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2 Commits
a330946f71
...
060fa5bff1
| Author | SHA1 | Date | |
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060fa5bff1 | ||
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ebebd2d481 |
@ -35,6 +35,8 @@ class EnergySystem:
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# 如果ESS也不足以满足需求，再从电网获取 - 当ESS中的存储电量也不足以补充时，再从电网购买剩余所需电量。
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def simulate(self, data, time_interval):
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total_benefit = 0
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total_netto_benefit = 0
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total_gen = 0
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for index, row in data.iterrows():
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time = row['time']
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sunlight_intensity = row['sunlight']
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@ -109,9 +111,11 @@ class EnergySystem:
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# 工厂需求量-总能量
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# unmet_demand = max(0, factory_demand * time_interval - total_energy)
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# benefit = (total_energy - unmet_demand) * electricity_price
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total_gen += saved_energy
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benefit = (saved_energy) * electricity_price
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cost = net_grid * electricity_price
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# print(f"time:{time} benefit: {benefit}, cost: {cost}")
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total_netto_benefit += benefit
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total_benefit += benefit - cost
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# # spring
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week_start = self.season_start
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@ -137,4 +141,4 @@ class EnergySystem:
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# self.winter_week_gen.append(generated_pv_power)
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# self.winter_week_soc.append(self.ess.storage / self.ess.capacity)
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return total_benefit
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return (total_benefit, total_netto_benefit, total_gen)
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10
config.json
10
config.json
@ -17,12 +17,12 @@
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"pv_capacities":{
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"begin": 0,
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"end": 50000,
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"groups": 5
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"groups": 11
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},
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"ess_capacities":{
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"begin": 0,
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"end": 100000,
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"groups": 10
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"groups": 11
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},
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"time_interval":{
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"numerator": 15,
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@ -31,7 +31,8 @@
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"annotated": {
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"unmet_prob": false,
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"benefit": false,
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"cost": false
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"cost": false,
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"roi": false
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},
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"figure_size":{
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"height": 9,
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@ -40,6 +41,7 @@
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"plot_title":{
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"unmet_prob": "Coverage Rate of Factory Electrical Demands",
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"cost": "Costs of Microgrid system [m-EUR]",
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"benefit": "Financial Profit Based on Py & Ess Configuration (k-EUR / year)"
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"benefit": "Financial Profit Based on Py & Ess Configuration (k-EUR / year)",
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"roi": "ROI"
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}
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}
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File diff suppressed because it is too large
Load Diff
35041
electricity_price_data_sell.csv
Normal file
35041
electricity_price_data_sell.csv
Normal file
File diff suppressed because it is too large
Load Diff
125
main.ipynb
125
main.ipynb
@ -52,7 +52,7 @@
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"source": [
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"import json\n",
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"\n",
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"print(\"Version 0.0.4\")\n",
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"print(\"Version 0.0.5\")\n",
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"\n",
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"with open('config.json', 'r') as f:\n",
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" js_data = json.load(f)\n",
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@ -61,6 +61,7 @@
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" \n",
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"\n",
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"time_interval = js_data[\"time_interval\"][\"numerator\"] / js_data[\"time_interval\"][\"denominator\"]\n",
