Repository: EnergyAssetOptimization/EAO (language: python)

Issue:
Update samples.rst
Add split optimization notebook


Discussion / hints:

Fix patch:
diff --git a/doc/source/samples.rst b/doc/source/samples.rst
index ddb0e4d..15442fd 100644
--- a/doc/source/samples.rst
+++ b/doc/source/samples.rst
@@ -54,4 +54,12 @@ Robust optimization
    samples/robust_optimization/robust_optimization_szenarios
 
 
+Split optimization
+------------------
+
+.. toctree:: 
+
+   samples/split_optimization/split_optimization
+
+
 
diff --git a/doc/source/samples/split_optimization/split_optimization.ipynb b/doc/source/samples/split_optimization/split_optimization.ipynb
new file mode 100644
index 0000000..38fbbe5
--- /dev/null
+++ b/doc/source/samples/split_optimization/split_optimization.ipynb
@@ -0,0 +1,282 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Split Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import sys\n",
+    "import os\n",
+    "myDir = os.path.join(os.getcwd(), '../..')\n",
+    "sys.path.append(myDir)\n",
+    "addDir = os.path.join(os.getcwd(), '../../../..')\n",
+    "sys.path.append(addDir)\n",
+    "import eaopack as eao"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_results(portfolio, op, res, prices, num_imgs_per_row=2):\n",
+    "    out = eao.io.extract_output(portfolio, op, res, prices)\n",
+    "    num_nodes = len(portfolio.nodes)\n",
+    "    num_img_rows=int(np.ceil((num_nodes+2)/num_imgs_per_row))\n",
+    "    fig, ax = plt.subplots(num_img_rows, num_imgs_per_row, tight_layout = True, figsize=(18,5*num_img_rows))\n",
+    "    ax = ax.reshape(-1)\n",
+    "\n",
+    "    # Plot the dispatch for each node:\n",
+    "    current_ax=0\n",
+    "    dispatch_per_node = {node: [] for node in portfolio.nodes}\n",
+    "    for mycol in out['dispatch'].columns.values:\n",
+    "        for node in dispatch_per_node:\n",
+    "            if '(' + node + ')' in mycol:\n",
+    "                dispatch_per_node[node].append(mycol)\n",
+    "    for node in dispatch_per_node:\n",
+    "        out['dispatch'][dispatch_per_node[node]].plot(ax=ax[current_ax], style='-x')\n",
+    "        ax[current_ax].set_title(\"Dispatch \" + node)\n",
+    "        current_ax+=1\n",
+    "\n",
+    "    # Plot internal variables:\n",
+    "    for v in out['internal_variables']:\n",
+    "        out['internal_variables'][v].plot(ax=ax[current_ax], style='-x', label=v)\n",
+    "    if out['internal_variables'].shape[1] == 0:\n",
+    "        ax[current_ax].text(0.4, 0.5, \"No bool variables present\")\n",
+    "    ax[current_ax].set_title('Bool Variables')\n",
+    "    ax[current_ax].legend()\n",
+    "\n",
+    "    # Remove empty axes:\n",
+    "    for i in range(current_ax+1, len(ax)):\n",
+    "        fig.delaxes(ax[i])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Portfolio Definition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "timegrid = eao.assets.Timegrid(dt.date(2021, 1, 1), dt.date(2021, 4, 1), freq='h', main_time_unit='h')\n",
+    "\n",
+    "prices = {\n",
+    "    'price_gas': np.ones(timegrid.T)*2,\n",
+    "    'price_heat': np.cos(4*np.pi * np.arange(timegrid.T)/timegrid.T)+2,\n",
+    "    'price_power': np.sin(4*np.pi * np.arange(timegrid.T)/timegrid.T)+2,          \n",
+    "}\n",
+    "\n",
+    "prices = timegrid.prices_to_grid(prices)\n",
+    "\n",
+    "prices.plot(title=\"Prices\")\n",
+    "\n",
+    "node_power = eao.assets.Node('node_power')\n",
+    "node_heat = eao.assets.Node('node_heat')\n",
+    "node_gas = eao.assets.Node('node_gas')\n",
+    "\n",
+    "simple_contract_power = eao.assets.SimpleContract(name='SC_power', price='price_power', nodes=node_power,\n",
+    "                                             min_cap=-30., max_cap=20)\n",
+    "simple_contract_heat = eao.assets.SimpleContract(name='SC_heat', price='price_heat', nodes=node_heat,\n",
+    "                                             min_cap=-30., max_cap=20.)\n",
+    "simple_contract_gas = eao.assets.SimpleContract(name='SC_gas', price='price_gas', nodes=node_gas,\n",
+    "                                             min_cap=-30., max_cap=20.)\n",
+    "\n",
+    "\n",
+    "chpasset = eao.assets.CHPAsset(name='CHP',\n",
+    "                               nodes=[node_power, node_heat, node_gas],\n",
+    "                               min_cap=5.,\n",
+    "                               max_cap=30.,\n",
+    "                               ramp=10)\n",
+    "portfolio = eao.portfolio.Portfolio([chpasset, simple_contract_power, simple_contract_heat, simple_contract_gas])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Split Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "op_split = portfolio.setup_split_optim_problem(prices, timegrid, interval_size=\"1w\")\n",
+    "res_split = op_split.optimize(solver=\"GLPK_MI\")\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value: 23393.663322608252\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1296x1080 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Value:\", res_split.value)\n",
+    "plot_results(portfolio, op_split, res_split, prices)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Full Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n"
+     ]
+    }
+   ],
+   "source": [
+    "op_full = portfolio.setup_optim_problem(prices, timegrid)\n",
+    "res_full = op_full.optimize(solver=\"GLPK_MI\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Value: 23464.337087301334\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1296x1080 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Value:\", res_full.value)\n",
+    "plot_results(portfolio, op_full, res_full, prices)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aifa",
+   "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": "3.9.7"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "73bdf4260f2f723a1d590868acdf3c8896a7cafe5288e8a9ee857f3c50f50e00"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/eaopack/assets.py b/eaopack/assets.py
index 0ff7213..f889dd8 100644
--- a/eaopack/assets.py
+++ b/eaopack/assets.py
@@ -289,6 +289,10 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
         assert hasattr(self, 'timegrid'), 'Set timegrid of asset before creating optim problem. Asset: '+ self.name
 
