Repository: EnergyAssetOptimization/EAO (language: python)

Issue:
Added PyPI (test) Packaging
Die Konfiguration läuft jetzt erst mal gegen den PyPI-Testserver. Dafür habe ich einen Account mit dem Namen "EnergyAssetOptimization" erstellt. Die Github-Action muss manuell angestoßen werden. Optional können wir es irgendwann so umstellen, dass mit dem Merge nach Main automatisch pupliziert wird.

Schaut euch an ob die Konfiguration passt.
* Package name: 'eaopack' (eao ist leider schon vergeben)
* Die Liste der Emailadressen müsst Ihr definieren
 * Namen / Beschreibung könnt Ihr ggfls. noch anpassen




Discussion / hints:
CI scheint mit dem PR nicht mehrzu laufen. Ich löse das Problem im Fork und öffne den PR anschließend wieder.
Das Problem lag bei GitHub, jetzt geht es wieder.
Mit der aktuellen Konfiguration würde das Package so aussehen: https://test.pypi.org/project/eaopack/1.0.0/

Fix patch:
diff --git a/doc/source/samples.rst b/doc/source/samples.rst
index 04cc639..b8d45bd 100644
--- a/doc/source/samples.rst
+++ b/doc/source/samples.rst
@@ -23,6 +23,13 @@ Multi commodity optimization
 
    samples/cross_commodity/cross_commodity_sample
 
+Combining Heat and Power
+----------------------------
+
+.. toctree::
+
+   samples/combined_heat_power/chp_sample
+
 Efficient optimization across different time scales
 ---------------------------------------------------
 
diff --git a/doc/source/samples/combined_heat_power/chp_sample.ipynb b/doc/source/samples/combined_heat_power/chp_sample.ipynb
new file mode 100644
index 0000000..cbe771e
--- /dev/null
+++ b/doc/source/samples/combined_heat_power/chp_sample.ipynb
@@ -0,0 +1,314 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Combining heat and power\n",
+    "The CHPAsset class represents an asset that can generate both heat and power at the same time and models starting procedures, minimum loads, ramping and a minimum runtime."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Technical Details\n",
+    "### Input Parameters\n",
+    "Possible input parameters of the CHPAsset are\n",
+    "- Ramp $r$\n",
+    "- Conversion efficiency $\\alpha$ from heat to power.\n",
+    "- Maximum share heat $\\beta$\n",
+    "- Minimum capacity $l_t$ for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "- Maximum capacity $u_t$ for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "- Dispatch power $x_{-1}$ from the previous timestep\n",
+    "- Dispatch heat $w_{-1}$ from the previous timestep\n",
+    "- Number $k$ of timesteps the asset has already been running\n",
+    "- Minimum runtime $m$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Output variables\n",
+    "The following variables are computed for a CHPAsset that runs for $T$ timesteps:\n",
+    "- Dispatch power $x_t \\in \\mathbb{R}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "- Dispatch heat $w_t \\in \\mathbb{R}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "- \"On\"-variable $a_t \\in \\left\\{0,1\\right\\}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "- \"Start\"-variable $s_t \\in \\left\\{0,1\\right\\}$, for every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$\n",
+    "\n",
+    "When they are not needed to enforce a minimum runtime, start costs or similar, the \"on\" and \"start\" variables are removed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variable bounds\n",
+    "The output variables are within the following bounds in every timestep $t \\in \\left\\{0,\\dots, T-1\\right\\}$:\n",
+    "- $0 \\leq x_t \\leq u_t$\n",
+    "- $0 \\leq w_t \\leq \\beta u_t$\n",
+    "- $0 \\leq a_t \\leq 1$\n",
+    "- $0 \\leq s_t \\leq 1$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Constraints\n",
+    "The following constraints are used in the asset\n",
+    "\n",
+    "- **Ramp**: Ensure that the increase/decrease of the virtual dispatch $\\tilde{x}_t = x_t + \\alpha w_t$ is bounded by $r$, i.e.   \n",
+    "\t\\begin{align*}\n",
+    "\t\t\t\t\t-r \\leq \\tilde{x}_t-\\tilde{x}_{t-1} \\leq r,\n",
+    "\t\\end{align*} \n",
+    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{0,\\dots, T-1\\right\\}$.\n",
+    "\n",
+    "-  **Minimum and maximum capacity**: The virtual dispatch $\\tilde{x}_t = x_t + \\alpha w_t$ is 0 when $a_t = 0$, i.e. the asset is \"off\", or between minimum and maximum capacity, i.e.\n",
+    "\\begin{align*} a_t l_t \\leq \\tilde{x}_t \\leq a_t u_t, \\end{align*} \n",
+    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{0,\\dots, T-1\\right\\}$.\n",
+    "\n",
+    "- **Start constraints**: When the \"on\" variable changes from \"off\" to \"on\", the start variable has to be 1, i.e.\n",
+    "\\begin{align*}s_t \\geq a_t - a_{t-1},\\end{align*} \n",
+    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{1,\\dots, T-1\\right\\}$. <br>\n",
+    "&emsp;&nbsp;&nbsp;&nbsp; If $k = 0$, i.e. previous state $a_{-1}$ is \"off\", we need the additional condition \n",
+    "\\begin{align*}s_0 \\geq a_0.\\end{align*}\n",
+    "\n",
+    "- **Minimum runtime**: Ensure that the asset follows the minimum runtime $m$, i.e.\n",
+    "\\begin{align*}a_t \\geq s_{t-i}\\end{align*} \n",
+    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{1,\\dots, T-1\\right\\}$ and $i \\in \\left\\{1, \\dots, m-1\\right\\}$. <br>\n",
+    "&emsp;&nbsp;&nbsp;&nbsp; If $k>0$ and $m > k$, we need the additional condition \n",
+    "\\begin{align*}a_i \\geq 1,\\end{align*}\n",
+    "&emsp;&nbsp;&nbsp;&nbsp; for all $i \\in \\left\\{0, \\dots, m -k-1\\right\\}$.\n",
+    "\n",
+    "- **Maximum share heat**: Bound the heat dispatch by a percentage of the power dispatch, i.e.\n",
+    "\\begin{align*}w_t \\leq \\beta x_t,\\end{align*} \n",
+    "&emsp;&nbsp;&nbsp;&nbsp; for $t \\in \\left\\{0, \\dots, T-1\\right\\}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example Portfolio\n",
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "\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",
+    "\n",
+    "import eaopack as eao\n",
+    "from eaopack.portfolio import Portfolio\n",
+    "from eaopack.basic_classes import Timegrid\n",
+    "from eaopack.optimization import Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining a portfolio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "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",
+    "timegrid = eao.assets.Timegrid(dt.date(2021, 1, 1), dt.date(2021, 1, 2), freq='h', main_time_unit='h')\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",
+    "                               extra_costs=0,\n",
+    "                               freq='h',\n",
+    "                               conversion_factor_power_heat=0.5, \n",
+    "                               max_share_heat=0.5,\n",
+    "                               ramp=20, \n",
+    "                               start_costs=1., \n",
+    "                               running_costs=0., \n",
+    "                               min_runtime=0, \n",
+    "                               time_already_running=0., \n",
+    "                               last_dispatch=0., \n",
+    "                               start_fuel=0)\n",
+    "\n",
+    "prices = {'price_power': np.sin(0.5*np.pi * np.arange(timegrid.T)*np.pi/10)+2,\n",
+    "          'price_heat': np.cos(0.5*np.pi * np.arange(timegrid.T)*np.pi/10)+2, \n",
+    "          'price_gas': np.ones(timegrid.T)*2, \n",
+    "          }\n",
+    "\n",
+    "portfolio = eao.portfolio.Portfolio([chpasset, simple_contract_power, simple_contract_heat, simple_contract_gas])\n",
+    "eao.network_graphs.create_graph(portf = portfolio)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing the Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n"
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "               Values\nParameter            \nstatus     successful\nvalue      254.108917",
+      "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>Values</th>\n    </tr>\n    <tr>\n      <th>Parameter</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>status</th>\n      <td>successful</td>\n    </tr>\n    <tr>\n      <th>value</th>\n      <td>254.108917</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "op = portfolio.setup_optim_problem(prices, timegrid)\n",
+    "res = op.optimize(solver=\"GLPK_MI\")\n",
+    "# res = op.optimize(solver=\"GLPK\", make_soft_problem=True)  # Relax bool variables, i.e. allow \"half on\"\n",
+    "out = eao.io.extract_output(portfolio, op, res, prices)\n",
+    "out['summary']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 1296x720 with 5 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "# Define the grid size to display the figures:\n",
+    "num_imgs_per_row = 3\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",
+    "current_ax+=1\n",
+    "\n",
+    "# Plot prices\n",
+    "for key in out['prices']:\n",
+    "    out['prices'][key].plot(ax=ax[current_ax], style='-x', label=key)\n",
+    "ax[current_ax].set_title('Prices')\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])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python371164bit6bbe9e9d94b3492cb1573c25f16af2f8",
+   "display_name": "Python 3.7.11 64-bit"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/doc/source/samples/combined_heat_power/linkedAsset_sample.ipynb b/doc/source/samples/combined_heat_power/linkedAsset_sample.ipynb
new file mode 100644
index 0000000..3e423f0
--- /dev/null
+++ b/doc/source/samples/combined_heat_power/linkedAsset_sample.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linking Assets\n",
+    "The LinkedAsset class can link two Assets and wrap them as one asset with additional constraints."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example Portfolio\n",
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import datetime as dt\n",
+    "import matplotlib.pyplot as plt\n",
+    "\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",
+    "\n",
+    "import eaopack as eao\n",
+    "from eaopack.portfolio import Portfolio, LinkedAsset\n",
+    "from eaopack.basic_classes import Timegrid\n",
+    "from eaopack.optimization import Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining a portfolio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "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",
+    "timegrid = eao.assets.Timegrid(dt.date(2021, 1, 1), dt.date(2021, 1, 2), freq='h', main_time_unit='h')\n",
+    "simple_contract_power = eao.assets.SimpleContract(name='SC_power', price='price_power', nodes=node_power,\n",
+    "                                             min_cap=-100., max_cap=0)\n",
+    "simple_contract_heat = eao.assets.SimpleContract(name='SC_heat', price='price_heat', nodes=node_heat,\n",
+    "                                             min_cap=-100., max_cap=0.)\n",
+    "simple_contract_gas = eao.assets.SimpleContract(name='SC_gas', price='price_gas', nodes=node_gas,\n",
+    "                                             min_cap=0., max_cap=100.)\n",
+    "\n",
+    "\n",
+    "chpasset1 = eao.assets.CHPAsset(name='CHP1', \n",
+    "                               nodes=[node_power, node_heat, node_gas],\n",
+    "                               min_cap=5., \n",
+    "                               max_cap=30.,\n",
+    "                               extra_costs=0,\n",
+    "                               freq='h',\n",
+    "                               conversion_factor_power_heat=0.5, \n",
+    "                               max_share_heat=0.5,\n",
+    "                               ramp=20, \n",
+    "                               start_costs=0., \n",
+    "                               running_costs=0., \n",
+    "                               min_runtime=0, \n",
+    "                               time_already_running=0., \n",
+    "                               last_dispatch=0., \n",
+    "                               start_fuel=0)\n",
+    "chpasset2 = eao.assets.CHPAsset(name='CHP2', \n",
+    "                               nodes=[node_power, node_heat, node_gas],\n",
+    "                               min_cap=5., \n",
+    "                               max_cap=30.,\n",
+    "                               extra_costs=0,\n",
+    "                               freq='h',\n",
+    "                               conversion_factor_power_heat=0.5, \n",
+    "                               max_share_heat=0.5,\n",
+    "                               ramp=20, \n",
+    "                               start_costs=0., \n",
+    "                               running_costs=0., \n",
+    "                               min_runtime=0, \n",
+    "                               time_already_running=1., \n",
+    "                               last_dispatch=0., \n",
+    "                               start_fuel=0)\n",
+    "\n",
+    "prices = {'price_power': np.sin(0.5*np.pi * np.arange(timegrid.T)*np.pi/10)+2,\n",
+    "          'price_heat': np.cos(0.5*np.pi * np.arange(timegrid.T)*np.pi/10)+2, \n",
+    "          'price_gas': np.ones(timegrid.T)*2, \n",
+    "          }\n",
+    "\n",
+    "linkedAsset_portfolio = Portfolio([chpasset1, chpasset2])\n",
+    "linkedAsset = LinkedAsset(portfolio=linkedAsset_portfolio, asset1_variable=(chpasset1, \"bool_on\", None), asset2_variable=(chpasset2, \"bool_on\", None), name=\"linkedAsset\", nodes=[node_power, node_heat, node_gas], time_back=2, time_forward=1)\n",
+    "\n",
+    "portfolio = eao.portfolio.Portfolio([linkedAsset, simple_contract_power, simple_contract_heat, simple_contract_gas])\n",
+    "eao.network_graphs.create_graph(portf = portfolio)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Performing the Optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "...MIP problem configured. Beware of potentially long optimization and other issues inherent to MIP\n"
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "               Values\nParameter            \nstatus     successful\nvalue      714.195228",
+      "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>Values</th>\n    </tr>\n    <tr>\n      <th>Parameter</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>status</th>\n      <td>successful</td>\n    </tr>\n    <tr>\n      <th>value</th>\n      <td>714.195228</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "op = portfolio.setup_optim_problem(prices, timegrid)\n",
+    "res = op.optimize(solver=\"GLPK_MI\")\n",
+    "# res = op.optimize(solver=\"GLPK\", make_soft_problem=True)  # Relax bool variables, i.e. allow \"half on\"\n",
+    "out = eao.io.extract_output(portfolio, op, res, prices)\n",
+    "out['summary']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 1296x1080 with 5 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "# Define the grid size to display the figures:\n",
+    "num_imgs_per_row = 3\n",
+    "num_nodes = len(portfolio.nodes)\n",
+    "num_imgs = num_nodes+2\n",
+    "num_img_rows=int(np.ceil(num_imgs/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",
+    "current_ax+=1\n",
+    "\n",
+    "# Plot prices\n",
+    "for key in out['prices']:\n",
+    "    out['prices'][key].plot(ax=ax[current_ax], style='-x', label=key)\n",
+    "ax[current_ax].set_title('Prices')\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])"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python371164bit6bbe9e9d94b3492cb1573c25f16af2f8",
+   "display_name": "Python 3.7.11 64-bit"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/eaopack/assets.py b/eaopack/assets.py
index 5b5a338..6269344 100644
--- a/eaopack/assets.py
+++ b/eaopack/assets.py
@@ -1,8 +1,9 @@
-from typing import Union, List, Dict
+from typing import Union, List, Dict, Sequence
 import datetime as dt
 import abc
 import numpy as np
 import pandas as pd
+from pandas.tseries.frequencies import to_offset
 import scipy.sparse as sp
 from scipy.sparse.lil import lil_matrix
 