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"print(time_interval)\n",
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"\n",
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"pv_loss = js_data[\"pv\"][\"loss\"]\n",
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"pv_cost_per_kW = js_data[\"pv\"][\"cost_per_kW\"]\n",
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@ -85,10 +86,13 @@
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"annot_unmet = js_data[\"annotated\"][\"unmet_prob\"]\n",
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"annot_benefit = js_data[\"annotated\"][\"benefit\"]\n",
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"annot_cost = js_data[\"annotated\"][\"cost\"]\n",
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"annot_roi = js_data[\"annotated\"][\"roi\"]\n",
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"\n",
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"title_unmet = js_data[\"plot_title\"][\"unmet_prob\"]\n",
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"title_cost = js_data[\"plot_title\"][\"cost\"]\n",
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"title_benefit = js_data[\"plot_title\"][\"benefit\"]\n",
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"title_roi = js_data[\"plot_title\"][\"roi\"]\n",
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"\n",
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"\n",
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"figure_size = (js_data[\"figure_size\"][\"length\"], js_data[\"figure_size\"][\"height\"])\n",
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"\n",
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@ -181,6 +185,59 @@
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" plt.savefig(filename)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, figure_size=(10, 10)):\n",
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" costs = costs.astype(float)\n",
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" costs = costs / 365 \n",
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" costs = costs * days\n",
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"\n",
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" df = results\n",
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" df = costs / df\n",
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" if 0 in df.index and 0 in df.columns:\n",
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" df.loc[0,0] = 100\n",
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" df[df > 80] = 100\n",
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" print(df)\n",
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"\n",
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" df = df.astype(float)\n",
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" df.index = df.index / 1000\n",
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" df.index = df.index.map(int)\n",
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" df.columns = df.columns / 1000\n",
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" df.columns = df.columns.map(int)\n",
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" min_value = df.min().min()\n",
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" max_value = df.max().max()\n",
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" print(max_value)\n",
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" max_scale = max(abs(min_value), abs(max_value))\n",
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"\n",
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" df[df.columns[-1] + 1] = df.iloc[:, -1] \n",
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" new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)\n",
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" for i in df.columns:\n",
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" new_Data[i] = df[i].iloc[-1]\n",
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" df = pd.concat([df, new_Data])\n",
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"\n",
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" X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
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"\n",
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" def fmt(x,pos):\n",
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" return '{:.0f}'.format(x)\n",
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"\n",
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" cmap = sns.color_palette(\"Greys\", as_cmap=True)\n",
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" plt.figure(figsize=figure_size)\n",
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" ax = sns.heatmap(df, fmt=\".1f\", cmap=cmap, vmin=0, vmax=100, annot=annot_benefit)\n",
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" CS = ax.contour(X, Y, df, colors='black', alpha=0.5)\n",
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" ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))\n",
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" plt.title(title_roi)\n",
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" plt.gca().invert_yaxis()\n",
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" plt.xlim(0, df.shape[1] - 1)\n",
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" plt.ylim(0, df.shape[0] - 1)\n",
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" plt.xlabel('ESS Capacity (MWh)')\n",
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" plt.ylabel('PV Capacity (MW)')\n",
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" plt.savefig(filename)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -253,8 +310,8 @@
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" ax = sns.heatmap(df, fmt=\".00%\", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)\n",
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"\n",
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" cbar = ax.collections[0].colorbar\n",
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" cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
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" cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])\n",
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" cbar.set_ticks([0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1])\n",
