def maximize_points(self, players=None):
model = pyomo.ConcreteModel()
model.starting_roster = constants.starting_roster
model.positions = [
position for position in model.starting_roster.keys()
]
model.players = self.players if players is None else players
# Decision variable: who is being used?
model.used = pyomo.Var(model.players, within=pyomo.Binary)
model.used_by_pos = pyomo.Var(model.players,
model.positions,
within=pyomo.Binary)
# Decision variable: who is playing what position?
model.games_played = pyomo.Var(model.players,
model.positions,
within=pyomo.NonNegativeIntegers)
# Limit number of players used
model.cap_players_used = pyomo.Constraint(rule=self.cap_players_used)
# Limit starting roster
model.limit_roster_rule = pyomo.Constraint(model.positions,
rule=self.limit_roster)
# Limit positions players can play
model.limit_positions_rule = pyomo.Constraint(
model.players, model.positions, rule=self.limit_positions)
# Limit to 1 position per player
model.limit_playtime_rule = pyomo.Constraint(model.players,
rule=self.limit_playtime)
model.limit_playtime_bypos_rule1 = \
pyomo.Constraint(model.players, model.positions, rule=self.limit_playtime_bypos1)
model.limit_playtime_bypos_rule2 = \
pyomo.Constraint(model.players, model.positions, rule=self.limit_playtime_bypos2)
# Only true RP play RP - due to starts limit
model.only_true_rp_rule1 = pyomo.Constraint(model.players,
rule=self.only_true_rp1)
model.only_true_rp_rule2 = pyomo.Constraint(model.players,
rule=self.only_true_rp2)
# Constrain which players can be modeled as 'replaceable' to fill out remaining playing time at position
model.limit_replaceability = pyomo.Constraint(
model.players, model.positions, rule=self.limit_replaceability)
# Objective function
model.objective = pyomo.Objective(rule=self.objective,
sense=pyomo.maximize)
return model
def add_variable(self, name, **kwargs):
'''create a new variable and add it to the root problem'''
def map_args(kind='Continuous', low=None, high=None):
return dict(bounds=(low, high), domain=variable_kinds[kind])
var = pyomo.Var(name=name, **map_args(**kwargs))
self._model.add_component(name, var)
def add_variable(self,
name,
time=None,
fixed_value=None,
index=None,
**kwargs):
'''
Create a new variable and add it to the object's variables and the model's variables.
:param name: name of optimization variable.
:param kind: type of variable, specified by string. {Continuous or Binary/Boolean}
:param low: low limit of variable
:param high: high limit of variable
:param fixed_value: a fixed value for a variable (making it a parameter)
:param time: a single time for a variable
:param index: a :class:`pyomo.Set` over which a variable is created
'''
def map_args(kind='Continuous', low=None, high=None):
return dict(bounds=(low, high), domain=variable_kinds[kind])
orig_name = name
if index is None:
name = self._t_id(name, time)
if fixed_value is None:
var = pyomo.Var(name=name, **map_args(**kwargs))
self._parent_problem().add_component_to_problem(var)
else:
var = pyomo.Param(name=name, default=fixed_value)
# add var
self._parent_problem().add_component_to_problem(var)
# and set value
var = self.get_variable(orig_name, time)
var[None] = fixed_value
else:
name = self._id(name)
if fixed_value is None:
var = pyomo.Var(index, name=name, **map_args(**kwargs))
self._parent_problem().add_component_to_problem(var)
else:
var = pyomo.Param(index, name=name, default=fixed_value)
self._parent_problem().add_component_to_problem(var)
var = self._parent_problem().get_component(name)
for i in index:
var[i] = fixed_value
# add number of linear constraints as attribute
model.m = pyomo.Param(within=pyomo.NonNegativeIntegers)
model.n = pyomo.Param(within=pyomo.NonNegativeIntegers)
# although not required, it is convenient to define index sets
model.I = pyomo.RangeSet(1, model.m)
model.J = pyomo.RangeSet(1, model.n)
# now we define the coefficients (which are themselves defined over index sets!)
model.a = pyomo.Param(model.I, model.J)
model.b = pyomo.Param(model.I)
model.c = pyomo.Param(model.J)
# the next line declares a variable indexed by the set J
model.x = pyomo.Var(model.J, domain=pyomo.NonNegativeReals)
def objective(model):
"""Abstract representation of our model objective."""
