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 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 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