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edited and renamed third example
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kais-siala committed Mar 10, 2021
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134 changes: 79 additions & 55 deletions ...03_Energy_system_expansion_planning.ipynb → teaching/03_Sector_coupling.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 3: Energy system expansion planning"
"# Example 3: Sector coupling"
]
},
{
Expand Down Expand Up @@ -93,8 +93,8 @@
" mydata[\"sites\"] = [\"OldTown\", \"NewTown\", \"FutureTown\"]\n",
" mydata[\"commodities\"] = [\"Elec\", \"Heat\", \"Gas\", \"SolarPV\", \"SolarThermal\", \"CO2\", \"AmbientTemp\"]\n",
" mydata[\"com_type\"] = [\"Demand\", \"Stock\", \"Env\", \"SupIm\"] # (i.e. SupIm, Demand, Stock, Env)\n",
" mydata[\"process\"] = [\"Gas plant\", \"PV plant\", \n",
" \"Gas boiler\", \"Solar thermal\",\n",
" mydata[\"process\"] = [\"Gas CC\", \"PV plant\", \n",
" \"Gas heating plant\", \"Solar thermal\",\n",
" \"Gas CHP\", # CHP: combined heat and power\n",
" \"Heat pump\", \"Electric resistance\"] \n",
" mydata[\"cost_type\"] = ['Invest', 'Fixed', 'Variable', 'Fuel', 'Environmental']\n",
Expand All @@ -104,7 +104,7 @@
" mydata[\"com_max\"] = {}\n",
" for stf in mydata[\"support_timeframes\"]:\n",
" for sit in mydata[\"sites\"]:\n",
" mydata[\"com_prices\"].update({(stf, sit, \"Gas\", \"Stock\"): 24.8,\n",
" mydata[\"com_prices\"].update({(stf, sit, \"Gas\", \"Stock\"): 25.2,\n",
" (stf, sit, \"SolarPV\", \"SupIm\"): 0,\n",
" (stf, sit, \"SolarThermal\", \"SupIm\"): 0,\n",
" (stf, sit, \"AmbientTemp\", \"SupIm\"): 0,\n",
Expand All @@ -122,18 +122,18 @@
"\n",
" # Dictionaries - processes\n",
" mydata[\"pro_capup\"] = {\n",
" (2021, \"OldTown\", \"Gas boiler\"): np.inf, # for heat\n",
" (2021, \"OldTown\", \"Gas heating plant\"): np.inf, # for heat\n",
" (2021, \"OldTown\", \"Solar thermal\"): np.inf, # for heat\n",
" (2021, \"OldTown\", \"Gas plant\"): np.inf, # for electricity\n",
" (2021, \"OldTown\", \"Gas CC\"): np.inf, # for electricity\n",
" (2021, \"OldTown\", \"PV plant\"): np.inf, # for electricity\n",
" \n",
" (2021, \"NewTown\", \"Gas boiler\"): np.inf, # for heat\n",
" (2021, \"NewTown\", \"Gas heating plant\"): np.inf, # for heat\n",
" (2021, \"NewTown\", \"Solar thermal\"): np.inf, # for heat\n",
" (2021, \"NewTown\", \"Gas plant\"): np.inf, # for electricity\n",
" (2021, \"NewTown\", \"Gas CC\"): np.inf, # for electricity\n",
" (2021, \"NewTown\", \"PV plant\"): np.inf, # for electricity\n",
" (2021, \"NewTown\", \"Gas CHP\"): np.inf, # for electricity and heat\n",
" \n",
" (2021, \"FutureTown\", \"Gas plant\"): np.inf, # for electricity\n",
" (2021, \"FutureTown\", \"Gas CC\"): np.inf, # for electricity\n",
" (2021, \"FutureTown\", \"PV plant\"): np.inf, # for electricity\n",
" (2021, \"FutureTown\", \"Heat pump\"): np.inf, # for heat from electricity\n",
" (2021, \"FutureTown\", \"Electric resistance\"): np.inf, # for heat from electricity\n",
Expand All @@ -153,22 +153,22 @@
" mydata[\"pro_depreciation\"] = mydata[\"pro_instcap\"].copy()\n",
