')
# commodity type subsets
m.com_supim = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'SupIm'),
doc='Commodities that have intermittent (timeseries) input')
m.com_demand = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Demand'),
doc='Commodities that have a demand (implies timeseries)')
m.com_env = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Env'),
doc='Commodities that (might) have a maximum creation limit')
# process tuples for area rule
m.pro_area_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro,
initialize=tuple(m.proc_area_dict.keys()),
doc='Processes and Sites with area Restriction')
# process input/output
m.pro_input_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro * m.com,
initialize=[(stf, site, process, commodity)
for (stf, site, process) in m.pro_tuples
for (s, pro, commodity) in tuple(m.r_in_dict.keys())
if process == pro and s == stf],
doc='Commodities consumed by process by site,'
'e.g. (2020,Mid,PV,Solar)')
m.pro_output_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro * m.com,
initialize=[(stf, site, process, commodity)
for (stf, site, process) in m.pro_tuples
for (s, pro, commodity) in tuple(m.r_out_dict.keys())
if process == pro and s == stf],
doc='Commodities produced by process by site, e.g. (2020,Mid,PV,Elec)')
# process tuples for maximum gradient feature
m.pro_maxgrad_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro,
initialize=[(stf, sit, pro) for (stf, sit, pro) in m.pro_tuples
if m.process_dict['max-grad'][stf, sit, pro] < 1.0 / dt],
doc='Processes with maximum gradient smaller than timestep length')
# process tuples for partial feature
m.pro_partial_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro,
initialize=[(stf, site, process)
for (stf, site, process) in m.pro_tuples
for (s, pro, _) in tuple(m.r_in_min_fraction_dict.keys())
if process == pro and s == stf],
doc='Processes with partial input')
m.pro_partial_input_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro * m.com,
initialize=[(stf, site, process, commodity)
for (stf, site, process) in m.pro_partial_tuples
for (s, pro,
commodity) in tuple(m.r_in_min_fraction_dict.keys())
if process == pro and s == stf],
doc='Commodities with partial input ratio,'
'e.g. (2020,Mid,Coal PP,Coal)')
m.pro_partial_output_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro * m.com,
initialize=[(stf, site, process, commodity)
for (stf, site, process) in m.pro_partial_tuples
for (s, pro,
commodity) in tuple(m.r_out_min_fraction_dict.keys())
if process == pro and s == stf],
doc='Commodities with partial input ratio, e.g. (Mid,Coal PP,CO2)')
# Variables
# costs
m.costs = pyomo.Var(m.cost_type,
within=pyomo.Reals,
doc='Costs by type (EUR/a)')
# commodity
m.e_co_stock = pyomo.Var(
m.tm,
m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of stock commodity source (MW) per timestep')
# process
m.cap_pro_new = pyomo.Var(m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='New process capacity (MW)')
# process capacity as expression object
# (variable if expansion is possible, else static)
m.cap_pro = pyomo.Expression(m.pro_tuples,
rule=def_process_capacity_rule,
doc='total process capacity')
m.tau_pro = pyomo.Var(m.t,
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow (MW) through process')
m.e_pro_in = pyomo.Var(
m.tm,
m.pro_input_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow of commodity into process (MW) per timestep')
m.e_pro_out = pyomo.Var(m.tm,
m.pro_output_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of process (MW) per timestep')
# Add additional features
# called features are declared in distinct files in features folder
if m.mode['tra']:
if m.mode['dpf']:
m = transmission.add_transmission_dc(m)
else:
m = add_transmission(m)
if m.mode['sto']:
m = add_storage(m)
if m.mode['dsm']:
m = add_dsm(m)
if m.mode['bsp']:
m = add_buy_sell_price(m)
if m.mode['tve']:
m = add_time_variable_efficiency(m)
else:
m.pro_timevar_output_tuples = pyomo.Set(
within=m.stf * m.sit * m.pro * m.com,
doc='empty set needed for (partial) process output')
# Equation declarations
# equation bodies are defined in separate functions, referred to here by
# their name in the "rule" keyword.
# commodity
m.res_vertex = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_vertex_rule,
doc='storage + transmission + process + source + buy - sell == demand')
m.res_stock_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_stock_step_rule,
doc='stock commodity input per step <= commodity.maxperstep')
m.res_stock_total = pyomo.Constraint(
m.com_tuples,
rule=res_stock_total_rule,
doc='total stock commodity input <= commodity.max')
m.res_env_step = pyomo.Constraint(
m.tm,
m.com_tuples,
rule=res_env_step_rule,
doc='environmental output per step <= commodity.maxperstep')
m.res_env_total = pyomo.Constraint(
m.com_tuples,
rule=res_env_total_rule,
doc='total environmental commodity output <= commodity.max')
# process
m.def_process_input = pyomo.Constraint(
m.tm,
m.pro_input_tuples - m.pro_partial_input_tuples,
rule=def_process_input_rule,
doc='process input = process throughput * input ratio')
m.def_process_output = pyomo.Constraint(
m.tm, (m.pro_output_tuples - m.pro_partial_output_tuples -
m.pro_timevar_output_tuples),
rule=def_process_output_rule,
doc='process output = process throughput * output ratio')
m.def_intermittent_supply = pyomo.Constraint(
m.tm,
m.pro_input_tuples,
rule=def_intermittent_supply_rule,
doc='process output = process capacity * supim timeseries')
m.res_process_throughput_by_capacity = pyomo.Constraint(
m.tm,
m.pro_tuples,
rule=res_process_throughput_by_capacity_rule,
doc='process throughput <= total process capacity')
m.res_process_maxgrad_lower = pyomo.Constraint(
m.tm,
m.pro_maxgrad_tuples,
rule=res_process_maxgrad_lower_rule,
doc='throughput may not decrease faster than maximal gradient')
m.res_process_maxgrad_upper = pyomo.Constraint(
m.tm,
m.pro_maxgrad_tuples,
rule=res_process_maxgrad_upper_rule,
doc='throughput may not increase faster than maximal gradient')
m.res_process_capacity = pyomo.Constraint(
m.pro_tuples,
rule=res_process_capacity_rule,
doc='process.cap-lo <= total process capacity <= process.cap-up')
m.res_area = pyomo.Constraint(
m.sit_tuples,
rule=res_area_rule,
doc='used process area <= total process area')
m.res_throughput_by_capacity_min = pyomo.Constraint(
m.tm,
m.pro_partial_tuples,
rule=res_throughput_by_capacity_min_rule,
doc='cap_pro * min-fraction <= tau_pro')
m.def_partial_process_input = pyomo.Constraint(
m.tm,
m.pro_partial_input_tuples,
rule=def_partial_process_input_rule,
doc='e_pro_in = '
' cap_pro * min_fraction * (r - R) / (1 - min_fraction)'
' + tau_pro * (R - min_fraction * r) / (1 - min_fraction)')
m.def_partial_process_output = pyomo.Constraint(
m.tm, (m.pro_partial_output_tuples -
(m.pro_partial_output_tuples & m.pro_timevar_output_tuples)),
rule=def_partial_process_output_rule,
doc='e_pro_out = '
' cap_pro * min_fraction * (r - R) / (1 - min_fraction)'
' + tau_pro * (R - min_fraction * r) / (1 - min_fraction)')
if m.mode['int']:
m.res_global_co2_limit = pyomo.Constraint(
m.stf,
rule=res_global_co2_limit_rule,
doc='total co2 commodity output <= global.prop CO2 limit')
# costs
m.def_costs = pyomo.Constraint(m.cost_type,
rule=def_costs_rule,
doc='main cost function by cost type')
# objective and global constraints
if m.obj.value == 'cost':
if m.mode['int']:
m.res_global_co2_budget = pyomo.Constraint(
rule=res_global_co2_budget_rule,
doc='total co2 commodity output <= global.prop CO2 budget')
else:
m.res_global_co2_limit = pyomo.Constraint(
m.stf,
rule=res_global_co2_limit_rule,
doc='total co2 commodity output <= Global CO2 limit')
m.objective_function = pyomo.Objective(
rule=cost_rule,
sense=pyomo.minimize,
doc='minimize(cost = sum of all cost types)')
elif m.obj.value == 'CO2':
m.res_global_cost_limit = pyomo.Constraint(
rule=res_global_cost_limit_rule,
doc='total costs <= Global cost limit')
m.objective_function = pyomo.Objective(
rule=co2_rule,
sense=pyomo.minimize,
doc='minimize total CO2 emissions')
else:
raise NotImplementedError("Non-implemented objective quantity. Set "
"either 'cost' or 'CO2' as the objective in "
"runme.py!")
if dual:
m.dual = pyomo.Suffix(direction=pyomo.Suffix.IMPORT)
return m
def generate_model(model_data):
"""
Generate a Pyomo model.