         dt =  self.timegrid.restricted.dt
+
+        if len(dt) == 0: # no overlap between timegrids, asset not active
+            return OptimProblem(c=np.array([]),l=np.array([]), u=np.array([]), cType='', mapping =  pd.DataFrame(),
+                                timegrid = self.timegrid)
         n = self.timegrid.restricted.T # moved to Timegrid
 
         ct = self.cap_out * dt #  Adjust capacity (unit is in vol/h)
diff --git a/eaopack/basic_classes.py b/eaopack/basic_classes.py
index c86da98..45f49ed 100644
--- a/eaopack/basic_classes.py
+++ b/eaopack/basic_classes.py
@@ -51,7 +51,7 @@ def __init__(self, start:dt.datetime, end:dt.datetime, freq:str='h', main_time_u
             freq (str, optional): Frequency for discretization according to pandas notation ('15min', 'h', 'd', ...). Defaults to 'h'
             main_time_unit (str, optional): All times in the optimization problem are measured in the main_time_unit. Pandas notation. Defaults to 'h'
             timezone: Timezone for times. String according to pandas tz definitions (e.g. CET). Defaults to None (naive timezone)
-
+            ref_timegrid (Timegrid, optional): reference TG in case this timegrid is a subset of a suber grid
         Returns:
             timepoints (daterange): specific points in time of mesh
             T (float): number of time steps
@@ -93,7 +93,7 @@ def __init__(self, start:dt.datetime, end:dt.datetime, freq:str='h', main_time_u
                     self.end = pd.Timestamp(self.end)          
             # restricted timegrid is generated by filtering the main timegrid
             if freq == ref_timegrid.freq:
-                I = (ref_timegrid.timepoints>=self.start) & (ref_timegrid.timepoints<self.end)         
+                I = (ref_timegrid.timepoints>=self.start) & (ref_timegrid.timepoints<self.end)
                 self.I = ref_timegrid.I[I]
                 self.timepoints = ref_timegrid.timepoints[I]
                 self.dt = ref_timegrid.dt[I]
@@ -165,6 +165,34 @@ def set_wacc(self, wacc:float):
         d = (1.+wacc)**(1./365.) # convert interest rate to daily
         self.discount_factors =  1./d**(self.Dt*pd.Timedelta(1, self.main_time_unit)/pd.Timedelta(1, 'd')) 
 