" \n",
" for (stf, sit, pro) in mydata[\"pro_invcost\"].keys():\n",
" mydata[\"pro_wacc\"][(stf, sit, pro)] = 0.07 # weigthed average cost of capital (in % of capital cost)\n",
" if pro == \"Gas plant\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 850000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 21250\n",
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 2\n",
" mydata[\"pro_wacc\"][(stf, sit, pro)] = 0.05 # weigthed average cost of capital (in % of capital cost)\n",
" if pro == \"Gas CC\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 900000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 22000\n",
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 4\n",
" mydata[\"pro_depreciation\"][(stf, sit, pro)] = 30\n",
" if pro == \"PV plant\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 800000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 13600\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 700000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 22000\n",
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 0\n",
" mydata[\"pro_depreciation\"][(stf, sit, pro)] = 25\n",
" if pro == \"Gas boiler\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 500000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 25000\n",
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 3\n",
" mydata[\"pro_depreciation\"][(stf, sit, pro)] = 15\n",
" if pro == \"Gas heating plant\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 400000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 12000\n",
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 6.7\n",
" mydata[\"pro_depreciation\"][(stf, sit, pro)] = 35\n",
" if pro == \"Solar thermal\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 800000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 13600\n",
Expand All @@ -185,34 +185,34 @@
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 0\n",
" mydata[\"pro_depreciation\"][(stf, sit, pro)] = 15\n",
" if pro == \"Electric resistance\":\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 100000\n",
" mydata[\"pro_invcost\"][(stf, sit, pro)] = 150000\n",
" mydata[\"pro_fixcost\"][(stf, sit, pro)] = 5000\n",
" mydata[\"pro_varcost\"][(stf, sit, pro)] = 0\n",
" mydata[\"pro_depreciation\"][(stf, sit, pro)] = 10\n",
"\n",
" # Dictionaries - conversion ratios\n",
" mydata[\"ratio_in\"] = {\n",
" (2021, \"Gas plant\", \"Gas\"): 1.67,\n",
" (2021, \"Gas CC\", \"Gas\"): 1.67,\n",
" (2021, \"PV plant\", \"SolarPV\"): 1,\n",
" (2021, \"Gas boiler\", \"Gas\"): 1.09,\n",
" (2021, \"Gas heating plant\", \"Gas\"): 1.25,\n",
" (2021, \"Solar thermal\", \"SolarThermal\"): 1,\n",
" (2021, \"Gas CHP\", \"Gas\"): 2.22,\n",
" (2021, \"Heat pump\", \"Elec\"): 1,\n",
" (2021, \"Heat pump\", \"AmbientTemp\"): 1,\n",
" (2021, \"Electric resistance\", \"Elec\"): 1,\n",
" }\n",
" mydata[\"ratio_out\"] = {\n",
" (2021, \"Gas plant\", \"Elec\"): 1,\n",
" (2021, \"Gas plant\", \"CO2\"): 0.3,\n",
" (2021, \"Gas CC\", \"Elec\"): 1,\n",
" (2021, \"Gas CC\", \"CO2\"): 0.3,\n",