@@ -147,6 +148,35 @@ def __extend_mapping_to_minor_grid__(self, mapping:pd.DataFrame):
         mapping['time_step'] = mapping['time_step'].astype('int64')
         return mapping
 
+    def convert_to_timegrid_freq(self, time_value: float, attribute_name: str, timegrid:Timegrid = None, round: bool = True) -> Union[float, int]:
+        """ Convert time_value from the timegrids main_time_unit to the timegrid.freq
+
+        Args:
+            time_value (float): The time value in timegrid.main_time_unit
+            attribute_name (str): The name of the attribute to be converted (only relevant for more specific warning)
+            timegrid (Timegrid): The timegrid from with main_time_unit and freq are used. If timegrid is None,
+                                 the asset's own timegrid self.timegrid is taken instead. Defaults to None.
+            round: If true the result is rounded to the next highest integer.
+
+        Returns:
+            converted_time_value: time_value converted to timegrid.freq
+        """
+        if timegrid is None:
+            timegrid = self.timegrid
+            if timegrid is None:
+                raise ValueError("Timegrid is not specified.")
+        time_value_converted = convert_time_unit(time_value, old_freq=timegrid.main_time_unit, new_freq=timegrid.freq)
+        if round:
+            if not time_value_converted.is_integer():
+                print("Warning for asset ", self.name, ": ", attribute_name, " is ", time_value,
+                      " in freq '", timegrid.main_time_unit,
+                      "' which corresponds to ", time_value_converted, " in freq '", timegrid.freq,"'. ",
+                      "This is not an integer and will therefore be rounded to ", np.ceil(time_value_converted),
+                      " in freq '", timegrid.freq, "'.", sep='')
+                time_value_converted = np.ceil(time_value_converted)
+            time_value_converted = int(time_value_converted)
+        return time_value_converted
+
 ##########################
 
 class Storage(Asset):
@@ -478,7 +508,7 @@ def __init__(self,
                 end:   dt.datetime = None,
                 wacc: float = 0,
                 price:str = None,
-                extra_costs:float = 0.,
+                extra_costs: Union[float, Dict, str] = 0.,
                 min_cap: Union[float, Dict, str] = 0.,
                 max_cap: Union[float, Dict, str] = 0.,
                 freq: str = None,
@@ -508,7 +538,12 @@ def __init__(self,
                                            dict['value'] = array
                                     str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
             price (str): Name of price vector for buying / selling. Defaults to None
-            extra_costs (float, optional): extra costs added to price vector (in or out). Defaults to 0.
+            extra_costs (float, dict, str): extra costs added to price vector (in or out). Defaults to 0.
+                                            float: constant value
+                                            dict:  dict['start'] = array
+                                                   dict['end']   = array
+                                                   dict['value'] = array
+                                            str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
 
             periodicity (str, pd freq style): Makes assets behave periodicly with given frequency. Periods are repeated up to freq intervals (defaults to None)
             periodicity_duration (str, pd freq style): Intervals in which periods repeat (e.g. repeat days ofer whole weeks)  (defaults to None)
@@ -586,22 +621,9 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
         ## if it's zero, one variable is enough
 