+    def prices_to_grid(self, prices: dict):
+        """
+        Convert prices into a dataframe with index self.timepoints.
+        If the price points already have (possibly different) timepoints, then the prices at self.timepoints
+        are interpolated.
+
+        Args:
+            prices: This can be either a pandas dataframe with a Datetimeindex where each column corresponds to a price,
+                or a dictionary of prices. In the case of a dict, the items can have the following forms:
+                - array of length self.T. In this case it is assumed that the i-th entry of the array corresponds to the
+                    i-th point in self.timepoints.
+                - dict where the keys are timepoints and the corresponding items depict the prices at the specific
+                    timepoints
+
+        Returns:
+            prices:
+        """
+        if not isinstance(prices, pd.DataFrame):
+            prices = pd.DataFrame.from_dict(prices)
+        if not isinstance(prices.index, pd.DatetimeIndex):
+            if prices.index.is_numeric():
+                prices.index = self.timepoints
+            else:
+                prices.index = pd.to_datetime(prices.index)
+        prices = prices.reindex(prices.index.union(self.timepoints))
+        prices = prices.interpolate(method='time', limit_direction="both")
+        prices = prices.loc[self.timepoints]
+        return prices
 
     def set_restricted_grid(self,start:dt.datetime = None, end:dt.datetime = None, freq:bool = None):
         """ return dictionary of arrays restricted to start/end
diff --git a/eaopack/io.py b/eaopack/io.py
index 04c50bb..cf9792a 100644
--- a/eaopack/io.py
+++ b/eaopack/io.py
@@ -83,16 +83,12 @@ def extract_output(portf: Portfolio, op: OptimProblem, res:Results, prices: dict
         # extract duals from nodal restrictions
         # looping through nodes and their recorded nodal restrictions and extract dual
         if not res.duals is None and not res.duals['N'] is None:
-            for i_node, n in enumerate(portf.nodes):
-                name_nodal_price = 'nodal price: '+n
-                duals[name_nodal_price] = np.nan # initialize column
-                my_mapping = op.mapping.loc[op.mapping['node']==n,:]
-                all_nr = my_mapping.loc[~my_mapping['nodal_restr'].isnull(),'nodal_restr'].unique()
-                for nr in all_nr:
-                    # relevant info same for all; get first row that fits
-                    r = my_mapping[my_mapping['nodal_restr'] == nr].iloc[0]
-                    # attention: change sign to obtain nodal price from dual
-                    duals.loc[times[r.time_step], name_nodal_price] = -res.duals['N'][r['nodal_restr']]
+            ### new version withour nodal_restr column in mapping, rather explicit reference to time and node in OP
+            for ii, id in enumerate(op.map_nodal_restr):
+                name_nodal_price = 'nodal price: '+ id[1]
+                duals.loc[times[id[0]], name_nodal_price] = -res.duals['N'][ii]
+                pass
+            pass
         else:   
             duals = pd.DataFrame()
         # extract specific parameters for all assets
diff --git a/eaopack/optimization.py b/eaopack/optimization.py
index ae784b7..2cd374e 100644
--- a/eaopack/optimization.py
+++ b/eaopack/optimization.py
@@ -31,7 +31,8 @@ def __init__(self,
                  mapping:pd.DataFrame = None,
                  timegrid:Timegrid = None,  # needed if periodic
                  periodic_period_length:str = None,
-                 periodic_duration:str = None
+                 periodic_duration:str = None,
+                 map_nodal_restr:list = None 
                  ):
         """ Formulated optimization problem. LP problem.
 
