diff --git a/andes/core/model/model.py b/andes/core/model/model.py
index ac3bae20b..339c4dc0b 100644
--- a/andes/core/model/model.py
+++ b/andes/core/model/model.py
@@ -526,7 +526,7 @@ def set(self, src, idx, attr, value):
 
         return True
 
-    def alter(self, src, idx, value):
+    def alter(self, src, idx, value, attr='v'):
         """
         Alter values of input parameters or constant service.
 
@@ -546,17 +546,43 @@ def alter(self, src, idx, value):
             The device to alter
         value : float
             The desired value
+        attr : str
+            The attribute to alter, default is 'v'.
+
+        Notes
+        -----
+        New in version 1.9.3: Added the `attr` parameter and the feature to alter
+        specific attributes. This feature is useful when you need to manipulate parameter
+        values in the system base and ensure that these changes are reflected in the
+        dumped case file.
+
+        Examples
+        --------
+        >>> import andes
+        >>> ss = andes.load(andes.get_case('5bus/pjm5bus.xlsx'), setup=True)
+        >>> ss.GENCLS.alter(src='M', idx=2, value=1, attr='v')
+        >>> ss.GENCLS.get(src='M', idx=2, attr='v')
+        3.0
+        >>> ss.GENCLS.alter(src='M', idx=2, value=1, attr='vin')
+        >>> ss.GENCLS.get(src='M', idx=2, attr='v')
+        1.0
         """
 
         instance = self.__dict__[src]
 
         if hasattr(instance, 'vin') and (instance.vin is not None):
-            self.set(src, idx, 'vin', value)
-
             uid = self.idx2uid(idx)
-            self.set(src, idx, 'v', value * instance.pu_coeff[uid])
-        else:
+            if attr == 'vin':
+                self.set(src, idx, 'vin', value / instance.pu_coeff[uid])
+                self.set(src, idx, 'v', value=value)
+            else:
+                self.set(src, idx, 'vin', value)
+                self.set(src, idx, 'v', value * instance.pu_coeff[uid])
+        elif not hasattr(instance, 'vin') and attr == 'vin':
+            logger.warning(f"{self.class_name}.{src} has no `vin` attribute, changing `v`.")
             self.set(src, idx, 'v', value)
+        else:
+            self.set(src, idx, attr=attr, value=value)
 
     def get_inputs(self, refresh=False):
         """
diff --git a/andes/core/model/modeldata.py b/andes/core/model/modeldata.py
index a66f2cb8d..47811c5df 100644
--- a/andes/core/model/modeldata.py
+++ b/andes/core/model/modeldata.py
@@ -202,12 +202,15 @@ def as_df(self, vin=False):
         Export all parameters as a `pandas.DataFrame` object.
         This function utilizes `as_dict` for preparing data.
 
+        Parameters
+        ----------
+        vin : bool
+            If True, export all parameters from original input (``vin``).
+
         Returns
         -------
         DataFrame
             A dataframe containing all model data. An `uid` column is added.
-        vin : bool
-            If True, export all parameters from original input (``vin``).
         """
         if vin is False:
             out = pd.DataFrame(self.as_dict()).set_index('uid')
diff --git a/andes/models/group.py b/andes/models/group.py
index 85518d03e..49c3dba9c 100644
--- a/andes/models/group.py
+++ b/andes/models/group.py
@@ -5,6 +5,7 @@
 import numpy as np
 
 from andes.core.service import BackRef
+from andes.shared import pd
 from andes.utils.func import list_flatten, validate_keys_values
 
 logger = logging.getLogger(__name__)
@@ -243,6 +244,38 @@ def set(self, src: str, idx, attr, value):
 
         return True
 
+    def alter(self, src, idx, value, attr='v'):
+        """
+        Alter values of input parameters or constant service for a group of models.
+
+        .. note::
+            New in version 1.9.3.
+
+        Parameters
+        ----------
+        src : str
+            The parameter name to alter
+        idx : str, float, int
+            The unique identifier for the device to alter
+        value : float
+            The desired value
+        attr : str, optional
+            The attribute to alter. Default is 'v'.
+        """
+        self._check_src(src)
+        self._check_idx(idx)
+
+        idx, _ = self._1d_vectorize(idx)
+        models = self.idx2model(idx)
+
+        if isinstance(value, (str, int, float, np.integer, np.floating)):
+            value = [value] * len(idx)
+
+        for mdl, ii, val in zip(models, idx, value):
+            mdl.alter(src, ii, val, attr=attr)
+
+        return True
+
     def find_idx(self, keys, values, allow_none=False, default=None, allow_all=False):
         """
         Find indices of devices that satisfy the given `key=value` condition.
@@ -442,6 +475,9 @@ def get_all_idxes(self):
         """
         Return all the devices idx in this group.
 
