Updating and cleaning the code

opf_losses_v2.py

@@ -1,21 +1,15 @@
-### this code is for OPF with voltage graph
 import itertools
-
-import numpy as np
 import pyomo.environ as py
-import matplotlib as plt
-import matplotlib.pyplot as plt
 import pandas as pd
 import pylab as pl
-# to ignore the warning for dividing by 0
-
-import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib import pyplot as plt
-from matplotlib import cm
-from pyomo.core import Objective
 
+
+
+# to ignore the warning for dividing by 0
 np.seterr(divide='ignore', invalid='ignore')
+
 ############################################################First Step#############################################
 # impedance matrix
 # importing the data from the excel for the bus
@@ -46,7 +40,6 @@ for y in range(len(dataimpedence)):
 print(shunt_matrix)
 
 # converting the impedance to admittance
-
 yb = -1 / Imp_matrix
 yb[np.isnan(yb)] = 0
 
@@ -62,8 +55,7 @@ y_offdiagonal = pyb
 y_offdiagonal_sum = np.sum(y_offdiagonal, axis=1)
 # print(y_offdiagonal_sum)
 
-# now i will add the y_offdiagonal_sum with shunt_matrix_sum for diagonal matrix
+# now adding the y_offdiagonal_sum with shunt_matrix_sum for diagonal matrix
-
 yii = np.add(shunt_matrix_sum, y_offdiagonal_sum)  # get the diaginal matrix
 # print(yii)
 
@@ -73,7 +65,6 @@ ybus = yb
 print('Y bus: ', ybus)
 
 # calculating the magnitude and angle of ybus matrix
-
 y_mag = abs(ybus)
 print('Y bus magnitude : ', y_mag)
 y_rad = np.angle(ybus)
@@ -92,7 +83,6 @@ print('Susseptance: ', ybus_imag)
 
 
 # optimal power flow
-
 model = py.ConcreteModel()
 # Set
 # model.b = py.Set(initialize=[0, 1, 2, 3, 4], doc='bus')
@@ -112,20 +102,7 @@ model.qdmand = py.Param(model.b, model.t, initialize=dict(
     zip(list(itertools.product(range(1, len(databus.bus.values) + 1), range(1, len(model.t.data()) + 1))),
         np.append(datademand.reactive_power.values, len(model.t.data())))),
                         doc='Reactive power demand')
-# fixed demand
-# model.pdmand = py.Param(model.b, model.t, initialize=dict(
-#     zip(list(itertools.product(range(1,len(databus.bus.values)+1), range(1,len(model.t.data())+1))),
-#         np.repeat(datademand.active_power1.values, len(model.t.data())))),mutable=True,
-#                         doc='Active power demand')
-# model.qdmand = py.Param(model.b, model.t, initialize=dict(
-#     zip(list(itertools.product(range(1,len(databus.bus.values)+1), range(1,len(model.t.data())+1))),
-#         np.repeat(datademand.reactive_power1.values, len(model.t.data())))),mutable=True,
-#                         doc='Reactive power demand')
-
-
 # Variable
-
-# todo do we need to put some bound on the voltage and angle need to fixed it
 model.v = py.Var(model.index_i, model.t, initialize=1, bounds=(0.9, 1.07))
 model.v[1, :].fix(1.06)
 model.v[2, :].fix(1.045)
@@ -134,8 +111,6 @@ model.theta = py.Var(model.index_i, model.t, initialize=0, bounds=(-np.pi, np.pi
 model.theta[1, :].fix(0)
 
 # bounds for generator active power
-
-
 model.plb = py.Param(model.b, initialize=dict(zip(databus.bus, datagen.pl)),
                      doc='active power lower bound')
 model.pub = py.Param(model.b, initialize=dict(zip(databus.bus, datagen.pu)),
@@ -150,9 +125,8 @@ def pgenbound(model, i, t):
 model.pgen = py.Var(model.b, model.t,bounds=(0, None))
 model.pgen[4, :].fix(0)
 model.pgen[5, :].fix(0)
-# bounds for generator reactive power
-# Todo should i need to put j with each element
 
+# bounds for generator reactive power
 model.qlb = py.Param(model.b, initialize=dict(zip(databus.bus, datagen.ql)), doc='reactive power lower bound')
 model.qub = py.Param(model.b, initialize=dict(zip(databus.bus, datagen.qu)), doc='reactive power upper bound')
 
@@ -169,17 +143,12 @@ print('model data is ok')
 
 
 # Constraints
-# Todo check the error for indexing
 def active_power(model, i, t):
     return model.pgen[i, t] - model.pdmand[i, t]- sum(
         [y_mag[i - 1, j - 1] * (model.v[i, t]) * (model.v[j, t]) * py.cos(
             y_rad[i - 1, j - 1] + model.theta[j, t] - model.theta[i, t])
          for j in model.index_j]) ==0
 
-
-# i is not equal to j
-
-
 model.Const_Active_power = py.Constraint(model.index_i, model.t, rule=active_power, doc='active power injection')
 
 
@@ -190,24 +159,17 @@ def reactive_power(model, i, t):
             y_rad[i - 1, j - 1] + model.theta[j, t] - model.theta[i, t])
          for j in model.index_j])==0
 
-
 model.Const_Reactive_power = py.Constraint(model.index_i, model.t, rule=reactive_power,
                                            doc='Reactive power injection')
 
 print('model constraint are ok')
 