"""
backend_model = po.ConcreteModel()
# Sets
for coord in list(model_data.coords):
set_data = list(model_data.coords[coord].data)
# Ensure that time steps are pandas.Timestamp objects
if isinstance(set_data[0], np.datetime64):
set_data = pd.to_datetime(set_data)
setattr(backend_model, coord, po.Set(initialize=set_data,
ordered=True))
# "Parameters"
model_data_dict = {
'data': {
k: v.to_series().dropna().replace('inf', np.inf).to_dict()
for k, v in model_data.data_vars.items() if
v.attrs['is_result'] == 0 or v.attrs.get('operate_param', 0) == 1
},
'dims': {
k: v.dims
for k, v in model_data.data_vars.items() if
v.attrs['is_result'] == 0 or v.attrs.get('operate_param', 0) == 1
},
'sets': list(model_data.coords),
'attrs':
{k: v
for k, v in model_data.attrs.items() if k is not 'defaults'}
}
# Dims in the dict's keys are ordered as in model_data, which is enforced
# in model_data generation such that timesteps are always last and the
# remainder of dims are in alphabetic order
backend_model.__calliope_model_data = model_data_dict
backend_model.__calliope_defaults = AttrDict.from_yaml_string(
model_data.attrs['defaults'])
backend_model.__calliope_run_config = AttrDict.from_yaml_string(
model_data.attrs['run_config'])
for k, v in model_data_dict['data'].items():
if k in backend_model.__calliope_defaults.keys():
setattr(
backend_model, k,
po.Param(*[
getattr(backend_model, i)
for i in model_data_dict['dims'][k]
],
initialize=v,
mutable=True,
default=backend_model.__calliope_defaults[k]))
# In operate mode, e.g. energy_cap is a parameter, not a decision variable,
# so add those in.
elif (backend_model.__calliope_run_config['mode'] == 'operate'
and model_data[k].attrs.get('operate_param') == 1):
setattr(
backend_model, k,
po.Param(getattr(backend_model, model_data_dict['dims'][k][0]),
initialize=v,
mutable=True))
else: # no default value to look up
setattr(
backend_model, k,
po.Param(*[
getattr(backend_model, i)
for i in model_data_dict['dims'][k]
],
initialize=v,
mutable=True))
# Variables
load_function(
'calliope.backend.pyomo.variables.initialize_decision_variables')(
backend_model)
# Constraints
constraints_to_add = [
i.split('.py')[0] for i in os.listdir(constraints.__path__[0])
if not i.startswith('_') and not i.startswith('.')
]
# The list is sorted to ensure that some constraints are added after pyomo
# expressions have been created in other constraint files.
# Ordering is given by the number assigned to the variable ORDER within each
# file (higher number = added later).
try:
constraints_to_add.sort(key=lambda x: load_function(
'calliope.backend.pyomo.constraints.' + x + '.ORDER'))
except AttributeError as e:
raise AttributeError(
'{}. This attribute must be set to an integer value based '
'on the order in which the constraints in the file {}.py should be '
'loaded relative to constraints in other constraint files. If order '
'does not matter, set ORDER to a value of 10.'.format(
e.args[0], e.args[0].split('.')[-1].split("'")[0]))
logger.info('constraints are loaded in the following order: {}'.format(
constraints_to_add))
for c in constraints_to_add:
load_function('calliope.backend.pyomo.constraints.' + c +
'.load_constraints')(backend_model)
# FIXME: Optional constraints
# optional_constraints = model_data.attrs['constraints']
# if optional_constraints:
# for c in optional_constraints:
# self.add_constraint(load_function(c))
# Objective function
# FIXME re-enable loading custom objectives
# fetch objective function by name, pass through objective options
# if they are present
objective_function = ('calliope.backend.pyomo.objective.' +
backend_model.__calliope_run_config['objective'])
objective_args = backend_model.__calliope_run_config['objective_options']
load_function(objective_function)(backend_model, **objective_args)
return backend_model
def generate_model(model_data):
"""
Generate a Pyomo model.
"""
backend_model = po.ConcreteModel()
mode = model_data.attrs['run.mode'] # 'plan' or 'operate'
backend_model.mode = mode
# Sets
for coord in list(model_data.coords):
set_data = list(model_data.coords[coord].data)
# Ensure that time steps are pandas.Timestamp objects
if isinstance(set_data[0], np.datetime64):
set_data = pd.to_datetime(set_data)
setattr(
backend_model, coord,
po.Set(initialize=set_data, ordered=True)
)
# "Parameters"
model_data_dict = {
'data': {
k: v.to_series().dropna().replace('inf', np.inf).to_dict()
for k, v in model_data.data_vars.items()
if v.attrs['is_result'] == 0 or v.attrs.get('operate_param', 0) == 1
},
'dims': {
k: v.dims
for k, v in model_data.data_vars.items()
if v.attrs['is_result'] == 0 or v.attrs.get('operate_param', 0) == 1
},
'sets': list(model_data.coords),
'attrs': {k: v for k, v in model_data.attrs.items() if k is not 'defaults'}
}
# Dims in the dict's keys are ordered as in model_data, which is enforced
# in model_data generation such that timesteps are always last and the
# remainder of dims are in alphabetic order
backend_model.__calliope_model_data__ = model_data_dict
backend_model.__calliope_defaults__ = (
ruamel.yaml.load(model_data.attrs['defaults'], Loader=ruamel.yaml.Loader)
)
for k, v in model_data_dict['data'].items():
if k in backend_model.__calliope_defaults__.keys():
setattr(
backend_model, k,
po.Param(*[getattr(backend_model, i)
for i in model_data_dict['dims'][k]],
initialize=v, mutable=True,
default=backend_model.__calliope_defaults__[k])
)
elif k == 'timestep_resolution' or k == 'timestep_weights': # no default value to look up
setattr(
backend_model, k,
po.Param(backend_model.timesteps, initialize=v, mutable=True)
)
elif mode == 'operate' and model_data[k].attrs.get('operate_param') == 1:
setattr(
backend_model, k,
po.Param(getattr(backend_model, model_data_dict['dims'][k][0]),
initialize=v, mutable=True)
)
# Variables
load_function(
'calliope.backend.pyomo.variables.initialize_decision_variables'
)(backend_model)
# Constraints
constraints_to_add = [
'energy_balance.load_constraints',
'dispatch.load_constraints',
'network.load_constraints',
'costs.load_constraints',
'policy.load_constraints'
]
if mode != 'operate':
constraints_to_add.append('capacity.load_constraints')
if hasattr(backend_model, 'loc_techs_conversion'):
constraints_to_add.append('conversion.load_constraints')
if hasattr(backend_model, 'loc_techs_conversion_plus'):
constraints_to_add.append('conversion_plus.load_constraints')
if hasattr(backend_model, 'loc_techs_milp') or hasattr(backend_model, 'loc_techs_purchase'):
constraints_to_add.append('milp.load_constraints')
# Export comes last as it can add to the cost expression, this could be
# overwritten if it doesn't come last
if hasattr(backend_model, 'loc_techs_export'):
constraints_to_add.append('export.load_constraints')
for c in constraints_to_add:
load_function(
'calliope.backend.pyomo.constraints.' + c
)(backend_model)
# FIXME: Optional constraints
# optional_constraints = model_data.attrs['constraints']
# if optional_constraints:
# for c in optional_constraints:
# self.add_constraint(load_function(c))
# Objective function
objective_name = model_data.attrs['run.objective']
objective_function = 'calliope.backend.pyomo.objective.' + objective_name
load_function(objective_function)(backend_model)
# delattr(backend_model, '__calliope_model_data__')
return backend_model
def add_transmission_dc(m):
# defining transmission tuple sets for transport and DCPF model separately
tra_tuples = set()
tra_tuples_dc = set()
for key in m.transmission_dict['reactance']:
tra_tuples.add(tuple(key))
for key in m.transmission_dc_dict['reactance']:
tra_tuples_dc.add(tuple(key))
tra_tuples_tp = tra_tuples - tra_tuples_dc
tra_tuples_dc = remove_duplicate_transmission(tra_tuples_dc)
tra_tuples = tra_tuples_dc | tra_tuples_tp
# tranmission (e.g. hvac, hvdc, pipeline...)
indexlist = set()
for key in m.transmission_dict["eff"]:
indexlist.add(tuple(key)[3])
m.tra = pyomo.Set(
initialize=indexlist,
doc='Set of transmission technologies')
# Transport and DCPF transmission tuples
m.tra_tuples = pyomo.Set(
within=m.stf * m.sit * m.sit * m.tra * m.com,
initialize=tuple(tra_tuples),
doc='Combinations of possible transmissions,'
'without duplicate dc transmissions'
' e.g. (2020,South,Mid,hvac,Elec)')
# DCPF transmission tuples
m.tra_tuples_dc = pyomo.Set(
within=m.stf * m.sit * m.sit * m.tra * m.com,
initialize=tuple(tra_tuples_dc),
doc='Combinations of possible bidirectional dc'
'transmissions, e.g. (2020,South,Mid,hvac,Elec)')
# Transport transmission tuples
m.tra_tuples_tp = pyomo.Set(
within=m.stf * m.sit * m.sit * m.tra * m.com,
initialize=tuple(tra_tuples_tp),
doc='Combinations of possible transport transmissions,'
'e.g. (2020,South,Mid,hvac,Elec)')
if m.mode['int']:
m.operational_tra_tuples = pyomo.Set(
within=m.sit * m.sit * m.tra * m.com * m.stf * m.stf,
initialize=[(sit, sit_, tra, com, stf, stf_later)
for (sit, sit_, tra, com, stf, stf_later)
in op_tra_tuples(m.tra_tuples, m)],
doc='Transmissions that are still operational through stf_later'
'(and the relevant years following), if built in stf'
'in stf.')