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" cbar.set_ticklabels(['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'])\n",
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" cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))\n",
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" X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n",
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"\n",
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@ -289,7 +346,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"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",
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"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",
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" pv = pv_config(capacity=pv_capacity, \n",
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" cost_per_kW=pv_cost_per_kW,\n",
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" lifetime=pv_lifetime, \n",
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@ -306,11 +363,11 @@
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" energySystem = EnergySystem(pv_type=pv, \n",
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" ess_type=ess, \n",
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" grid_type= grid)\n",
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" benefit = energySystem.simulate(data, time_interval)\n",
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" (benefit, netto_benefit, gen_energy) = energySystem.simulate(data, time_interval)\n",
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" results = cal_profit(energySystem, benefit, days)\n",
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" overload_cnt = energySystem.overload_cnt\n",
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" costs = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW\n",
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" return (results, overload_cnt, costs)\n"
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" return (results, overload_cnt, costs, netto_benefit, gen_energy, energySystem.generated)\n"
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]
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},
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{
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@ -322,26 +379,38 @@
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"months_results = []\n",
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"months_costs = []\n",
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"months_overload = []\n",
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"months_nettos = []\n",
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"months_gen_energy = []\n",
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"months_gen_energy2 = []\n",
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"for index, month_data in enumerate(months_data):\n",
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" results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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" costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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" overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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" nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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" gen_energies = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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" gen_energies2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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" for pv_capacity in pv_capacities:\n",
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" for ess_capacity in ess_capacities:\n",
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" (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",
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" (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",
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" results.loc[pv_capacity,ess_capacity] = result\n",
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" overload_cnt.loc[pv_capacity,ess_capacity] = overload\n",
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" costs.loc[pv_capacity,ess_capacity] = cost\n",
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" nettos.loc[pv_capacity,ess_capacity] = netto\n",
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" gen_energies.loc[pv_capacity, ess_capacity] = gen_energy\n",
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" gen_energies2.loc[pv_capacity, ess_capacity] = gen_energy2\n",
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" months_results.append(results)\n",
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" months_costs.append(costs)\n",
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" months_overload.append(overload_cnt)\n",
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" months_nettos.append(nettos)\n",
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" months_gen_energy.append(gen_energies)\n",
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" months_gen_energy2.append(gen_energies2)\n",
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" draw_results(results=results, \n",
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" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-benefit.png',\n",
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" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-benefit.png',\n",
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" title_benefit=title_benefit,\n",
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" annot_benefit=annot_benefit,\n",
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" figure_size=figure_size)\n",
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" draw_overload(overload_cnt=overload_cnt, \n",
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" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-unmet.png',\n",
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" filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-unmet.png',\n",