-# Objective Function  * .25
-
-
 for k in model.index_i:
     for l in model.index_j:
         ybus_real[k - 1, l - 1] = (-(ybus_real[k - 1, l - 1]))
 
 
 def objective_rule(model):
-    # for m in model.index_i:
-    #     for n in model.index_j:
-    #         if m != n:
     return sum(
         sum(0.5 * (ybus_real[m - 1, n - 1]) * ((model.v[m, l]) ** 2 + (model.v[n, l]) ** 2 - 2 * (
                 (model.v[m, l]) * (model.v[n, l])) * py.cos(model.theta[n, l] - model.theta[m, l])) for m in
@@ -216,7 +178,6 @@ def objective_rule(model):
 
 model.objective = py.Objective(rule=objective_rule, sense=py.minimize, doc='Definition of objective function')
 
-print(' model objective is ok ')
 # Solver
 
 opt = py.SolverFactory('ipopt')
@@ -224,7 +185,6 @@ result = opt.solve(model)
 model.write('model_opf.nl', io_options={'symbolic_solver_labels': True})
 
 print('objective function = ', py.value(model.objective) * 100)
-# model.display()
 
 if (result.solver.status == py.SolverStatus.ok) and (
         result.solver.termination_condition == py.TerminationCondition.optimal):
@@ -236,92 +196,6 @@ else:
     print('Solver Status: ', result.solver.status)
 
 # ploting:
-
-# pl_pgen = []
-# pl_pdem = []
-# pl_qdem = []
-#
-# pl_qgen = []
-# pl_v = [[] for j in model.b]
-# pl_theta = [[] for j in model.b]
-#
-# for ll in range(
-#         len([model.pgen, model.qgen, model.v, model.theta, model.pdmand, model.qdmand])):
-#     if ll == 0:
-#         for i in model.pgen:
-#             pl_pgen.append(py.value(model.pgen[i]))
-#     if ll == 1:
-#         for i in model.qgen:
-#             pl_qgen.append(py.value(model.qgen[i]))
-#
-#     # if ll == 2:
-#     #     for i in model.v:
-#     #         pl_v.append(py.value(model.v[i]))
-#     # if ll == 3:
-#     #     for i in model.theta:
-#     #         pl_theta.append(py.value(model.theta[i]))
-#     if ll == 3:
-#         for i in model.pdmand:
-#             pl_pdem.append(py.value(model.pdmand[i]))
-#     if ll == 3:
-#         for i in model.qdmand:
-#             pl_qdem.append(py.value(model.qdmand[i]))
-# for j in model.b:
-#     for k, v in model.v.items():
-#         if k[1] == j:
-#             pl_v[j -1].append(py.value(v))
-# for j in model.b:
-#     for k, v in model.theta.items():
-#         if k[1] == j:
-#             pl_theta[j-1 ].append(py.value(v))
-# # width = 0.2
-# b = np.arange(len(model.b))
-# t = np.arange(len(model.t))
-# x = b  # number of buss
-# y = t  # time
-# z = pl_v  # votlage
-# X, Y = np.meshgrid(y, x)
-# Z = np.reshape(z, X.shape)  # Z.shape must be equal to X.shape = Y.shape
-# fig = plt.figure()
-# ax = fig.gca(projection='3d')
-# ax.plot_surface(X, Y, Z, cmap=cm.coolwarm)
-# ax.set_xlabel('time')
-# ax.set_ylabel('bus')
-# ax.set_zlabel('voltage')
-
-# plt.show()
-# fig = plt.figure()
-# ax = fig.add_subplot(111, projection='3d')
-# ax.plot_surface(X, Y, Z)
-# ax.set_xlabel('time')
-# ax.set_ylabel('bus')
-# ax.set_zlabel('voltage')
-# plt.show()
-
-# ax1.set_xlabel('x axis')
-# ax1.set_ylabel('y axis')
-# ax1.set_zlabel('z axis')
-# fig2, ax = plt.subplots()
-# v = ax.bar(x1, pl_v, label='voltage-bus_i')
-# ax.set_ylabel('Voltages')
-# ax.set_title('Bus')
-# ax.set_xticks(x)
-# ax.set_xticklabels(x)
-# ax.bar_label(v)
-# pl.ylabel('Voltage (PU)')
-# pl.title('Voltages of the buses')
-# fig2.tight_layout()
-# pl.show()
-
-# fig2, ax = plt.subplots(2, len(b), constrained_layout=True, figsize=(10, 10))
-# for k in range(len(b)):
-#     ax[0, k].bar(t, pl_v[k], color="blue")
-#     ax[1, k].bar(t, pl_theta[k], color="blue")
-#     ax[0, 0].set_ylabel('Voltage')
-#     ax[0, k].set_title(f"Bus {k + 1}")
-#     fig2.suptitle('Grid Data', fontsize=10)
-# pl.show()
-
 pl_pdem = [[] for j in model.b]
 pl_pgen = [[] for j in model.b]
 time = [i for i in model.t]
@@ -366,7 +240,9 @@ t = np.arange(len(model.t))
 x = b  # number of buss
 y = t  # time
 z = pl_v  # votlage
-#
+
+
+#3d voltage profile
 # X, Y = np.meshgrid(y, x)
 # Z = np.reshape(z, X.shape)  # Z.shape must be equal to X.shape = Y.shape
 # fig = plt.figure()