def add_generators_to_topology(AMB_network):
file_path = stored_path.main_path + '/data/generators/generator_adjacency_matrix_dict1.json'
with open(file_path) as f:
data = json.loads(f.read())
number_of_added_generators = 0
for name_generator in data.keys():
L_ = data[name_generator]
try:
if type(L_[0]) != float:
if not math.isnan(L_[-2]):
if L_[-1] == 'Hydro':
P_min = L_[-2]
else:
P_min = 0
g = Generator(name_generator,
L_[0],
0,
L_[-1],
Pmax=L_[-2],
Pmin=P_min,
marginal_cost=np.array(L_[1]))
AMB_network.add_generator(g)
number_of_added_generators += 1
except:
pass
return AMB_network
def get_basics(Horizon_T, day):
AMB_network = top(network="ABM")
AMB_network = add_loads_to_topology(AMB_network)
AMB_network = add_generators_to_topology(AMB_network)
H, h = AMB_network.H, AMB_network.h
print("Topology loaded")
"""
Tweak case : add a fake generator
"""
node_name = "MDN"
AMB_network.add_generator(
Generator("diesel_gen",
node_name,
0,
0,
Pmax=200,
Pmin=0,
marginal_cost=[0, 0]))
"""
Get the load data
"""
d = get_load_matrix(AMB_network, day, Horizon_T)
d = tweak_d(d, load_factor=1, index_to_tweak=10, load_factor_for_node=1)
print("Load historical data loaded and tweaked")
"""
Get the bid matrices
"""
b, P_max, P_min = get_producers_matrices(AMB_network, day, Horizon_T)
print("Load historical bids")
"""
Load now the topology of generators
"""
Mn = AMB_network.Mn
return H, h, Mn, b, P_max, P_min, d
def launch_model():
AMB_network = top(network="ABM")
"""
Create loads on each node
"""
Existing_sub_nodes = ld.get_existing_subnodes()
historical_loads = ld.get_historical_loads()
Simp_nodes_dict = ld.get_nodes_to_subnodes()
Simp_nodes_dict["MAN"] = ["MAN2201"]
Existing_sub_nodes.append("MAN2201")
nodes_to_index = pd.read_csv(stored_path.main_path +
'/data/ABM/ABM_Nodes.csv')
for i, node in enumerate(AMB_network.names_2_nodes.keys()):
# print("Load added at node : " + node)
index = nodes_to_index[nodes_to_index["Node names"] ==
node]["Node index"].values[0]
load = Load(node, node, index, type="real_load")
load.add_load_data(historical_loads, Simp_nodes_dict,
Existing_sub_nodes)
AMB_network.add_load(load)
file_path = stored_path.main_path + '/data/generators/generator_adjacency_matrix_dict1.json'
with open(file_path) as f:
data = json.loads(f.read())
number_of_added_generators = 0
for name_generator in data.keys():
L_ = data[name_generator]
try:
if type(L_[0]) != float:
if not math.isnan(L_[-2]):
if L_[-1] == "Gas" or L_[-1] == "Biogas":
add = 35
if L_[-1] == "Geothermal":
add = 10
if L_[-1] == "Coal":
add = 50
if L_[-1] == "Diesel":
add = 150
if L_[-1] == 'Hydro':
P_min = 0.1 * L_[-2]
else:
P_min = 0
g = Generator(name_generator,
L_[0],
0,
L_[-1],
Pmax=L_[-2],
Pmin=P_min,
marginal_cost=add + np.array(L_[1]))
AMB_network.add_generator(g)
number_of_added_generators += 1
except:
pass
node_name = "MDN"
# index = 10
AMB_network.add_generator(
Generator("diesel_gen",
node_name,
0,
0,
Pmax=200,
Pmin=0,
marginal_cost=[0, 0]))
# AMB_network.generators[10]
"""
get d_t for day 12 and trading period 1
"""
Horizon_T = 48
day = 2
d = []
for k, node in enumerate(AMB_network.loads.keys()):
d.append([])
for j in range(day * 48, day * 48 + Horizon_T):
d[k].append(
1 * 1000 *
AMB_network.loads[node][0].return_d(1 + j // 48, j % 48 + 1))
# d[1] = d[1]*2
d = np.array(d)
# # d[1] = d[1]*10
#
# import matplotlib.pyplot as plt
# plt.plot(sum(d))
# plt.show()
#
# sum(d[:,0])
# d = np.zeros((d.shape[0], Horizon_T))
# d = P_min
"""
Add topology specific characteristics
"""
n_generator = AMB_network.get_number_of_gen()
b = np.zeros((n_generator, Horizon_T))
P_max = np.zeros((n_generator, Horizon_T))
P_min = np.zeros((n_generator, Horizon_T))
for node in AMB_network.generators.keys():
for g in AMB_network.generators[node]:
for i, j in enumerate(range(day * 48, day * 48 + Horizon_T)):