m.inst_tra_tuples = pyomo.Set(
within=m.sit * m.sit * m.tra * m.com * m.stf,
initialize=[(sit, sit_, tra, com, stf)
for (sit, sit_, tra, com, stf)
in inst_tra_tuples(m)],
doc='Installed transmissions that are still operational'
'through stf')
# Variables
m.cap_tra_new = pyomo.Var(
m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='New transmission capacity (MW)')
# transmission capacity as expression object
m.cap_tra = pyomo.Expression(
m.tra_tuples,
rule=def_transmission_capacity_rule,
doc='total transmission capacity')
m.e_tra_abs = pyomo.Var(
m.tm, m.tra_tuples_dc,
within=pyomo.NonNegativeReals,
doc='Absolute power flow on transmission line (MW) per timestep')
m.e_tra_in = pyomo.Var(
m.tm, m.tra_tuples,
within=e_tra_domain_rule,
doc='Power flow into transmission line (MW) per timestep')
m.e_tra_out = pyomo.Var(
m.tm, m.tra_tuples,
within=e_tra_domain_rule,
doc='Power flow out of transmission line (MW) per timestep')
m.voltage_angle = pyomo.Var(
m.tm, m.stf, m.sit,
within=pyomo.Reals,
doc='Voltage angle of a site')
# transmission
m.def_transmission_output = pyomo.Constraint(
m.tm, m.tra_tuples,
rule=def_transmission_output_rule,
doc='transmission output = transmission input * efficiency')
m.def_dc_power_flow = pyomo.Constraint(
m.tm, m.tra_tuples_dc,
rule=def_dc_power_flow_rule,
doc='transmission output = (angle(in)-angle(out))/ 57.2958 '
'* -1 *(-1/reactance) * (base voltage)^2')
m.def_angle_limit = pyomo.Constraint(
m.tm, m.tra_tuples_dc,
rule=def_angle_limit_rule,
doc='-angle limit < angle(in) - angle(out) < angle limit')
m.e_tra_abs1 = pyomo.Constraint(
m.tm, m.tra_tuples_dc,
rule=e_tra_abs_rule1,
doc='transmission dc input <= absolute transmission dc input')
m.e_tra_abs2 = pyomo.Constraint(
m.tm, m.tra_tuples_dc,
rule=e_tra_abs_rule2,
doc='-transmission dc input <= absolute transmission dc input')
m.res_transmission_input_by_capacity = pyomo.Constraint(
m.tm, m.tra_tuples,
rule=res_transmission_input_by_capacity_rule,
doc='transmission input <= total transmission capacity')
m.res_transmission_dc_input_by_capacity = pyomo.Constraint(
m.tm, m.tra_tuples_dc,
rule=res_transmission_dc_input_by_capacity_rule,
doc='-dcpf transmission input <= total transmission capacity')
m.res_transmission_capacity = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_capacity_rule,
doc='transmission.cap-lo <= total transmission capacity <= '
'transmission.cap-up')
m.res_transmission_symmetry = pyomo.Constraint(
m.tra_tuples_tp,
rule=res_transmission_symmetry_rule,
doc='total transmission capacity must be symmetric in both directions')
return m
def add_transmission(m):
# tranmission (e.g. hvac, hvdc, pipeline...)
indexlist = set()
for key in m.transmission_dict["eff"]:
indexlist.add(tuple(key)[3])
m.tra = pyomo.Set(
initialize=indexlist,
doc='Set of transmission technologies')
# transmission tuples
m.tra_tuples = pyomo.Set(
within=m.stf * m.sit * m.sit * m.tra * m.com,
initialize=tuple(m.transmission_dict["eff"].keys()),
doc='Combinations of possible transmissions, e.g. '
'(2020,South,Mid,hvac,Elec)')
if m.mode['int']:
m.operational_tra_tuples = pyomo.Set(
within=m.sit * m.sit * m.tra * m.com * m.stf * m.stf,
initialize=[(sit, sit_, tra, com, stf, stf_later)
for (sit, sit_, tra, com, stf, stf_later)
in op_tra_tuples(m.tra_tuples, m)],
doc='Transmissions that are still operational through stf_later'
'(and the relevant years following), if built in stf'
'in stf.')
m.inst_tra_tuples = pyomo.Set(
within=m.sit * m.sit * m.tra * m.com * m.stf,
initialize=[(sit, sit_, tra, com, stf)
for (sit, sit_, tra, com, stf)
in inst_tra_tuples(m)],
doc='Installed transmissions that are still operational'
'through stf')
# Variables
m.cap_tra_new = pyomo.Var(
m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='New transmission capacity (MW)')
# transmission capacity as expression object
m.cap_tra = pyomo.Expression(
m.tra_tuples,
rule=def_transmission_capacity_rule,
doc='total transmission capacity')
m.e_tra_in = pyomo.Var(
m.tm, m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into transmission line (MW) per timestep')
m.e_tra_out = pyomo.Var(
m.tm, m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of transmission line (MW) per timestep')
# transmission
m.def_transmission_output = pyomo.Constraint(
m.tm, m.tra_tuples,
rule=def_transmission_output_rule,
doc='transmission output = transmission input * efficiency')
m.res_transmission_input_by_capacity = pyomo.Constraint(
m.tm, m.tra_tuples,
rule=res_transmission_input_by_capacity_rule,
doc='transmission input <= total transmission capacity')
m.res_transmission_capacity = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_capacity_rule,
doc='transmission.cap-lo <= total transmission capacity <= '
'transmission.cap-up')
m.res_transmission_symmetry = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_symmetry_rule,
doc='total transmission capacity must be symmetric in both directions')
return m
def add_storage(m):
# storage (e.g. hydrogen, pump storage)
indexlist = set()
for key in m.storage_dict["eff-in"]:
indexlist.add(tuple(key)[2])
m.sto = pyomo.Set(initialize=indexlist, doc='Set of storage technologies')
# storage tuples
m.sto_tuples = pyomo.Set(within=m.stf * m.sit * m.sto * m.com,
initialize=tuple(m.storage_dict["eff-in"].keys()),
doc='Combinations of possible storage by site,'
'e.g. (2020,Mid,Bat,Elec)')
# tuples for intertemporal operation
if m.mode['int']:
m.operational_sto_tuples = pyomo.Set(
within=m.sit * m.sto * m.com * m.stf * m.stf,
initialize=[(sit, sto, com, stf, stf_later)
for (sit, sto, com, stf,
stf_later) in op_sto_tuples(m.sto_tuples, m)],
doc='Processes that are still operational through stf_later'
'(and the relevant years following), if built in stf'
'in stf.')