+        .. note::
+            New in version 1.9.3.
+
         Returns
         -------
         list
@@ -463,6 +499,63 @@ def get_all_idxes(self):
         """
         return list(self._idx2model.keys())
 
+    def as_dict(self, vin=False):
+        """
+        Export group common parameters as a dictionary.
+
+        .. note::
+            New in version 1.9.3.
+
+        This method returns a dictionary where the keys are the `ModelData` parameter names
+        and the values are array-like structures containing the data in the order they were added.
+        Unlike `ModelData.as_dict()`, this dictionary does not include the `uid` field.
+
+        Parameters
+        ----------
+        vin : bool, optional
+            If True, includes the `vin` attribute in the dictionary. Default is False.
+
+        Returns
+        -------
+        dict
+            A dictionary of common parameters.
+        """
+        out_all = []
+        out_params = self.common_params.copy()
+        out_params.insert(2, 'idx')
+
+        for mdl in self.models.values():
+            if mdl.n <= 0:
+                continue
+            mdl_data = mdl.as_df(vin=True) if vin else mdl.as_dict()
+            mdl_dict = {k: mdl_data.get(k) for k in out_params if k in mdl_data}
+            out_all.append(mdl_dict)
+
+        if not out_all:
+            return {}
+
+        out = {key: np.concatenate([item[key] for item in out_all]) for key in out_all[0].keys()}
+        return out
+
+    def as_df(self, vin=False):
+        """
+        Export group common parameters as a `pandas.DataFrame` object.
+
+        .. note::
+            New in version 1.9.3.
+
+        Parameters
+        ----------
+        vin : bool
+            If True, export all parameters from original input (``vin``).
+
+        Returns
+        -------
+        DataFrame
+            A dataframe containing all model data. An `uid` column is added.
+        """
+        return pd.DataFrame(self.as_dict(vin=vin))
+
     def doc(self, export='plain'):
         """
         Return the documentation of the group in a string.
@@ -579,7 +672,7 @@ class StaticGen(GroupBase):
 
     def __init__(self):
         super().__init__()
-        self.common_params.extend(('Sn', 'Vn', 'p0', 'q0', 'ra', 'xs', 'subidx'))
+        self.common_params.extend(('bus', 'Sn', 'Vn', 'p0', 'q0', 'ra', 'xs', 'subidx'))
         self.common_vars.extend(('q', 'a', 'v'))
 
         self.SynGen = BackRef()
@@ -642,7 +735,7 @@ class SynGen(GroupBase):
 
     def __init__(self):
         super().__init__()
-        self.common_params.extend(('Sn', 'Vn', 'fn', 'bus', 'M', 'D', 'subidx'))
+        self.common_params.extend(('bus', 'gen', 'Sn', 'Vn', 'fn', 'M', 'D', 'subidx'))
         self.common_vars.extend(('omega', 'delta', ))
         self.idx_island = []
         self.uid_island = []
diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst
index 64d2992a9..e06172dc7 100644
--- a/docs/source/release-notes.rst
+++ b/docs/source/release-notes.rst
@@ -11,12 +11,12 @@ v1.9 Notes
 
 v1.9.3 (2024-04-XX)
 -------------------
-Development of connectivity manager `ConnMan`:
+Development of connectivity manager ``ConnMan``:
 
-- Add case `ieee14_conn.xlsx` for demonstration.
-- Add `ConnMan` class to manage connectivity.
-- Add `ConnMan` to `System` to as an attribute `conn`.
-- Add a demo notebook for `ConnMan`.
+- Add case ``ieee14_conn.xlsx`` for demonstration.
+- Add ``ConnMan`` class to manage connectivity.
+- Add ``ConnMan`` to `System` to as an attribute ``conn``.
+- Add a demo notebook ``ConnMan.ipynb``. See folder ``andes/examples/demonstration``.
 
 Other changes:
 