         # Make vector of single min/max capacities.
-        if isinstance(self.max_cap, (float, int, np.ndarray)):
-            max_cap = self.max_cap*np.ones(T)
-        elif isinstance(self.max_cap, str):
-            assert (self.max_cap in prices), 'data for max_cap not found for asset  '+self.name
-            max_cap = prices[self.max_cap].copy()
-            max_cap = max_cap[I] #  only in asset time window
-        else: # given in form of dict (start/end/values)
-            max_cap = timegrid.restricted.values_to_grid(self.max_cap)
-        if isinstance(self.min_cap, (float, int, np.ndarray)):
-            min_cap = self.min_cap*np.ones(T)
-        elif isinstance(self.min_cap, str):
-            assert (self.min_cap in prices), 'data for min_cap not found for asset  '+self.name
-            min_cap = prices[self.min_cap].copy()
-            min_cap = min_cap[I] #  only in asset time window
-        else: # given in form of dict (start/end/values)
-            min_cap = timegrid.restricted.values_to_grid(self.min_cap)
+        max_cap = self.make_vector(self.max_cap, prices)
+        min_cap = self.make_vector(self.min_cap, prices)
+
         # check integrity
         if any(min_cap>max_cap):
             raise ValueError('Asset --' + self.name+'--: Contract with min_cap > max_cap leads to ill-posed optimization problem')
@@ -609,16 +631,19 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
         min_cap = min_cap * self.timegrid.restricted.dt
         max_cap = max_cap * self.timegrid.restricted.dt
 
+        # Make vector of extra_costs:
+        extra_costs = self.make_vector(self.extra_costs, prices, default_value=0)
+
         mapping = pd.DataFrame() ## mapping of variables for use in portfolio
-        if (self.extra_costs == 0) or (all(max_cap<=0.)) or (all(min_cap>=0.)):
+        if (all(extra_costs==0.)) or (all(max_cap<=0.)) or (all(min_cap>=0.)):
             # in this case no need for two variables per time step
             u =  max_cap # upper bound
             l =  min_cap # lower
-            if self.extra_costs !=0:
+            if any(extra_costs !=0):
                 if (all(max_cap<=0.)): # dispatch always negative
-                    price = price - self.extra_costs
+                    price = price - extra_costs
                 if (all(min_cap>=0.)): # dispatch always negative
-                    price = price + self.extra_costs
+                    price = price + extra_costs
             c = price * discount_factors # set price and discount
             mapping['time_step'] = I
             mapping['var_name']  = 'disp' # name variables for use e.g. in RI
@@ -629,7 +654,7 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
             # in full contract there may be different prices for in/out
             c = np.tile(price, (2,1))
             # add extra costs to in/out dispatch
-            ec = np.vstack((-np.ones(T)*self.extra_costs, np.ones(T)*self.extra_costs))
+            ec = np.vstack((-extra_costs, extra_costs))
             c  = c + ec
             # discount the cost vectors:
             c = c * (np.tile(discount_factors, (2,1)))
@@ -664,6 +689,38 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
             return OptimProblem(c = c, l = l, u = u,
                                 mapping = mapping)
 
+    def make_vector(self, value:  Union[float, Dict, str], prices:dict, default_value: float = None):
+        """
+        Make a vector out of value
+        Args:
+            value (float, dict, str): The value to be converted to a vector
+                                      float: constant value
+                                      dict:  dict['start'] = array
+                                             dict['end']   = array
+                                             dict['value'] = array
+                                      str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
+            prices (dict): Dictionary of price arrays needed by assets in portfolio
+            default_value (float): The value that is used if any of the entries of the resulting vector are not specified
+
+        Returns:
+
+        """
+        I = self.timegrid.restricted.I  # indices of restricted time grid
+        T = self.timegrid.restricted.T
+        if value is None:
+            return value
+        elif isinstance(value, (float, int, np.ndarray)):
+            vec = value * np.ones(T)
+        elif isinstance(value, str):
+            assert (value in prices), 'data for ' + value + 'not found for asset  ' + self.name
+            vec = prices[value].copy()
+            vec = vec[I]  # only in asset time window
+        else:  # given in form of dict (start/end/values)
+            vec = self.timegrid.restricted.values_to_grid(value)
+            if default_value is not None:
+                vec[np.isnan(vec)] = default_value
+        return vec
+
 
 class Transport(Asset):
     """ Contract Class """
@@ -887,7 +944,7 @@ def __init__(self,
                 end:   dt.datetime = None,
                 wacc: float = 0,
                 price:str = None,
-                extra_costs:float = 0.,
+                extra_costs: Union[float, Dict, str] = 0.,
                 min_cap: Union[float, Dict, str] = 0.,
                 max_cap: Union[float, Dict, str] = 0.,
                 min_take: dict = None,
@@ -928,7 +985,12 @@ def __init__(self,
                                      dict['end']   = np.array
                                      dict['value'] = np.array
             price (str): Name of price vector for buying / selling
-            extra_costs (float, optional): extra costs added to price vector (in or out). Defaults to 0.
+            extra_costs (float, dict, str): extra costs added to price vector (in or out). Defaults to 0.
+                                            float: constant value
+                                            dict:  dict['start'] = array
+                                                   dict['end']   = array
+                                                   dict['value'] = array
+                                            str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
 
             periodicity (str, pd freq style): Makes assets behave periodicly with given frequency. Periods are repeated up to freq intervals (defaults to None)
             periodicity_duration (str, pd freq style): Intervals in which periods repeat (e.g. repeat days ofer whole weeks)  (defaults to None)
@@ -1020,6 +1082,391 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
             op.__make_periodic__(freq_period = self.periodicity, freq_duration = self.periodicity_duration, timegrid = timegrid)
         return op
 