m.inst_sto_tuples = pyomo.Set(
within=m.sit * m.sto * m.com * m.stf,
initialize=[(sit, sto, com, stf)
for (sit, sto, com, stf) in inst_sto_tuples(m)],
doc='Installed storages that are still operational through stf')
# storage tuples for storages with fixed initial state
m.sto_init_bound_tuples = pyomo.Set(
within=m.stf * m.sit * m.sto * m.com,
initialize=tuple(m.stor_init_bound_dict.keys()),
doc='storages with fixed initial state')
# storage tuples for storages with given energy to power ratio
m.sto_ep_ratio_tuples = pyomo.Set(
within=m.stf * m.sit * m.sto * m.com,
initialize=tuple(m.sto_ep_ratio_dict.keys()),
doc='storages with given energy to power ratio')
# Variables
m.cap_sto_c_new = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage size (MWh)')
m.cap_sto_p_new = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage power (MW)')
# storage capacities as expression objects
m.cap_sto_c = pyomo.Expression(m.sto_tuples,
rule=def_storage_capacity_rule,
doc='Total storage size (MWh)')
m.cap_sto_p = pyomo.Expression(m.sto_tuples,
rule=def_storage_power_rule,
doc='Total storage power (MW)')
m.e_sto_in = pyomo.Var(m.tm,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into storage (MW) per timestep')
m.e_sto_out = pyomo.Var(m.tm,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of storage (MW) per timestep')
m.e_sto_con = pyomo.Var(m.t,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Energy content of storage (MWh) in timestep')
# storage rules
m.def_storage_state = pyomo.Constraint(
m.tm,
m.sto_tuples,
rule=def_storage_state_rule,
doc='storage[t] = (1 - sd) * storage[t-1] + in * eff_i - out / eff_o')
m.res_storage_input_by_power = pyomo.Constraint(
m.tm,
m.sto_tuples,
rule=res_storage_input_by_power_rule,
doc='storage input <= storage power')
m.res_storage_output_by_power = pyomo.Constraint(
m.tm,
m.sto_tuples,
rule=res_storage_output_by_power_rule,
doc='storage output <= storage power')
m.res_storage_state_by_capacity = pyomo.Constraint(
m.t,
m.sto_tuples,
rule=res_storage_state_by_capacity_rule,
doc='storage content <= storage capacity')
m.res_storage_power = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_power_rule,
doc='storage.cap-lo-p <= storage power <= storage.cap-up-p')
m.res_storage_capacity = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_capacity_rule,
doc='storage.cap-lo-c <= storage capacity <= storage.cap-up-c')
m.def_initial_storage_state = pyomo.Constraint(
m.sto_init_bound_tuples,
rule=def_initial_storage_state_rule,
doc='storage content initial == and final >= storage.init * capacity')
m.res_storage_state_cyclicity = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_state_cyclicity_rule,
doc='storage content initial <= final, both variable')
m.def_storage_energy_power_ratio = pyomo.Constraint(
m.sto_ep_ratio_tuples,
rule=def_storage_energy_power_ratio_rule,
doc='storage capacity = storage power * storage E2P ratio')
return m
def create_model(data, dt=1, timesteps=None, dual=False):
"""Create a pyomo ConcreteModel urbs object from given input data.
Args:
data: a dict of 6 DataFrames with the keys 'commodity', 'process',
'transmission', 'storage', 'demand' and 'supim'.
dt: timestep duration in hours (default: 1)
timesteps: optional list of timesteps, default: demand timeseries
dual: set True to add dual variables to model (slower); default: False
Returns:
a pyomo ConcreteModel object
"""
# Optional
if not timesteps:
timesteps = data['demand'].index.tolist()
m = pyomo_model_prep(data, timesteps) # preparing pyomo model
m.name = 'urbs'
m.created = datetime.now().strftime('%Y%m%dT%H%M')
m._data = data
# Parameters
# weight = length of year (hours) / length of simulation (hours)
# weight scales costs and emissions from length of simulation to a full
# year, making comparisons among cost types (invest is annualized, fixed
# costs are annual by default, variable costs are scaled by weight) and
# among different simulation durations meaningful.
m.weight = pyomo.Param(
initialize=float(8760) / (len(m.timesteps) * dt),
doc='Pre-factor for variable costs and emissions for an annual result')
# dt = spacing between timesteps. Required for storage equation that
# converts between energy (storage content, e_sto_con) and power (all other
# quantities that start with "e_")
m.dt = pyomo.Param(
initialize=dt,
doc='Time step duration (in hours), default: 1')
# Sets
# ====
# Syntax: m.{name} = Set({domain}, initialize={values})
# where name: set name
# domain: set domain for tuple sets, a cartesian set product
# values: set values, a list or array of element tuples
# generate ordered time step sets
m.t = pyomo.Set(
initialize=m.timesteps,
ordered=True,
doc='Set of timesteps')
# modelled (i.e. excluding init time step for storage) time steps
m.tm = pyomo.Set(
within=m.t,
initialize=m.timesteps[1:],
ordered=True,
doc='Set of modelled timesteps')
# site (e.g. north, middle, south...)
m.sit = pyomo.Set(
initialize=m.commodity.index.get_level_values('Site').unique(),
doc='Set of sites')
# commodity (e.g. solar, wind, coal...)
m.com = pyomo.Set(
initialize=m.commodity.index.get_level_values('Commodity').unique(),
doc='Set of commodities')
# commodity type (i.e. SupIm, Demand, Stock, Env)
m.com_type = pyomo.Set(
initialize=m.commodity.index.get_level_values('Type').unique(),
doc='Set of commodity types')
# process (e.g. Wind turbine, Gas plant, Photovoltaics...)
m.pro = pyomo.Set(
initialize=m.process.index.get_level_values('Process').unique(),
doc='Set of conversion processes')
# tranmission (e.g. hvac, hvdc, pipeline...)
m.tra = pyomo.Set(
initialize=m.transmission.index.get_level_values('Transmission')
.unique(),
doc='Set of transmission technologies')
# storage (e.g. hydrogen, pump storage)
m.sto = pyomo.Set(
initialize=m.storage.index.get_level_values('Storage').unique(),
doc='Set of storage technologies')
# cost_type
m.cost_type = pyomo.Set(
initialize=['Invest', 'Fixed', 'Variable', 'Fuel',
'Environmental'],
doc='Set of cost types (hard-coded)')
# tuple sets
m.com_tuples = pyomo.Set(
within=m.sit*m.com*m.com_type,
initialize=m.commodity.index,
doc='Combinations of defined commodities, e.g. (Mid,Elec,Demand)')
m.pro_tuples = pyomo.Set(
within=m.sit*m.pro,
initialize=m.process.index,
doc='Combinations of possible processes, e.g. (North,Coal plant)')
m.tra_tuples = pyomo.Set(
within=m.sit*m.sit*m.tra*m.com,
initialize=m.transmission.index,
doc='Combinations of possible transmissions, e.g. '
'(South,Mid,hvac,Elec)')
m.sto_tuples = pyomo.Set(
within=m.sit*m.sto*m.com,
initialize=m.storage.index,
doc='Combinations of possible storage by site, e.g. (Mid,Bat,Elec)')
# commodity type subsets
m.com_supim = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'SupIm'),
doc='Commodities that have intermittent (timeseries) input')
m.com_stock = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Stock'),
doc='Commodities that can be purchased at some site(s)')
m.com_demand = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Demand'),
doc='Commodities that have a demand (implies timeseries)')
m.com_env = pyomo.Set(
within=m.com,
initialize=commodity_subset(m.com_tuples, 'Env'),
doc='Commodities that (might) have a maximum creation limit')
# process input/output
m.pro_input_tuples = pyomo.Set(
within=m.sit*m.pro*m.com,
initialize=[(site, process, commodity)
for (site, process) in m.pro_tuples
for (pro, commodity) in m.r_in.index
if process == pro],
doc='Commodities consumed by process by site, e.g. (Mid,PV,Solar)')
m.pro_output_tuples = pyomo.Set(
within=m.sit*m.pro*m.com,
initialize=[(site, process, commodity)
for (site, process) in m.pro_tuples
for (pro, commodity) in m.r_out.index
if process == pro],
doc='Commodities produced by process by site, e.g. (Mid,PV,Elec)')
# storage tuples for storages with fixed initial state
m.sto_init_bound_tuples = pyomo.Set(
within=m.sit*m.sto*m.com,
initialize=m.stor_init_bound.index,
doc='storages with fixed initial state')
# storage tuples for storages with given energy to power ratio
m.sto_ep_ratio_tuples = pyomo.Set(
within=m.sit*m.sto*m.com,
initialize=m.sto_ep_ratio.index,
doc='storages with given energy to power ratio')
# Variables
# costs
m.costs = pyomo.Var(
m.cost_type,
within=pyomo.Reals,
doc='Costs by type (EUR/a)')
# commodity
m.e_co_stock = pyomo.Var(
m.tm, m.com_tuples,
within=pyomo.NonNegativeReals,
doc='Use of stock commodity source (MW) per timestep')
# process
m.cap_pro = pyomo.Var(
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Total process capacity (MW)')
m.cap_pro_new = pyomo.Var(
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='New process capacity (MW)')
m.tau_pro = pyomo.Var(
m.t, m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow (MW) through process')
m.e_pro_in = pyomo.Var(
m.tm, m.pro_tuples, m.com,
within=pyomo.NonNegativeReals,
doc='Power flow of commodity into process (MW) per timestep')
m.e_pro_out = pyomo.Var(
m.tm, m.pro_tuples, m.com,
within=pyomo.NonNegativeReals,
doc='Power flow out of process (MW) per timestep')
# transmission
m.cap_tra = pyomo.Var(
m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Total transmission capacity (MW)')
m.cap_tra_new = pyomo.Var(
m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='New transmission capacity (MW)')
m.e_tra_in = pyomo.Var(
m.tm, m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into transmission line (MW) per timestep')
m.e_tra_out = pyomo.Var(
m.tm, m.tra_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of transmission line (MW) per timestep')
# storage
m.cap_sto_c = pyomo.Var(
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Total storage size (MWh)')
m.cap_sto_c_new = pyomo.Var(
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage size (MWh)')
m.cap_sto_p = pyomo.Var(
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Total storage power (MW)')
m.cap_sto_p_new = pyomo.Var(
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage power (MW)')
m.e_sto_in = pyomo.Var(
m.tm, m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into storage (MW) per timestep')
m.e_sto_out = pyomo.Var(
m.tm, m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of storage (MW) per timestep')
m.e_sto_con = pyomo.Var(
m.t, m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Energy content of storage (MWh) in timestep')