@@ -46,6 +47,7 @@ def __init__(self,
                                                          sum of dispatch at node zero: N
                                                       Optional. Defaults to None (no restrictions given)
             mapping (pd.DataFrame): Mapping of variables to 'asset', 'node', 'type' ('d' - dispatch and 'i' internal variable) and 'time_step' 
+            map_nodal_restr (list): Mapping to identify node and timepoint for duals in the solution of the LP
 
             --- if periodic
             timegrid (Timegrid)          : timegrid underneath optim problem (defaults to None)
@@ -59,7 +61,7 @@ def __init__(self,
         self.b     = b     # restriction result vector
         self.cType = cType # GLPK type of restriction (le, ge, ...)      
         self.mapping = mapping 
-
+        self.map_nodal_restr = map_nodal_restr
         assert not np.isnan(c.sum()), 'nan value in optim problem. Check input data -- c'
         assert not np.isnan(l.sum()), 'nan value in optim problem. Check input data -- l'
         assert not np.isnan(u.sum()), 'nan value in optim problem. Check input data -- u'
@@ -223,7 +225,7 @@ def optimize(self, target = 'value',
                     my_bools = False
                 else:
                     isMIP = True ### !!! Need to change solver
-                    print('...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP')
+                    # print('...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP')
             else:
                 my_bools = False
             x = CVX.Variable(self.c.size, boolean = my_bools)
@@ -342,3 +344,42 @@ def optimize(self, target = 'value',
         return results
 
 
+class SplitOptimProblem(OptimProblem):
+    def __init__(self, ops, mapping):
+        """ Collection of consecutive OptimProblems
+
+            Args:
+                ops: List of OptimProblems
+                mapping (pd.DataFrame): Mapping of all result variables to 'asset', 'node', 'type' ('d' - dispatch and 'i' internal variable) and 'time_step'
+        """
+        self.ops = ops
+        self.mapping = mapping
+        self.c = np.hstack([op.c for op in ops])
+        if not ops[0].map_nodal_restr is None:
+            self.map_nodal_restr = []
+            for op in ops:
+                self.map_nodal_restr += op.map_nodal_restr
+        else:
+             self.map_nodal_restr = None
+
+    def optimize(self, *args, **kwargs) -> Results:
+        """ Optimize all OptimProblems in self.ops and piece the results together in one Result
+        """
+        res = Results(0, np.array([]), None)
+        for op in self.ops:
+            res_tmp = op.optimize(*args, **kwargs)
+            res.value += res_tmp.value
+            res.x = np.hstack((res.x, res_tmp.x))
+            if res_tmp.duals:
+                if res.duals:
+                    for key in res_tmp.duals:
+                        if res.duals[key] is not None:
+                            if res_tmp.duals[key] is None:
+                                res.duals[key] = None
+                            else:
+                                res.duals[key] = np.hstack((res.duals[key], res_tmp.duals[key]))
+                else:
+                    res.duals = res_tmp.duals
+        return res
+
+
diff --git a/eaopack/portfolio.py b/eaopack/portfolio.py
index f54ea82..1977264 100644
--- a/eaopack/portfolio.py
+++ b/eaopack/portfolio.py
@@ -3,9 +3,10 @@
 import datetime as dt
 import abc
 import scipy.sparse as sp
+from copy import deepcopy
 from typing import Union, List, Tuple, Dict, Sequence
 from eaopack.assets import Node, Asset, Timegrid, convert_time_unit
-from eaopack.optimization import OptimProblem          
+from eaopack.optimization import OptimProblem, SplitOptimProblem
 from eaopack.optimization import Results 
 
 class Portfolio:
@@ -44,7 +45,6 @@ def set_timegrid(self, timegrid:Timegrid):
         """
         self.timegrid = timegrid
 
-
     def setup_optim_problem(self, prices: dict = None, 
                             timegrid:Timegrid = None, 
                             costs_only:bool = False, 
@@ -129,7 +129,7 @@ def setup_optim_problem(self, prices: dict = None,
         if 'disp_factor' not in mapping.columns:
             mapping['disp_factor'] = 1.
         mapping['disp_factor'].fillna(1., inplace = True)
-        mapping['nodal_restr'] = None
+        # mapping['nodal_restr'] = None
 