+def convert_time_unit(value: float, old_freq:str, new_freq:str) -> float:
+    """
+    Convert time value from old_freq to new_freq
+    Args:
+        value (float): the time value to convert
+        old_freq: pandas frequency string, e.g. 'd', 'h', 'min', '15min', '1d1h'
+        new_freq: pandas frequency string, e.g. 'd', 'h', 'min', '15min', '1d1h'
+
+    Returns:
+        the time value converted from old_freq to new_freq
+    """
+    return value * pd.to_timedelta(to_offset(old_freq)) / pd.to_timedelta(to_offset(new_freq))
+
+class CHPAsset(Contract):
+    def __init__(self,
+                 name: str = 'default_name_contract',
+                 nodes: List[Node] = [Node(name = 'default_node_power'), Node(name = 'default_node_heat')],
+                 start: dt.datetime = None,
+                 end:   dt.datetime = None,
+                 wacc: float = 0,
+                 price:str = None,
+                 extra_costs: Union[float, Dict, str] = 0.,
+                 min_cap: Union[float, Dict, str] = 0.,
+                 max_cap: Union[float, Dict, str] = 0.,
+                 min_take:dict = None,
+                 max_take:dict = None,
+                 freq: str = None,
+                 profile: pd.Series = None,
+                 periodicity: str = None,
+                 periodicity_duration: str = None,
+                 conversion_factor_power_heat: Union[float, Dict, str] = 1.,
+                 max_share_heat: Union[float, Dict, str] = None,
+                 ramp: float = None,
+                 start_costs: Union[float, Sequence[float], Dict] = 0.,
+                 running_costs: Union[float, Dict, str] = 0.,
+                 min_runtime: float = 0,
+                 time_already_running: float = 0,
+                 last_dispatch: float = 0,
+                 start_fuel: Union[float, Dict, str] = 0.,
+                 fuel_efficiency: Union[float, Dict, str] = 1.,
+                 consumption_if_on: Union[float, Dict, str] = 0.
+                 ):
+        """ CHPContract: Generate heat and power
+            Restrictions
+            - time dependent capacity restrictions
+            - MinTake & MaxTake for a list of periods
+            - start costs
+            - minimum runtime
+            - ramps
+        Args:
+            name (str): Unique name of the asset                                              (asset parameter)
+            nodes (Node): One node each for generated power and heat                          (asset parameter)
+                          optional: node for fuel (e.g. gas)
+            start (dt.datetime) : start of asset being active. defaults to none (-> timegrid start relevant)
+            end (dt.datetime)   : end of asset being active. defaults to none (-> timegrid start relevant)
+            timegrid (Timegrid): Timegrid for discretization                                  (asset parameter)
+            wacc (float): Weighted average cost of capital to discount cash flows in target   (asset parameter)
+            freq (str, optional):   Frequency for optimization - in case different from portfolio (defaults to None, using portfolio's freq)
+                                    The more granular frequency of portf & asset is used
+            profile (pd.Series, optional):  If freq(asset) > freq(portf) assuming this profile for granular dispatch (e.g. scaling hourly profile to week).
+                                            Defaults to None, only relevant if freq is not none
+            min_cap (float) : Minimum capacity for generating virtual dispatch (power + conversion_factor_power_heat * heat). Has to be greater or equal to 0. Defaults to 0.
+            max_cap (float) : Maximum capacity for generating virtual dispatch (power + conversion_factor_power_heat * heat). Has to be greater or equal to 0. Defaults to 0.
+            min_take (float) : Minimum volume within given period. Defaults to None
+            max_take (float) : Maximum volume within given period. Defaults to None
+                              float: constant value
+                              dict:  dict['start'] = np.array
+                                     dict['end']   = np.array
+                                     dict['value'] = np.array
+            price (str): Name of price vector for buying / selling
+            extra_costs (float, dict, str): extra costs added to price vector (in or out). Defaults to 0.
+                                            float: constant value
+                                            dict:  dict['start'] = array
+                                                   dict['end']   = array
+                                                   dict['value'] = array
+                                            str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
+            periodicity (str, pd freq style): Makes assets behave periodicly with given frequency. Periods are repeated up to freq intervals (defaults to None)
+            periodicity_duration (str, pd freq style): Intervals in which periods repeat (e.g. repeat days ofer whole weeks)  (defaults to None)
+            conversion_factor_power_heat (float, dict, str): Conversion efficiency from heat to power. Defaults to 1.
+            max_share_heat (float, dict, str): Defines upper bound for the heat dispatch as a percentage of the power dispatch. Defaults to 1.
+            ramp (float): Maximum increase/decrease of virtual dispatch (power + conversion_factor_power_heat * heat) in one timestep. Defaults to 1.
+            start_costs (float): Costs for starting. Defaults to 0.
+            running_costs (float): Costs when on. Defaults to 0.
+            min_runtime (int): Minimum runtime in timegrids main_time_unit. Defaults to 0.
+            time_already_running (int): The number of timesteps the asset is already running in timegrids main_time_unit. Defaults to 0.
+            last_dispatch (float): Previous virtual dispatch (power + conversion_factor_power_heat * heat). Defaults to 0.
+
+            Optional: Explicit fuel consumption (e.g. gas) for multi-commodity simulation
+                 start_fuel (float, dict, str): detaults to  0
+                 fuel_efficiency (float, dict, str): defaults to 1
+                 consumption_if_on (float, dict, str): defaults to 0
+        """
+        super(CHPAsset, self).__init__(name=name,
+                                       nodes=nodes,
+                                       start=start,
+                                       end=end,
+                                       wacc=wacc,
+                                       freq=freq,
+                                       profile=profile,
+                                       price=price,
+                                       extra_costs=extra_costs,
+                                       min_cap=min_cap,
+                                       max_cap=max_cap,
+                                       min_take=min_take,
+                                       max_take=max_take,
+                                       periodicity=periodicity,
+                                       periodicity_duration=periodicity_duration)
+        self.conversion_factor_power_heat                = conversion_factor_power_heat
+        self.max_share_heat                 = max_share_heat
+        self.ramp                 = ramp
+        self.start_costs          = start_costs
+        self.running_costs        = running_costs
+        self.min_runtime          = min_runtime
+        self.time_already_running = time_already_running
+        self.last_dispatch = last_dispatch
+        if len(nodes) >= 3:
+            self.fuel_efficiency      = fuel_efficiency
+            self.consumption_if_on    = consumption_if_on
+            self.start_fuel           = start_fuel
+
+        if len(nodes) not in (2,3):
+            raise ValueError('Length of nodes has to be 2 or 3; power, heat and optionally fuel. Asset: ' + self.name)
+
+    def setup_optim_problem(self, prices: dict, timegrid: Timegrid = None,
+                            costs_only: bool = False) -> OptimProblem:
+        """ Set up optimization problem for asset
+
+        Args:
+            prices (dict): Dictionary of price arrays needed by assets in portfolio
+            timegrid (Timegrid, optional): Discretization grid for asset. Defaults to None,
+                                           in which case it must have been set previously
+            costs_only (bool): Only create costs vector (speed up e.g. for sampling prices). Defaults to False
+
+        Returns:
+            OptimProblem: Optimization problem to be used by optimizer
+        """
+        if self.freq is not None and self.freq != timegrid.freq:
+            raise ValueError('Freq of asset' + self.name + ' is ' + str(self.freq) + ' which is unequal to freq ' + timegrid.freq + ' of timegrid. Asset: ' + self.name)
+
+        # convert min_runtime and time_already_running from timegrids main_time_unit to timegrid.freq
+        min_runtime = self.convert_to_timegrid_freq(self.min_runtime, "min_runtime", timegrid)
+        time_already_running = self.convert_to_timegrid_freq(self.time_already_running, "time_already_running", timegrid)
+
+        op = super().setup_optim_problem(prices=prices, timegrid=timegrid, costs_only=costs_only)
+
+        # Check that min_cap and max_cap are >= 0
+        assert np.all(op.l >= 0.), 'min_cap has to be greater or equal to 0. Asset: ' + self.name
+        assert np.all(op.u >= 0.), 'max_cap has to be greater or equal to 0. Asset: ' + self.name
+
+        # Make vectors of input params:
+        start_costs = self.make_vector(self.start_costs, prices, default_value=0.)
+        running_costs = self.make_vector(self.running_costs, prices, default_value=0.)
+        max_share_heat = self.make_vector(self.max_share_heat, prices, default_value=1.)
+        conversion_factor_power_heat = self.make_vector(self.conversion_factor_power_heat, prices, default_value=1.)
+        assert np.all(conversion_factor_power_heat != 0), 'conversion_factor_power_heat must not be zero. Asset: ' + self.name
+        if len(self.nodes) >= 3:
+            start_fuel = self.make_vector(self.start_fuel, prices, default_value=0.)
+            fuel_efficiency = self.make_vector(self.fuel_efficiency, prices, default_value=1.)
+            consumption_if_on = self.make_vector(self.consumption_if_on, prices, default_value=0.)
+            assert np.all(fuel_efficiency != 0), 'fuel efficiency must not be zero. Asset: ' + self.name
+
+        # calculate costs:
+        if costs_only:
+            c = op
+        else:
+            c = op.c
+        c = np.hstack([c, conversion_factor_power_heat * c])  # costs for power and heat dispatch
+        if len(self.nodes)==2:
+            include_start_variables = min_runtime > 1 or np.any(start_costs != 0)
+            include_on_variables = include_start_variables or np.any(self.min_cap != 0.)
+        else:
+            include_start_variables = min_runtime > 1 or np.any(start_costs != 0) or np.any(start_fuel !=0.)
+            include_on_variables = include_start_variables or np.any(self.min_cap != 0.) or np.any(consumption_if_on != 0.)
+
+        if include_on_variables:
+            c = np.hstack([c, running_costs])  # add costs for on variables
+        if include_start_variables:
+            c = np.hstack([c, start_costs])  # add costs for start variables
+        if costs_only:
+            return c
+        op.c = c
+
+        # Check that if include_on_variables is True, the minimum capacity is not 0. Otherwise the "on" variables cannot be computed correctly.
+        if np.any(op.l == 0) and include_on_variables:
+            print("Warning for asset " + self.name + ": The minimum capacity is 0 at some point and 'on'-variables are included" 
+                  ". This can lead to incorrect 'on' and 'start' variables. "
+                  "To prevent this either set min_cap>0 or set min_runtime=0 and start_costs=0 and start_fuel=0"
+                  " and consumption_if_on=0.")
+
+        # Prepare matrix A:
+        n = len(op.l)
+        if op.A is None:
+            op.A = sp.lil_matrix((0, n))
+            op.cType = ''
+            op.b = np.zeros(0)
+
+        # Ramp constraints:
+        if self.ramp is not None:
+            variables = op.mapping[["asset", "node", "var_name"]].drop_duplicates()
+            for i in range(len(variables)):
+                I_past = None
+                for t in range(self.timegrid.restricted.T):
+                    I_curr = np.where((op.mapping["asset"] == variables.iloc[i]["asset"])
+                                      & (op.mapping["node"] == variables.iloc[i]["node"])
+                                      & (op.mapping["var_name"] == variables.iloc[i]["var_name"])
+                                      & (op.mapping["time_step"] == self.timegrid.restricted.I[t]))
+                    if I_past and I_curr:
+                        a = sp.lil_matrix((1, n))
+                        a[0, I_curr] = 1
+                        a[0, I_past] = -1
+                        op.A = sp.vstack([op.A, a])
+                        op.cType += 'L'
+                        op.b = np.hstack([op.b, -self.ramp])
+                        op.A = sp.vstack([op.A, a])
+                        op.cType += 'U'
+                        op.b = np.hstack([op.b, self.ramp])
+                    I_past = I_curr
+
+            # Initial ramp constraint
+            a = sp.lil_matrix((1, n))
+            a[0, 0] = 1
+            op.A = sp.vstack([op.A, a])
+            op.cType += 'L'
+            op.b = np.hstack([op.b, -self.ramp + self.last_dispatch])
+            op.A = sp.vstack([op.A, a])
+            op.cType += 'U'
+            op.b = np.hstack([op.b, self.ramp + self.last_dispatch])
+
+        # Make sure that op.mapping contains only dispatch variables (i.e. with type=='d')
+        var_types = op.mapping['type'].unique()
+        assert np.all(var_types=='d'), "Only variables of type 'd' (i.e. dispatch variables) are allowed in op.mapping at this point. " \
+                                       "However, there are variables with types " + str(var_types[var_types != 'd']) + " in the mapping." \
+                                       "This is likely due to a change in a superclass."
+
+        # Divide each dispatch variable in power and heat:
+        new_map = pd.DataFrame()
+        for i, mynode in enumerate(self.nodes):
+            if i>=2: continue # do only for power and heat
+            initial_map = op.mapping[op.mapping['type'] == 'd'].copy()
+            initial_map['node'] = mynode.name
+            new_map = pd.concat([new_map, initial_map.copy()])
+        op.mapping = new_map
+        op.A = sp.hstack([op.A, sp.coo_matrix(conversion_factor_power_heat * op.A.toarray())])
+
+        # Add on variables
+        if include_on_variables:
+            op.mapping['bool'] = False
+            map_bool = pd.DataFrame()
+            map_bool['time_step'] = self.timegrid.restricted.I
+            map_bool['node'] = np.nan
+            map_bool['asset'] = self.name
+            map_bool['type'] = 'i'  # internal
+            map_bool['bool'] = True
+            map_bool['var_name'] = 'bool_on'
+            op.mapping = pd.concat([op.mapping, map_bool])
+
+            # extend A for on variables (not relevant in exist. restrictions)
+            op.A = sp.hstack((op.A, sp.lil_matrix((op.A.shape[0], len(map_bool)))))
+
+        # Add start variables
+        if include_start_variables:
+            map_bool['var_name'] = 'bool_start'
+            op.mapping = pd.concat([op.mapping, map_bool])
+
+            # extend A for start variables (not relevant in exist. restrictions)
+            op.A = sp.hstack((op.A, sp.lil_matrix((op.A.shape[0], len(map_bool)))))
+
+        # Minimum and maximum capacity:
+        A_lower_bounds = sp.lil_matrix((n, op.A.shape[1]))
+        A_upper_bounds = sp.lil_matrix((n, op.A.shape[1]))
+        for i in range(n):
+            var = op.mapping.iloc[i]
+            on_variable = np.where((op.mapping["asset"] == var["asset"])
+                                   & (op.mapping["var_name"] == "bool_on")
+                                   & (op.mapping["time_step"] == var["time_step"]))
+
+            A_lower_bounds[i, i] = 1
+            A_lower_bounds[i, n + i] = conversion_factor_power_heat[i]
+            A_lower_bounds[i, on_variable] = - op.l[i]  # has no effect if no on variables found
+
+            A_upper_bounds[i, i] = 1
+            A_upper_bounds[i, n + i] = conversion_factor_power_heat[i]
+            A_upper_bounds[i, on_variable] = - op.u[i]  # has no effect if no on variables found
+        op.A = sp.vstack((op.A, A_lower_bounds))
+        op.cType += 'L' * n
+        op.b = np.hstack((op.b, np.zeros(n)))
+
+        op.A = sp.vstack((op.A, A_upper_bounds))
+        op.cType += 'U' * n
+        if include_on_variables:
+            op.b = np.hstack((op.b, np.zeros(n)))
+        else:
+            op.b = np.hstack((op.b, op.u))
+
+        # Start constraints:
+        if include_start_variables:
+            myA = sp.lil_matrix((self.timegrid.restricted.T-1, op.A.shape[1]))
+            for i in range(self.timegrid.restricted.T-1):
+                myA[i, 2 * n + i + 1] = 1
+                myA[i, 2 * n + i] = - 1
+                myA[i, 2 * n + self.timegrid.restricted.T + i + 1] = -1
+            op.A = sp.vstack((op.A, myA))
+            op.cType += 'U' * (self.timegrid.restricted.T - 1)
+            op.b = np.hstack((op.b, np.zeros(self.timegrid.restricted.T-1)))
+
+            if time_already_running==0:
+                a = sp.lil_matrix((1, op.A.shape[1]))
+                a[0, 2*n] = 1
+                a[0, 2*n + self.timegrid.restricted.T] = -1
+                op.A = sp.vstack((op.A, a))
+                op.cType += 'U'
+                op.b = np.hstack((op.b, 0))
+
+            # Minimum runtime:
+            for t in range(self.timegrid.restricted.T):
+                for i in range(1, min_runtime):
+                    if i > t:
+                        continue
+                    a = sp.lil_matrix((1, op.A.shape[1]))
+                    a[0, 2 * n + t] = 1
+                    a[0, 2 * n + self.timegrid.restricted.T + t - i] = -1
+                    op.A = sp.vstack((op.A, a))
+                    op.cType += 'L'
+                    op.b = np.hstack((op.b, 0))
+
+        # Boundaries for the heat variable:
+        if max_share_heat is not None:
+            myA = sp.lil_matrix((n, op.A.shape[1]))
+            for i in range(n):
+                myA[i, n + i] = 1
+                myA[i, i] = - max_share_heat[i]
+            op.A = sp.vstack((op.A, myA))
+            op.cType += 'U' * n
+            op.b = np.hstack((op.b, np.zeros(n)))
+
+        # Set lower and upper bounds for all variables
+        op.l = np.zeros(op.A.shape[1])
+        if max_share_heat is not None:
+            u_heat = max_share_heat * op.u
+        else:
+            u_heat = op.u / conversion_factor_power_heat
+        op.u = np.hstack((op.u, u_heat, np.ones(op.A.shape[1] - op.u.shape[0] * 2)))
+
+        # Enforce minimum runtime if asset already on
+        if time_already_running > 0 and min_runtime - time_already_running>0:
+            op.l[2*n:2*n+ min_runtime - time_already_running] = 1
+
+        # Reset mapping index:
+        op.mapping.reset_index(inplace=True, drop=True)  # need to reset index (which enumerates variables)
+
+        # in case there is an explicit node for fuel, extend mapping
+        # idea: fuel consumption is  power disp + conversion_factor_power_heat * heat disp
+        # mapping extention equivalent to simpler asset type "MultiCommodityContract"
+        if len(self.nodes) >= 3:
+            # disp_factor determines the factor with which fuel is consumed
+            if 'disp_factor' not in op.mapping: op.mapping['disp_factor'] = np.nan
+            new_map     = op.mapping.copy()
+            for i in [0,1]: # nodes power and heat
+                initial_map = op.mapping[(op.mapping['var_name']=='disp') & (op.mapping['node']== self.node_names[i])].copy()
+                initial_map['node']        = self.node_names[2] # fuel node
+                if i == 0:
+                    initial_map['disp_factor'] = -1./fuel_efficiency
+                elif i == 1:
+                    initial_map['disp_factor'] = -conversion_factor_power_heat / fuel_efficiency
+                new_map = pd.concat([new_map, initial_map.copy()])
+            # consumption  if on
+            if include_on_variables:
+                initial_map = op.mapping[op.mapping['var_name']=='bool_on'].copy()
+                initial_map['node'] = self.node_names[2] # fuel node
+                #initial_map['var_name'] = 'fuel_if_on'
+                initial_map['type'] = 'd'
+                initial_map['disp_factor'] = -consumption_if_on
+                new_map = pd.concat([new_map, initial_map.copy()])
+            # consumption on start
+            if include_start_variables:
+                initial_map = op.mapping[op.mapping['var_name']=='bool_start'].copy()
+                initial_map['node'] = self.node_names[2] # fuel node
+                #initial_map['var_name'] = 'fuel_start'
+                initial_map['type'] = 'd'
+                initial_map['disp_factor'] = -start_fuel
+                new_map = pd.concat([new_map, initial_map.copy()])
+
+            op.mapping = new_map
+        return op
+
 