obj = pyomo.summation(model.c, model.x)
return obj
# add the objective function to the model object as an attribute (OBJ can be arbitrary!)
model.OBJ = pyomo.Objective(rule=objective)
def constraints(model, i):
"""Abstract representation of model constraints."""
# return the expression for the constraint for i
return sum(model.a[i,j] * model.x[j] for j in model.J) >= model.b[i]
# the next line creates one constraint for each member of the set model.I (CONS can be arbitrary!)
# define borrowing constraint
model.minimum_capital = pyomo.Param(doc='lower bound on capital holdings.')
##### Define model variables #####
# declare consumption variable
def initial_consumption(model, t):
"""Rule for initial choice of consumption."""
return 0.5
model.consumption = pyomo.Var(
model.periods,
name='consumption',
doc="agent's consumption choice is a flow variable!",
domain=pyomo.PositiveReals,
initialize=initial_consumption)
# declare investment variable
def initial_investment(model, t):
"""Rule for initial choice of consumption."""
return 0.5
model.investment = pyomo.Var(
model.periods,
name='investment',
doc="agent's investment choice is a flow variable!",
domain=pyomo.Reals,
def create_optimal_model(self, team_players, available_players,
prob_avail_after):
model = pyomo.ConcreteModel()
# Set bounds of problem
model.weeks = constants.week_weights.keys()
model.positions = constants.roster.keys()
model.roster_size = sum(constants.roster.values())
mcfadden = PlayerClass.Player('D. McFadden Dal - RB')
points = {
1: 4.74,
2: 11.89,
3: 12.09,
4: 11.99,
5: 12.2,
6: 0,
7: 11.61,
8: 13.25,
9: 3.43,
10: 3.21,
11: 3.44,
12: 3.52,
13: 3.44,
14: 3.27,
15: 3.24,
16: 3.33
}
mcfadden.add_points(points)
# Determine current and available players
model.team_players = team_players + [mcfadden]
model.avail_players = available_players
model.players = model.team_players + model.avail_players
model.prob_avail_after = {
key: prob_avail_after[key]
for key in prob_avail_after.keys()
}
for player in model.team_players:
model.prob_avail_after[player] = 1.0
########### Decision Variables ###########
model.starting = pyomo.Var(model.players,
model.positions,
model.weeks,
within=pyomo.Binary)
model.starts = pyomo.Var(model.players,
within=pyomo.NonNegativeIntegers)
model.used = pyomo.Var(model.players, within=pyomo.Binary)
########### Constraints ###########
model.position_eligibility = pyomo.Constraint(
model.players,
model.positions,
model.weeks,
rule=self.position_eligibility)
model.weekly_roster_size = pyomo.Constraint(
model.positions, model.weeks, rule=self.weekly_roster_size)
model.only_one_position = pyomo.Constraint(model.players,
model.weeks,
rule=self.only_one_position)
model.set_starts = pyomo.Constraint(model.players,
rule=self.set_starts)
model.set_used = pyomo.Constraint(model.players, rule=self.set_used)
model.set_used2 = pyomo.Constraint(model.players, rule=self.set_used2)
model.cap_used = pyomo.Constraint(model.players, rule=self.cap_used)
########### Objective function ###########
model.objective = pyomo.Objective(rule=self.objective,
sense=pyomo.maximize)
return model
# define borrowing constraint
model.minimum_assets = pyomo.Param(doc='lower bound on assets.')