         def create_nodal_restr(nodes, map_nodes, map_types, map_idx, map_dispf, map_times, timegrid_I, skip_nodes, n_vars):
             """ Specific function creating nodal restrictions """
@@ -138,6 +138,7 @@ def create_nodal_restr(nodes, map_nodes, map_types, map_idx, map_dispf, map_time
             cols = np.zeros(0)
             rows = np.zeros(0)
             vals = np.zeros(0)
+            nodal_restr_map_expl = []
             for n in nodes:
                 if (skip_nodes is None) or (not n in skip_nodes):
                     Inode = (map_types=='d') & (map_nodes==n)
@@ -155,8 +156,9 @@ def create_nodal_restr(nodes, map_nodes, map_types, map_idx, map_dispf, map_time
                             Itemp[Itemp] = I
                             map_nodal_restr[Itemp] = n_nodal_restr
                             n_nodal_restr +=1
+                            nodal_restr_map_expl.append((t, n))
                             # A = sp.vstack((A, myA))
-            return cols, rows, vals, map_nodal_restr, n_nodal_restr
+            return cols, rows, vals, nodal_restr_map_expl, n_nodal_restr
 
         # # easily readable version -  loop
         # perf = time.perf_counter()
@@ -191,7 +193,7 @@ def create_nodal_restr(nodes, map_nodes, map_types, map_idx, map_dispf, map_time
                                                                                 map_times, self.timegrid.I,my_skip_nodes, 
                                                                                 n_vars)
         A = sp.vstack((A, sp.csr_matrix((vals, (rows.astype(np.int64), cols.astype(np.int64))), shape = (n_nodal_restr, n_vars))))
-        mapping['nodal_restr'] = map_nodal_restr.astype(np.int64)
+        # mapping['nodal_restr'] = map_nodal_restr.astype(np.int64)
         ### end cryptic version
 
         b = np.hstack((b,np.zeros(n_nodal_restr))) # must add to zero
@@ -215,7 +217,76 @@ def create_nodal_restr(nodes, map_nodes, map_types, map_idx, map_dispf, map_time
             I = mapping['time_step'].isin(timegrid.I[fix_time_window['I']])
             l[I] = fix_time_window['x'][I]
             u[I] = fix_time_window['x'][I]
-        return OptimProblem(c = c, l = l, u = u, A = A, b = b, cType = cType, mapping = mapping)
+        return OptimProblem(c = c, l = l, u = u, A = A, b = b, cType = cType, mapping = mapping, map_nodal_restr = map_nodal_restr)
+
+    def setup_split_optim_problem(self, prices: dict = None,
+                                  timegrid: Timegrid = None,
+                                  interval_size: str = 'd',
+                                  skip_nodes: list = [],
+                                  fix_time_window: Dict = None):
+        """ Set up a split optimization problem for portfolio, i.e. split the timegrid into intervals of size
+            interval_size and create a separate optimization problem for each interval
+
+        Args:
+            prices (dict): Dictionary of price arrays needed by assets in portfolio. Defaults to None
+            timegrid (Timegrid, optional): Discretization grid for portfolio and all assets within.
+                                           Defaults to None, in which case it must have been set previously
+            interval_size (bool): Interval size according to pandas notation ('15min', 'h', 'd', ...). Defaults to 'd'
+            skip_nodes (List): Nodes to be skipped in nodal restrictions (defaults to [])
+            fix_time_window (Dict): Fix results for given indices on time grid to given values. Defaults to None
+                           fix_time_window['I']: Indices on timegrid or alternatively date (all dates before date taken)
+                           fix_time_window['x']: Results.x that results are to be fixed to in time window(full array, all times)
+
+            Returns:
+                SplitOptimProblem: A Split Optimization problem
+        """
+        interval_timepoints = pd.date_range(start=timegrid.start, end=timegrid.end, freq=interval_size,
+                                            tz=timegrid.tz)
+        interval_timepoints = interval_timepoints.append(pd.to_datetime([timegrid.end]))
+        if interval_timepoints[0] != pd.Timestamp(timegrid.start):
+            interval_timepoints = interval_timepoints.insert(0, timegrid.start)
+        prices = timegrid.prices_to_grid(prices)
+        ops = []
+        mappings = []
+        t = 0
+        len_res = 0
+        for i in range(len(interval_timepoints) - 1):
+            start_tmp = interval_timepoints[i]
+            end_tmp = interval_timepoints[i + 1]
+            timegrid_tmp = Timegrid(start_tmp, end_tmp, timegrid.freq, ref_timegrid=timegrid)
+            if timegrid_tmp.T == 0: continue
+            tmp_I = timegrid_tmp.I # the original time steps
+            timegrid_tmp.I = np.array(range(0, timegrid_tmp.T))  
+            # timegrid_tmp.Dt = np.cumsum(timegrid_tmp.dt)           ### use the Dt from the reference --> for discounting
+            prices_tmp = timegrid_tmp.prices_to_grid(prices)
+            op_tmp = self.setup_optim_problem(prices_tmp, timegrid_tmp, skip_nodes=skip_nodes,
+                                              fix_time_window=fix_time_window)
+            mapping_tmp = deepcopy(op_tmp.mapping)
+            # mapping_tmp["time_step"] += t
+            # write original time step IDs to mapping
+            orig_I = [tmp_I[a] for a in mapping_tmp["time_step"]]
+            mapping_tmp["time_step"] = orig_I.copy()
+            if i > 0:
+                # mapping_tmp["nodal_restr"] += mappings[-1]["nodal_restr"].max()
+                for asset in self.assets:
+                    shift = mappings[-1][mappings[-1]["asset"] == asset.name]["index_assets"].max() + 1
+                    mapping_tmp.loc[mapping_tmp["asset"] == asset.name, "index_assets"] += shift
+            mapping_tmp.index += len_res
+            mappings.append(mapping_tmp)
+            # adjust numbering for time steps in map_nodal_restr
+            tmp_nod_rest = []
+            for mynr in op_tmp.map_nodal_restr:
+                tmp_nod_rest.append((tmp_I[mynr[0]], mynr[1]))
+            op_tmp.map_nodal_restr = tmp_nod_rest
+            ops.append(op_tmp)
+            len_res += op_tmp.c.shape[0]
+            t += timegrid_tmp.T
+        mapping = pd.concat(mappings)
+        op = SplitOptimProblem(ops, mapping)
+        self.set_timegrid(timegrid)
+        for a in self.assets:
+            a.set_timegrid(timegrid)
+        return op
 