@@ -25,13 +25,27 @@ Other changes:
 - In symbolic processor, most variables are assumed to be real, except some
   services that are specified as complex. This will allow generating simplified
   expressions.
-- Adjust `BusFreq.Tw.default` to 0.1.
+- Adjust ``BusFreq.Tw.default`` to 0.1.
 - Add parameter from_csv=None in TDS.run() to allow loading data from CSV files at TDS begining.
-- Fix `TDS.init()` and `TDS._csv_step()` to fit loading from CSV when `Output` exists.
-- Add parameter `allow_all=False` to `ModelData.find_idx()` `GroupBase.find_idx()` to allow searching all matches.
-- Add method `GroupBase.get_all_idxes()` to get all indices of a group.
-- Enhanced three-winding transformer parsing in PSS/E raw files by assigning the equivalent star bus `area`, `owner`, and `zone` using the high-voltage bus values.
-- Specify `multiprocess <=0.70.16` in requirements as 0.70.17 does not support Linux.
+- Fix ``TDS.init()`` and ``TDS._csv_step()`` to fit loading from CSV when ``Output`` exists.
+- Add function signature ``allow_all=False`` to ``ModelData.find_idx()`` ``GroupBase.find_idx()``
+  to allow searching all matches.
+- Enhanced three-winding transformer parsing in PSS/E raw files by assigning the equivalent star bus ``area``,
+  ``owner``, and ``zone`` using the high-voltage bus values.
+- Add function signature ``attr='v'`` to method ``Model.alter`` for altering parameter values without manual
+  per unit conversion
+- Add following methods to ``GroupBase`` for easier usage: ``get_all_idxes``, ``alter``, ``as_dict``, and ``as_df``
+- Add two demo ``add_RenGen.ipynb`` and ``replace_SynGen.ipynb`` to show how to do renewable penetration via code.
+  See folder ``andes/examples/demonstration``
+- Add a demo ``manipulate_params.ipynb`` to compare the differnce between ``set`` and ``alter``
+- Extend ``SynGen`` common parameters with ``bus``, ``gen``, ``Sn``, ``Vn``, and ``fn``
+- Extend ``StaticGen`` common parameters with ``bus``
+- Fix ``TDS.init()`` and ``TDS._csv_step()`` to fit loading from CSV when ``Output`` exists.
+- Add parameter ``allow_all=False`` to ``ModelData.find_idx()`` ``GroupBase.find_idx()`` to allow searching all matches.
+- Add method ``GroupBase.get_all_idxes()`` to get all indices of a group.
+- Enhanced three-winding transformer parsing in PSS/E raw files by assigning the equivalent star bus ``area``,
+  ``owner``, and ``zone`` using the high-voltage bus values.
+- Specify `multiprocess<=0.70.16` in requirements as 0.70.17 does not support Linux.
 