# Equation declarations
# equation bodies are defined in separate functions, referred to here by
# their name in the "rule" keyword.
# commodity
m.res_vertex = pyomo.Constraint(
m.tm, m.com_tuples,
rule=res_vertex_rule,
doc='storage + transmission + process + source == demand')
# process
m.def_process_capacity = pyomo.Constraint(
m.pro_tuples,
rule=def_process_capacity_rule,
doc='total process capacity = inst-cap + new capacity')
m.def_process_input = pyomo.Constraint(
m.tm, m.pro_input_tuples,
rule=def_process_input_rule,
doc='process input = process throughput * input ratio')
m.def_process_output = pyomo.Constraint(
m.tm, m.pro_output_tuples,
rule=def_process_output_rule,
doc='process output = process throughput * output ratio')
m.def_intermittent_supply = pyomo.Constraint(
m.tm, m.pro_input_tuples,
rule=def_intermittent_supply_rule,
doc='process output = process capacity * supim timeseries')
m.res_process_throughput_by_capacity = pyomo.Constraint(
m.tm, m.pro_tuples,
rule=res_process_throughput_by_capacity_rule,
doc='process throughput <= total process capacity')
m.res_process_capacity = pyomo.Constraint(
m.pro_tuples,
rule=res_process_capacity_rule,
doc='process.cap-lo <= total process capacity <= process.cap-up')
# transmission
m.def_transmission_capacity = pyomo.Constraint(
m.tra_tuples,
rule=def_transmission_capacity_rule,
doc='total transmission capacity = inst-cap + new capacity')
m.def_transmission_output = pyomo.Constraint(
m.tm, m.tra_tuples,
rule=def_transmission_output_rule,
doc='transmission output = transmission input * efficiency')
m.res_transmission_input_by_capacity = pyomo.Constraint(
m.tm, m.tra_tuples,
rule=res_transmission_input_by_capacity_rule,
doc='transmission input <= total transmission capacity')
m.res_transmission_capacity = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_capacity_rule,
doc='transmission.cap-lo <= total transmission capacity <= '
'transmission.cap-up')
m.res_transmission_symmetry = pyomo.Constraint(
m.tra_tuples,
rule=res_transmission_symmetry_rule,
doc='total transmission capacity must be symmetric in both directions')
# storage
m.def_storage_state = pyomo.Constraint(
m.tm, m.sto_tuples,
rule=def_storage_state_rule,
doc='storage[t] = (1 - sd) * storage[t-1] + in * eff_i - out / eff_o')
m.def_storage_power = pyomo.Constraint(
m.sto_tuples,
rule=def_storage_power_rule,
doc='storage power = inst-cap + new power')
m.def_storage_capacity = pyomo.Constraint(
m.sto_tuples,
rule=def_storage_capacity_rule,
doc='storage capacity = inst-cap + new capacity')
m.res_storage_input_by_power = pyomo.Constraint(
m.tm, m.sto_tuples,
rule=res_storage_input_by_power_rule,
doc='storage input <= storage power')
m.res_storage_output_by_power = pyomo.Constraint(
m.tm, m.sto_tuples,
rule=res_storage_output_by_power_rule,
doc='storage output <= storage power')
m.res_storage_state_by_capacity = pyomo.Constraint(
m.t, m.sto_tuples,
rule=res_storage_state_by_capacity_rule,
doc='storage content <= storage capacity')
m.res_storage_power = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_power_rule,
doc='storage.cap-lo-p <= storage power <= storage.cap-up-p')
m.res_storage_capacity = pyomo.Constraint(
m.sto_tuples,
rule=res_storage_capacity_rule,
doc='storage.cap-lo-c <= storage capacity <= storage.cap-up-c')
m.res_initial_and_final_storage_state = pyomo.Constraint(
m.t, m.sto_init_bound_tuples,
rule=res_initial_and_final_storage_state_rule,
doc='storage content initial == and final >= storage.init * capacity')
m.res_initial_and_final_storage_state_var = pyomo.Constraint(
m.t, m.sto_tuples - m.sto_init_bound_tuples,
rule=res_initial_and_final_storage_state_var_rule,
doc='storage content initial <= final, both variable')
m.def_storage_energy_power_ratio = pyomo.Constraint(
m.sto_ep_ratio_tuples,
rule=def_storage_energy_power_ratio_rule,
doc='storage capacity = storage power * storage E2P ratio')
# costs
m.def_costs = pyomo.Constraint(
m.cost_type,
rule=def_costs_rule,
doc='main cost function by cost type')
m.obj = pyomo.Objective(
rule=obj_rule,
sense=pyomo.minimize,
doc='minimize(cost = sum of all cost types)')
# global
m.res_global_co2_limit = pyomo.Constraint(
rule=res_global_co2_limit_rule,
doc='total co2 commodity output <= Global CO2 limit')
if dual:
m.dual = pyomo.Suffix(direction=pyomo.Suffix.IMPORT)
return m
def create_model(ems_local):
""" create one optimization instance and parameterize it with the input data in ems model
Args:
- ems_local: ems model which has been parameterized
Return:
- m: optimization model instance created according to ems model
"""
# record the time
t0 = tm.time()
# get all the data from the external file
# ems_local = ems_loc(initialize=True, path='C:/Users/ge57vam/emsflex/opentumflex/ems01_ems.txt')
devices = ems_local['devices']
# read data from excel file
# print('Data Read. time: ' + "{:.1f}".format(tm.time() - t0) + ' s\n')
# print('Prepare Data ...\n')
t = tm.time()
time_interval = ems_local['time_data'][
't_inval'] # x minutes for one time step
# write in the time series from the data
df_time_series = ems_local['fcst']
time_series = pd.DataFrame.from_dict(df_time_series)
# time = time_series.index.values
# print('Data Prepared. time: ' + "{:.1f}".format(tm.time() - t0) + ' s\n')
# system
# get the initial time step
# time_step_initial = parameter.loc['System']['value']
time_step_initial = ems_local['time_data']['isteps']
# time_step_end = int(60 / time_interval * 24)
time_step_end = ems_local['time_data']['nsteps']
timesteps = np.arange(time_step_initial, time_step_end)
# timestep_1 = timesteps[0]
# timesteps = timesteps_all[time_step_initial:time_step_end]
t_dn = 4
# 6*time_step_end/96
t_up = 4
# 6*time_step_end/96
timesteps_dn = timesteps[time_step_initial + 1:time_step_end - t_dn]
timesteps_up = timesteps[time_step_initial + 1:time_step_end - t_up]
# 15 min for every timestep/ timestep by one hour
# create the concrete model
p2e = time_interval / 60
# create the model object m
m = pyen.ConcreteModel()
# create the parameter
# print('Define Model ...\n')
m.t = pyen.Set(ordered=True, initialize=timesteps)
m.t_DN = pyen.Set(ordered=True, initialize=timesteps_dn)
m.t_UP = pyen.Set(ordered=True, initialize=timesteps_up)
# heat_storage
sto_param = devices['sto']
m.sto_max_cont = pyen.Param(initialize=sto_param['stocap'])
m.SOC_init = pyen.Param(initialize=sto_param['initSOC'])
m.temp_min = pyen.Param(initialize=sto_param['mintemp'])
m.temp_max = pyen.Param(initialize=sto_param['maxtemp'])
# battery
bat_param = devices['bat']