" (2021, \"PV plant\", \"Elec\"): 1,\n",
" (2021, \"Gas boiler\", \"Heat\"): 1,\n",
" (2021, \"Gas boiler\", \"CO2\"): 0.2,\n",
" (2021, \"Gas heating plant\", \"Heat\"): 1,\n",
" (2021, \"Gas heating plant\", \"CO2\"): 0.23,\n",
" (2021, \"Solar thermal\", \"Heat\"): 1,\n",
" (2021, \"Gas CHP\", \"Elec\"): 1,\n",
" (2021, \"Gas CHP\", \"Heat\"): 0.88,\n",
" (2021, \"Gas CHP\", \"CO2\"): 0.4,\n",
" (2021, \"Heat pump\", \"Heat\"): 5,\n",
" (2021, \"Electric resistance\", \"Heat\"): 2.7,\n",
" (2021, \"Electric resistance\", \"Heat\"): 0.95,\n",
" }\n",
"\n",
" # Dictionaries - time series\n",
Expand All @@ -222,13 +222,14 @@
" ts_SolarPV = [0, 0, 0, 0, 0, 0.05, 0.1, 0.15, 0.22, 0.35, 0.4, 0.55, 0.5, 0.45, 0.39, 0.35, 0.3, 0.2, 0.05, 0, 0, 0, 0, 0]\n",
" ts_SolarThermal = [0, 0, 0, 0, 0, 0.05, 0.1, 0.15, 0.22, 0.35, 0.4, 0.55, 0.5, 0.45, 0.39, 0.35, 0.3, 0.2, 0.05, 0, 0, 0, 0, 0]\n",
"\n",
" # Scale the ambient temperature time series so that the efficiency is 1 if temperature <= 5°C,\n",
" # and decreases to 0.2 if >= 25°C\n",
" # Scale the ambient temperature time series so that the efficiency increases almost linearly with the temperature\n",
" # for the range that we are interested in.\n",
" # Approximation: https://www.researchgate.net/publication/273458507_A_new_two-degree-of-freedom_space_heating_model_for_demand_response/figures?lo=1\n",
" ts_AmbientTemp = np.array(ts_AmbientTemp, dtype=float)\n",
" ts_AmbientTemp[np.array(ts_AmbientTemp)<=5] = 1\n",
" ts_AmbientTemp[ts_AmbientTemp>=25] = 0.2\n",
" ts_filter = ((ts_AmbientTemp>5) & (ts_AmbientTemp<25))\n",
" ts_AmbientTemp[ts_filter] = np.exp(np.log(5)/20*(5-ts_AmbientTemp[ts_filter]))\n",
" ts_AmbientTemp = 0.03 * (ts_AmbientTemp + 15) + 3.1\n",
" ts_AmbientTemp[ts_AmbientTemp<=1] = 1\n",
" # What we have here is a COP... let's scale it so that it is lower than 1\n",
" ts_AmbientTemp = ts_AmbientTemp / 5\n",
"\n",
" mydata[\"demand\"] = {}\n",
" mydata[\"supim\"] = {}\n",
Expand Down Expand Up @@ -264,8 +265,8 @@
"outputs": [],
"source": [
"# Generate the data for the reference scenario\n",
"data_ref = generate_scenario()\n",
"%matplotlib inline"
"%matplotlib inline\n",
"data_ref = generate_scenario()"
]
},
{
Expand Down Expand Up @@ -324,7 +325,7 @@
"ax2.set_title(\"Temperature\")\n",
"plt.plot(AmbientTemp, color=\"red\")\n",
"ax2b = ax2.twinx()\n",
"ax2b.set_ylim(0, 6)\n",
"ax2b.set_ylim(3.5, 4.5)\n",
"ax2b.set_ylabel(\"Heat pump COP [1]\")\n",
"ax2b.plot(ts_AmbientTemp[1:], color=\"black\")"
]
Expand All @@ -345,7 +346,31 @@
"metadata": {},
"outputs": [],
"source": [
"data_ref[\"pro_invcost\"]"
"# Retrieve the SolarPV and SolarThermal timeseries\n",
"ts_SolarPV = []\n",
"ts_SolarThermal = []\n",
"for (stf, sit, com, tm), v in data_ref[\"supim\"].items():\n",
" if ((sit==\"OldTown\") and (com==\"SolarPV\")):\n",
" ts_SolarPV.append(v)\n",
" if ((sit==\"OldTown\") and (com==\"SolarThermal\")):\n",
" ts_SolarThermal.append(v)\n",
"# Plot them side by side\n",
"fig1 = plt.figure(figsize=[12, 5])\n",
"ax1a = fig1.add_subplot(1,2,1)\n",
"ax1a.set_xlim(1, 24)\n",
