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tep_v1.0.py
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# -*- coding: utf-8 -*-
"""
Created on Fri May 28 12:09:38 2021
@author: Kyle Skolfield
"""
import gurobipy as gp
import networkx as nx
import numpy as np
import mpinput as mp
import time
#import cProfile
import sys
from itertools import islice
#from itertools import combinations
import random
import math
import miplearn
from miplearn import LearningSolver
import pyomo.environ as pe
from scipy.stats import uniform, randint, bernoulli
from miplearn import Instance
#import nicify_tree_decomp as ntd
#import os
########################################
########### HELPER FUNCTIONS ###########
########################################
def path_lengths(graph,path_set):
lengths = []
for path in path_set:
length = 0
for edge in path:
length += graph.edges[edge]['branch_CR']
lengths.append(length)
return lengths
def k_shortest_paths(G, source, target, k, weight=None):
return list(islice(nx.shortest_simple_paths(G, source, target, weight=weight), k))
#returns edge list of a decently long path between start node and end node
#may not actually reach end node
#what to do if it doesn't reach?
def path_expander(graph,start_node,end_node):
used_v = []
used_v.append(start_node)
curr_node = start_node
main_subgraph = graph.copy()
while len(main_subgraph.nodes) > 1:
#print(curr_node, list(main_subgraph.nodes))
curr_weight = 0
neighbors = nx.neighbors(main_subgraph,curr_node)
main_subgraph.remove_node(curr_node)
for adj_node in neighbors:
reachable_nodes = nx.dfs_preorder_nodes(main_subgraph,adj_node)
temp_subgraph = nx.subgraph(main_subgraph,reachable_nodes)
temp_weight = temp_subgraph.size(weight = 'branch_CR')
if temp_weight >= curr_weight:
curr_weight = temp_weight
new_node = adj_node
used_v.append(new_node)
if curr_node == new_node or curr_node == end_node:
break
curr_node = new_node
return list(nx.utils.pairwise(used_v))
def induced_path(start_node,graph):
temp_graph = graph.copy()
neighbors = list(temp_graph.neighbors(start_node))
path_nodes = [start_node]
while len(neighbors) > 0:
next_node = random.choice(neighbors)
#print('traveling to node', next_node)
path_nodes.append(next_node)
#print('path so far', path_nodes)
neighbors.remove(next_node)
for node in neighbors:
temp_graph.remove_node(node)
#print('removing node', node)
neighbors.remove(node)
neighbors = list(set(temp_graph.neighbors(next_node)) - set(path_nodes))
path_edges = list(nx.utils.pairwise(path_nodes))
temp_graph = temp_graph.edge_subgraph(path_edges).copy()
return temp_graph
def long_induced_path(graph, repeats):
best_length = 0
#can specificy subset of nodes
subset = graph.nodes
for node in subset:
#temp_best = 0
#print(node)
for i in range(repeats):
path_graph = induced_path(node,graph)
path_length = path_graph.size(weight='branch_CR')
if path_length > best_length:
best_length = path_length
return best_length
###################################
########### END HELPERS ###########
###################################
########################################
########### BASE DCOPF MODEL ###########
########################################
#For full DCOPF, use flow capacity constraints from OTS and force all switch variables to be constant 1
#Load flow equations
def load_cons_flow_eq(mod,graph):
M = 2*.6*max({key: 1/value for (key,value) in nx.get_edge_attributes(graph,'branch_x').items()}.values())
#M = 2*6.8
edge_x = nx.get_edge_attributes(graph, 'branch_x')