 class MultiCommodityContract(Contract):
     """ Multi commodity contract class - implements a Contract that generates two or more commoditites at a time.
@@ -1034,7 +1481,7 @@ def __init__(self,
                 end:   dt.datetime = None,
                 wacc: float = 0,
                 price:str = None,
-                extra_costs:float = 0.,
+                extra_costs: Union[float, Dict, str] = 0.,
                 min_cap: Union[float, Dict, str] = 0.,
                 max_cap: Union[float, Dict, str] = 0.,
                 min_take: dict = None,
@@ -1068,15 +1515,19 @@ def __init__(self,
                                     dict:  dict['start'] = array
                                            dict['end']   = array
                                            dict['value'] = array
-                                    str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)            
+                                    str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
             min_take (dict) : Minimum volume within given period. Defaults to None
             max_take (dict) : Maximum volume within given period. Defaults to None
                               dict:  dict['start'] = np.array
                                      dict['end']   = np.array
                                      dict['value'] = np.array
             price (str): Name of price vector for buying / selling
-            extra_costs (float, optional): extra costs added to price vector (in or out). Defaults to 0.
-
+            extra_costs (float, dict, str): extra costs added to price vector (in or out). Defaults to 0.
+                                            float: constant value
+                                            dict:  dict['start'] = array
+                                                   dict['end']   = array
+                                                   dict['value'] = array
+                                            str:   refers to column in "prices" data that provides time series to set up OptimProblem (as for "price" below)
             periodicity (str, pd freq style): Makes assets behave periodicly with given frequency. Periods are repeated up to freq intervals (defaults to None)
             periodicity_duration (str, pd freq style): Intervals in which periods repeat (e.g. repeat days ofer whole weeks)  (defaults to None)
 
@@ -1127,11 +1578,21 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
 
         # set up Contract optimProblem
         op = super().setup_optim_problem(prices= prices, timegrid=timegrid, costs_only = costs_only)
+
+        if costs_only:
+            return op
+
         # the optimization problem remains basically the same
         # only the mapping needs to be amended
         ##### adjusting the mapping
         # the contract mapping is the starting point
         # create a copy of the dispatching variables part for each commodity
+
+        # Make sure that op.mapping contains only dispatch variables (i.e. with type=='d')
+        var_types = op.mapping['type'].unique()
+        assert np.all(var_types == 'd'), "Only variables of type 'd' (i.e. dispatch variables) are allowed in op.mapping at this point. " \
+                                         "However, there are variables with types " + str(var_types[var_types != 'd']) + " in the mapping." \
+                                         "This is likely due to a change in a superclass."
         new_map     = pd.DataFrame()
         for i, mynode in enumerate(self.nodes):
             initial_map = op.mapping[op.mapping['type']=='d'].copy()
diff --git a/eaopack/io.py b/eaopack/io.py
index 6b47a86..04c50bb 100644
--- a/eaopack/io.py
+++ b/eaopack/io.py
@@ -18,6 +18,7 @@ def extract_output(portf: Portfolio, op: OptimProblem, res:Results, prices: dict
 