##### Define model variables #####
# declare consumption variable
def initial_consumption(model, t):
"""Rule for initial choice of consumption."""
return 0.5
model.consumption = pyomo.Var(
model.periods,
name='consumption',
doc="agent's consumption choice is a flow variable!",
domain=pyomo.PositiveReals,
initialize=initial_consumption)
# declare labor supply variable
def initial_labor_supply(model, t):
"""Rule for initial choice of labor supply."""
return 1.0
model.labor_supply = pyomo.Var(
model.periods,
name='labor supply',
doc="agent's labor supply choice is a flow variable!",
domain=pyomo.PositiveReals,
def HASM(shpfile,
field,
cellsize,
IniRasFile=None,
reTol=1e-5,
options='HASM6',
solver='cplex',
out_raster=None):
#get the initial values for a surface#
if (not options == 'HASM6') and (IniRasFile is None):
print 'initial values is needed for ' + options
return 0
import util
F0, Zs, RCix, extent, r, c = util.input(shpfile,
field,
cellsize,
IniRasFile=IniRasFile,
bndFile=bndFile)
print solver, ',', options, ",Beigin " + options + " modelling:" + str(
time.ctime())
t1 = time.time()
#set up HASM model and declare necessary variables
md = pym.ConcreteModel('HASMmodel')
k = 0 #declare a model for HASM application
md.F = pym.Var(range(r), range(c)) #declare a two dimension surface
#grid where there is a sample, is set with the sample's value directly
#md.constraints = pym.ConstraintList()
for i in range(len(Zs)): #Zs[pts*3], rcIx[pts*2]
md.F[RCix[i, 0], RCix[i, 1]].value = Zs[i]
md.F[RCix[i, 0], RCix[i, 1]].fixed = True #left
#md.constraints.add(md.F[RCix[i,0],RCix[i,1]] - Zs[i]==0)
#fix the four boundary side
if not (options == 'HASM6'): #boundary values is not fixed in HASM6
#fix the four boundary side
for i in range(r):
md.F[i, 0].value = F0[i, 0]
md.F[i, 0].fixed = True #left
md.F[i, c - 1].value = F0[i, c - 1]
md.F[i, c - 1].fixed = True #right
for j in range(c):
md.F[0, j].value = F0[0, j]
md.F[0, j].fixed = True #bottom
md.F[r - 1, j].value = F0[r - 1, j]
md.F[r - 1, j].fixed = True #up
rere0 = 1e38
print solver, ',', r, '*', c, ', reTol=', reTol
while (1): #iterate until HASM meet the precision
t11 = time.time()
if k > 0: md.del_component('smooth')
md.smooth = pym.Objective(expr=H456_rule(md, F0, cellsize, options),
sense=pym.minimize)
#md.smooth.pprint()
# Create a solver plugin, other solvers can also be tested here
optimizer = opt.SolverFactory(solver)
instance = md.create()
results = optimizer.solve(instance)
instance.load(results)
F1 = model2matrix(md, r, c)
rere = ((F1 - F0)**2).sum() / (F0**2).sum()
k += 1 #relative residuals
print k, ',relative Residual||x(n+1)-x(n)||2/||x(n)||2=,', rere, ', ', np.round(
time.time() - t11), ' seconds'
if rere > rere0:
print 'Divergence happened, previous results returned.'
print '#' * 50
F1 = F0
break
else:
rere0 = rere
if rere <= reTol: break
else: F0 = F1
return F1, extent
model.sigma = pyomo.Param(
doc='inverse of elasticity of substitution for consumption',
within=pyomo.NonNegativeReals)
##### Define model variables #####
# declare consumption variable
def initial_consumption(model, t):
"""Rule for initial choice of consumption."""
return 0.5
model.consumption = pyomo.Var(
model.periods,
name='consumption',
doc="agent's consumption choice is a flow variable!",
domain=pyomo.PositiveReals,
initialize=initial_consumption)
# declare debt variable
def initial_debt(model, t):
"""Rule for initial choice of debt."""
return 0.0
model.debt = pyomo.Var(pyomo.RangeSet(0, model.T + 1),
name='borrowing',
doc="agent's debt is a stock variable!",
domain=pyomo.Reals,
initialize=initial_debt)
def create_model(data, timesteps):
""" Create a VICUS model object from input data.