mod.addConstrs((-1/edge_x[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) - mod._corr_flow[i,j] + (1 - mod._switch[i,j])*M >= 0 for (i,j) in mod._corr_flow),
name = "F_eq_pos")
mod.addConstrs((-1/edge_x[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) - mod._corr_flow[i,j] - (1 - mod._switch[i,j])*M <= 0 for (i,j) in mod._corr_flow),
name = "F_eq_neg")
#Load bus angle difference constraints
def load_cons_bus_ang_diff(mod,graph,bus_ang):
mod.addConstrs((mod._bus_angle[i] - mod._bus_angle[j] <= bus_ang for (i,j) in mod._corr_flow))
mod.addConstrs((mod._bus_angle[j] - mod._bus_angle[i] <= bus_ang for (i,j) in mod._corr_flow))
#Load flow balance constraints
def load_cons_balance(mod,graph):
for i in graph.nodes:
mod.addConstr(gp.quicksum([mod._corr_flow[j,i] for j in graph.neighbors(i) if j < i]) -
gp.quicksum([mod._corr_flow[i,j] for j in graph.neighbors(i) if j > i]) +
(mod._gen.get(i) or 0) == nx.get_node_attributes(graph, 'bus_pd')[i])
#######################################
########### END DCOPF MODEL ###########
#######################################
#######################################
########### OTS CONSTRAINTS ###########
#######################################
#Load flow capacity constraints
def load_cons_OTS_flow_cap(mod,graph):
edge_caps = nx.get_edge_attributes(graph,'branch_cap')
mod.addConstrs((mod._corr_flow[i,j] <= edge_caps[i,j] * mod._switch[i,j] for (i,j) in mod._corr_flow), name="F_cap_pos")
mod.addConstrs((mod._corr_flow[i,j] >= -edge_caps[i,j] * mod._switch[i,j] for (i,j) in mod._corr_flow), name="F_cap_neg")
#Load maximum number of switched off lines constraint
def load_cons_switch_cap(mod,graph,cap):
mod.addConstr(len(mod._corr_flow) - gp.quicksum(mod._switch) <=cap, name='switch_cap')
###########################################
########### END OTS CONSTRAINTS ###########
###########################################
#######################################
########### TEP CONSTRAINTS ###########
#######################################
def load_cons_flow_eq_TEP(mod,graph):
M = 2*.6*max({key: 1/value for (key,value) in nx.get_edge_attributes(graph,'branch_x').items()}.values())
#M = 2*6.8
edge_x = nx.get_edge_attributes(graph, 'branch_x')
mod.addConstrs((-1/edge_x[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) - mod._corr_flow[i,j] +
(1 - mod._new_line[i,j])*M >= 0 for (i,j) in mod._corr_flow if edge_status[i,j] == 0),
name = "F_eq_pos")
mod.addConstrs((-1/edge_x[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) - mod._corr_flow[i,j] -
(1 - mod._new_line[i,j])*M <= 0 for (i,j) in mod._corr_flow if edge_status[i,j] == 0),
name = "F_eq_neg")
mod.addConstrs((-1/edge_x[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) == mod._corr_flow[i,j] for (i,j) in mod._corr_flow if edge_status[i,j] == 1),
name = "F_eq")
def load_TEP_cons_flow_cap(mod,graph):
edge_caps = nx.get_edge_attributes(graph,'branch_cap')
edge_status = nx.get_edge_attributes(graph,'branch_status')
mod.addConstrs((mod._corr_flow[i,j] <= edge_caps[i,j] * mod._new_line[i,j] for (i,j) in mod._corr_flow if edge_status[i,j] == 0), name="F_cap_pos")
mod.addConstrs((mod._corr_flow[i,j] >= -edge_caps[i,j] * mod._new_line[i,j] for (i,j) in mod._corr_flow if edge_status[i,j] == 0), name="F_cap_neg")
mod.addConstrs((mod._corr_flow[i,j] <= edge_caps[i,j] + edge_caps[i,j] * mod._cap_exp[i,j] for (i,j) in mod._corr_flow if edge_status[i,j] == 1), name="F_cap_pos")
mod.addConstrs((mod._corr_flow[i,j] >= -edge_caps[i,j] - edge_caps[i,j] * mod._cap_exp[i,j] for (i,j) in mod._corr_flow if edge_status[i,j] == 1), name="F_cap_neg")