 v1.9.2 (2024-03-25)
 -------------------
diff --git a/examples/demonstration/add_RenGen.ipynb b/examples/demonstration/add_RenGen.ipynb
new file mode 100644
index 000000000..4fe5ff5b1
--- /dev/null
+++ b/examples/demonstration/add_RenGen.ipynb
@@ -0,0 +1,1101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Add Renewable Generators\n",
+    "\n",
+    "This demo shows how to add renewable generators without removing the existing synchronous generators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import andes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "andes.config_logger(stream_level=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Working directory: \"/Users/jinningwang/work/andes/examples/demonstration\"\n",
+      "> Loaded config from file \"/Users/jinningwang/.andes/andes.rc\"\n",
+      "> Loaded generated Python code in \"/Users/jinningwang/.andes/pycode\".\n",
+      "Parsing input file \"/Users/jinningwang/work/andes/andes/cases/ieee14/ieee14_linetrip.xlsx\"...\n",
+      "Input file parsed in 0.1935 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ss = andes.load(andes.get_case('ieee14/ieee14_linetrip.xlsx'),\n",
+    "                setup=False, no_output=True, default_config=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idx</th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>Vn</th>\n",
+       "      <th>vmax</th>\n",
+       "      <th>vmin</th>\n",
+       "      <th>v0</th>\n",
+       "      <th>a0</th>\n",
+       "      <th>xcoord</th>\n",
+       "      <th>ycoord</th>\n",
+       "      <th>area</th>\n",
+       "      <th>zone</th>\n",
+       "      <th>owner</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS1</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.03000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS2</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.01970</td>\n",
+       "      <td>-0.027981</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS3</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.00042</td>\n",
+       "      <td>-0.060097</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS4</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.99858</td>\n",
+       "      <td>-0.074721</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS5</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.00443</td>\n",
+       "      <td>-0.064315</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS6</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.99871</td>\n",
+       "      <td>-0.109998</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS7</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.00682</td>\n",
+       "      <td>-0.084285</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS8</td>\n",
+       "      <td>69</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.01895</td>\n",
+       "      <td>-0.024339</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS9</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>1.00193</td>\n",
+       "      <td>-0.127502</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS10</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.99351</td>\n",
+       "      <td>-0.130202</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS11</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.99245</td>\n",
+       "      <td>-0.122948</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS12</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.98639</td>\n",
+       "      <td>-0.128934</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS13</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.98403</td>\n",
+       "      <td>-0.133786</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>BUS14</td>\n",
+       "      <td>138</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.99063</td>\n",
+       "      <td>-0.166916</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     idx  u   name   Vn  vmax  vmin       v0        a0  xcoord  ycoord  area  \\\n",
+       "uid                                                                            \n",
+       "0      1  1   BUS1   69   1.1   0.9  1.03000  0.000000       0       0     1   \n",
+       "1      2  1   BUS2   69   1.1   0.9  1.01970 -0.027981       0       0     1   \n",
+       "2      3  1   BUS3   69   1.1   0.9  1.00042 -0.060097       0       0     1   \n",
+       "3      4  1   BUS4   69   1.1   0.9  0.99858 -0.074721       0       0     1   \n",
+       "4      5  1   BUS5   69   1.1   0.9  1.00443 -0.064315       0       0     1   \n",
+       "5      6  1   BUS6  138   1.1   0.9  0.99871 -0.109998       0       0     2   \n",
+       "6      7  1   BUS7  138   1.1   0.9  1.00682 -0.084285       0       0     2   \n",
+       "7      8  1   BUS8   69   1.1   0.9  1.01895 -0.024339       0       0     2   \n",
+       "8      9  1   BUS9  138   1.1   0.9  1.00193 -0.127502       0       0     2   \n",
+       "9     10  1  BUS10  138   1.1   0.9  0.99351 -0.130202       0       0     2   \n",
+       "10    11  1  BUS11  138   1.1   0.9  0.99245 -0.122948       0       0     2   \n",
+       "11    12  1  BUS12  138   1.1   0.9  0.98639 -0.128934       0       0     2   \n",
+       "12    13  1  BUS13  138   1.1   0.9  0.98403 -0.133786       0       0     2   \n",
+       "13    14  1  BUS14  138   1.1   0.9  0.99063 -0.166916       0       0     2   \n",
+       "\n",
+       "     zone  owner  \n",
+       "uid               \n",
+       "0       1      1  \n",
+       "1       1      1  \n",
+       "2       1      1  \n",
+       "3       1      1  \n",
+       "4       1      1  \n",
+       "5       2      2  \n",
+       "6       2      2  \n",
+       "7       2      2  \n",
+       "8       2      2  \n",
+       "9       2      2  \n",
+       "10      2      2  \n",
+       "11      2      2  \n",
+       "12      2      2  \n",
+       "13      2      2  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.Bus.as_df()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: The method used below, `GroupBase.as_df()`, is newly added in version 1.9.3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>idx</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>Vn</th>\n",
+       "      <th>p0</th>\n",
+       "      <th>q0</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>xs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.40000</td>\n",
+       "      <td>0.15000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.40000</td>\n",