     def create_cost_samples(self, price_samples: List, timegrid: Timegrid = None) -> List:
         """ create costs vectors for LP on basis of price samples
@@ -335,7 +406,7 @@ class LinkedAsset(StructuredAsset):
     This can be used to ensure that one asset turns on only after another asset has been running for at
     least a set amount of time.
     """
-    def __init__(self, portfolio,
+    def __init__(self, portfolio:Portfolio,
                  asset1_variable: Tuple[Union[Asset, str], str, Union[Node, str]],
                  asset2_variable: Tuple[Union[Asset, str], str, Union[Node, str]],
                  asset2_time_already_running: Union[str, float] = "time_already_running",
diff --git a/setup.cfg b/setup.cfg
index 062fb85..b721c49 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = eaopack
-version = 1.2.3
+version = 1.2.4
 author = The EAO development Team
 description = A Framework for Optimizing Decentralized Portfolios and Green Supply
 long_description = file: README.md


Test patch:
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
index bf96fcc..46469e9 100644
--- a/tests/test_optimization.py
+++ b/tests/test_optimization.py
@@ -130,6 +130,39 @@ def test_fixing_results(self):
         resC = opC.optimize()                           
         self.assertAlmostEqual(sum(abs((resA.x[I] - resC.x[I]))), 0., 5)  
 
