/
gas_network_model.py
426 lines (334 loc) · 17.6 KB
/
gas_network_model.py
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# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
import pyomo.environ as aml
import pyomo.dae as dae
import numpy as np
import networkx
import json
def create_model(demand_factor=1.0):
model = aml.ConcreteModel()
# sets
model.TIME = dae.ContinuousSet(bounds=(0.0, 24.0))
model.DIS = dae.ContinuousSet(bounds=(0.0, 1.0))
model.S = aml.Param(initialize=1)
model.SCEN = aml.RangeSet(1, model.S)
# links
model.LINK = aml.Set(initialize=['l1', 'l2', 'l3', 'l4', 'l5', 'l6', 'l7', 'l8', 'l9', 'l10', 'l11', 'l12'])
def rule_startloc(m, l):
ll = ['l1', 'l2', 'l3', 'l4', 'l5', 'l6', 'l7', 'l8', 'l9', 'l10', 'l11', 'l12']
ls = ['n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12']
start_locations = dict(zip(ll, ls))
return start_locations[l]
model.lstartloc = aml.Param(model.LINK, initialize=rule_startloc)
def rule_endloc(m, l):
ll = ['l1', 'l2', 'l3', 'l4', 'l5', 'l6', 'l7', 'l8', 'l9', 'l10', 'l11', 'l12']
ls = ['n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13']
end_locations = dict(zip(ll, ls))
return end_locations[l]
model.lendloc = aml.Param(model.LINK, initialize=rule_endloc)
model.ldiam = aml.Param(model.LINK, initialize=920.0, mutable=True)
def rule_llength(m, l):
if l == 'l1' or l == 'l12':
return 300.0
return 100.0
model.llength = aml.Param(model.LINK, initialize=rule_llength, mutable=True)
def rule_ltype(m, l):
if l == 'l1' or l == 'l12':
return 'p'
return 'a'
model.ltype = aml.Param(model.LINK, initialize=rule_ltype)
def link_a_init_rule(m):
return (l for l in m.LINK if m.ltype[l] == "a")
model.LINK_A = aml.Set(initialize=link_a_init_rule)
def link_p_init_rule(m):
return (l for l in m.LINK if m.ltype[l] == "p")
model.LINK_P = aml.Set(initialize=link_p_init_rule)
# nodes
model.NODE = aml.Set(initialize=['n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13'])
def rule_pmin(m, n):
if n == 'n1':
return 57.0
elif n == 'n13':
return 39.0
else:
return 34.0
model.pmin = aml.Param(model.NODE, initialize=rule_pmin, mutable=True)
def rule_pmax(m, n):
if n == 'n13':
return 41.0
return 70.0
model.pmax = aml.Param(model.NODE, initialize=rule_pmax, mutable=True)
# supply
model.SUP = aml.Set(initialize=[1])
model.sloc = aml.Param(model.SUP, initialize='n1')
model.smin = aml.Param(model.SUP, within=aml.NonNegativeReals, initialize=0.000, mutable=True)
model.smax = aml.Param(model.SUP, within=aml.NonNegativeReals, initialize=30, mutable=True)
model.scost = aml.Param(model.SUP, within=aml.NonNegativeReals)
# demand
model.DEM = aml.Set(initialize=[1])
model.dloc = aml.Param(model.DEM, initialize='n13')
model.d = aml.Param(model.DEM, within=aml.PositiveReals, initialize=10, mutable=True)
# physical data
model.TDEC = aml.Param(initialize=9.5)
model.eps = aml.Param(initialize=0.025, within=aml.PositiveReals)
model.z = aml.Param(initialize=0.80, within=aml.PositiveReals)
model.rhon = aml.Param(initialize=0.72, within=aml.PositiveReals)
model.R = aml.Param(initialize=8314.0, within=aml.PositiveReals)
model.M = aml.Param(initialize=18.0, within=aml.PositiveReals)