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" title_unmet=title_unmet,\n",
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" annot_unmet=annot_unmet,\n",
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" figure_size=figure_size,\n",
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@ -351,6 +420,11 @@
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"annual_result = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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"annual_costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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"annual_overload = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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"annual_nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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"annual_gen = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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"annual_gen2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n",
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"\n",
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"\n",
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"\n",
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"# get the yearly results\n",
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"for pv_capacity in pv_capacities:\n",
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@ -358,13 +432,22 @@
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" results = 0\n",
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" costs = 0\n",
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" overload_cnt = 0\n",
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" nettos = 0\n",
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" gen = 0\n",
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" gen2 = 0\n",
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" for index, month_data in enumerate(months_data):\n",
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" results += months_results[index].loc[pv_capacity,ess_capacity]\n",
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" costs += months_costs[index].loc[pv_capacity,ess_capacity]\n",
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" overload_cnt += months_overload[index].loc[pv_capacity, ess_capacity]\n",
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" nettos += months_nettos[index].loc[pv_capacity, ess_capacity]\n",
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" gen += months_gen_energy[index].loc[pv_capacity, ess_capacity]\n",
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" gen2 += months_gen_energy[index].loc[pv_capacity, ess_capacity]\n",
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" annual_result.loc[pv_capacity, ess_capacity] = results\n",
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" annual_costs.loc[pv_capacity, ess_capacity] = costs\n",
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" annual_overload.loc[pv_capacity, ess_capacity] = overload_cnt\n",
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" annual_nettos.loc[pv_capacity, ess_capacity] = nettos\n",
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" annual_gen.loc[pv_capacity, ess_capacity] = gen\n",
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" annual_gen2.loc[pv_capacity, ess_capacity] = gen2\n",
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"\n",
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"draw_cost(costs=annual_costs,\n",
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" filename='plots/annual_cost.png',\n",
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@ -403,9 +486,27 @@
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"if not os.path.isdir('data'):\n",
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" os.makedirs('data')\n",
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"\n",
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"save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n",
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"save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n",
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"save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')"
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"save_data(annual_result, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n",
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"save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n",
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"save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')"
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]
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},
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||||
{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"draw_results(annual_result, 'plots/test.png', 'test', False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png', title_roi, 365, annot_benefit, figure_size)\n"
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]
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}
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],
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560
main.py
560
main.py
@ -1,12 +1,21 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[14]:
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# In[ ]:
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import os
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import glob
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import shutil
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import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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from matplotlib.ticker import FuncFormatter
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import numpy as np
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import pandas as pd
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import os
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import seaborn as sns
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import json
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from matplotlib.colors import LinearSegmentedColormap
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def clear_folder_make_ess_pv(folder_path):