+       "      <td>0.15000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>100</td>\n",
+       "      <td>138</td>\n",
+       "      <td>0.30000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.81442</td>\n",
+       "      <td>0.01962</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   u  name  idx  bus   Sn   Vn       p0       q0  ra    xs\n",
+       "0  1     2    2    2  100   69  0.40000  0.15000   0  0.13\n",
+       "1  1     3    3    3  100   69  0.40000  0.15000   0  0.13\n",
+       "2  1     4    4    6  100  138  0.30000  0.10000   0  0.12\n",
+       "3  1     5    5    8  100   69  0.35000  0.10000   0  0.12\n",
+       "4  1     1    1    1  100   69  0.81442  0.01962   0  0.23"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.StaticGen.as_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>idx</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>gen</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>Vn</th>\n",
+       "      <th>fn</th>\n",
+       "      <th>M</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_1</td>\n",
+       "      <td>GENROU_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_2</td>\n",
+       "      <td>GENROU_2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_3</td>\n",
+       "      <td>GENROU_3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_4</td>\n",
+       "      <td>GENROU_4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100</td>\n",
+       "      <td>138</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_5</td>\n",
+       "      <td>GENROU_5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   u      name       idx  bus  gen   Sn   Vn  fn   M  D\n",
+       "0  1  GENROU_1  GENROU_1    1    1  100   69  60   8  0\n",
+       "1  1  GENROU_2  GENROU_2    2    2  100   69  60  13  0\n",
+       "2  1  GENROU_3  GENROU_3    3    3  100   69  60  10  0\n",
+       "3  1  GENROU_4  GENROU_4    6    4  100  138  60  10  0\n",
+       "4  1  GENROU_5  GENROU_5    8    5  100   69  60  10  0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.SynGen.as_df()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After inspecting the case, we find that there are 5 existing synchronous generators connected to 5 different buses.\n",
+    "To add renewable generators, we need 1) add new static generators, and 2) add new corresponding renweable generators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'PV7'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.add(model='PV', param_dict=dict(idx='PV4', bus=4, p0=0.1, q0=0.1))\n",
+    "ss.add(model='PV', param_dict=dict(idx='PV7', bus=7, p0=0.1, q0=0.1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'REGCV2_2'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Kp, Ki = 0.001, 0.01\n",
+    "ss.add(model='REGCV2', param_dict=dict(bus=4, gen='PV4', Sn=100, Kpvd=Kp, Kivd=Ki, Kpvq=Kp, Kivq=Ki))\n",
+    "ss.add(model='REGCV2', param_dict=dict(bus=7, gen='PV7', Sn=100, Kpvd=Kp, Kivd=Ki, Kpvq=Kp, Kivq=Ki))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we successfully add additional renewable generators to the system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idx</th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>gen</th>\n",
+       "      <th>coi2</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>fn</th>\n",
+       "      <th>Tc</th>\n",
+       "      <th>kw</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>xs</th>\n",
+       "      <th>gammap</th>\n",
+       "      <th>gammaq</th>\n",
+       "      <th>Kpvd</th>\n",
+       "      <th>Kivd</th>\n",
+       "      <th>Kpvq</th>\n",
+       "      <th>Kivq</th>\n",
+       "      <th>Tiq</th>\n",
+       "      <th>Tid</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>REGCV2_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>REGCV2_1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>PV4</td>\n",
+       "      <td>None</td>\n",
+       "      <td>100</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>REGCV2_2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>REGCV2_2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>PV7</td>\n",
+       "      <td>None</td>\n",
+       "      <td>100</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          idx  u      name  bus  gen  coi2   Sn    fn    Tc   kw  ...   ra  \\\n",
+       "uid                                                               ...        \n",
+       "0    REGCV2_1  1  REGCV2_1    4  PV4  None  100  60.0  0.01  0.0  ...  0.0   \n",
+       "1    REGCV2_2  1  REGCV2_2    7  PV7  None  100  60.0  0.01  0.0  ...  0.0   \n",
+       "\n",
+       "      xs  gammap  gammaq   Kpvd  Kivd   Kpvq  Kivq   Tiq   Tid  \n",
+       "uid                                                             \n",
+       "0    0.2     1.0     1.0  0.001  0.01  0.001  0.01  0.01  0.01  \n",
+       "1    0.2     1.0     1.0  0.001  0.01  0.001  0.01  0.01  0.01  \n",
+       "\n",
+       "[2 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.REGCV2.as_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "System internal structure set up in 0.0198 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.setup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: Please note that adding new static generators will alter the system’s power flow solution. Make sure that the power flow remains solvable and reasonable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "-> System connectivity check results:\n",
+      "  No islanded bus detected.\n",
+      "  System is interconnected.\n",
+      "  Each island has a slack bus correctly defined and enabled.\n",
+      "\n",
+      "-> Power flow calculation\n",
+      "           Numba: Off\n",
+      "   Sparse solver: KLU\n",
+      " Solution method: NR method\n",
+      "Power flow initialized in 0.0039 seconds.\n",
+      "0: |F(x)| = 0.5605182134\n",
+      "1: |F(x)| = 0.00578100059\n",
+      "2: |F(x)| = 4.88447848e-06\n",
+      "3: |F(x)| = 3.587970199e-12\n",
+      "Converged in 4 iterations in 0.0030 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.PFlow.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initialization for dynamics completed in 0.0368 seconds.\n",
+      "Initialization was successful.\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = ss.TDS.init()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-> Time Domain Simulation Summary:\n",
+      "Sparse Solver: KLU\n",
