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v0.0.5 code

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Hanzhang Ma
2024-05-10 23:57:58 +02:00
parent ebebd2d481
commit 060fa5bff1
6 changed files with 70547 additions and 35277 deletions
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main.ipynb
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@@ -52,7 +52,7 @@
"source": [
"import json\n",
"\n",
"print(\"Version 0.0.4\")\n",
"print(\"Version 0.0.5\")\n",
"\n",
"with open('config.json', 'r') as f:\n",
" js_data = json.load(f)\n",
@@ -61,6 +61,7 @@
" \n",
"\n",
"time_interval = js_data[\"time_interval\"][\"numerator\"] / js_data[\"time_interval\"][\"denominator\"]\n",
"print(time_interval)\n",
"\n",
"pv_loss = js_data[\"pv\"][\"loss\"]\n",
"pv_cost_per_kW = js_data[\"pv\"][\"cost_per_kW\"]\n",
@@ -85,10 +86,13 @@
"annot_unmet = js_data[\"annotated\"][\"unmet_prob\"]\n",
"annot_benefit = js_data[\"annotated\"][\"benefit\"]\n",
"annot_cost = js_data[\"annotated\"][\"cost\"]\n",
"annot_roi = js_data[\"annotated\"][\"roi\"]\n",
"\n",
"title_unmet = js_data[\"plot_title\"][\"unmet_prob\"]\n",
"title_cost = js_data[\"plot_title\"][\"cost\"]\n",
"title_benefit = js_data[\"plot_title\"][\"benefit\"]\n",
"title_roi = js_data[\"plot_title\"][\"roi\"]\n",
"\n",
"\n",
"figure_size = (js_data[\"figure_size\"][\"length\"], js_data[\"figure_size\"][\"height\"])\n",
"\n",
@@ -181,6 +185,59 @@
" plt.savefig(filename)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, figure_size=(10, 10)):\n",
" costs = costs.astype(float)\n",
" costs = costs / 365 \n",
" costs = costs * days\n",
"\n",
" df = results\n",
" df = costs / df\n",
" if 0 in df.index and 0 in df.columns:\n",
" df.loc[0,0] = 100\n",
" df[df > 80] = 100\n",
" print(df)\n",
"\n",
" df = df.astype(float)\n",
" df.index = df.index / 1000\n",
" df.index = df.index.map(int)\n",
" df.columns = df.columns / 1000\n",
" df.columns = df.columns.map(int)\n",
" min_value = df.min().min()\n",
" max_value = df.max().max()\n",
" print(max_value)\n",
" max_scale = max(abs(min_value), abs(max_value))\n",
"\n",
" df[df.columns[-1] + 1] = df.iloc[:, -1] \n",
" new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)\n",
" for i in df.columns:\n",
" new_Data[i] = df[i].iloc[-1]\n",
" df = pd.concat([df, new_Data])\n",
"\n",
" X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
"\n",
" def fmt(x,pos):\n",
" return '{:.0f}'.format(x)\n",
"\n",
" cmap = sns.color_palette(\"Greys\", as_cmap=True)\n",
" plt.figure(figsize=figure_size)\n",
" ax = sns.heatmap(df, fmt=\".1f\", cmap=cmap, vmin=0, vmax=100, annot=annot_benefit)\n",
" CS = ax.contour(X, Y, df, colors='black', alpha=0.5)\n",
" ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))\n",
" plt.title(title_roi)\n",
" plt.gca().invert_yaxis()\n",
" plt.xlim(0, df.shape[1] - 1)\n",
" plt.ylim(0, df.shape[0] - 1)\n",
" plt.xlabel('ESS Capacity (MWh)')\n",
" plt.ylabel('PV Capacity (MW)')\n",
" plt.savefig(filename)"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -253,8 +310,8 @@
" ax = sns.heatmap(df, fmt=\".00%\", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)\n",
"\n",
" cbar = ax.collections[0].colorbar\n",
" cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
" cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])\n",
" cbar.set_ticks([0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1])\n",
" cbar.set_ticklabels(['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'])\n",
" cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))\n",
" X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
"\n",
@@ -289,7 +346,7 @@
"metadata": {},
"outputs": [],
"source": [
"def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price,time_interval, data, days):\n",
"def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price, time_interval, data, days):\n",
" pv = pv_config(capacity=pv_capacity, \n",
" cost_per_kW=pv_cost_per_kW,\n",
" lifetime=pv_lifetime, \n",
@@ -306,11 +363,11 @@
" energySystem = EnergySystem(pv_type=pv, \n",
" ess_type=ess, \n",
" grid_type= grid)\n",
" benefit = energySystem.simulate(data, time_interval)\n",
" (benefit, netto_benefit, gen_energy) = energySystem.simulate(data, time_interval)\n",
" results = cal_profit(energySystem, benefit, days)\n",
" overload_cnt = energySystem.overload_cnt\n",
" costs = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW\n",
" return (results, overload_cnt, costs)\n"
" return (results, overload_cnt, costs, netto_benefit, gen_energy, energySystem.generated)\n"
]
},
{
@@ -322,26 +379,38 @@
"months_results = []\n",
"months_costs = []\n",
"months_overload = []\n",
"months_nettos = []\n",
"months_gen_energy = []\n",
"months_gen_energy2 = []\n",