m.bat_cont_max = pyen.Param(initialize=bat_param['stocap'])
m.bat_SOC_init = pyen.Param(initialize=bat_param['initSOC'])
m.bat_power_max = pyen.Param(initialize=bat_param['maxpow'])
m.bat_eta = pyen.Param(initialize=bat_param['eta'])
# heat pump
hp_param = devices['hp']
hp_elec_cap = pd.DataFrame.from_dict(hp_param['powmap'])
hp_cop = pd.DataFrame.from_dict(hp_param['COP'])
hp_supply_temp = hp_param['supply_temp']
m.hp_ther_pow = pyen.Param(m.t,
initialize=1,
mutable=True,
within=pyen.NonNegativeReals)
m.hp_COP = pyen.Param(m.t,
initialize=1,
mutable=True,
within=pyen.NonNegativeReals)
m.hp_elec_pow = pyen.Param(m.t,
initialize=1,
mutable=True,
within=pyen.NonNegativeReals)
m.T_DN = pyen.Param(initialize=t_dn, mutable=True)
m.T_UP = pyen.Param(initialize=t_up, mutable=True)
m.hp_themInertia = pyen.Param(initialize=hp_param['thermInertia'])
m.hp_minTemp = pyen.Param(initialize=hp_param['minTemp'])
m.hp_maxTemp = pyen.Param(initialize=hp_param['maxTemp'])
m.hp_heatgain = pyen.Param(initialize=hp_param['heatgain'])
# elec_vehicle
ev_param = devices['ev']
ev_aval = ev_param['aval']
m.ev_min_pow = pyen.Param(initialize=ev_param['minpow'])
m.ev_max_pow = pyen.Param(initialize=ev_param['maxpow'])
m.ev_sto_cap = pyen.Param(initialize=ev_param['stocap'])
m.ev_eta = pyen.Param(initialize=ev_param['eta'])
m.ev_aval = pyen.Param(m.t, initialize=1, mutable=True)
m.ev_charg_amount = m.ev_sto_cap * (ev_param['endSOC'][-1] -
ev_param['initSOC'][0]) / 100
ev_soc_init = ev_param['initSOC']
ev_soc_end = ev_param['endSOC']
ev_init_soc_check = ev_param['init_soc_check']
ev_end_soc_check = ev_param['end_soc_check']
# boilder
boil_param = devices['boiler']
m.boiler_max_cap = pyen.Param(initialize=boil_param['maxpow'])
m.boiler_eff = pyen.Param(initialize=boil_param['eta'])
# CHP
chp_param = devices['chp']
m.chp_elec_effic = pyen.Param(m.t, initialize=chp_param['eta'][0])
m.chp_ther_effic = pyen.Param(m.t, initialize=chp_param['eta'][1])
m.chp_elec_run = pyen.Param(m.t, initialize=chp_param['maxpow'])
m.chp_heat_run = pyen.Param(m.t, initialize=0, mutable=True)
m.chp_gas_run = pyen.Param(m.t, initialize=0, mutable=True)
# solar
pv_param = devices['pv']
# m.pv_effic = pyen.Param(initialize=pv_param['eta'])
m.pv_peak_power = pyen.Param(initialize=pv_param['maxpow'])
m.solar = pyen.Param(m.t, initialize=1, mutable=True)
# for t in m.t_UP:
# m.t_dn[t] = t_dn
# m.t_up[t] = t_dn
# price
m.ele_price_in, m.ele_price_out, m.gas_price = (pyen.Param(m.t,
initialize=1,
mutable=True)
for i in range(3))
# lastprofil
m.lastprofil_heat, m.lastprofil_elec = (pyen.Param(m.t,
initialize=1,
mutable=True)
for i in range(2))
for t in m.t:
# weather data
m.ele_price_in[t] = time_series.loc[t]['ele_price_in']
m.gas_price[t] = time_series.loc[t]['gas_price']
m.ele_price_out[t] = time_series.loc[t]['ele_price_out']
m.lastprofil_heat[t] = time_series.loc[t]['load_heat']
m.lastprofil_elec[t] = time_series.loc[t]['load_elec']
m.solar[t] = time_series.loc[t]['solar_power']
# fill the ev availability
m.ev_aval[t] = ev_aval[t]
# calculate the spline function for thermal power of heat pump
spl_elec_pow = UnivariateSpline(
list(map(float, hp_elec_cap.columns.values)),
list(hp_elec_cap.loc[hp_supply_temp, :]))
m.hp_elec_pow[t] = spl_elec_pow(time_series.loc[t]['temperature'] +
273.15).item(0)
# calculate the spline function for COP of heat pump
spl_cop = UnivariateSpline(list(map(float, hp_cop.columns.values)),
list(hp_cop.loc[hp_supply_temp, :]))
m.hp_COP[t] = spl_cop(time_series.loc[t]['temperature'] +
273.15).item(0)
m.hp_ther_pow[t] = m.hp_elec_pow[t] * m.hp_COP[t]
# calculate the chp electric and thermal power when it's running
m.chp_heat_run[
t] = m.chp_elec_run[t] / m.chp_elec_effic[t] * m.chp_ther_effic[t]
m.chp_gas_run[t] = m.chp_elec_run[t] / m.chp_elec_effic[t]
# m.ele_price = ele_price
# Variables
m.hp_run = pyen.Var(m.t,
within=pyen.Boolean,
doc='operation of the heat pump')
m.CHP_run = pyen.Var(m.t, within=pyen.Boolean, doc='operation of the CHP')
m.ev_power = pyen.Var(m.t,
within=pyen.NonNegativeReals,
bounds=(ev_param['minpow'], ev_param['maxpow']),
doc='power of the EV')
m.boiler_cap, m.PV_cap, m.elec_import, m.elec_export, m.bat_cont, m.sto_e_cont, m.bat_pow_pos, m.bat_pow_neg, \
m.ev_cont, m.ev_var_pow, m.soc_diff, m.roomtemp = (pyen.Var(m.t, within=pyen.NonNegativeReals) for i in range(12))
m.sto_e_pow, m.costs, m.heatextra = (pyen.Var(m.t, within=pyen.Reals)
for i in range(3))
# Constrains
# heat_storage
def sto_e_cont_def_rule(m, t):
if t > m.t[1]:
return m.sto_e_cont[t] == m.sto_e_cont[t -
1] + m.sto_e_pow[t] * p2e
else:
return m.sto_e_cont[
t] == m.sto_max_cont * m.SOC_init / 100 + m.sto_e_pow[t] * p2e
m.sto_e_cont_def = pyen.Constraint(m.t,
rule=sto_e_cont_def_rule,
doc='heat_storage_balance')
def heat_balance_rule(m, t):
return m.boiler_cap[t] + m.CHP_run[t] * m.chp_heat_run[t] + \
m.hp_run[t] * m.hp_ther_pow[t] - m.lastprofil_heat[t] - m.sto_e_pow[t] == 0
m.heat_power_balance = pyen.Constraint(m.t,
rule=heat_balance_rule,
doc='heat_storage_balance')
# the room in the building
# def heat_room_rule(m, t):
# if t > m.t[1]:
# return m.roomtemp[t] == m.roomtemp[t - 1] + (m.heatextra[t] + m.hp_heatgain) / m.hp_themInertia
# else:
# return m.roomtemp[t] == 23
#
# m.heat_room_balance = pyen.Constraint(m.t, rule=heat_room_rule, doc='heat_room_balance')
#
# def heat_room_maxtemp_rule(m, t):
# return m.roomtemp[t] <= m.hp_maxTemp
#
# m.heat_room_maxtemp = pyen.Constraint(m.t, rule=heat_room_maxtemp_rule)
#
# def heat_room_mintemp_rule(m, t):
# return m.roomtemp[t] >= m.hp_minTemp
#
# m.heat_room_mintemp = pyen.Constraint(m.t, rule=heat_room_mintemp_rule)
#
# def heat_room_end_rule(m, t):
# if t == m.t[-1]:
# return m.roomtemp[t] == 23
# else:
# return Constraint.Skip
#
# m.heat_room_end = pyen.Constraint(m.t, rule=heat_room_end_rule)
# battery
def battery_e_cont_def_rule(m, t):
if t > m.t[1]:
return m.bat_cont[t] == m.bat_cont[
t - 1] + (m.bat_pow_pos[t] * m.bat_eta -
m.bat_pow_neg[t] / m.bat_eta) * p2e
else:
return m.bat_cont[t] == m.bat_cont_max * m.bat_SOC_init / 100 + (
m.bat_pow_pos[t] * m.bat_eta -
m.bat_pow_neg[t] / m.bat_eta) * p2e
m.bat_e_cont_def = pyen.Constraint(m.t,
rule=battery_e_cont_def_rule,
doc='battery_balance')
def elec_balance_rule(m, t):
return m.elec_import[t] + m.CHP_run[t] * m.chp_elec_run[t] + m.PV_cap[t] * m.solar[t] - \
m.elec_export[t] - m.hp_run[t] * m.hp_elec_pow[t] - m.lastprofil_elec[t] - \
(m.bat_pow_pos[t] - m.bat_pow_neg[t]) - m.ev_power[t] == 0
m.elec_power_balance = pyen.Constraint(m.t,
rule=elec_balance_rule,