Creates and returns a Pyomo ConcreteModel object, given a model
input file (supported formats: Excel spreadsheet [planned: SQLite DB])
Args:
data: input dict with fields for Commodities, Processes,
Storage and timeseries for Demand and SupIm
timesteps: numpy array of timestep labels, matching the ones
used in the Demand and SupIm timeseries
Returns:
A coopr.pyomo ConcreteModel object, ready to be instantiated and
solved.
"""
m = pyomo.ConcreteModel()
m.name = 'VICUS'
m.created = datetime.now().strftime('%Y%m%dT%H%M%S', )
# Preparations
# ============
# Data import. Syntax to access a value within equation definitions looks
# like this:
#
# m.process.loc[sit, pro, coin, cout][attribute]
#
get_inputs = itemgetter("commodity", "process", "storage", "demand",
"supim")
(m.commodity, m.process, m.storage, m.demand, m.supim) = get_inputs(data)
# 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
m.t = pyomo.Set(ordered=True, initialize=timesteps)
m.tm = pyomo.Set(within=m.t, initialize=timesteps[1:])
m.co = pyomo.Set(initialize=m.commodity.index.levels[0])
m.coin = pyomo.Set(within=m.co, initialize=m.process.index.levels[1])
m.cout = pyomo.Set(within=m.co, initialize=m.process.index.levels[2])
m.co_type = pyomo.Set(initialize=m.commodity.index.levels[1])
m.pro = pyomo.Set(initialize=m.process.index.levels[0])
m.sto = pyomo.Set(initialize=m.storage.index.levels[0])
m.cost_type = pyomo.Set(initialize=['Inv', 'Fix', 'Var', 'Fuel'])
# sets of existing tuples:
# co_tuples = [('Coal', 'Stock'), ('Wind', 'SupIm'), ('ElecAC', 'Demand')...]
# pro_tuples = [('pp', 'Coal', 'ElecAC'), ('wt', 'Wind', 'ElecAC')...]
# sto_tuples = [('bat', 'ElecDC'), ('pst', 'ElecAC')...]
m.co_tuples = pyomo.Set(within=m.co * m.co_type,
initialize=m.commodity.index)
m.pro_tuples = pyomo.Set(within=m.pro * m.coin * m.cout,
initialize=m.process.index)
m.sto_tuples = pyomo.Set(within=m.sto * m.co, initialize=m.storage.index)
# subsets of commodities by type
# for shorter equations that apply to only one commodity type
m.co_supim = pyomo.Set(within=m.co,
initialize=(c[0] for c in m.co_tuples
if c[1] == 'SupIm'))
m.co_stock = pyomo.Set(within=m.co,
initialize=(c[0] for c in m.co_tuples
if c[1] == 'Stock'))
m.co_demand = pyomo.Set(within=m.co,
initialize=(c[0] for c in m.co_tuples
if c[1] == 'Demand'))