def load_cons_flow_eq_TEP_hyb(mod,graph):
edge_x = nx.get_edge_attributes(graph, 'branch_x')
mod.addConstrs((-1/edge_x[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) == mod._corr_flow[i,j] for (i,j) in mod._corr_flow if edge_status[i,j] == 1),
name = "F_eq")
###########################################
########### END TEP CONSTRAINTS ###########
###########################################
###########################################
########### Low-Effort Heuristic ##########
###########################################
def fused_graph_pos(graph, model_list, lin_mod, hyb_mod, trans_mod):
pos_flow = graph.copy()
for edge in pos_flow.edges:
if model_list == ['lin']:
if lin_mod._corr_flow[edge].x <= 0:
pos_flow.remove_edge(edge[0],edge[1])
elif model_list == ['hyb']:
if hyb_mod._corr_flow[edge].x <= 0:
pos_flow.remove_edge(edge[0],edge[1])
elif model_list == ['trans']:
if trans_mod._corr_flow[edge].x <= 0:
pos_flow.remove_edge(edge[0],edge[1])
elif 'lin' in model_list and 'hyb' in model_list:
if lin_mod._corr_flow[edge].x <= 0 or hyb_mod._corr_flow[edge].x <=0:
pos_flow.remove_edge(edge[0],edge[1])
elif 'lin' in model_list and 'trans' in model_list:
if lin_mod._corr_flow[edge].x <= 0 or trans_mod._corr_flow[edge].x <=0:
pos_flow.remove_edge(edge[0],edge[1])
elif 'trans' in model_list and 'hyb' in model_list:
if trans_mod._corr_flow[edge].x <= 0 or hyb_mod._corr_flow[edge].x <=0:
pos_flow.remove_edge(edge[0],edge[1])
elif 'lin' in model_list and 'hyb' in model_list and 'trans' in model_list:
if lin_mod._corr_flow[edge].x <= 0 or hyb_mod._corr_flow[edge].x <=0 or trans_mod._corr_flow[edge].x <= 0:
pos_flow.remove_edge(edge[0],edge[1])
return pos_flow
def fused_graph_neg(graph, model_list, lin_mod, hyb_mod, trans_mod):
neg_flow = graph.copy()
for edge in neg_flow.edges:
if model_list == ['lin']:
if lin_mod._corr_flow[edge].x >= 0:
neg_flow.remove_edge(edge[0],edge[1])
elif model_list == ['hyb']:
if hyb_mod._corr_flow[edge].x >= 0:
neg_flow.remove_edge(edge[0],edge[1])
elif model_list == ['trans']:
if trans_mod._corr_flow[edge].x >= 0:
neg_flow.remove_edge(edge[0],edge[1])
elif 'lin' in model_list and 'hyb' in model_list:
if lin_mod._corr_flow[edge].x >= 0 or hyb_mod._corr_flow[edge].x >=0:
neg_flow.remove_edge(edge[0],edge[1])
elif 'lin' in model_list and 'trans' in model_list:
if lin_mod._corr_flow[edge].x >= 0 or trans_mod._corr_flow[edge].x >=0:
neg_flow.remove_edge(edge[0],edge[1])
elif 'trans' in model_list and 'hyb' in model_list:
if trans_mod._corr_flow[edge].x >= 0 or hyb_mod._corr_flow[edge].x >=0:
neg_flow.remove_edge(edge[0],edge[1])
elif 'lin' in model_list and 'hyb' in model_list and 'trans' in model_list:
if lin_mod._corr_flow[edge].x >= 0 or hyb_mod._corr_flow[edge].x >=0 or trans_mod._corr_flow[edge].x >= 0:
neg_flow.remove_edge(edge[0],edge[1])
return neg_flow
###########################################
######### End Low-Effort Heuristic ########
###########################################
##########################################
########### VALID INEQUALITIES ###########
##########################################
def theorem_7_cycle_basis(model,where):
if where == gp.GRB.Callback.MIPSOL:
path = random.choice(cycle_paths)
cycle_paths.remove(path)
path1 = list(nx.utils.pairwise(path[0]))
path2 = list(nx.utils.pairwise(path[1]))
mp_length = graph.edge_subgraph(path1).size(weight = 'branch_CR')
start = path[0][0]
end = path[-1][1]
sp_e = path2
sp_length = graph.edge_subgraph(sp_e).size(weight = 'branch_CR')
if sp_length>mp_length:
sp_e,path1 = path1,sp_e