+      "Simulation time: 0.0-20.0 s.\n",
+      "Fixed step size: h=33.33 ms. Shrink if not converged.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81757ba79e324a6eba58a4458a6e9309",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?%/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Toggle Toggler_1>: Line.Line_1 status changed to 0 at t=1.0 sec.\n",
+      "<Toggle Toggler_2>: Line.Line_1 status changed to 1 at t=1.1 sec.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Simulation to t=20.00 sec completed in 1.0539 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can see that now the replaced synchronous generator `GENROU_5` is replaced by a renewable generator `REGCV2_1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <Axes: xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot(ss.REGCV2.Pe, latex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <Axes: xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot([ss.GENROU.Pe, ss.REGCV2.Pe], latex=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ams",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/demonstration/manipulate_params.ipynb b/examples/demonstration/manipulate_params.ipynb
new file mode 100644
index 000000000..9085e7756
--- /dev/null
+++ b/examples/demonstration/manipulate_params.ipynb
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Manipulate Parameters Value\n",
+    "\n",
+    "This demo shows how to manipulate the parameters value of a model or group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import andes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, load a small system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating code for 1 models on 4 processes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ss = andes.load(andes.get_case('5bus/pjm5bus.xlsx'), setup=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Per Unit Conversion in ANDES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the system base is 100 MVA."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.config.mva"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Attribute `vin` stores the original input values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4., 4., 4., 4.])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.M.vin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After inspecting the `M` property, we can notice that `power=True`. This means `M` is a power per-unit quantity under the device base."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'non_zero': True,\n",
+       " 'non_positive': False,\n",
+       " 'non_negative': True,\n",
+       " 'mandatory': False,\n",
+       " 'power': True,\n",
+       " 'ipower': False,\n",
+       " 'voltage': False,\n",
+       " 'current': False,\n",
+       " 'z': False,\n",
+       " 'y': False,\n",
+       " 'r': False,\n",
+       " 'g': False,\n",
+       " 'dc_current': False,\n",
+       " 'dc_voltage': False}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.M.property"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Note: in ANDES, `StaticGen` inlcudeing both `PV` and `Slack`, they do have parameter `Sn`.\n",
+    "> However, parameters `p0`, `q0`, `pmax`, `pmin`, `qmax`, and `qmin` are under **system base**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After successful set up, `M.v` is converted to system base from device base."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idx</th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>M</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     idx    u  name     Sn     M    D\n",
+       "uid                                  \n",
+       "0      0  1.0     0  200.0   8.0  0.0\n",
+       "1      2  1.0     2  300.0  12.0  0.0\n",
+       "2      3  1.0     3  200.0   8.0  0.0\n",
+       "3      4  1.0     4  300.0  12.0  0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.as_df()[['idx', 'u', 'name', 'Sn', 'M', 'D']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Manipulate Parameters Value\n",
+    "\n",
+    "In ANDES, there are mainly two ways to manipulate parameters value, i.e., `set` and `alter`.\n",
+    "\n",
+    "`set` is used when you do not wish the changes to be reflected in the dumped case file, while `alter` is used when you want to reflect the changes to the case file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`set` can directly work on the attribute `v` regardless the per unit conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.0)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.set(src='M', idx=2, value=1, attr='v')\n",
+    "ss.GENCLS.get(src='M', idx=2, attr='v')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In contrast, `alter` treats the parameter as device base value, and will convert it to system base value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(3.0)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.alter(src='M', idx=2, value=1, attr='v')\n",
+    "ss.GENCLS.get(src='M', idx=2, attr='v')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "if you wish to change a parameter value in system base, but also want to reflect the changes in the case file, you can use `alter` with `attr='vin'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.0)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.alter(src='M', idx=2, value=1, attr='vin')\n",
+    "ss.GENCLS.get(src='M', idx=2, attr='v')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the `v` attribute is also changed using per unit conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.3333333333333333)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.GENCLS.get(src='M', idx=2, attr='vin')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ams",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/demonstration/replace_SynGen.ipynb b/examples/demonstration/replace_SynGen.ipynb
new file mode 100644
index 000000000..91802c271
--- /dev/null
+++ b/examples/demonstration/replace_SynGen.ipynb
@@ -0,0 +1,882 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Replace Synchronous Generators\n",
+    "\n",
+    "This demo shows how to replace synchronous generators with renewable generators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import andes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If matplotlib inline plots are not showing, use the following line magic to enable it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "andes.config_logger(stream_level=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When loading the case file, don't set it up because adding new devices is not allowed after `System.setup()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Working directory: \"/Users/jinningwang/work/andes/examples/demonstration\"\n",