"ax1a.set_xlabel(\"tm\")\n",
"ax1a.set_ylim(0, 1)\n",
"ax1a.set_ylabel(\"SE Elec [MWh]\")\n",
"ax1a.set_title(\"SolarPV\")\n",
"plt.plot(ts_SolarPV, color=\"blue\")\n",
"fig1.add_subplot(1,2,2)\n",
"ax1b = fig1.add_subplot(1,2,2)\n",
"ax1b.set_xlim(1, 24)\n",
"ax1b.set_xlabel(\"tm\")\n",
"ax1b.set_ylim(0, 1)\n",
"ax1b.set_ylabel(\"SE Heat [MWh]\")\n",
"ax1b.set_title(\"SolarThermal\")\n",
"plt.plot(ts_SolarThermal, color=\"orange\")"
]
},
{
Expand Down Expand Up @@ -412,23 +437,24 @@
"# You can access the variables, for example the output of the processes\n",
"supply_data = {}\n",
"for (tm, stf, sit, com, com_type), x in model_ref.e_pro_out.items():\n",
" supply_data[(tm, stf, sit, com, com_type)] = pyo.value(x)\n",
" supply_data[(tm, sit, com, com_type)] = pyo.value(x)\n",
"\n",
"df_supply = pd.DataFrame.from_dict(supply_data, orient=\"index\", columns=[\"SE [MWh]\"])\n",
"df_supply.index = pd.MultiIndex.from_tuples(df_supply.index, names=('t', 'stf', 'Site', 'Technology', 'Commodity'))\n",
"df_supply.reorder_levels([2,4,3,0,1])"
"df_supply = pd.DataFrame.from_dict(supply_data, orient=\"index\", columns=[\"SE [MWh] or emissions [t_CO2]\"])\n",
"df_supply.index = pd.MultiIndex.from_tuples(df_supply.index, names=('t', 'Site', 'Technology', 'Commodity'))\n",
"df_supply = df_supply.reorder_levels([1,3,2,0])\n",
"df_supply.head()"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"***\n",
"### <span style=\"color:blue\">Task</span>\n",
"1. Report the most important results (new capacities, costs, power mix over time) using the techniques that we learned on Day 01.\n",
"2. Experiment with different scenarios (i.e. vary the settings when generating the data and solve the different models).\n",
"3. Analyze the results and draw some conclusions.\n",
"***"
"df_supply_elec = df_supply[df_supply.index.get_level_values(1) == 'Elec']\n",
"df_supply_elec = df_supply_elec.droplevel(1)\n",
"df_supply_elec.rename({\"SE [MWh] or emissions [kgCO2]\": \"SE [MWh]\"}, axis=1, inplace=True)\n",
"df_supply_elec.head()"
]
},
{
Expand All @@ -437,11 +463,9 @@
"source": [
"***\n",
"## <span style=\"color:red\">Homework</span>\n",
"1. Add another energy sector (transportation). The demand of transportation can be expressed in vehicle-kilometers. The processes that convert gasoline/gas or electricity to vehicle-kilometers are conventional cars or battery electric vehicles (BEVs). Beware that we have not modeled storage so far.\n",
"2. Perform a sensitivity analysis: <br>\n",
" a. Experiment with different investment cost assumptions for BEVs<br>\n",
" b. Experiment with different CO2 prices<br>\n",
"3. In which town is it cheaper to decarbonize the system?\n",
"1. Report the most important results (new capacities, costs, power mix over time) using the techniques that we learned on Day 01.\n",
"2. Experiment with different scenarios (i.e. vary the settings when generating the data and solve the different models).\n",
"3. Analyze the results and draw some conclusions.\n",
"***"
]
}
Expand Down

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