sp_length,mp_length = mp_length,sp_length
for i in range(len(path1)):
if path1[i][0] > path1[i][1]:
path1[i] = tuple(reversed(path1[i]))
for i in range(len(sp_e)):
if sp_e[i][0] > sp_e[i][1]:
sp_e[i] = tuple(reversed(sp_e[i]))
edge_status = nx.get_edge_attributes(graph,'branch_status')
model.cbLazy(model._bus_angle[start] - model._bus_angle[end] <= sp_length +
(mp_length -sp_length) * (len(sp_e) - gp.quicksum([mod._expansion[edge] for edge in sp_e if edge_status[edge] == 0])))
model.cbLazy(model._bus_angle[end] - model._bus_angle[start] <= sp_length +
(mp_length -sp_length) * (len(sp_e) - gp.quicksum([mod._expansion[edge] for edge in sp_e if edge_status[edge] == 0])))
def theorem_7_heuristic(model,where):
if where == gp.GRB.Callback.MIPSOL:
if n[0] <= 0:
#print('*****************')
#print('none added')
#print('*****************')
return
if len(path_pairs) == 0:
return
#path = random.choice(path_pairs)
path = path_pairs[0]
path_pairs.remove(path)
path1 = list(nx.utils.pairwise(path[0]))
path2 = list(nx.utils.pairwise(path[1]))
mp_length = graph.edge_subgraph(path1).size(weight = 'branch_CR')
start = path[0][0]
end = path[-1][1]
sp_e = path2
sp_length = graph.edge_subgraph(sp_e).size(weight = 'branch_CR')
if sp_length>mp_length:
sp_e,path1 = path1,sp_e
sp_length,mp_length = mp_length,sp_length
for i in range(len(path1)):
if path1[i][0] > path1[i][1]:
path1[i] = tuple(reversed(path1[i]))
for i in range(len(sp_e)):
if sp_e[i][0] > sp_e[i][1]:
sp_e[i] = tuple(reversed(sp_e[i]))
n[0] = n[0] - 2
#print('*****************')
#print(n[0])
#print('*****************')
edge_status = nx.get_edge_attributes(graph,'branch_status')
model.cbLazy(model._bus_angle[start] - model._bus_angle[end] <= sp_length +
(mp_length -sp_length) * (len(sp_e) - gp.quicksum([mod._expansion[edge] for edge in sp_e if edge_status[edge] == 0])))
model.cbLazy(model._bus_angle[end] - model._bus_angle[start] <= sp_length +
(mp_length -sp_length) * (len(sp_e) - gp.quicksum([mod._expansion[edge] for edge in sp_e if edge_status[edge] == 0])))
##############################################
########### END VALID INEQUALITIES ###########
##############################################
##############################################
############ INPUT SETTINGS ##################
##############################################
if len(sys.argv) > 1:
model_flag = int(sys.argv[1])
filenames = [sys.argv[2]]
output_filename = sys.argv[3]
SEED = int(sys.argv[4])
pySEED = int(sys.argv[5])
n = int(sys.argv[6])
else:
model_flag = 8
filename = "pglib-opf-master/pglib_opf_case500_goc_tep.m"
filenames = [filename]
output_filename = ""
SEED = 0
pySEED = 0
#ieee 300
if '300' in filenames[0]:
LP_Length = [1945.15]
cap = 1
#goc 500
elif '500' in filenames[0]:
LP_Length = [20692]
cap = 1.1
#goc 793
elif '793' in filenames[0]:
LP_Length = [1147.08]
cap = 1
#############################################
########### EXECUTION BEGINS ################
#############################################
pySeed = 876245
SEED = 876245
random.seed(pySEED)
#cap = .9
demand = .4
filenames = ["pglib-opf-master/pglib_opf_case14_ieee.m"]
model_flag = 10
#LP_Length = 3015
cap = .7
#for goc 500 tep, used demand = .1, cap = .7
#for 2383, demand = .33, cap = .4
n= 0
for filename in filenames:
bus_data = np.array([])
gen_data = np.array([])
branch_data = np.array([])
bus_data,gen_data,branch_data = mp.load_data(filename)
graph = nx.Graph()
mp.encode_graph(graph, bus_data, gen_data, branch_data,demand,cap,1,1)
graph_lr = graph.copy()
##########################################