"for index, month_data in enumerate(months_data):\n",
" results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
" costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
" overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
" nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
" gen_energies = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
" gen_energies2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
" for pv_capacity in pv_capacities:\n",
" for ess_capacity in ess_capacities:\n",
" (result, overload, cost) = generate_data(pv_capacity=pv_capacity,pv_cost_per_kW=pv_cost_per_kW, pv_lifetime=pv_lifetime, pv_loss=pv_loss, ess_capacity=ess_capacity, ess_cost_per_kW=ess_cost_per_kW, ess_lifetime=ess_lifetime, ess_loss=ess_loss, grid_capacity=grid_capacity, grid_loss=grid_loss, sell_price=sell_price, time_interval=time_interval, data=month_data, days=months_days[index])\n",
" (result, overload, cost, netto, gen_energy, gen_energy2) = generate_data(pv_capacity=pv_capacity,pv_cost_per_kW=pv_cost_per_kW, pv_lifetime=pv_lifetime, pv_loss=pv_loss, ess_capacity=ess_capacity, ess_cost_per_kW=ess_cost_per_kW, ess_lifetime=ess_lifetime, ess_loss=ess_loss, grid_capacity=grid_capacity, grid_loss=grid_loss, sell_price=sell_price, time_interval=time_interval, data=month_data, days=months_days[index])\n",
" results.loc[pv_capacity,ess_capacity] = result\n",
" overload_cnt.loc[pv_capacity,ess_capacity] = overload\n",
" costs.loc[pv_capacity,ess_capacity] = cost\n",
" nettos.loc[pv_capacity,ess_capacity] = netto\n",
" gen_energies.loc[pv_capacity, ess_capacity] = gen_energy\n",
" gen_energies2.loc[pv_capacity, ess_capacity] = gen_energy2\n",
" months_results.append(results)\n",
" months_costs.append(costs)\n",
" months_overload.append(overload_cnt)\n",
" months_nettos.append(nettos)\n",
" months_gen_energy.append(gen_energies)\n",
" months_gen_energy2.append(gen_energies2)\n",
" draw_results(results=results, \n",
" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-benefit.png',\n",
" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-benefit.png',\n",
" title_benefit=title_benefit,\n",
" annot_benefit=annot_benefit,\n",
" figure_size=figure_size)\n",
" draw_overload(overload_cnt=overload_cnt, \n",
" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-unmet.png',\n",
" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-unmet.png',\n",
" title_unmet=title_unmet,\n",
" annot_unmet=annot_unmet,\n",
" figure_size=figure_size,\n",
@@ -351,6 +420,11 @@
"annual_result = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
"annual_costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
"annual_overload = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
"annual_nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
"annual_gen = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
"annual_gen2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
"\n",
"\n",
"\n",
"# get the yearly results\n",
"for pv_capacity in pv_capacities:\n",
@@ -358,13 +432,22 @@
" results = 0\n",
" costs = 0\n",
" overload_cnt = 0\n",
" nettos = 0\n",
" gen = 0\n",
" gen2 = 0\n",
" for index, month_data in enumerate(months_data):\n",
" results += months_results[index].loc[pv_capacity,ess_capacity]\n",
" costs += months_costs[index].loc[pv_capacity,ess_capacity]\n",
" overload_cnt += months_overload[index].loc[pv_capacity, ess_capacity]\n",
" nettos += months_nettos[index].loc[pv_capacity, ess_capacity]\n",
" gen += months_gen_energy[index].loc[pv_capacity, ess_capacity]\n",
" gen2 += months_gen_energy[index].loc[pv_capacity, ess_capacity]\n",
" annual_result.loc[pv_capacity, ess_capacity] = results\n",
" annual_costs.loc[pv_capacity, ess_capacity] = costs\n",
" annual_overload.loc[pv_capacity, ess_capacity] = overload_cnt\n",
" annual_nettos.loc[pv_capacity, ess_capacity] = nettos\n",
" annual_gen.loc[pv_capacity, ess_capacity] = gen\n",
" annual_gen2.loc[pv_capacity, ess_capacity] = gen2\n",
"\n",
"draw_cost(costs=annual_costs,\n",
" filename='plots/annual_cost.png',\n",
@@ -403,9 +486,27 @@
"if not os.path.isdir('data'):\n",
" os.makedirs('data')\n",
"\n",
"save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n",
"save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n",
"save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')"
"save_data(annual_result, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n",
"save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n",
"save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"draw_results(annual_result, 'plots/test.png', 'test', False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png', title_roi, 365, annot_benefit, figure_size)\n"
]
}
],