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if os.path.isdir(folder_path):
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@ -19,7 +28,7 @@ folder_path = 'plots'
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clear_folder_make_ess_pv(folder_path)
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||||
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# In[15]:
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||||
# In[ ]:
|
||||
|
||||
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import matplotlib.pyplot as plt
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@ -30,18 +39,21 @@ from EnergySystem import EnergySystem
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from config import pv_config, grid_config, ess_config
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||||
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||||
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# In[16]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
import json
|
||||
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print("Version 0.0.2")
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print("Version 0.0.5")
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with open('config.json', 'r') as f:
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js_data = json.load(f)
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||||
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data = pd.read_csv('combined_data.csv')
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||||
|
||||
|
||||
|
||||
time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"]
|
||||
print(time_interval)
|
||||
|
||||
pv_loss = js_data["pv"]["loss"]
|
||||
pv_cost_per_kW = js_data["pv"]["cost_per_kW"]
|
||||
@ -66,22 +78,45 @@ ess_groups = js_data["ess_capacities"]["groups"]
|
||||
annot_unmet = js_data["annotated"]["unmet_prob"]
|
||||
annot_benefit = js_data["annotated"]["benefit"]
|
||||
annot_cost = js_data["annotated"]["cost"]
|
||||
annot_roi = js_data["annotated"]["roi"]
|
||||
|
||||
title_unmet = js_data["plot_title"]["unmet_prob"]
|
||||
title_cost = js_data["plot_title"]["cost"]
|
||||
title_benefit = js_data["plot_title"]["benefit"]
|
||||
title_roi = js_data["plot_title"]["roi"]
|
||||
|
||||
|
||||
figure_size = (js_data["figure_size"]["length"], js_data["figure_size"]["height"])
|
||||
|
||||
data = pd.read_csv('combined_data.csv')
|
||||
|
||||
granularity = js_data["time_interval"]["numerator"]
|
||||
|
||||
months_days = [31,28,31,30,31,30,31,31,30,31,30,31]
|
||||
def get_month_coe(num, granularity):
|
||||
return 60 / granularity * 24 * months_days[num]
|
||||
|
||||
months_index = [get_month_coe(num, granularity) for num in range(12)]
|
||||
months_data = []
|
||||
for i in range(1,12):
|
||||
months_index[i] += months_index[i-1]
|
||||
for i in range(12):
|
||||
start = 0 if i == 0 else months_index[i-1]
|
||||
end = months_index[i]
|
||||
months_data.append(data.iloc[int(start):int(end)])
|
||||
|
||||
|
||||
|
||||
|
||||
pv_capacities = np.linspace(pv_begin, pv_end, pv_groups)
|
||||
ess_capacities = np.linspace(ess_begin, ess_end, ess_groups)
|
||||
results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
affords = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
# results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
# affords = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
# costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
# overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
|
||||
|
||||
# In[17]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
hour_demand = []
|
||||
@ -97,164 +132,309 @@ plt.savefig('plots/demand.png')
|
||||
plt.close()
|
||||
|
||||
|
||||
# In[18]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
def cal_profit(es: EnergySystem, saved_money):
|
||||
profit = saved_money - es.ess.get_cost_per_year() - es.pv.get_cost_per_year()
|
||||
def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)):
|
||||
df=results
|
||||
df = df.astype(float)
|
||||
df.index = df.index / 1000
|
||||
df.index = df.index.map(int)
|
||||
df.columns = df.columns / 1000
|
||||
df.columns = df.columns.map(int)
|
||||
min_value = df.min().min()
|
||||
max_value = df.max().max()
|
||||
max_scale = max(abs(min_value/1000), abs(max_value/1000))
|
||||
|
||||
df[df.columns[-1] + 1] = df.iloc[:, -1]
|
||||
new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
|
||||
for i in df.columns:
|
||||
new_Data[i] = df[i].iloc[-1]
|
||||
df = pd.concat([df, new_Data])
|
||||
|
||||
X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
|
||||
|
||||
def fmt(x,pos):
|
||||
return '{:.0f}'.format(x/1000)
|
||||
|
||||
cmap = sns.color_palette("coolwarm", as_cmap=True)
|
||||
plt.figure(figsize=figure_size)
|
||||
ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
|
||||
CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
|
||||
ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
|
||||
plt.title(title_benefit)
|
||||
plt.gca().invert_yaxis()
|
||||
plt.xlim(0, df.shape[1] - 1)
|
||||
plt.ylim(0, df.shape[0] - 1)
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig(filename)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, figure_size=(10, 10)):
|
||||
costs = costs.astype(float)
|
||||
costs = costs / 365
|
||||
costs = costs * days
|
||||
|
||||
df = results
|
||||
df = costs / df
|
||||
if 0 in df.index and 0 in df.columns:
|
||||
df.loc[0,0] = 100
|
||||
df[df > 80] = 100
|
||||
print(df)
|
||||
|
||||
df = df.astype(float)
|
||||
df.index = df.index / 1000
|
||||
df.index = df.index.map(int)
|
||||
df.columns = df.columns / 1000
|
||||
df.columns = df.columns.map(int)
|
||||
min_value = df.min().min()
|
||||
max_value = df.max().max()
|
||||
print(max_value)
|
||||
max_scale = max(abs(min_value), abs(max_value))
|
||||
|
||||
df[df.columns[-1] + 1] = df.iloc[:, -1]
|
||||
new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
|
||||
for i in df.columns:
|
||||
new_Data[i] = df[i].iloc[-1]
|
||||
df = pd.concat([df, new_Data])
|
||||
|
||||
X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
|
||||
|
||||
def fmt(x,pos):
|
||||
return '{:.0f}'.format(x)
|
||||
|
||||
cmap = sns.color_palette("Greys", as_cmap=True)
|
||||
plt.figure(figsize=figure_size)
|
||||