if g.name == "diesel_gen":
pmax, pmin, a = 500, 0, 200
else:
pmax, pmin, a = LMP.get_P_min_a(g.name, 1 + j // 48,
j % 48 + 1, g.type)
P_max[g.index, i] = pmax
P_min[g.index, i] = pmin if g.type == "Hydro" else 0
b[g.index, i] = a if a > 0 else np.random.randint(0, 100)
print("Loading data done")
list_of_generator = {}
for node in AMB_network.generators.keys():
for g in AMB_network.generators[node]:
list_of_generator[g.index] = g
new_dict = {}
for key in sorted(list_of_generator.keys()):
new_dict[key] = list_of_generator[key]
H, h = AMB_network.H, AMB_network.h
Mn = AMB_network.Mn
save = d[10].copy()
save_d = d.copy()
d[10] = save
d = 1.3 * save_d.copy()
d[10] = save * 12.1
cost_of_battery = 200 * 1000 / (2 * 365)
model = run_program(d,
b,
P_max,
P_min,
H,
h,
Mn,
i_battery=None,
z_start=1,
cost_of_battery=cost_of_battery)
beta = np.array([[pyo.value(model.beta[t, i]) for i in range(Horizon_T)]
for t in range(H.shape[0])])
save_results(model, i_test=4)
print("\n___ OBJ ____")
print(pyo.value(model.obj))
from lmpnz.Network.PriceBids.Load.Load import Load
from lmpnz.Network.PriceBids.Generator.Generator import Generator
from lmpnz.MarketOperator.marketOperator import marketOperator
import lmpnz.Network.PriceBids.Load.Load as ld
import pandas as pd
from cvxpy import *
import numpy as np
"""
One node test : 1 GEN, 1 LD
"""
# Initializing topology, generator and load
ONDE = Topology(network="ONDE")
GEN = Generator("GEN", "NDE", 0, "dummy", Pmax=20, Pmin=0, marginal_cost=10)
LD = Load(
"LD",
"NDE",
0,
)
ONDE_market = marketOperator
# Adding generator and load to topology
ONDE.add_generator(GEN)
ONDE.add_load(LD)
# Updating Topology characteristics
ONDE.create_Mn()
ONDE.create_Pmin_Pmax()
ONDE.create_Ag_qg()
- aggregate and add the generators to "AMB network"
"""
AMB_network = Topology.add_loads_to_topology(AMB_network)
AMB_network = Topology.add_generators_to_topology(AMB_network)
#plot the grid
nzgridvisu.plot_19_nodes()
print("Topology loaded")
"""
Get the bid matrices from historical bid
"""
# Average congestion on MDN
node_name = "MDN"
AMB_network.add_generator(Generator("swing_generator", node_name, 0, 0, Pmax=200, Pmin=0,
marginal_cost=[0, 0]))
b, P_max, P_min = dataPackage.get_producers_matrices(AMB_network, day, Horizon_T, random_a=False)
print("historical bids loaded")
"""
Load now the topology of generators
"""
Mn = AMB_network.Mn
"""
Get LMP without Battery and plot them
"""
d = dataPackage.get_load_matrix(AMB_network, day, Horizon_T)
d = dataPackage.tweak_d(d, load_factor=1.3, index_to_tweak=10, load_factor_for_node=12.7) #1.3, 12.7
plotprog.plot_nodal_demand(d)
AMB_network.add_load(load)
file_path = stored_path.main_path + '/data/generators/generator_adjacency_matrix_dict.json'
with open(file_path) as f:
data = json.loads(f.read())
number_of_added_generators = 0
for name_generator in data.keys():
L_ = data[name_generator]
try:
if type(L_[0]) != float:
if not math.isnan(L_[-2]):
g = Generator(name_generator,
L_[0],
0,
L_[-1],
Pmax=L_[-2],
Pmin=L_[-3],
marginal_cost=L_[1])
AMB_network.add_generator(g)
number_of_added_generators += 1
except:
pass
"""
get d_t for day 12 and trading period 1
"""
Horizon_T = 24
d = []
for k, node in enumerate(AMB_network.loads.keys()):
d.append([])
for j in range(Horizon_T):
Test : Two Node network
"""
TwoNode_network = Topology(network="NSNDE")
"""
Test : AMB Network
"""
AMB_network = Topology(network="ABM")
# AMB_network.names_2_nodes
"""
Create two generators and add them to the network
"""
g = Generator("GuillaumeGenerator",
"HEN",
0,
"dummy",
Pmax=20,
Pmin=0,
marginal_cost=10)
AMB_network.add_generator(g)
a = Generator("AliceGenerator",
"HEN",
2,
"dummy",
Pmax=200,
Pmin=0,
marginal_cost=200)
AMB_network.add_generator(a)
k = Generator("KieranGenerator",
"MAN",
1,