########### Declare TEP Model ############
##########################################
mod = gp.Model()
mod.modelSense = gp.GRB.MINIMIZE
mod.Params.LogFile = 'anor_test_orig.txt'
mod.Params.MIPGap = .001
#mod.Params.OutputFlag = 0
#Gather line status and cost properties for full graph
M = 2*.6*max({key: 1/value for (key,value) in nx.get_edge_attributes(graph,'branch_b').items()}.values())
edge_status = nx.get_edge_attributes(graph,'branch_status')
edge_b = nx.get_edge_attributes(graph, 'branch_b')
expand_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==0]
recond_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==1]
expand_cost = nx.get_edge_attributes(graph,'branch_cand_cost')
#Add binary decision variables
mod._expansion = mod.addVars(expand_lines, vtype = gp.GRB.BINARY, name = 'expansion_status', obj = expand_cost)
#Add continuous variables
######## Flow Variables #######
mod._corr_flow = mod.addVars(graph.edges, name='corr_flow', ub = gp.GRB.INFINITY, lb = -gp.GRB.INFINITY)
######## Generation Variables ########
mod._gen = mod.addVars(graph.nodes, obj = nx.get_node_attributes(graph, 'gen_cost'), name = 'gen', ub = nx.get_node_attributes(graph, 'gen_Pmax'),
lb= nx.get_node_attributes(graph, 'gen_Pmin'))
######## Angle Variables ########
mod._bus_angle = mod.addVars(graph.nodes, name = 'bus_angle', ub = 30, lb = -30)
####### Load All Constraints #######
#Load flow constraints
mod.addConstrs((-1/edge_b[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) - mod._corr_flow[i,j] +
(1 - mod._expansion[i,j])*M >= 0 for (i,j) in expand_lines),
name = "F_eq_pos")
mod.addConstrs((-1/edge_b[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) - mod._corr_flow[i,j] -
(1 - mod._expansion[i,j])*M <= 0 for (i,j) in expand_lines),
name = "F_eq_neg")
mod.addConstrs((-1/edge_b[i,j] * (mod._bus_angle[i] - mod._bus_angle[j] ) == mod._corr_flow[i,j] for (i,j) in recond_lines),
name = "F_eq")
#Load capacity constraints
mod.addConstrs((mod._corr_flow[i,j] <= graph.edges[i,j]['branch_cap'] * mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_pos")
mod.addConstrs((mod._corr_flow[i,j] >= -graph.edges[i,j]['branch_cap'] * mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_neg")
#Load balance constraints
mod.addConstrs(gp.quicksum([mod._corr_flow[j,i] for j in graph.neighbors(i) if j < i]) -
gp.quicksum([mod._corr_flow[i,j] for j in graph.neighbors(i) if j > i]) +
(mod._gen.get(i) or 0) == nx.get_node_attributes(graph, 'bus_pd')[i] for i in graph.nodes)
##########################################
############# End TEP Model ##############
##########################################
##########################################
########### Declare Linear Model #########
##########################################
linear_mod = gp.Model()
linear_mod.modelSense = gp.GRB.MINIMIZE
linear_mod.Params.LogFile = 'anor_test_l1.txt'
linear_mod.Params.MIPGap = .001
#linear_mod.Params.OutputFlag = 0
#Gather line status and cost properties for full graph
M = 2*.6*max({key: 1/value for (key,value) in nx.get_edge_attributes(graph,'branch_b').items()}.values())
edge_status = nx.get_edge_attributes(graph,'branch_status')
edge_b = nx.get_edge_attributes(graph, 'branch_b')
expand_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==0]
recond_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==1]
expand_cost = nx.get_edge_attributes(graph,'branch_cand_cost')
#Add binary decision variables
linear_mod._expansion = linear_mod.addVars(expand_lines, ub = 1, name = 'expansion_status', obj = expand_cost)
#Add continuous variables