model.pi = aml.Param(initialize=3.14, within=aml.PositiveReals)
model.nu2 = aml.Param(within=aml.PositiveReals,mutable=True)
model.lam = aml.Param(model.LINK, within=aml.PositiveReals, mutable=True)
model.A = aml.Param(model.LINK, within=aml.NonNegativeReals, mutable=True)
model.Tgas = aml.Param(initialize=293.15, within=aml.PositiveReals)
model.Cp = aml.Param(initialize=2.34, within=aml.PositiveReals)
model.Cv = aml.Param(initialize=1.85, within=aml.PositiveReals)
model.gam = aml.Param(initialize=model.Cp/model.Cv, within=aml.PositiveReals)
model.om = aml.Param(initialize=(model.gam-1.0)/model.gam, within=aml.PositiveReals)
# scaling and constants
model.ffac = aml.Param(within=aml.PositiveReals, initialize=(1.0e+6*model.rhon)/(24.0*3600.0))
model.ffac2 = aml.Param(within=aml.PositiveReals, initialize=3600.0/(1.0e+4 * model.rhon))
model.pfac = aml.Param(within=aml.PositiveReals, initialize=1.0e+5)
model.pfac2 = aml.Param(within=aml.PositiveReals, initialize=1.0e-5)
model.dfac = aml.Param(within=aml.PositiveReals, initialize=1.0e-3)
model.lfac = aml.Param(within=aml.PositiveReals, initialize=1.0e+3)
model.c1 = aml.Param(model.LINK, within=aml.PositiveReals, mutable=True)
model.c2 = aml.Param(model.LINK, within=aml.PositiveReals, mutable=True)
model.c3 = aml.Param(model.LINK, within=aml.PositiveReals, mutable=True)
model.c4 = aml.Param(within=aml.PositiveReals, mutable=True)
# cost factors
model.ce = aml.Param(initialize=0.1, within=aml.NonNegativeReals)
model.cd = aml.Param(initialize=1.0e+6, within=aml.NonNegativeReals)
model.cT = aml.Param(initialize=1.0e+6, within=aml.NonNegativeReals)
model.cs = aml.Param(initialize=0.0, within=aml.NonNegativeReals)
# define stochastic info
model.rand_d = aml.Param(model.SCEN, model.DEM, within=aml.NonNegativeReals, mutable=True)
# convert units for input data
def rescale_rule(m):
for i in m.LINK:
m.ldiam[i] = m.ldiam[i]*m.dfac
m.llength[i] = m.llength[i]*m.lfac
# m.dx[i] = m.llength[i]/float(m.DIS.last())
for i in m.SUP:
m.smin[i] = m.smin[i]*m.ffac*m.ffac2 # from scmx106/day to kg/s and then to scmx10-4/hr
m.smax[i] = m.smax[i]*m.ffac*m.ffac2 # from scmx106/day to kg/s and then to scmx10-4/hr
for i in m.DEM:
m.d[i] = m.d[i]*m.ffac*m.ffac2
for i in m.NODE:
m.pmin[i] = m.pmin[i]*m.pfac*m.pfac2 # from bar to Pascals and then to bar
m.pmax[i] = m.pmax[i]*m.pfac*m.pfac2 # from bar to Pascals and then to bar
rescale_rule(model)
def compute_constants(m):
for i in m.LINK:
m.lam[i] = (2.0*aml.log10(3.7*m.ldiam[i]/(m.eps*m.dfac)))**(-2.0)
m.A[i] = (1.0/4.0)*m.pi*m.ldiam[i]*m.ldiam[i]
m.nu2 = m.gam*m.z*m.R*m.Tgas/m.M
m.c1[i] = (m.pfac2/m.ffac2)*(m.nu2/m.A[i])
m.c2[i] = m.A[i]*(m.ffac2/m.pfac2)
m.c3[i] = m.A[i]*(m.pfac2/m.ffac2)*(8.0*m.lam[i]*m.nu2)/(m.pi*m.pi*(m.ldiam[i]**5.0))
m.c4 = (1/m.ffac2)*(m.Cp*m.Tgas)
compute_constants(model)
# set stochastic demands
def compute_demands_rule(m):
for k in m.SCEN:
for j in m.DEM:
m.rand_d[k, j] = demand_factor*m.d[j]
compute_demands_rule(model)
def stochd_init(m, k, j, t):
# What it should be to match description in paper
# if t < m.TDEC:
# return m.d[j]
# if t >= m.TDEC and t < m.TDEC+5:
# return m.rand_d[k,j]
# if t >= m.TDEC+5:
# return m.d[j]
if t < m.TDEC+1:
return m.d[j]
if t >= m.TDEC+1 and t < m.TDEC+1+4.5:
return m.rand_d[k, j]
if t >= m.TDEC+1+4.5:
return m.d[j]
model.stochd = aml.Param(model.SCEN, model.DEM, model.TIME, within=aml.PositiveReals, mutable=True, default=stochd_init)
# define temporal variables
def p_bounds_rule(m, k, j, t):
return aml.value(m.pmin[j]), aml.value(m.pmax[j])
model.p = aml.Var(model.SCEN, model.NODE, model.TIME, bounds=p_bounds_rule, initialize=50.0)
model.dp = aml.Var(model.SCEN, model.LINK_A, model.TIME, bounds=(0.0, 100.0), initialize=10.0)
model.fin = aml.Var(model.SCEN, model.LINK, model.TIME, bounds=(1.0, 500.0), initialize=100.0)
model.fout = aml.Var(model.SCEN, model.LINK, model.TIME, bounds=(1.0, 500.0), initialize=100.0)
def s_bounds_rule(m, k, j, t):
return 0.01, aml.value(m.smax[j])
model.s = aml.Var(model.SCEN, model.SUP, model.TIME, bounds=s_bounds_rule, initialize=10.0)
model.dem = aml.Var(model.SCEN, model.DEM, model.TIME, initialize=100.0)
model.pow = aml.Var(model.SCEN, model.LINK_A, model.TIME, bounds=(0.0, 3000.0), initialize=1000.0)
model.slack = aml.Var(model.SCEN, model.LINK, model.TIME, model.DIS, bounds=(0.0, None), initialize=10.0)
# define spatio-temporal variables
# average 55.7278214666423
model.px = aml.Var(model.SCEN, model.LINK, model.TIME, model.DIS, bounds=(10.0, 100.0), initialize=50.0)
# average 43.19700578593625
model.fx = aml.Var(model.SCEN, model.LINK, model.TIME, model.DIS, bounds=(1.0, 100.0), initialize=100.0)
# define derivatives
model.dpxdt = dae.DerivativeVar(model.px, wrt=model.TIME, initialize=0)
model.dpxdx = dae.DerivativeVar(model.px, wrt=model.DIS, initialize=0)
model.dfxdt = dae.DerivativeVar(model.fx, wrt=model.TIME, initialize=0)
model.dfxdx = dae.DerivativeVar(model.fx, wrt=model.DIS, initialize=0)
# ----------- MODEL --------------
# compressor equations
def powereq_rule(m, j, i, t):
return m.pow[j, i, t] == m.c4 * m.fin[j, i, t] * (((m.p[j, m.lstartloc[i], t]+m.dp[j, i, t])/m.p[j, m.lstartloc[i], t])**m.om - 1.0)
model.powereq = aml.Constraint(model.SCEN, model.LINK_A, model.TIME, rule=powereq_rule)
# cvar model
model.cvar_lambda = aml.Param(initialize=0.0)
model.nu = aml.Var(initialize=100.0)
model.phi = aml.Var(model.SCEN, bounds=(0.0, None), initialize=100.0)
def cvarcost_rule(m):
return (1.0/m.S) * sum((m.phi[k]/(1.0-0.95) + m.nu) for k in m.SCEN)
model.cvarcost = aml.Expression(rule=cvarcost_rule)
# node balances
def nodeeq_rule(m, k, i, t):
return sum(m.fout[k, j, t] for j in m.LINK if m.lendloc[j] == i) + \
sum(m.s[k, j, t] for j in m.SUP if m.sloc[j] == i) - \
sum(m.fin[k, j, t] for j in m.LINK if m.lstartloc[j] == i) - \
sum(m.dem[k, j, t] for j in m.DEM if m.dloc[j] == i) == 0.0
model.nodeeq = aml.Constraint(model.SCEN, model.NODE, model.TIME, rule=nodeeq_rule)
# boundary conditions flow
def flow_start_rule(m, j, i, t):
return m.fx[j, i, t, m.DIS.first()] == m.fin[j, i, t]
model.flow_start = aml.Constraint(model.SCEN, model.LINK, model.TIME, rule=flow_start_rule)
def flow_end_rule(m, j, i, t):
return m.fx[j, i, t, m.DIS.last()] == m.fout[j, i, t]