+      "> Loaded config from file \"/Users/jinningwang/.andes/andes.rc\"\n",
+      "> Loaded generated Python code in \"/Users/jinningwang/.andes/pycode\".\n",
+      "Parsing input file \"/Users/jinningwang/work/andes/andes/cases/ieee14/ieee14_linetrip.xlsx\"...\n",
+      "Input file parsed in 0.1940 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ss = andes.load(andes.get_case('ieee14/ieee14_linetrip.xlsx'),\n",
+    "                setup=False, no_output=True, default_config=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: The methods used below, `GroupBase.as_df` and `GroupBase.as_dict`, are newly added in version 1.9.3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>idx</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>Vn</th>\n",
+       "      <th>p0</th>\n",
+       "      <th>q0</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>xs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.40000</td>\n",
+       "      <td>0.15000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.40000</td>\n",
+       "      <td>0.15000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>100</td>\n",
+       "      <td>138</td>\n",
+       "      <td>0.30000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.35000</td>\n",
+       "      <td>0.10000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>0.81442</td>\n",
+       "      <td>0.01962</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   u  name  idx  bus   Sn   Vn       p0       q0  ra    xs\n",
+       "0  1     2    2    2  100   69  0.40000  0.15000   0  0.13\n",
+       "1  1     3    3    3  100   69  0.40000  0.15000   0  0.13\n",
+       "2  1     4    4    6  100  138  0.30000  0.10000   0  0.12\n",
+       "3  1     5    5    8  100   69  0.35000  0.10000   0  0.12\n",
+       "4  1     1    1    1  100   69  0.81442  0.01962   0  0.23"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.StaticGen.as_df()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>idx</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>gen</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>Vn</th>\n",
+       "      <th>fn</th>\n",
+       "      <th>M</th>\n",
+       "      <th>D</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_1</td>\n",
+       "      <td>GENROU_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_2</td>\n",
+       "      <td>GENROU_2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_3</td>\n",
+       "      <td>GENROU_3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_4</td>\n",
+       "      <td>GENROU_4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100</td>\n",
+       "      <td>138</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>GENROU_5</td>\n",
+       "      <td>GENROU_5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>100</td>\n",
+       "      <td>69</td>\n",
+       "      <td>60</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   u      name       idx  bus  gen   Sn   Vn  fn   M  D\n",
+       "0  1  GENROU_1  GENROU_1    1    1  100   69  60   8  0\n",
+       "1  1  GENROU_2  GENROU_2    2    2  100   69  60  13  0\n",
+       "2  1  GENROU_3  GENROU_3    3    3  100   69  60  10  0\n",
+       "3  1  GENROU_4  GENROU_4    6    4  100  138  60  10  0\n",
+       "4  1  GENROU_5  GENROU_5    8    5  100   69  60  10  0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.SynGen.as_df()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that in this case, the synchronous generators are connected to static generators in a one-to-one relationship.\n",
+    "\n",
+    "To achieve a renewable penetrated system, we need to replace a synchronous generator with a renewable generator.\n",
+    "It can be done in steps:\n",
+    "1. Add a renewable generator\n",
+    "2. Turn off the replaced synchronous generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'REGCV2_1'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.add(model='REGCV2', param_dict=dict(bus=1, gen=1, Sn=100, Kpvd=0.1, Kivd=0.01, Kpvq=0.1, Kivq=0.01))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>idx</th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>gen</th>\n",
+       "      <th>coi2</th>\n",
+       "      <th>Sn</th>\n",
+       "      <th>fn</th>\n",
+       "      <th>Tc</th>\n",
+       "      <th>kw</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ra</th>\n",
+       "      <th>xs</th>\n",
+       "      <th>gammap</th>\n",
+       "      <th>gammaq</th>\n",
+       "      <th>Kpvd</th>\n",
+       "      <th>Kivd</th>\n",
+       "      <th>Kpvq</th>\n",
+       "      <th>Kivq</th>\n",
+       "      <th>Tiq</th>\n",
+       "      <th>Tid</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>REGCV2_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>REGCV2_1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>100</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          idx  u      name  bus  gen  coi2   Sn    fn    Tc   kw  ...   ra  \\\n",
+       "uid                                                               ...        \n",
+       "0    REGCV2_1  1  REGCV2_1    1    1  None  100  60.0  0.01  0.0  ...  0.0   \n",
+       "\n",
+       "      xs  gammap  gammaq  Kpvd  Kivd  Kpvq  Kivq   Tiq   Tid  \n",
+       "uid                                                           \n",
+       "0    0.2     1.0     1.0   0.1  0.01   0.1  0.01  0.01  0.01  \n",
+       "\n",
+       "[1 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.REGCV2.as_df()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> **Note**: The method used below, `GroupBase.alter`, is newly added in version 1.9.3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.SynGen.alter(src='u', idx='GENROU_1', value=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "System internal structure set up in 0.0196 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.setup()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "-> System connectivity check results:\n",
+      "  No islanded bus detected.\n",
+      "  System is interconnected.\n",
+      "  Each island has a slack bus correctly defined and enabled.\n",
+      "\n",
+      "-> Power flow calculation\n",
+      "           Numba: Off\n",