doc='elec_balance')
def cost_sum_rule(m, t):
return m.costs[t] == p2e * (
m.boiler_cap[t] / m.boiler_eff * m.gas_price[t] +
m.CHP_run[t] * m.chp_gas_run[t] * m.gas_price[t] +
m.elec_import[t] * m.ele_price_in[t] -
m.elec_export[t] * m.ele_price_out[t]) + m.soc_diff[t] * 1000
m.cost_sum = pyen.Constraint(m.t, rule=cost_sum_rule)
# ev battery balance
def ev_cont_def_rule(m, t):
if t > m.t[1]:
return m.ev_cont[t] == m.ev_cont[
t - 1] + m.ev_power[t] * p2e * m.ev_eta - m.ev_var_pow[t]
else:
return m.ev_cont[t] == m.ev_sto_cap * ev_soc_init[
0] / 100 + m.ev_power[t] * p2e * m.ev_eta
m.ev_cont_def = pyen.Constraint(m.t,
rule=ev_cont_def_rule,
doc='EV_balance')
def EV_end_soc_rule(m, t):
return m.ev_cont[
t] >= m.ev_sto_cap * ev_end_soc_check[t] / 100 - m.soc_diff[t]
m.EV_end_soc_def = pyen.Constraint(m.t, rule=EV_end_soc_rule)
def EV_init_soc_rule(m, t):
return m.ev_cont[t] <= m.ev_sto_cap * ev_init_soc_check[t] / 100
m.EV_init_soc_def = pyen.Constraint(m.t, rule=EV_init_soc_rule)
def EV_aval_rule(m, t):
return m.ev_power[t] <= m.ev_aval[t] * m.ev_max_pow
m.EV_aval_def = pyen.Constraint(m.t, rule=EV_aval_rule)
# hp
def hp_min_still_t_rule(m, t):
return (m.hp_run[t - 1] - m.hp_run[t]) * m.T_DN <= m.T_DN - (
m.hp_run[t] + m.hp_run[t + 1] + m.hp_run[t + 2] + m.hp_run[t + 3])
# m.hp_min_still_t_def = pyen.Constraint(m.t_DN, rule=hp_min_still_t_rule)
def hp_min_lauf_t_rule(m, t):
return (m.hp_run[t] - m.hp_run[t - 1]) * m.T_UP <= m.hp_run[t] + m.hp_run[t + 1] \
+ m.hp_run[t + 2] + m.hp_run[t + 3]
# m.hp_min_lauf_t_def = pyen.Constraint(m.t_UP, rule=hp_min_lauf_t_rule)
def chp_min_still_t_rule(m, t):
return (m.CHP_run[t - 1] - m.CHP_run[t]) * m.T_DN <= m.T_DN - (
m.CHP_run[t] + m.CHP_run[t + 1])
# m.chp_min_still_t_def = pyen.Constraint(m.t_DN, rule=chp_min_still_t_rule)
def chp_min_lauf_t_rule(m, t):
return (m.CHP_run[t] -
m.CHP_run[t - 1]) * m.T_UP <= m.CHP_run[t] + m.CHP_run[t + 1]
# m.chp_min_lauf_t_def = pyen.Constraint(m.t_UP, rule=chp_min_lauf_t_rule)
# boiler
def boiler_max_cap_rule(m, t):
return m.boiler_cap[t] <= m.boiler_max_cap
m.boiler_max_cap_def = pyen.Constraint(m.t, rule=boiler_max_cap_rule)
# PV
def pv_max_cap_rule(m, t):
return m.PV_cap[t] <= m.pv_peak_power
m.pv_max_cap_def = pyen.Constraint(m.t, rule=pv_max_cap_rule)
# elec_import
def elec_import_rule(m, t):
return m.elec_import[t] <= 50 * 5000
m.elec_import_def = pyen.Constraint(m.t, rule=elec_import_rule)
# elec_export
def elec_export_rule(m, t):
return m.elec_export[t] <= 50 * 5000
m.elec_export_def = pyen.Constraint(m.t, rule=elec_export_rule)
# storage
# storage content
if m.sto_max_cont > 0:
def sto_e_cont_min_rule(m, t):
return m.sto_e_cont[t] / m.sto_max_cont >= 0.1
m.sto_e_cont_min = pyen.Constraint(m.t, rule=sto_e_cont_min_rule)
def sto_e_cont_max_rule(m, t):
return m.sto_e_cont[t] / m.sto_max_cont <= 0.9
m.sto_e_cont_max = pyen.Constraint(m.t, rule=sto_e_cont_max_rule)
if m.bat_cont_max > 0:
def bat_e_cont_min_rule(m, t):
return m.bat_cont[t] / m.bat_cont_max >= 0.1
m.bat_e_cont_min = pyen.Constraint(m.t, rule=bat_e_cont_min_rule)
def bat_e_cont_max_rule(m, t):
return m.bat_cont[t] / m.bat_cont_max <= 0.9
m.bat_e_cont_max = pyen.Constraint(m.t, rule=bat_e_cont_max_rule)
# storage power
def sto_e_max_pow_rule_1(m, t):
return m.sto_e_pow[t] <= m.sto_max_cont
m.sto_e_pow_max_1 = pyen.Constraint(m.t, rule=sto_e_max_pow_rule_1)
def sto_e_max_pow_rule_2(m, t):
return m.sto_e_pow[t] >= -m.sto_max_cont
m.sto_e_pow_max_2 = pyen.Constraint(m.t, rule=sto_e_max_pow_rule_2)
def bat_e_max_pow_rule_1(m, t):
return m.bat_pow_pos[t] <= min(m.bat_power_max, m.bat_cont_max)
m.bat_e_pow_max_1 = pyen.Constraint(m.t, rule=bat_e_max_pow_rule_1)
def bat_e_max_pow_rule_2(m, t):
return m.bat_pow_neg[t] <= min(m.bat_power_max, m.bat_cont_max)
m.bat_e_pow_max_2 = pyen.Constraint(m.t, rule=bat_e_max_pow_rule_2)
# end state of storage and battery
m.sto_e_cont_end = pyen.Constraint(
expr=(m.sto_e_cont[m.t[-1]] >= 0.5 * m.sto_max_cont))
m.bat_e_cont_end = pyen.Constraint(
expr=(m.bat_cont[m.t[-1]] >= 0.5 * m.bat_cont_max))
def obj_rule(m):
# Return sum of total costs over all cost types.
# Simply calculates the sum of m.costs over all m.cost_types.
return pyen.summation(m.costs)
m.obj = pyen.Objective(sense=pyen.minimize,
rule=obj_rule,
doc='Sum costs by cost type')
return m
def add_dsm(m):
# modelled Demand Side Management time steps (downshift):
# downshift effective in tt to compensate for upshift in t
m.tt = pyomo.Set(within=m.t,
initialize=m.timesteps[1:],
ordered=True,
doc='Set of additional DSM time steps')
# DSM Tuples
m.dsm_site_tuples = pyomo.Set(
within=m.stf * m.sit * m.com,
initialize=tuple(m.dsm_dict["delay"].keys()),
doc='Combinations of possible dsm by site, e.g. '
'(2020, Mid, Elec)')
m.dsm_down_tuples = pyomo.Set(
within=m.tm * m.tm * m.stf * m.sit * m.com,
initialize=[(t, tt, stf, site, commodity)
for (t, tt, stf, site, commodity) in dsm_down_time_tuples(
m.timesteps[1:], m.dsm_site_tuples, m)],
doc='Combinations of possible dsm_down combinations, e.g. '
'(5001,5003,2020,Mid,Elec)')
# Variables
m.dsm_up = pyomo.Var(m.tm,
m.dsm_site_tuples,
within=pyomo.NonNegativeReals,
doc='DSM upshift')
m.dsm_down = pyomo.Var(m.dsm_down_tuples,
within=pyomo.NonNegativeReals,
doc='DSM downshift')
# DSM rules
m.def_dsm_variables = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=def_dsm_variables_rule,
doc='DSMup * efficiency factor n == DSMdo (summed)')
m.res_dsm_upward = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_upward_rule,
doc='DSMup <= Cup (threshold capacity of DSMup)')
m.res_dsm_downward = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_downward_rule,
doc='DSMdo (summed) <= Cdo (threshold capacity of DSMdo)')
m.res_dsm_maximum = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_maximum_rule,
doc='DSMup + DSMdo (summed) <= max(Cup,Cdo)')
m.res_dsm_recovery = pyomo.Constraint(
m.tm,
m.dsm_site_tuples,
rule=res_dsm_recovery_rule,
doc='DSMup(t, t + recovery time R) <= Cup * delay time L')
return m
def create_model(vertex, edge, params={}, timesteps=[]):
"""return a DHMIN model instance from nodes and edges DataFrame
Args:
vertex: DataFrame of vertex with index and attributes
edges: DataFrame of edges with (Vertex1, Vertex2) MultiIndex and attributes
params: dict of cost and technical parameters
timesteps: list of timestep tuples (duration, scaling factor)
Returns:
m: a coopr.pyomo ConcreteModel object
Usage:
see rundh.py
The optional argument params can be used to specify any of the
technical and cost parameters.