######## Flow Variables #######
linear_mod._corr_flow = linear_mod.addVars(graph.edges, name='corr_flow', ub = gp.GRB.INFINITY, lb = -gp.GRB.INFINITY)
######## Generation Variables ########
linear_mod._gen = linear_mod.addVars(graph.nodes, obj = nx.get_node_attributes(graph, 'gen_cost'), name = 'gen', ub = nx.get_node_attributes(graph, 'gen_Pmax'),
lb= nx.get_node_attributes(graph, 'gen_Pmin'))
######## Angle Variables ########
linear_mod._bus_angle = linear_mod.addVars(graph.nodes, name = 'bus_angle', ub = 30, lb = -30)
####### Load All Constraints #######
#Load flow constraints
linear_mod.addConstrs((-1/edge_b[i,j] * (linear_mod._bus_angle[i] - linear_mod._bus_angle[j] ) - linear_mod._corr_flow[i,j] +
(1 - linear_mod._expansion[i,j])*M >= 0 for (i,j) in expand_lines),
name = "F_eq_pos")
linear_mod.addConstrs((-1/edge_b[i,j] * (linear_mod._bus_angle[i] - linear_mod._bus_angle[j] ) - linear_mod._corr_flow[i,j] -
(1 - linear_mod._expansion[i,j])*M <= 0 for (i,j) in expand_lines),
name = "F_eq_neg")
linear_mod.addConstrs((-1/edge_b[i,j] * (linear_mod._bus_angle[i] - linear_mod._bus_angle[j] ) == linear_mod._corr_flow[i,j] for (i,j) in recond_lines),
name = "F_eq")
#Load capacity constraints
linear_mod.addConstrs((linear_mod._corr_flow[i,j] <= graph.edges[i,j]['branch_cap'] * linear_mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_pos")
linear_mod.addConstrs((linear_mod._corr_flow[i,j] >= -graph.edges[i,j]['branch_cap'] * linear_mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_neg")
#Load balance constraints
linear_mod.addConstrs(gp.quicksum([linear_mod._corr_flow[j,i] for j in graph.neighbors(i) if j < i]) -
gp.quicksum([linear_mod._corr_flow[i,j] for j in graph.neighbors(i) if j > i]) +
(linear_mod._gen.get(i) or 0) == nx.get_node_attributes(graph, 'bus_pd')[i] for i in graph.nodes)
##########################################
############# End Linear Model ###########
##########################################
##########################################
########### Declare Transport Model ######
##########################################
transport_mod = gp.Model()
transport_mod.modelSense = gp.GRB.MINIMIZE
transport_mod.Params.LogFile = 'anor_test_t1.txt'
transport_mod.Params.MIPGap = .001
#transport_mod.Params.OutputFlag = 0
#Gather line status and cost properties for full graph
M = 2*.6*max({key: 1/value for (key,value) in nx.get_edge_attributes(graph,'branch_b').items()}.values())
edge_status = nx.get_edge_attributes(graph,'branch_status')
edge_b = nx.get_edge_attributes(graph, 'branch_b')
expand_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==0]
recond_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==1]
expand_cost = nx.get_edge_attributes(graph,'branch_cand_cost')
#Add binary decision variables
transport_mod._expansion = transport_mod.addVars(expand_lines, ub = 1, name = 'expansion_status', obj = expand_cost)
#Add continuous variables
######## Flow Variables #######
transport_mod._corr_flow = transport_mod.addVars(graph.edges, name='corr_flow', ub = gp.GRB.INFINITY, lb = -gp.GRB.INFINITY)
######## Generation Variables ########
transport_mod._gen = transport_mod.addVars(graph.nodes, obj = nx.get_node_attributes(graph, 'gen_cost'), name = 'gen', ub = nx.get_node_attributes(graph, 'gen_Pmax'),
lb= nx.get_node_attributes(graph, 'gen_Pmin'))
######## Angle Variables ########
transport_mod._bus_angle = transport_mod.addVars(graph.nodes, name = 'bus_angle', ub = 30, lb = -30)
####### Load All Constraints #######
#Load capacity constraints