model.flow_end = aml.Constraint(model.SCEN, model.LINK, model.TIME, rule=flow_end_rule)
# First PDE for gas network model
def flow_rule(m, j, i, t, k):
if t == m.TIME.first() or k == m.DIS.last():
return aml.Constraint.Skip # Do not apply pde at initial time or final location
return m.dpxdt[j, i, t, k]/3600.0 + m.c1[i]/m.llength[i] * m.dfxdx[j, i, t, k] == 0
model.flow = aml.Constraint(model.SCEN, model.LINK, model.TIME, model.DIS, rule=flow_rule)
# Second PDE for gas network model
def press_rule(m, j, i, t, k):
if t == m.TIME.first() or k == m.DIS.last():
return aml.Constraint.Skip # Do not apply pde at initial time or final location
return m.dfxdt[j, i, t, k]/3600 == -m.c2[i]/m.llength[i]*m.dpxdx[j, i, t, k] - m.slack[j, i, t, k]
model.press = aml.Constraint(model.SCEN, model.LINK, model.TIME, model.DIS, rule=press_rule)
def slackeq_rule(m, j, i, t, k):
if t == m.TIME.last():
return aml.Constraint.Skip
return m.slack[j, i, t, k] * m.px[j, i, t, k] == m.c3[i] * m.fx[j, i, t, k] * m.fx[j, i, t, k]
model.slackeq = aml.Constraint(model.SCEN, model.LINK, model.TIME, model.DIS, rule=slackeq_rule)
# boundary conditions pressure, passive links
def presspas_start_rule(m, j, i, t):
return m.px[j, i, t, m.DIS.first()] == m.p[j, m.lstartloc[i], t]
model.presspas_start = aml.Constraint(model.SCEN, model.LINK_P, model.TIME, rule=presspas_start_rule)
def presspas_end_rule(m, j, i, t):
return m.px[j, i, t, m.DIS.last()] == m.p[j, m.lendloc[i], t]
model.presspas_end = aml.Constraint(model.SCEN, model.LINK_P, model.TIME, rule=presspas_end_rule)
# boundary conditions pressure, active links
def pressact_start_rule(m, j, i, t):
return m.px[j, i, t, m.DIS.first()] == m.p[j, m.lstartloc[i], t] + m.dp[j, i, t]
model.pressact_start = aml.Constraint(model.SCEN, model.LINK_A, model.TIME, rule=pressact_start_rule)
def pressact_end_rule(m, j, i, t):
return m.px[j, i, t, m.DIS.last()] == m.p[j, m.lendloc[i], t]
model.pressact_end = aml.Constraint(model.SCEN, model.LINK_A, model.TIME, rule=pressact_end_rule)
# fix pressure at supply nodes
def suppres_rule(m, k, j, t):
return m.p[k, m.sloc[j], t] == m.pmin[m.sloc[j]]
model.suppres = aml.Constraint(model.SCEN, model.SUP, model.TIME, rule=suppres_rule)
# discharge pressure for compressors
def dispress_rule(m, j, i, t):
return m.p[j, m.lstartloc[i], t] + m.dp[j, i, t] <= m.pmax[m.lstartloc[i]]
model.dispress = aml.Constraint(model.SCEN, model.LINK_A, model.TIME, rule=dispress_rule)
# ss constraints
def flow_ss_rule(m, j, i, k):
if k == m.DIS.last():
return aml.Constraint.Skip
return m.dfxdx[j, i, m.TIME.first(), k] == 0.0
model.flow_ss = aml.Constraint(model.SCEN, model.LINK, model.DIS, rule=flow_ss_rule)
def pres_ss_rule(m, j, i, k):
if k == m.DIS.last():
return aml.Constraint.Skip
return 0.0 == - m.c2[i]/m.llength[i] * m.dpxdx[j, i, m.TIME.first(), k] - m.slack[j, i, m.TIME.first(), k];
model.pres_ss = aml.Constraint(model.SCEN, model.LINK, model.DIS, rule=pres_ss_rule)
# non-anticipativity constraints
def nonantdq_rule(m, j, i, t):
if j == 1:
return aml.Constraint.Skip
if t >= m.TDEC+1:
return aml.Constraint.Skip
return m.dp[j, i, t] == m.dp[1, i, t]