The optional argument timesteps is given, DHMIN is run in multi-
seasonal mode that includes a simplified time model. Each (t,p)
tuple encodes a time interval of length (1 hour)*t and relative
peak power requirement (peak)*p of all consumers. Note that sum(t)
must be equal to 8760. The inequalities 0 <= t <= 8760 and 0 <= p <= 1
are to be respected.
"""
m = pyomo.ConcreteModel()
m.name = 'DHMIN'
# DATA PREPARATION
tech_parameters = {
'c_fix': 600, # (€/m) fixed pipe investment
'c_var': 0.015, # (€/kW/m) variable pipe investment
'c_om': 5, # (€/m) operation & maintenance
'r_heat': 0.07, # (€/kWh) retail price for heat
'annuity': anf(40, 0.06), # (%) annuity factor (years, interest)
'thermal_loss_fix': 20e-3, # (kW/m) fixed thermal losses
'thermal_loss_var': 1e-7, # (kW/kW/m) variable thermal losses
'concurrence': 1, # (%) concurrence effect
}
# Entity edge contains column 'Edge' as index. This model (in contrast to
# the old GAMS version) does not use the 'Edge' ID on its own, so remove the
# edge ID from the index ('Edge', 'Vertex1', 'Vertex2')
edges = edge.reset_index('Edge')
# replace default parameter values with user-defined ones, if specified
tech_parameters.update(params)
# make edges symmetric by duplicating each row (i,j) to (j,i)
edges_tmp = edges
edges_tmp.index.names = ['Vertex2', 'Vertex1']
edges_tmp = edges_tmp.reorder_levels(['Vertex1', 'Vertex2'])
edges = edges_tmp.append(edges, verify_integrity=True)
del edges_tmp
# derive list of neighbours for each vertex
m.neighbours = {}
for (i, j) in edges.index:
m.neighbours.setdefault(i, [])
m.neighbours[i].append(j)
#
m.vertices = vertex.copy()
m.edges = edges.copy()
cost_types = [
'network', # pipe construction, maintenance
'heat', # heating plants, operation
'revenue', # sold heat
]
# derive subset of source vertices, i.e. those with column 'init' set to 1
source_vertex = vertex[vertex.init == 1].index
# timestep preparation
if timesteps:
# extend timesteps with (name, duration, scaling factor) tuples and
# add a near-zero (here: 1 hour) legnth, nominal power timestep 'Pmax'
timesteps = [('t{}'.format(t[0]), t[0], t[1]) for t in timesteps]
timesteps.append(('Pmax', 1 , 1))
# now get a list of all source nodes
# for each source, add a non-availability timestep ('v0', 1, 1)
# and set availability matrix so that 'v0' is off in that timestep
availability = np.ones((len(timesteps) + len(source_vertex),
len(source_vertex)),
dtype=np.int)
for i, v0 in enumerate(source_vertex):
availability[len(timesteps), i] = 0
timesteps.append(('v{}'.format(v0), 1, 1))
else:
# no timesteps: create single dummy timestep with 100% availability
timesteps = [('t0', 1, 1)]
availability = np.ones((1,
len(source_vertex)),
dtype=np.int)
# MODEL
# Sets
m.vertex = pyomo.Set(initialize=vertex.index)
m.edge = pyomo.Set(within=m.vertex*m.vertex, initialize=edges.index)
m.cost_types = pyomo.Set(initialize=cost_types)
m.tech_params = pyomo.Set(initialize=tech_parameters.keys())
m.timesteps = pyomo.Set(initialize=[t[0] for t in timesteps])
m.source_vertex = pyomo.Set(initialize=source_vertex)
# Parameters
m.tech_parameters = pyomo.Param(m.tech_params, initialize=tech_parameters)
# derive delta and eta from edge attributes
m.delta = pyomo.Param(m.edge, initialize=dict(
edges['peak']
* edges['cnct_quota']
* tech_parameters['concurrence'] +
edges['length']
* tech_parameters['thermal_loss_fix']
))
m.eta = pyomo.Param(m.edge, initialize=dict(
1 - (edges['length']
* tech_parameters['thermal_loss_var'])
))
# cost coefficients for objective function
# k_fix: power-independent investment and operation & maintenance costs for
# pipes (EUR/a)
m.k_fix = pyomo.Param(m.edge, initialize=dict(
edges['length']
* 0.5 # x and Pmax are forced in both directions (i,j),(j,i)
* tech_parameters['c_fix']
* tech_parameters['annuity']
* (1 - edges['pipe_exist']) +
edges['length']
* 0.5 # x and Pmax are forced in both directions (i,j),(j,i)
* tech_parameters['c_om']
))
# k_var: power-dependent pipe investment costs (EUR/kW/a)
m.k_var = pyomo.Param(m.edge, initialize=dict(
edges['length']
* 0.5 # x and Pmax are forced in both directions (i,j),(j,i)
* tech_parameters['c_var']
* tech_parameters['annuity']
* (1- edges['pipe_exist'])
))
# k_heat: costs for heat generation (EUR/h)
# as the source-term for power flow is lowered by concurrence effect (cf.
# m.delta), for conversion to energy integral, it must be removed again
m.k_heat = pyomo.Param(m.vertex, initialize=dict(
vertex['cost_heat']
/ tech_parameters['concurrence']
))
# r_heat: revenue for heat delivery (EUR/h)
#
m.r_heat = pyomo.Param(m.edge, initialize=dict(
edges['peak']
* 0.5 # x and Pmax are forced in both directions (i,j),(j,i)
* edges['cnct_quota']
* tech_parameters['r_heat']
))
m.availability = pyomo.Param(m.source_vertex, m.timesteps, initialize={
(s,t[0]): availability[x,y]
for y,s in enumerate(source_vertex)
for x,t in enumerate(timesteps)
})
m.dt = pyomo.Param(m.timesteps, initialize={t[0]:t[1] for t in timesteps})
m.scaling_factor = pyomo.Param(m.timesteps, initialize={t[0]:t[2] for t in timesteps})
# Variables
m.costs = pyomo.Var(m.cost_types)
m.x = pyomo.Var(m.edge, within=pyomo.Binary)
m.Pmax = pyomo.Var(m.edge, within=pyomo.NonNegativeReals)
m.Pin = pyomo.Var(m.edge, m.timesteps, within=pyomo.NonNegativeReals)
m.Pot = pyomo.Var(m.edge, m.timesteps, within=pyomo.NonNegativeReals)
m.Q = pyomo.Var(m.vertex, m.timesteps, within=pyomo.NonNegativeReals)
m.y = pyomo.Var(m.edge, m.timesteps, within=pyomo.Binary)
m.energy_conservation = pyomo.Constraint(
m.vertex, m.timesteps,
doc='Power flow is conserved in vertex',
rule=energy_conservation_rule)
m.demand_satisfaction = pyomo.Constraint(
m.edge, m.timesteps,
doc='Peak demand (delta) must be satisfied in edge, if pipe is built',
rule=demand_satisfaction_rule)
m.pipe_capacity = pyomo.Constraint(
m.edge, m.timesteps,
doc='Power flow is smaller than pipe capacity Pmax',
rule=pipe_capacity_rule)
m.pipe_usage = pyomo.Constraint(
m.edge, m.timesteps,
doc='Power flow through pipe=0 if y[i,j,t]=0',
rule=pipe_usage_rule)
m.must_build = pyomo.Constraint(
m.edge,
doc='Pipe must be built if must_build == 1',
rule=must_build_rule)
m.build_capacity = pyomo.Constraint(
m.edge,
doc='Pipe capacity Pmax must be smaller than edge attribute cap_max',
rule=build_capacity_rule)
m.unidirectionality = pyomo.Constraint(
m.edge, m.timesteps,
doc='Power flow only in one direction per timestep',
rule=unidirectionality_rule)
m.symmetry_x = pyomo.Constraint(
m.edge,
doc='Pipe may be used in both directions, if built',
rule=symmetry_x_rule)
m.symmetry_Pmax = pyomo.Constraint(
m.edge,
doc='Pipe has same capacity in both directions, if built',
rule=symmetry_Pmax_rule)
m.built_then_use = pyomo.Constraint(
m.edge, m.timesteps,
doc='Demand must be satisfied from at least one direction, if built',
rule=built_then_use_rule)
m.source_vertices = pyomo.Constraint(
m.vertex, m.timesteps,
doc='Non-zero source term Q is only allowed in source vertices',
rule=source_vertices_rule)
# Objective
m.def_costs = pyomo.Constraint(
m.cost_types,
doc='Cost definitions by type',
rule=cost_rule)
m.obj = pyomo.Objective(
sense=pyomo.minimize,
doc='Minimize costs = network + heat - revenue',
rule=obj_rule)
return m