     Returns: dictionary with results
         disp:    dataframe with dispatch per asset
+        internal_variables: dataframe with internal variables per asset
         dcfs:    dataframe with discounted cash flows per asset
         prices:  dataframe with nodal prices and given prices
         special: dataframe with specific asset parameters (such as size in scaled assets)
@@ -28,6 +29,7 @@ def extract_output(portf: Portfolio, op: OptimProblem, res:Results, prices: dict
         output['summary']['status']  = res
         output['DCF']       = None
         output['dispatch']  = None
+        output['internal_variables']  = None
         output['prices']    = None        
         output['special']   = None
     else:
@@ -41,6 +43,7 @@ def extract_output(portf: Portfolio, op: OptimProblem, res:Results, prices: dict
         times   = portf.timegrid.timepoints # using time as main index
         dcfs    = pd.DataFrame(index = times) # dcf
         disp    = pd.DataFrame(index = times) # dispatch
+        internal_variables = pd.DataFrame(index = times)
         duals   = pd.DataFrame(index = times) # duals of problem -  correspond to nodal prices
         special = pd.DataFrame(columns=['asset', 'variable', 'value', 'costs']) # one line per asset / parameter
         # extract dcf for all assets and set together
@@ -64,9 +67,22 @@ def extract_output(portf: Portfolio, op: OptimProblem, res:Results, prices: dict
                 disp[myCol] = 0.
                 for i,r in my_mapping.iterrows():
                     disp.loc[times[r.time_step], myCol] += res.x[i]*r.disp_factor
+        # extract internal variables per asset
+        for a in portf.assets:
+            variable_names = op.mapping[(op.mapping['asset'] == a.name)& (op.mapping['type'] == 'i')]['var_name'].unique()
+            for v in variable_names:
+                I =   (op.mapping['asset'] == a.name) \
+                    & (op.mapping['type'] == 'i')      \
+                    & (op.mapping['var_name'] == v)
+                my_mapping = op.mapping.loc[I,:]
+                myCol = (a.name +' ('+  v + ')')
+                internal_variables[myCol] = None
+                for i,r in my_mapping.iterrows():
+                    pass
+                    internal_variables.loc[times[r.time_step], myCol] = res.x[i]
         # extract duals from nodal restrictions
         # looping through nodes and their recorded nodal restrictions and extract dual
-        if not res.duals is None:
+        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
@@ -108,6 +124,7 @@ def extract_output(portf: Portfolio, op: OptimProblem, res:Results, prices: dict
             disp.loc[times[I_t],:] = disp.loc[times[I_t],:]/n_samples
         output['DCF']       = dcfs
         output['dispatch']  = disp
+        output['internal_variables'] = internal_variables
         output['prices']    = duals
         output['special']   = special
 
diff --git a/eaopack/optimization.py b/eaopack/optimization.py
index cd372b1..ae784b7 100644
--- a/eaopack/optimization.py
+++ b/eaopack/optimization.py
@@ -185,7 +185,8 @@ def __make_periodic__(self, freq_period:str, freq_duration:str, timegrid:Timegri
     def optimize(self, target = 'value',
                        samples = None,
                        interface:str = 'cvxpy', 
-                       solver = None, 
+                       solver = None,
+                       make_soft_problem=False,
                        rel_tol:float = 1e-3, 
                        iterations:int = 5000)->Results:
         """ optimize the optimization problem
@@ -197,6 +198,7 @@ def optimize(self, target = 'value',
                             - Robust optimization: list of costs arrays (maximizing minimal DCF)
             interface (str, optional): Chosen interface architecture. Defaults to 'cvxpy'.
             solver (str, optional): Solver for interface. Defaults to None
+            make_soft_problem (bool, optional): If true, relax the boolean variables and allow float values instead. Defaults to False
             INACTIVE   rel_tol (float): relative tolerance for solver
             INACTIVE   iterations (int): max number of iterations for solver
             INACTIVE   decimals_res (int): rounding results to ... decimals. Defaults to 5
@@ -208,7 +210,11 @@ def optimize(self, target = 'value',
             # Construct the problem
 
             # variable to optimize. Note: may add differentiation of variables and constants in case lower and upper bounds are equal
-            map = self.mapping # abbreviation
+            map = self.mapping.copy()  # abbreviation
+
+            if make_soft_problem:
+                map['bool'] = False
+
             isMIP = False
             if 'bool' in map:
                 my_bools = map.loc[(~map.index.duplicated(keep='first'))&(map['bool'])].index.values.tolist()
diff --git a/eaopack/portfolio.py b/eaopack/portfolio.py
index 2b9dbe4..f54ea82 100644
--- a/eaopack/portfolio.py
+++ b/eaopack/portfolio.py
@@ -3,8 +3,8 @@
 import datetime as dt
 import abc
 import scipy.sparse as sp
-from typing import Union, List, Dict
-from eaopack.assets import Node, Asset, Timegrid
+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 Results 
 
@@ -233,6 +233,34 @@ def create_cost_samples(self, price_samples: List, timegrid: Timegrid = None) ->
             res.append(self.setup_optim_problem(ps, timegrid, costs_only = True))
         return res
 
+    def get_asset(self, asset_name: str) -> Asset:
+        """ Return the asset with name asset_name or None if no asset with this name exists in the portfolio.
+
+            Args:
+                asset_name(str): The name of the asset
+
+            Returns:
+                asset (Asset): The asset with name asset_name
+        """
+        if asset_name in self.asset_names:
+            idx = self.asset_names.index(asset_name)
+            return self.assets[idx]
+        else:
+            return None
+
+    def get_node(self, node_name: str) -> Node:
+        """ Return the node with name node_name or None if no nodes with this name exists in the portfolio.
+
+            Args:
+                node_name(str): The name of the node
+
+            Returns:
+                node (Node): The node with name node_name
+        """
+        if node_name in self.nodes:
+            return self.nodes[node_name]
+        else:
+            return None
 
 class StructuredAsset(Asset):
     """ Structured asset that wraps a portfolio in one asset
@@ -301,5 +329,146 @@ def setup_optim_problem(self, prices: dict, timegrid:Timegrid = None, costs_only
         return op
 