+class SplitOptimizationTests(unittest.TestCase):
+    def test_same_same(self):
+        node1 = eao.assets.Node('node_1')
+        node2 = eao.assets.Node('node_2')
+        Start = dt.date(2021,2,10) 
+        End   = dt.date(2021,3,12)
+        timegrid = eao.assets.Timegrid(Start, End, freq = 'h')
+        a1 = eao.assets.SimpleContract(name = 'SC_1', price = 'rand_price_1', nodes = node1 ,
+                        min_cap= -20., max_cap=20., start = dt.date(2021,2,10), end = dt.date(2021,3,20), wacc=0.2)
+        a2 = eao.assets.SimpleContract(name = 'SC_2', price = 'rand_price_2', nodes = node1 ,
+                        min_cap= -5., max_cap=10., wacc=0.)
+        a3 = eao.assets.SimpleContract(name = 'SC_3', price = 'rand_price_2', nodes = node2 ,
+                        min_cap= -1., max_cap=10., extra_costs= 1.)
+        a5 = eao.assets.Storage('storage', nodes = node1, \
+             start=dt.date(2021,1,1), end=dt.date(2021,2,1),size=10, \
+             cap_in=1.0/24.0, cap_out=1.0/24.0, start_level=5, end_level=5,
+             block_size='d')
+        pricesA ={'rand_price_1': np.sin(np.linspace(0,10,timegrid.T)),
+                'rand_price_2': np.cos(np.linspace(0,10,timegrid.T))}
+
+        portf = eao.portfolio.Portfolio([a1, a2, a3, a5])
+        ### original
+        opA    = portf.setup_optim_problem(pricesA, timegrid)
+        resA = opA.optimize()
+        outA = eao.io.extract_output(portf, opA, resA)
+        ### split_optim
+        opB    = portf.setup_split_optim_problem(pricesA, timegrid, interval_size='d')
+        resB   = opB.optimize()
+        outB = eao.io.extract_output(portf, opB, resB)
+        # all results must be equal        
+        self.assertAlmostEqual(resA.value, resB.value, 4)
+        self.assertTrue(all(abs(outA['dispatch']-outB['dispatch']).sum()<1e-5))
+        self.assertTrue(all(abs(outA['prices']-outB['prices']).sum()<1e-3))
 ###########################################################################################################
 ###########################################################################################################
 ###########################################################################################################
diff --git a/tests/test_portfolio.py b/tests/test_portfolio.py
index 10ea884..786fa9a 100644
--- a/tests/test_portfolio.py
+++ b/tests/test_portfolio.py
@@ -140,6 +140,31 @@ def test_structured_asset(self):
         eao.io.output_to_file(output=outp, file_name= 'test.xlsx')
 
 
+    def test_linked_asset(self):
+        """ Unit test. Linked asset with asset 1 having higher costs. Runs before asset 2
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        timegrid = eao.assets.Timegrid(dt.date(2021,1,1), dt.date(2021,2,1), freq = 'h')
+        demand = eao.assets.SimpleContract(name = 'demand', nodes = node_power ,     min_cap= -10., max_cap=-10.)
+        a1 =     eao.assets.CHPAsset(name='CHP1', extra_costs = 10, nodes = (node_power, node_heat), min_cap=5., max_cap= 5.)
+        a2 =     eao.assets.CHPAsset(name='CHP2', extra_costs = 5, nodes = (node_power, node_heat), min_cap=2., max_cap=15.)
+        p = eao.portfolio.Portfolio([a1, a2]) # collect the two assets
+        linked = eao.portfolio.LinkedAsset(p, nodes = [node_power, node_heat], 
+                                            asset1_variable=[a2, 'disp', node_power], 
+                                            asset2_variable=[a1, 'bool_on', None],
+                                            time_back=0,
+                                            name = 'extra_CHP')
+        check_op = linked.setup_optim_problem(prices = [], timegrid=timegrid)
+        portf = eao.portfolio.Portfolio([linked, demand])
+        op = portf.setup_optim_problem(prices = [], timegrid=timegrid)
+        res = op.optimize()
+        out = eao.io.extract_output(portf, op, res)
+        # if asset a1 must run first, before a2 can gererate, costs must be 5*10 + 5*5 = 75
+        self.assertAlmostEqual(np.abs(out['DCF']['extra_CHP'].values + 75.).sum(),0, 4)
+
+
+
     def test_various_vars_in_mapping(self):
         """ testing more efficient approach where more than one row in mapping
         can exist per variable """
@@ -210,6 +235,8 @@ def test_portf_min_cap_from_price_DF(self):
         self.assertAlmostEqual(abs(disp[19:]).sum(), 0., 5)
         self.assertAlmostEqual(abs(disp[9:19]-prices['CAP'][9:19]).sum(), 0., 5)
 
+
+
 ###########################################################################################################
 
 ###########################################################################################################