+      "   Sparse solver: KLU\n",
+      " Solution method: NR method\n",
+      "Power flow initialized in 0.0033 seconds.\n",
+      "0: |F(x)| = 0.5605182134\n",
+      "1: |F(x)| = 0.006202200332\n",
+      "2: |F(x)| = 5.819382825e-06\n",
+      "3: |F(x)| = 6.964193111e-12\n",
+      "Converged in 4 iterations in 0.0031 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.PFlow.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we manually tune two parameters to fix the TDS initialization:\n",
+    "1. Extend `TGOV1.VMIN` to a low value to alleviate the limiter `TGOV1.LAG_LIM`\n",
+    "2. Turn off the stability criteria check by setting `TDS.criteria = 0`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss.TGOV1.alter(src='VMIN', idx=ss.TGOV1.idx.v, value=-10)\n",
+    "ss.TDS.config.criteria = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GENROU (vf range) out of typical lower limit.\n",
+      "\n",
+      "   idx    | values | limit\n",
+      "----------+--------+------\n",
+      " GENROU_1 | 0      | 1    \n",
+      "\n",
+      "\n",
+      "Initialization for dynamics completed in 0.0374 seconds.\n",
+      "Initialization was successful.\n"
+     ]
+    }
+   ],
+   "source": [
+    "_ = ss.TDS.init()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-> Time Domain Simulation Summary:\n",
+      "Sparse Solver: KLU\n",
+      "Simulation time: 0.0-20.0 s.\n",
+      "Fixed step size: h=33.33 ms. Shrink if not converged.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "986e2a2f554643a19652e211ec8501f3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?%/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<Toggle Toggler_1>: Line.Line_1 status changed to 0 at t=1.0 sec.\n",
+      "<Toggle Toggler_2>: Line.Line_1 status changed to 1 at t=1.1 sec.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Simulation to t=20.00 sec completed in 0.8834 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can see that now the replaced synchronous generator `GENROU_5` is replaced by a renewable generator `REGCV2_1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <Axes: xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot([ss.GENROU.Pe, ss.REGCV2.Pe], latex=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's inspect the excitation system."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>u</th>\n",
+       "      <th>name</th>\n",
+       "      <th>idx</th>\n",
+       "      <th>syn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>EXST1_1</td>\n",
+       "      <td>EXST1_1</td>\n",
+       "      <td>GENROU_2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>ESST3A_2</td>\n",
+       "      <td>ESST3A_2</td>\n",
+       "      <td>GENROU_1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>ESST3A_3</td>\n",
+       "      <td>ESST3A_3</td>\n",
+       "      <td>GENROU_3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>ESST3A_4</td>\n",
+       "      <td>ESST3A_4</td>\n",
+       "      <td>GENROU_4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>ESST3A_5</td>\n",
+       "      <td>ESST3A_5</td>\n",
+       "      <td>GENROU_5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     u      name       idx       syn\n",
+       "0  1.0   EXST1_1   EXST1_1  GENROU_2\n",
+       "1  1.0  ESST3A_2  ESST3A_2  GENROU_1\n",
+       "2  1.0  ESST3A_3  ESST3A_3  GENROU_3\n",
+       "3  1.0  ESST3A_4  ESST3A_4  GENROU_4\n",
+       "4  1.0  ESST3A_5  ESST3A_5  GENROU_5"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.Exciter.as_df()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the excitation voltage for `GENROU_1` is also turned off as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 640x480 with 1 Axes>, <Axes: xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot([ss.EXST1.vf, ss.ESST3A.vf], latex=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ams",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tests/test_group.py b/tests/test_group.py
index c0539eae2..6b5f6602e 100644
--- a/tests/test_group.py
+++ b/tests/test_group.py
@@ -108,3 +108,41 @@ def test_group_access(self):
                             set(ss.PVD1.idx.v))
         self.assertSetEqual(set(ss.StaticGen.get_all_idxes()),
                             set(ss.PV.idx.v + ss.Slack.idx.v))
+
+
+class TestGroupAdditional(unittest.TestCase):
+    """
+    Test additional group functions.
+    """
+
+    def setUp(self):
+        self.ss = andes.load(
+            andes.get_case('5bus/pjm5bus.xlsx'),
+            setup=True,
+            default_config=True,
+            no_output=True,
+        )
+
+    def test_group_alter(self):
+        """
+        Test `Group.alter` method.
+        """
+
+        # alter `v`
+        self.ss.SynGen.alter(src='M', idx=2, value=1, attr='v')
+        self.assertEqual(self.ss.GENCLS.M.v[1],
+                         1 * self.ss.GENCLS.M.pu_coeff[1])
+
+        # alter `vin`
+        self.ss.SynGen.alter(src='M', idx=2, value=1, attr='vin')
+        self.assertEqual(self.ss.GENCLS.M.v[1], 1)
+
+        # alter `vin` on instances without `vin` falls back to `v`
+        self.ss.SynGen.alter(src='p0', idx=2, value=1, attr='vin')
+        self.assertEqual(self.ss.GENCLS.p0.v[1], 1)
+
+    def test_as_dict(self):
+        """
+        Test `Group.as_dict()`.
+        """
+        self.assertIsInstance(self.ss.SynGen.as_dict(), dict)
diff --git a/tests/test_model_set.py b/tests/test_model_set.py
index df29b54d6..ed4d5ecf0 100644
--- a/tests/test_model_set.py
+++ b/tests/test_model_set.py
@@ -107,3 +107,27 @@ def test_find_idx(self):
         with self.assertRaises(ValueError):
             mdl.find_idx(keys=['gammap', 'name'],
                          values=[[0.1, 0.1], ['PVD1_1']])
+
+    def test_model_alter(self):
+        """
+        Test `Model.alter()` method.
+        """
+
+        ss = andes.run(
+            andes.get_case('5bus/pjm5bus.xlsx'),
+            default_config=True,
+            no_output=True,
+        )
+        ss.TDS.init()
+
+        # alter `v`
+        ss.GENCLS.alter(src='M', idx=2, value=1, attr='v')
+        self.assertEqual(ss.GENCLS.M.v[1], 1 * ss.GENCLS.M.pu_coeff[1])
+
+        # alter `vin`
+        ss.GENCLS.alter(src='M', idx=2, value=1, attr='vin')
+        self.assertEqual(ss.GENCLS.M.v[1], 1)
+
+        # alter `vin` on instances without `vin` falls back to `v`
+        ss.GENCLS.alter(src='p0', idx=2, value=1, attr='vin')
+        self.assertEqual(ss.GENCLS.p0.v[1], 1)