 
+class LinkedAsset(StructuredAsset):
+    """
+    Linked asset that wraps a portfolio in one asset and poses additional constraints on variables.
+    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,
+                 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",
+                 time_back: float = 1,
+                 time_forward: float = 0,
+                 name: str = 'default_name_linkedAsset',
+                 *args, **kwargs):
+        """ Linked asset that wraps a portfolio in one asset and poses the following additional constraints on variable
+        v1 of asset1 and (bool) variable v2 of asset2:
+
+        v1_t <= u1_t * v2_{t+i}, for all i = -time_back,...,time_forward   and   timesteps t = 0,...,timegrid.T
+
+        Here, v1_t and v2_t stand for variable v1 of asset1 at timestep t and variable v2 of asset2 at timestep t, respectively.
+        u1_t stands for the upper bound for variable v1_t as specified in asset1.
+
+        This can be used to ensure that a dispatch or "on" variable v1 is 0 (or "off") depending on the value of an "on" variable v2.
+        For example, it can be ensured that asset1 only turns "on" or has positive dispatch once asset2 has
+        been running for a minimum amount of time.
+
+        Args:
+            portf (Portfolio): Portfolio to be wrapped
+            nodes (nodes as in std. asset): where to connect the asset to the outside.
+                                            Must correspond to (a) node(s) of the internal structure
+            name (str): name of the linked asset
+            asset1_variable (Tuple[Union[Asset, str], str, Union[Node, str]]): Tuple specifying the variable v1 consisting of
+                - asset1 (Asset, str): asset or asset_name of asset in portfolio
+                - v1 (str): name of a variable in asset1
+                - node1 (Node, str): node or node_name of node in portfolio
+            asset2_variable (Tuple[Union[Asset, str], str, Union[Node, str]]): Tuple specifying the variable v2 consisting of
+                - asset2 (Asset, str): asset or asset_name of asset in portfolio
+                - v2 (str): name of a bool variable in asset2
+                - node2 (Node, str): node or node_name of node in portfolio
+            asset2_time_already_running (Union[str, float]): Indicating the runtime asset2 has already been running for
+                float: the time in the timegrids main_time_unit that asset2 has been 'on' for
+                str: the name of an attribute of asset2 that indicates the time asset2 has been running
+                This defaults to "time_already_running"
+            time_back(float): The minimum amount of time asset2 has to be running before v1 of asset1 can be > 0
+            time_forward(float): The minimum amount of time v1 of asset1 has to be 0 before asset2  is turned off
+        """
+
+        super().__init__(portfolio=portfolio, name=name, *args, **kwargs)
+
+        a1, v1, node1 = asset1_variable
+        a2, v2, node2 = asset2_variable
+
+        if isinstance(a1, Asset):
+            self.asset1 = a1
+        else:
+            self.asset1 = self.portfolio.get_asset(a1)
+
+        if isinstance(a2, Asset):
+            self.asset2 = a2
+        else:
+            self.asset2 = self.portfolio.get_asset(a2)
+
+        self.variable1_name = v1
+        self.variable2_name = v2
+
+        self.node1_name = node1
+        if self.node1_name is not None:
+            if isinstance(self.node1_name, Node):
+                self.node1_name = self.node1_name.name
+            if self.node1_name not in self.node_names:
+                self.node1_name = self.name + '_internal_' + self.node1_name
+
+        self.node2 = node2
+        if self.node2 is not None:
+            if isinstance(self.node2, Node):
+                self.node2 = self.node2.name
+            if self.node2 not in self.node_names:
+                self.node2 = self.name + '_internal_' + self.node2
+
+        if isinstance(asset2_time_already_running, str):
+            self.asset2_time_already_running = getattr(self.asset2, asset2_time_already_running, None)
+            if self.asset2_time_already_running is None:
+                print("Warning: Asset", self.asset2.name, "has no attribute", asset2_time_already_running + ". "
+                      "Therefore, 0 is used per default.")
+                self.asset2_time_already_running = 0
+        else:
+            self.asset2_time_already_running = asset2_time_already_running
+        self.time_back = time_back
+        self.time_forward = time_forward
+
+    def setup_optim_problem(self, prices: dict, timegrid: Timegrid = None, costs_only: bool = False) -> OptimProblem:
+        """ set up optimization problem for the asset
+
+        Args:
+            prices (dict): dictionary of price np.arrays. dict must contain a key that corresponds
+                            to str "price" in asset (if prices are required by the asset)
+            timegrid (Timegrid): Grid to be used for optim problem. Defaults to none
+            costs_only (bool): Only create costs vector (speed up e.g. for sampling prices). Defaults to False
+
+        Returns:
+            OptimProblem: Optimization problem that may be used by optimizer
+        """
+        op = super().setup_optim_problem(prices, timegrid, costs_only)
+
+        # convert time_back and time_forward from timegrids main_time_unit to timegrid.freq
+        time_back = self.convert_to_timegrid_freq(self.time_back, "time_back")
+        time_forward = self.convert_to_timegrid_freq(self.time_forward, "time_forward")
+        asset2_time_already_running = self.convert_to_timegrid_freq(self.asset2_time_already_running, "asset2_time_already_running")
+
+        for t in range(self.timegrid.restricted.T):
+            condition = (op.mapping['var_name'] == self.variable1_name + '__' + self.asset1.name) & (op.mapping["time_step"] == t)
+            if self.node1_name is not None:
+                condition = condition & (op.mapping["node"] == self.node1_name)
+            else:
+                condition = condition & (op.mapping["node"].isnull())
+            I1_t = op.mapping.index[condition]
+            assert I1_t[0].size == 1
+            for i in np.arange(-time_back, time_forward + 1):
+                if i + t < -asset2_time_already_running:
+                    # asset2 has not been running long enough, so variable1 of asset1 has to be 0
+                    op.u[I1_t] = 0
+                    continue
+                if i + t < 0 or i + t >= self.timegrid.restricted.T:
+                    continue
+                condition = (op.mapping['var_name'] == self.variable2_name + '__' + self.asset2.name) & (op.mapping["time_step"] == i + t)
+                if self.node2 is not None:
+                    condition = condition & (op.mapping["node"] == self.node2)
+                else:
+                    condition = condition & (op.mapping["node"].isnull())
+                I2_it = op.mapping.index[condition]
+                assert I2_it[0].size == 1
+                a = sp.lil_matrix((1, op.A.shape[1]))
+                a[0, I1_t] = 1
+                a[0, I2_it] = -op.u[I1_t]
+                op.A = sp.vstack((op.A, a))
+                op.cType += 'U'
+                op.b = np.hstack((op.b, 0))
+        return op
+
+
+
 if __name__ == "__main__" :
     pass


Test patch:
diff --git a/tests/test_asset.py b/tests/test_asset.py
index dd0e32d..d0cbfe6 100644
--- a/tests/test_asset.py
+++ b/tests/test_asset.py
@@ -397,22 +397,315 @@ def test_contract_min_take(self):
 
     def test_max_cap_vector_from_pricesDF(self):
         node = eao.assets.Node('testNode')
-        Start = dt.date(2021,1,1)
-        End   = dt.date(2021,1,10)
-        timegrid = eao.assets.Timegrid(Start, End, freq = 'h')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 10)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
 
-        # capacities    
+        # capacities
         min_cap = np.random.rand(timegrid.T)
         prices = {'rand_price': -np.ones(timegrid.T), 'cap': min_cap}
 