# Parameters
# ==========
# for model entities (commodity, process, storage) no Pyomo params
# are needed, just use the DataFrames m.commodity, m.process and
# m.storage directly.
# Syntax: m.{name} = Param({domain}, initialize={values})
# where name: param name
# domain: one or multiple model sets; empty for scalar parameters
# values: dict of values, addressed by elements of domain sets
m.weight = pyomo.Param(initialize=float(8760) / len(m.t))
# Variables
# =========
# listed alphabetically
# Syntax: m.{name} = Var({domain}, within={range})
# where name: variable name
# domain: variable domain, consisting of one or multiple sets
# range: variable values, like Binary, Integer, NonNegativeReals
m.cap_pro = pyomo.Var(m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Total process capacity (kW)')
m.cap_pro_new = pyomo.Var(m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='New process capacity (kW)')
m.cap_sto_c = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Total storage size (kWh)')
m.cap_sto_c_new = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage capacity (kWh)')
m.cap_sto_p = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Total storage power (kW)')
m.cap_sto_p_new = pyomo.Var(m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='New storage power (kW)')
m.co2_pro_out = pyomo.Var(
m.tm,
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='CO2 emissions from processes (kg) per timestep')
m.costs = pyomo.Var(m.cost_type,
within=pyomo.NonNegativeReals,
doc='Costs by type (EUR/a)')
m.e_co_stock = pyomo.Var(
m.tm,
m.co_stock,
within=pyomo.NonNegativeReals,
doc='Source power flow from stock commodities (kW) per timestep')
m.e_pro_in = pyomo.Var(m.tm,
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into process (kW) per timestep')
m.e_pro_out = pyomo.Var(m.tm,
m.pro_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of process (kW) per timestep')
m.e_sto_in = pyomo.Var(m.tm,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow into storage (kW) per timestep')
m.e_sto_out = pyomo.Var(m.tm,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Power flow out of storage (kW) per timestep')
m.e_sto_con = pyomo.Var(m.t,
m.sto_tuples,
within=pyomo.NonNegativeReals,
doc='Energy content of storage (kWh) in timestep')
# Equation definition
# ===================
# listed by topic. All equations except the Objective function are
# of type Constraint, although there are two semantics for those,
# indicated by the name prefix (def, res).
# - def: definition, usually equations, defining variable values
# - res: restriction, usually inequalities, limiting variable values
# topics
# - commodity
# - process
# - storage
# - emissions
# - costs
# commodity
def res_demand_rule(m, tm, co, co_type):
if co not in m.co_demand:
return pyomo.Constraint.Skip
else:
provided_energy = -commodity_balance(m, tm, co)
return provided_energy >= \
m.demand.loc[tm][co] * \
m.commodity.loc[co, co_type]['peak']
def def_e_co_stock_rule(m, tm, co, co_type):
if co not in m.co_stock:
return pyomo.Constraint.Skip
else:
return m.e_co_stock[tm, co] == commodity_balance(m, tm, co)
def res_stock_hour_rule(m, tm, co, co_type):
if co not in m.co_stock:
return pyomo.Constraint.Skip
else:
return m.e_co_stock[tm, co] <= \
m.commodity.loc[co, co_type]['maxperhour']
def res_stock_total_rule(m, co, co_type):
if co not in m.co_stock:
return pyomo.Constraint.Skip
else:
# calculate total consumption of commodity co
total_consumption = 0
for tm in m.tm:
total_consumption += m.e_co_stock[tm, co] * m.weight
return total_consumption <= m.commodity.loc[co, co_type]['max']
# process
def def_process_capacity_rule(m, tm, pro, coin, cout):
return m.cap_pro[pro,coin,cout] == \
m.cap_pro_new[pro,coin,cout] + \
m.process.loc[pro,coin,cout]['inst-cap']
def def_process_output_rule(m, tm, pro, coin, cout):
return m.e_pro_out[tm, pro, coin, cout] == \
m.e_pro_in[tm, pro, coin, cout] * \
m.process.loc[pro, coin, cout]['eff']
def def_intermittent_supply_rule(m, tm, pro, coin, cout):
if coin in m.co_supim:
return m.e_pro_in[tm, pro, coin, cout] == \
m.cap_pro[pro, coin, cout] * m.supim.loc[tm][coin]
else:
return pyomo.Constraint.Skip
def def_co2_emissions_rule(m, tm, pro, coin, cout):
return m.co2_pro_out[tm, pro, coin, cout] == \
m.e_pro_in[tm, pro, coin, cout] * \
m.process.loc[pro, coin, cout]['co2'] * \
m.weight
def res_process_output_by_capacity_rule(m, tm, pro, coin, cout):
return m.e_pro_out[tm, pro, coin, cout] <= m.cap_pro[pro, coin, cout]
def res_process_capacity_rule(m, pro, coin, cout):
return (m.process.loc[pro, coin, cout]['cap-lo'],
m.cap_pro[pro, coin, cout], m.process.loc[pro, coin,
cout]['cap-up'])
# storage
def def_storage_state_rule(m, t, sto, co):
return m.e_sto_con[t, sto, co] == \
m.e_sto_con[t-1, sto, co] + \
m.e_sto_in[t, sto, co] * m.storage.loc[sto, co]['eff-in'] - \
m.e_sto_out[t, sto, co] / m.storage.loc[sto, co]['eff-out']
def def_storage_power_rule(m, sto, co):
return m.cap_sto_p[sto, co] == \
m.cap_sto_p_new[sto, co] + \
m.storage.loc[sto, co]['inst-cap-p']
def def_storage_capacity_rule(m, sto, co):
return m.cap_sto_c[sto, co] == \
m.cap_sto_c_new[sto, co] + \
m.storage.loc[sto, co]['inst-cap-p']
def res_storage_input_by_power_rule(m, t, sto, co):
return m.e_sto_in[t, sto, co] <= m.cap_sto_p[sto, co]
def res_storage_output_by_power_rule(m, t, sto, co):
return m.e_sto_out[t, sto, co] <= m.cap_sto_p[sto, co]
def res_storage_state_by_capacity_rule(m, t, sto, co):
return m.e_sto_con[t, sto, co] <= m.cap_sto_c[sto, co]
def res_storage_power_rule(m, sto, co):
return (m.storage.loc[sto, co]['cap-lo-p'], m.cap_sto_p[sto, co],
m.storage.loc[sto, co]['cap-up-p'])
def res_storage_capacity_rule(m, sto, co):
return (m.storage.loc[sto, co]['cap-lo-c'], m.cap_sto_c[sto, co],
m.storage.loc[sto, co]['cap-up-c'])
def res_initial_and_final_storage_state_rule(m, t, sto, co):
if t == m.t[1]: # first timestep (Pyomo uses 1-based indexing)
return m.e_sto_con[t, sto, co] == \
m.cap_sto_c[sto, co] * \
m.storage.loc[sto, co]['init']
elif t == m.t[-1]: # last timestep
return m.e_sto_con[t, sto, co] >= \
m.cap_sto_c[sto, co] * \
m.storage.loc[sto, co]['init']
else:
return pyomo.Constraint.Skip
# emissions
def res_co2_emission_rule(m):
return pyomo.summation(m.co2_pro_out) <= \
m.commodity.loc['CO2','Env']['max']
# costs
def def_costs_rule(m, cost_type):
""" Calculate total costs by cost type.
Sums up process activity and capacity expansions
and sums them in the cost types that are specified in the set
m.cost_type. To change or add cost types, add/change entries
there and modify the if/elif cases in this function accordingly.
Cost types are
- Investment costs for process power, storage power and
storage capacity, annualized.
- Fixed costs for process & storage power, storage capacity.
- Variable costs for process and storage activity.
- Fuel costs for purchased stock commodities.
"""
if cost_type == 'Inv':
return m.costs['Inv'] == \
sum(m.cap_pro_new[p] *
m.process.loc[p]['inv-cost'] *
m.process.loc[p]['annuity_factor']
for p in m.pro_tuples) + \
sum(m.cap_sto_p_new[s] *
m.storage.loc[s]['inv-cost-p'] *
m.storage.loc[s]['annuity_factor'] +
m.cap_sto_c_new[s] *
m.storage.loc[s]['inv-cost-c'] *
m.storage.loc[s]['annuity_factor']
for s in m.sto_tuples)
elif cost_type == 'Fix':
return m.costs['Fix'] == \
sum(m.cap_pro[p] * m.process.loc[p]['fix-cost']
for p in m.pro_tuples) + \
sum(m.cap_sto_p[s] * m.storage.loc[s]['fix-cost-p'] +
m.cap_sto_c[s] * m.storage.loc[s]['fix-cost-c']
for s in m.sto_tuples)
elif cost_type == 'Var':
return m.costs['Var'] == \
sum(m.e_pro_out[(tm,) + p] *
m.process.loc[p]['var-cost'] *
m.weight
for tm in m.tm for p in m.pro_tuples) + \
sum(m.e_sto_con[(tm,) + s] *
m.storage.loc[s]['var-cost-c'] * m.weight +
(m.e_sto_in[(tm,) + s] + m.e_sto_out[(tm,) + s]) *
m.storage.loc[s]['var-cost-p'] * m.weight
for tm in m.tm for s in m.sto_tuples)
elif cost_type == 'Fuel':
return m.costs['Fuel'] == \
sum(m.e_co_stock[(tm,c[0])] *
m.commodity.loc[c]['price'] *
m.weight
for tm in m.tm for c in m.co_tuples
if c[0] in m.co_stock)
else:
raise NotImplementedError("Unknown cost type!")
