diff --git a/API_Reference.py b/API_Reference.py
index dd4c215..c3cdb51 100644
--- a/API_Reference.py
+++ b/API_Reference.py
@@ -218,6 +218,8 @@ conductance = guan.calculate_conductance_with_disorder(fermi_energy, h00, h01, d
 
 conductance = guan.calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100)
 
+conductance = guan.calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100)
+
 conductance_array = guan.calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_concentration=1.0, length=100, calculation_times=1, print_show=0)
 
 conductance_array = guan.calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h01, disorder_concentration_array, disorder_intensity=2.0, length=100, calculation_times=1, print_show=0)
diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index 3a9cede..a470533 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.0.149
+version = 0.0.150
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO
index c6a5b40..e67a212 100644
--- a/PyPI/src/guan.egg-info/PKG-INFO
+++ b/PyPI/src/guan.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.0.149
+Version: 0.0.150
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/PyPI/src/guan/__init__.py b/PyPI/src/guan/__init__.py
index 9322df7..be81486 100644
--- a/PyPI/src/guan/__init__.py
+++ b/PyPI/src/guan/__init__.py
@@ -2,7 +2,7 @@
 
 # With this package, you can calculate band structures, density of states, quantum transport and topological invariant of tight-binding models by invoking the functions you need. Other frequently used functions are also integrated in this package, such as file reading/writing, figure plotting, data processing.
 
-# The current version is guan-0.0.149, updated on December 21, 2022.
+# The current version is guan-0.0.150, updated on December 22, 2022.
 
 # Installation: pip install --upgrade guan
 
@@ -1214,6 +1214,26 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
     conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
     return conductance
 
+def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100):
+    right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead(fermi_energy, h00, h01)
+    dim = np.array(h00).shape[0]
+    for ix in range(length):
+        random_vacancy = np.zeros((dim, dim))
+        for dim0 in range(dim):
+            if np.random.uniform(0, 1)<=vacancy_concentration:
+                random_vacancy[dim0, dim0] = vacancy_potential
+        if ix == 0:
+            green_nn_n = guan.green_function(fermi_energy, h00+random_vacancy, broadening=0, self_energy=left_self_energy)
+            green_0n_n = copy.deepcopy(green_nn_n)
+        elif ix != length-1:
+            green_nn_n = guan.green_function_nn_n(fermi_energy, h00+random_vacancy, h01, green_nn_n, broadening=0)
+            green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
+        else:
+            green_nn_n = guan.green_function_nn_n(fermi_energy, h00+random_vacancy, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
+            green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
+    conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
+    return conductance
+
 def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_concentration=1.0, length=100, calculation_times=1, print_show=0):
     dim = np.array(disorder_intensity_array).shape[0]
     conductance_array = np.zeros(dim)