-        a = eao.assets.Contract(name = 'SC', price = 'rand_price', nodes = node ,
-                        min_cap= 'cap', max_cap='cap')
-        
+        a = eao.assets.Contract(name='SC', price='rand_price', nodes=node,
+                                min_cap='cap', max_cap='cap')
+
         op = a.setup_optim_problem(prices, timegrid=timegrid)
         res = op.optimize()
-        self.assertAlmostEqual(abs(min_cap - res.x).sum(),0., 5)
+        self.assertAlmostEqual(abs(min_cap - res.x).sum(), 0., 5)
+
+
+class CHPAssetTest(unittest.TestCase):
+    def test_optimization(self):
+        """ Unit test. Setting up a CHPAsset with random prices
+            and check that it generates full load at negative prices and nothing at positive prices.
+        """
+        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 = 'd')
+        a = eao.assets.CHPAsset(name='CHP', price='rand_price', nodes = (node_power, node_heat),
+                                min_cap=5., max_cap=10.)
+        prices ={'rand_price': np.random.rand(timegrid.T)-0.5}
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        x_power = np.around(res.x[:timegrid.T], decimals = 3) # round
+        x_heat = np.around(res.x[timegrid.T:2*timegrid.T], decimals = 3) # round
+
+        check =     all(x_power[np.sign(op.c[:timegrid.T]) == -1] + a.conversion_factor_power_heat * x_heat[np.sign(op.c[:timegrid.T]) == -1] == op.u[:timegrid.T][np.sign(op.c[:timegrid.T]) == -1]) \
+                and all(x_power[np.sign(op.c[:timegrid.T]) == 1] + a.conversion_factor_power_heat * x_heat[np.sign(op.c[:timegrid.T]) == 1] == 0)
+        tot_dcf = np.around((a.dcf(op, res)).sum(), decimals = 3) # asset dcf, calculated independently
+        check = check and (tot_dcf == np.around(res.value , decimals = 3))
+        self.assertTrue(check)
+
+    def test_min_cap_vector(self):
+        """ Unit test. Setting up a CHPAsset with positive prices and a simple contract with a minimum demand that is
+            smaller than the min capacity. Check that it runs at minimum capacity.
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 10)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        # capacities
+        restr_times = pd.date_range(Start, End, freq='d', closed='left')
+        min_cap = {}
+        min_cap['start'] = restr_times.to_list()
+        min_cap['end'] = (restr_times + dt.timedelta(days=1)).to_list()
+        min_cap['values'] = np.random.rand(len(min_cap['start']))
+        max_cap = 1.
+
+        max_cap_sc = min_cap.copy()
+        max_cap_sc['values'] = -0.5*min_cap['values']
+
+        sc_power = eao.assets.SimpleContract(name='SC_power', price='rand_price', nodes=node_power,
+                                            min_cap=-20., max_cap=max_cap_sc)
+
+        a = eao.assets.CHPAsset(name='CHP', price='rand_price', nodes=(node_power, node_heat),
+                                min_cap=min_cap, max_cap=max_cap)
+
+        prices = {'rand_price': np.ones(timegrid.T)}
+        p = eao.portfolio.Portfolio([a, sc_power])
+
+        op = p.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        x_power = np.around(res.x[:timegrid.T], decimals=3)  # round
+        x_heat = np.around(res.x[timegrid.T:2 * timegrid.T], decimals=3)  # round
+
+        self.assertAlmostEqual(np.abs(timegrid.values_to_grid(min_cap) - x_power + a.conversion_factor_power_heat * x_heat).max(), 0., 2)
+
+    def test_max_cap_vector(self):
+        """ Unit test. Setting up a CHPAsset with negative prices
+            and check that it runs at maximum capacity.
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 10)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        # capacities
+        restr_times = pd.date_range(Start, End, freq='d', closed='left')
+        min_cap = {}
+        min_cap['start'] = restr_times.to_list()
+        min_cap['end'] = (restr_times + dt.timedelta(days=1)).to_list()
+        min_cap['values'] = np.random.rand(len(min_cap['start']))
+        max_cap = min_cap.copy()
+
+        a = eao.assets.CHPAsset(name='CHP', price='rand_price', nodes=(node_power, node_heat),
+                                min_cap=min_cap, max_cap=max_cap)
+
+        prices = {'rand_price': -np.ones(timegrid.T)}
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        self.assertAlmostEqual(np.abs(res.x[:timegrid.T] + a.conversion_factor_power_heat * res.x[timegrid.T:2 * timegrid.T] - timegrid.values_to_grid(max_cap)).sum(), 0., 5)
+
+    def test_start_variables(self):
+        """ Unit test. Setting up a CHPAsset with random prices
+            and check that it starts any time the prices change from positive to negative.
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 10)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        a = eao.assets.CHPAsset(name='CHP', price='rand_price', nodes=(node_power, node_heat), min_cap=1., max_cap=1, start_costs=0.001, running_costs=0)
+        prices ={'rand_price': np.random.rand(timegrid.T)-0.5}
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        start_variables = res.x[3*timegrid.T:]
+        for i in range(1, timegrid.T):
+            if np.sign(op.c[i]) == -1 and np.sign(np.sign(op.c[i-1])) == 1:
+                self.assertTrue(start_variables[i]==1)
+
+    def test_minruntime(self):
+        """ Unit test. Setting up a CHPAsset and check min run time restriction
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        # simple case, no min run time
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=1.,
+                                max_cap=10.,
+                                start_costs=1.,
+                                running_costs=5.)
+        prices ={'price': 1.*np.ones(timegrid.T)}
+        prices['price'][0:10] = -100.
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        on_variables = res.x[2*timegrid.T:3*timegrid.T]
+        disp_variables = res.x[0*timegrid.T:1*timegrid.T]
+        start_variables = res.x[3*timegrid.T:]
+        self.assertAlmostEqual(res.value, 10*1000. + 10*(-5)-1., 4) 
+        # min run time 20
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=1.,
+                                max_cap=10.,
+                                start_costs=1.,
+                                running_costs=5.,
+                                min_runtime=20)
+        prices ={'price': 1.*np.ones(timegrid.T)}
+        prices['price'][0:10] = -100.
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        on_variables = res.x[2*timegrid.T:3*timegrid.T]
+        disp_variables = res.x[0*timegrid.T:1*timegrid.T]
+        start_variables = res.x[3*timegrid.T:]
+        self.assertAlmostEqual(on_variables.sum(), 20., 4) 
+        # 10 times full load, 10 time min load
+        self.assertAlmostEqual(res.value, 10*10*100. - 10*1 + 20*(-5)-1., 4) 
+        # min run time 20 ... but 5 hours already on
+        a = eao.assets.CHPAsset(name='CHP',
+                                price='price',
+                                nodes=(node_power, node_heat),
+                                min_cap=1.,
+                                max_cap=10.,
+                                start_costs=1.,
+                                running_costs=5.,
+                                min_runtime=20,
+                                time_already_running=5)
+        prices ={'price': 1.*np.ones(timegrid.T)}
+        prices['price'][0:10] = -100.
+
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        on_variables = res.x[2*timegrid.T:3*timegrid.T]
+        disp_variables = res.x[0*timegrid.T:1*timegrid.T]
+        start_variables = res.x[3*timegrid.T:]
+        self.assertAlmostEqual(on_variables.sum(), 15., 4) 
+        # 10 times full load, 10 time min load, NO start!
+        self.assertAlmostEqual(res.value, 10*10*100. - 5*1 + 15*(-5), 4) 
+
+    def test_gas_consumption(self):
+        """ Unit test. Setting up a CHPAsset with explicit gas (fuel) consumption
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        node_gas = eao.assets.Node('node_gas')
+
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 2)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        # simple case, no min run time
+        a = eao.assets.CHPAsset(name='CHP',
+                                nodes=(node_power, node_heat, node_gas),
+                                min_cap=1.,
+                                max_cap=10.,
+                                start_costs=1.,
+                                running_costs=5.,
+                                conversion_factor_power_heat= 0.2,
+                                max_share_heat= 1,
+                                start_fuel = 10,
+                                fuel_efficiency= .5,
+                                consumption_if_on= .1)
+        b = eao.assets.SimpleContract(name = 'powerMarket', price='price', nodes = node_power, min_cap=-100, max_cap=100)
+        c = eao.assets.SimpleContract(name = 'gasMarket', price='priceGas', nodes = node_gas, min_cap=-100, max_cap=100)
+        d = eao.assets.SimpleContract(name = 'heatMarket', price='priceGas', nodes = node_heat, min_cap=-100, max_cap=100)
+        prices ={'price': 50.*np.ones(timegrid.T), 'priceGas': 0.1*np.ones(timegrid.T)}
+        prices['price'][0:5] = -100.
+        portf = eao.portfolio.Portfolio([a, b, c, d])
+        op = portf.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        out = eao.io.extract_output(portf, op, res, prices)
+        # check manually checked values
+        check = out['dispatch']['CHP (node_power)'].sum()
+        self.assertAlmostEqual(check, 190. , 4) 
+        check = out['dispatch']['CHP (node_heat)'].sum()
+        self.assertAlmostEqual(check, 0. , 4) 
+        check = out['dispatch']['gasMarket (node_gas)'].sum()
+        self.assertAlmostEqual(check, 391.9 , 4)
+
+    def test_min_take(self):
+        """
+        Unit test. Set up a CHPAsset with positive prices and check that the minTake is reached in the defined time frame.
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 10)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        min_take = {}
+        min_take['start'] = dt.date(2021, 1, 3)
+        min_take['end'] = dt.date(2021, 1, 5)
+        min_take['values'] = 20
+        # max_take = min_take.copy()
+        max_take = None
+        conversion_factor_power_heat = 0.5
+
+        a = eao.assets.CHPAsset(name='CHP', price='rand_price', nodes=(node_power, node_heat), min_cap=0., max_cap=20,
+                                min_take=min_take, max_take=max_take, conversion_factor_power_heat=conversion_factor_power_heat)
+        prices = {'rand_price': np.ones(timegrid.T)}
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        disp_power = res.x[:timegrid.T]
+        disp_heat = res.x[timegrid.T:2*timegrid.T]
+
+        self.assertAlmostEqual((disp_power[2*24:4*24]+conversion_factor_power_heat*disp_heat[2*24:4*24]).sum(), 20, 5)
+
+    def test_max_take(self):
+        """
+        Unit test. Set up a CHPAsset with negative prices and check that the max_Take is reached in the defined time frame.
+        """
+        node_power = eao.assets.Node('node_power')
+        node_heat = eao.assets.Node('node_heat')
+        Start = dt.date(2021, 1, 1)
+        End = dt.date(2021, 1, 10)
+        timegrid = eao.assets.Timegrid(Start, End, freq='h')
+
+        max_take = {}
+        max_take['start'] = dt.date(2021, 1, 3)
+        max_take['end'] = dt.date(2021, 1, 5)
+        max_take['values'] = 20
+        # max_take = min_take.copy()
+        min_take = None
+        conversion_factor_power_heat = 0.5
+
+        a = eao.assets.CHPAsset(name='CHP', price='rand_price', nodes=(node_power, node_heat), min_cap=0., max_cap=20,
+                                min_take=min_take, max_take=max_take, conversion_factor_power_heat=conversion_factor_power_heat)
+        prices = {'rand_price': -np.ones(timegrid.T)}
+        op = a.setup_optim_problem(prices, timegrid=timegrid)
+        res = op.optimize()
+        disp_power = res.x[:timegrid.T]
+        disp_heat = res.x[timegrid.T:2*timegrid.T]
+
+        self.assertAlmostEqual((disp_power[2*24:4*24]+conversion_factor_power_heat*disp_heat[2*24:4*24]).sum(), 20, 5)
+
+    def test_freq_conversion(self):
+        """
+        Check the time conversion function that is used to convert e.g. min_runtime and time_already_running in CHPAsset
+        """
+        old_value = np.random.rand() * 30
+        new_value = eao.assets.convert_time_unit(old_value, old_freq='h', new_freq='d')
+        self.assertAlmostEqual(new_value - old_value / 24, 0, 5)
+
+        old_value = np.random.rand() * 30
+        new_value = eao.assets.convert_time_unit(old_value, old_freq='h', new_freq='15min')
+        self.assertAlmostEqual(new_value - old_value * 4, 0, 5)
+
+        old_value = np.random.rand() * 30
+        new_value = eao.assets.convert_time_unit(old_value, old_freq='d', new_freq='15min')
+        self.assertAlmostEqual(new_value - old_value * 24 * 4, 0, 5)
+
+        old_value = np.random.rand() * 30
+        new_value = eao.assets.convert_time_unit(old_value, old_freq='30min', new_freq='min')
+        self.assertAlmostEqual(new_value - old_value * 30, 0, 5)
+
+        old_value = np.random.rand() * 30
+        new_value = eao.assets.convert_time_unit(old_value, old_freq='min', new_freq='h')
+        self.assertAlmostEqual(new_value - old_value / 60, 0, 5)
+
 
-        
 class MultiCommodity(unittest.TestCase):
 
     def test_predefined_multicommodity(self):
@@ -459,7 +752,7 @@ def test_scaled_asset(self):
         res = op.optimize()
         # out = eao.io.extract_output(portf=portf, op = op, res = res)
         # zero costs and limit at original size -- same result
-        self.assertAlmostEqual(res_std.value, res.value, 5)
+        self.assertAlmostEqual(res_std.value, res.value, 4)
 
 class DiscountRate(unittest.TestCase):
     def test_discount_simple_contract(self):