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 pyomo.summation(m.costs)
# Equation declaration
# ====================
# declarations connect rule functions to the model, specifying
# the model sets for which the constraints are enforced.
# commodity
m.res_demand = pyomo.Constraint(m.tm,
m.co_tuples,
doc='storage + process balance >= demand')
m.def_e_co_stock = pyomo.Constraint(
m.tm,
m.co_tuples,
doc='commodity source term = hourly commodity consumption')
m.res_stock_hour = pyomo.Constraint(
m.tm,
m.co_tuples,
doc='hourly commodity source term <= commodity.maxperhour')
m.res_stock_total = pyomo.Constraint(
m.co_tuples, doc='total commodity source term <= commodity.max')
# process
m.def_process_capacity = pyomo.Constraint(
m.tm,
m.pro_tuples,
doc='total process capacity = inst-cap + new capacity')
m.def_process_output = pyomo.Constraint(
m.tm, m.pro_tuples, doc='process output = process input * efficiency')
m.def_intermittent_supply = pyomo.Constraint(
m.tm,
m.pro_tuples,
doc='process output = process capacity * supim timeseries')
m.def_co2_emissions = pyomo.Constraint(
m.tm,
m.pro_tuples,
doc='process co2 output = process input * process.co2 * weight')
m.res_process_output_by_capacity = pyomo.Constraint(
m.tm, m.pro_tuples, doc='process output <= process capacity')
m.res_process_capacity = pyomo.Constraint(
m.pro_tuples,
doc='process.cap-lo <= process capacity <= process.cap-up')
# storage
m.def_storage_state = pyomo.Constraint(
m.tm, m.sto_tuples, doc='storage[t] = storage[t-1] + input - output')
m.def_storage_power = pyomo.Constraint(
m.sto_tuples, doc='storage power = inst-cap + new power')
m.def_storage_capacity = pyomo.Constraint(
m.sto_tuples, doc='storage capacity = inst-cap + new capacity')
m.res_storage_input_by_power = pyomo.Constraint(
m.tm, m.sto_tuples, doc='storage input <= storage power')
m.res_storage_output_by_power = pyomo.Constraint(
m.tm, m.sto_tuples, doc='storage output <= storage power')
m.res_storage_state_by_capacity = pyomo.Constraint(
m.t, m.sto_tuples, doc='storage content <= storage capacity')
m.res_storage_power = pyomo.Constraint(
m.sto_tuples, doc='storage.cap-lo <= storage power <= storage.cap-up')
m.res_storage_capacity = pyomo.Constraint(
m.sto_tuples,
doc='storage.cap-lo <= storage capacity <= storage.cap-up')
m.res_initial_and_final_storage_state = pyomo.Constraint(
m.t,
m.sto_tuples,
doc='storage content initial == and final >= storage.init * capacity')
# emissions
m.res_co2_emission = pyomo.Constraint(
doc='total co2 emissions <= commodity.co2.max')
# costs
m.def_costs = pyomo.Constraint(m.cost_type,
doc='main cost function by cost type')
m.obj = pyomo.Objective(sense=pyomo.minimize,
doc='cost = sum of all cost types')
return m