model.nonantdq = aml.Constraint(model.SCEN, model.LINK_A, model.TIME, rule=nonantdq_rule)
def nonantde_rule(m, j, i, t):
if j == 1:
return aml.Constraint.Skip
if t >= m.TDEC+1:
return aml.Constraint.Skip
return m.dem[j, i, t] == m.dem[1, i, t]
model.nonantde = aml.Constraint(model.SCEN, model.DEM, model.TIME, rule=nonantde_rule)
# discretize model
discretizer = aml.TransformationFactory('dae.finite_difference')
discretizer.apply_to(model, nfe=1, wrt=model.DIS, scheme='FORWARD')
discretizer2 = aml.TransformationFactory('dae.collocation')
#discretizer2.apply_to(model, nfe=47, ncp=1, wrt=model.TIME, scheme='LAGRANGE-RADAU')
# discretizer.apply_to(model, nfe=48, wrt=model.TIME, scheme='BACKWARD')
# What it should be to match description in paper
discretizer.apply_to(model, nfe=48, wrt=model.TIME, scheme='BACKWARD')
TimeStep = model.TIME[2] - model.TIME[1]
def supcost_rule(m, k):
return sum(m.cs * m.s[k, j, t] * TimeStep for j in m.SUP for t in m.TIME.get_finite_elements())
model.supcost = aml.Expression(model.SCEN, rule=supcost_rule)
def boostcost_rule(m, k):
return sum(m.ce * m.pow[k, j, t] * TimeStep for j in m.LINK_A for t in m.TIME.get_finite_elements())
model.boostcost = aml.Expression(model.SCEN, rule=boostcost_rule)
def trackcost_rule(m, k):
return sum(
m.cd * (m.dem[k, j, t] - m.stochd[k, j, t]) ** 2.0 for j in m.DEM for t in m.TIME.get_finite_elements())
model.trackcost = aml.Expression(model.SCEN, rule=trackcost_rule)
def sspcost_rule(m, k):
return sum(
m.cT * (m.px[k, i, m.TIME.last(), j] - m.px[k, i, m.TIME.first(), j]) ** 2.0 for i in m.LINK for j in m.DIS)
model.sspcost = aml.Expression(model.SCEN, rule=sspcost_rule)
def ssfcost_rule(m, k):
return sum(
m.cT * (m.fx[k, i, m.TIME.last(), j] - m.fx[k, i, m.TIME.first(), j]) ** 2.0 for i in m.LINK for j in m.DIS)
model.ssfcost = aml.Expression(model.SCEN, rule=ssfcost_rule)
def cost_rule(m, k):
return 1e-6 * (m.supcost[k] + m.boostcost[k] + m.trackcost[k] + m.sspcost[k] + m.ssfcost[k])
model.cost = aml.Expression(model.SCEN, rule=cost_rule)
def mcost_rule(m):
return sum(m.cost[k] for k in m.SCEN)
model.mcost = aml.Expression(rule=mcost_rule)
model.FirstStageCost = aml.Expression(expr=0.0)
model.SecondStageCost = aml.Expression(rule=mcost_rule)
model.obj = aml.Objective(expr=model.FirstStageCost + model.SecondStageCost)
return model
"""
instance = create_model(1.0)
solver = aml.SolverFactory("ipopt")
solver.solve(instance, tee=True)
import sys
sys.exit()
"""
# Define the scenario tree with networkx
nx_scenario_tree = networkx.DiGraph()
# first stage
nx_scenario_tree.add_node("R",
cost="FirstStageCost",
variables=["dp", "dem"])
# second stage
demand_factors = np.random.uniform(0.8, 2.5, 5)
n_scenarios = len(demand_factors)
for i, df in enumerate(demand_factors):
nx_scenario_tree.add_node("s{}".format(i),
cost="SecondStageCost")
nx_scenario_tree.add_edge("R", "s{}".format(i), weight=1/n_scenarios)
# Creates an instance for each scenario
def pysp_instance_creation_callback(scenario_name, node_names):
sid = scenario_name.strip("s")
df = demand_factors[int(sid)]
model = create_model(df)
return model