transport_mod.addConstrs((transport_mod._corr_flow[i,j] <= graph.edges[i,j]['branch_cap'] * transport_mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_pos")
transport_mod.addConstrs((transport_mod._corr_flow[i,j] >= -graph.edges[i,j]['branch_cap'] * transport_mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_neg")
#Load balance constraints
transport_mod.addConstrs(gp.quicksum([transport_mod._corr_flow[j,i] for j in graph.neighbors(i) if j < i]) -
gp.quicksum([transport_mod._corr_flow[i,j] for j in graph.neighbors(i) if j > i]) +
(transport_mod._gen.get(i) or 0) == nx.get_node_attributes(graph, 'bus_pd')[i] for i in graph.nodes)
##########################################
############# End Transport Model ########
##########################################
##########################################
########### Declare Hybrid Model #########
##########################################
hybrid_mod = gp.Model()
hybrid_mod.modelSense = gp.GRB.MINIMIZE
hybrid_mod.Params.LogFile = 'anor_test_0.txt'
hybrid_mod.Params.MIPGap = .001
#hybrid_mod.Params.OutputFlag = 0
#Gather line status and cost properties for full graph
M = 2*.6*max({key: 1/value for (key,value) in nx.get_edge_attributes(graph,'branch_b').items()}.values())
edge_status = nx.get_edge_attributes(graph,'branch_status')
edge_b = nx.get_edge_attributes(graph, 'branch_b')
expand_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==0]
recond_lines = [(i,j) for (i,j) in graph.edges if edge_status[i,j] ==1]
expand_cost = nx.get_edge_attributes(graph,'branch_cand_cost')
#Add binary decision variables
hybrid_mod._expansion = hybrid_mod.addVars(expand_lines, ub = 1, name = 'expansion_status', obj = expand_cost)
#Add continuous variables
######## Flow Variables #######
hybrid_mod._corr_flow = hybrid_mod.addVars(graph.edges, name='corr_flow', ub = gp.GRB.INFINITY, lb = -gp.GRB.INFINITY)
######## Generation Variables ########
hybrid_mod._gen = hybrid_mod.addVars(graph.nodes, obj = nx.get_node_attributes(graph, 'gen_cost'), name = 'gen', ub = nx.get_node_attributes(graph, 'gen_Pmax'),
lb= nx.get_node_attributes(graph, 'gen_Pmin'))
######## Angle Variables ########
hybrid_mod._bus_angle = hybrid_mod.addVars(graph.nodes, name = 'bus_angle', ub = 30, lb = -30)
####### Load All Constraints #######
#Load flow constraints
hybrid_mod.addConstrs((-1/edge_b[i,j] * (hybrid_mod._bus_angle[i] - hybrid_mod._bus_angle[j] ) == hybrid_mod._corr_flow[i,j] for (i,j) in recond_lines),
name = "F_eq")
#Load capacity constraints
hybrid_mod.addConstrs((hybrid_mod._corr_flow[i,j] <= graph.edges[i,j]['branch_cap'] * hybrid_mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_pos")
hybrid_mod.addConstrs((hybrid_mod._corr_flow[i,j] >= -graph.edges[i,j]['branch_cap'] * hybrid_mod._expansion[i,j] for (i,j) in expand_lines), name="F_cap_neg")
#Load balance constraints
hybrid_mod.addConstrs(gp.quicksum([hybrid_mod._corr_flow[j,i] for j in graph.neighbors(i) if j < i]) -
gp.quicksum([hybrid_mod._corr_flow[i,j] for j in graph.neighbors(i) if j > i]) +
(hybrid_mod._gen.get(i) or 0) == nx.get_node_attributes(graph, 'bus_pd')[i] for i in graph.nodes)
##########################################
############# End Hybrid Model ###########
##########################################
transport_mod.optimize()
linear_mod.optimize()
hybrid_mod.optimize()
#model_flag = 2
'''if model_flag >= 2:
linear_mod.optimize()
transport_mod.optimize()
hybrid_mod.optimize()
mod.Params.Presolve = 0
mod.Params.lazyConstraints = 1
mod.optimize()
sys.exit()'''
if model_flag == 2:
mod_list = ['lin']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('LR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 3:
mod_list = ['hyb']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('HR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 4:
mod_list = ['trans']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('TR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 5:
mod_list = ['lin','hyb']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
print(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('LR+HR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 6:
mod_list = ['lin','trans']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('LR+tR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 7:
mod_list = ['trans','hyb']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('TR+HR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 8:
mod_list = ['lin','hyb','trans']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),20))
sp = nx.utils.pairwise(sp)
path_pairs += sp
timing = open('records/' + filename[18:-2] + '_'.join(mod_list) + '.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_heuristic)
timing.write('LR+HR+TR VIs: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
elif model_flag == 0:
timing = open('records/' + filename[18:-2] + 'orig.txt', 'a')
t0 = time.time()
mod.Params.lazyConstraints = 1
mod.Params.presolve = 0
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize()
timing.write('orig solve: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
mod.write(filename[18:-2] + '.sol')
timing.close()
elif model_flag == 1:
timing = open('records/' + filename[18:-2]+ 'cycle_vis.txt', 'a')
t0 = time.time()
cycle_paths = []
cycleBasis = nx.cycle_basis(graph)
for cycle in cycleBasis:
for node in cycle[1:-1]:
path1 = cycle[:cycle.index(node)+1]
path2 = cycle[cycle.index(node)+1:]
path2.append(cycle[0])
cycle_paths.append((path1,path2))
mod.Params.lazyConstraints = 1
mod.Params.seed = SEED
mod.Params.MIPGap = .0001
mod.optimize(theorem_7_cycle_basis)
timing.write('cycle vis: ' + str(time.time()-t0) + ' obj:' + str(mod.objVal) + ' ' +
str(filename) + ' ' + str(SEED) + ' ' + str(pySEED) + ' \n')
timing.close()
if model_flag == 9:
mod_list = ['lin','hyb','trans']
pos_flow = fused_graph_pos(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
neg_flow = fused_graph_neg(graph, mod_list, linear_mod, hybrid_mod, transport_mod)
path_pairs = []
for start in pos_flow.nodes():
for end in pos_flow.nodes():
if end>start:
if nx.has_path(pos_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(pos_flow, start, end, weight = 'branch_CR'),200))
sp = nx.utils.pairwise(sp)
path_pairs += sp
for start in neg_flow.nodes():
for end in neg_flow.nodes():
if end>start:
if nx.has_path(neg_flow, start, end):
sp = list(islice(nx.shortest_simple_paths(neg_flow, start, end, weight = 'branch_CR'),20))
sp = nx.utils.pairwise(sp)
path_pairs += sp
while n > 0 and len(path_pairs) > 0:
path = random.choice(path_pairs)
#path = path_pairs[0]
path_pairs.remove(path)
path1 = list(nx.utils.pairwise(path[0]))
path2 = list(nx.utils.pairwise(path[1]))
mp_length = graph.edge_subgraph(path1).size(weight = 'branch_CR')
start = path[0][0]
end = path[-1][1]
sp_e = path2
sp_length = graph.edge_subgraph(sp_e).size(weight = 'branch_CR')
if sp_length>mp_length:
sp_e,path1 = path1,sp_e
sp_length,mp_length = mp_length,sp_length
for i in range(len(path1)):
if path1[i][0] > path1[i][1]:
path1[i] = tuple(reversed(path1[i]))
for i in range(len(sp_e)):
if sp_e[i][0] > sp_e[i][1]:
sp_e[i] = tuple(reversed(sp_e[i]))
n = n- 2