ax = sns.heatmap(df, fmt=".1f", cmap=cmap, vmin=0, vmax=100, annot=annot_benefit)
|
||||
CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
|
||||
ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
|
||||
plt.title(title_roi)
|
||||
plt.gca().invert_yaxis()
|
||||
plt.xlim(0, df.shape[1] - 1)
|
||||
plt.ylim(0, df.shape[0] - 1)
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig(filename)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
|
||||
df = costs
|
||||
df = df.astype(int)
|
||||
df.index = df.index / 1000
|
||||
df.index = df.index.map(int)
|
||||
df.columns = df.columns / 1000
|
||||
df.columns = df.columns.map(int)
|
||||
|
||||
df[df.columns[-1] + 1] = df.iloc[:, -1]
|
||||
new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
|
||||
for i in df.columns:
|
||||
new_Data[i] = df[i].iloc[-1]
|
||||
df = pd.concat([df, new_Data])
|
||||
X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
|
||||
|
||||
def fmt(x, pos):
|
||||
return '{:.0f}'.format(x / 1000000)
|
||||
|
||||
plt.figure(figsize=figure_size)
|
||||
ax = sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost)
|
||||
CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
|
||||
ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
|
||||
plt.title(title_cost)
|
||||
plt.gca().invert_yaxis()
|
||||
plt.xlim(0, df.shape[1] - 1)
|
||||
plt.ylim(0, df.shape[0] - 1)
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig(filename)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10), days=365, granularity=15):
|
||||
df = overload_cnt
|
||||
print(days, granularity)
|
||||
coef = 60 / granularity * days * 24
|
||||
print(coef)
|
||||
print(df)
|
||||
df = ( coef - df) / coef
|
||||
print(df)
|
||||
|
||||
df = df.astype(float)
|
||||
df.index = df.index / 1000
|
||||
df.index = df.index.map(int)
|
||||
df.columns = df.columns / 1000
|
||||
df.columns = df.columns.map(int)
|
||||
|
||||
|
||||
df[df.columns[-1] + 1] = df.iloc[:, -1]
|
||||
new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
|
||||
for i in df.columns:
|
||||
new_Data[i] = df[i].iloc[-1]
|
||||
# print(new_Data)
|
||||
df = pd.concat([df, new_Data])
|
||||
|
||||
|
||||
plt.figure(figsize=figure_size)
|
||||
cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
|
||||
ax = sns.heatmap(df, fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
|
||||
|
||||
cbar = ax.collections[0].colorbar
|
||||
cbar.set_ticks([0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1])
|
||||
cbar.set_ticklabels(['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'])
|
||||
cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))
|
||||
X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
|
||||
|
||||
def fmt(x, pos):
|
||||
return '{:.0f}%'.format(x * 100)
|
||||
CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
|
||||
|
||||
ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
|
||||
|
||||
plt.xlim(0, df.shape[1] - 1)
|
||||
plt.ylim(0, df.shape[0] - 1)
|
||||
plt.title(title_unmet)
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig(filename)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
def cal_profit(es: EnergySystem, saved_money, days):
|
||||
profit = saved_money - es.ess.get_cost_per_year() / 365 * days - es.pv.get_cost_per_year() / 365 * days
|
||||
return profit
|
||||
|
||||
|
||||
# In[24]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
for ess_capacity in ess_capacities:
|
||||
print(f"ess_capacity:{ess_capacity}")
|
||||
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):
|
||||
pv = pv_config(capacity=pv_capacity,
|
||||
cost_per_kW=pv_cost_per_kW,
|
||||
lifetime=pv_lifetime,
|
||||
loss=pv_loss)
|
||||
ess = ess_config(capacity=ess_capacity,
|
||||
cost_per_kW=ess_cost_per_kW,
|
||||
lifetime=ess_lifetime,
|
||||
loss=ess_loss,
|
||||
charge_power=ess_capacity,
|
||||
discharge_power=ess_capacity)
|
||||
grid = grid_config(capacity=grid_capacity,
|
||||
grid_loss=grid_loss,
|
||||
sell_price= sell_price)
|
||||
energySystem = EnergySystem(pv_type=pv,
|
||||
ess_type=ess,
|
||||
grid_type= grid)
|
||||
(benefit, netto_benefit, gen_energy) = energySystem.simulate(data, time_interval)
|
||||
results = cal_profit(energySystem, benefit, days)
|
||||
overload_cnt = energySystem.overload_cnt
|
||||
costs = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW
|
||||
return (results, overload_cnt, costs, netto_benefit, gen_energy, energySystem.generated)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
months_results = []
|
||||
months_costs = []
|
||||
months_overload = []
|
||||
months_nettos = []
|
||||
months_gen_energy = []
|
||||
months_gen_energy2 = []
|
||||
for index, month_data in enumerate(months_data):
|
||||
results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
gen_energies = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
gen_energies2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
for pv_capacity in pv_capacities:
|
||||
print(f"pv_capacity:{pv_capacity}")
|
||||
pv = pv_config(capacity=pv_capacity,
|
||||
cost_per_kW=pv_cost_per_kW,
|
||||
lifetime=pv_lifetime,
|
||||
loss=pv_loss)
|
||||
ess = ess_config(capacity=ess_capacity,
|
||||
cost_per_kW=ess_cost_per_kW,
|
||||
lifetime=ess_lifetime,
|
||||
loss=ess_loss,
|
||||
charge_power=ess_capacity,
|
||||
discharge_power=ess_capacity)
|
||||
grid = grid_config(capacity=grid_capacity,
|
||||
grid_loss=grid_loss,
|
||||
sell_price= sell_price)
|
||||
energySystem = EnergySystem(pv_type=pv,
|
||||
ess_type=ess,
|
||||
grid_type= grid)
|
||||
benefit = energySystem.simulate(data, time_interval)
|
||||
results.loc[pv_capacity,ess_capacity] = cal_profit(energySystem, benefit)
|
||||
affords.loc[pv_capacity,ess_capacity] = energySystem.afford
|
||||
overload_cnt.loc[pv_capacity,ess_capacity] = energySystem.overload_cnt
|
||||
costs.loc[pv_capacity,ess_capacity] = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW
|
||||
pv_generated = energySystem.day_generated
|
||||
ess_generated = energySystem.hour_stored
|
||||
ess_generated_2 = energySystem.hour_stored_2
|
||||
plt.figure(figsize=(10,8));
|
||||
plt.plot(ess_generated)
|
||||
plt.xlabel('day #')
|
||||
plt.ylabel('SoC %')
|
||||
plt.title(f'14:00 ESS SoC \n PV cap:{pv_capacity}, ESS cap:{ess_capacity}')
|
||||
plt.savefig(f'plots/ess/1400-{pv_capacity}-{ess_capacity}.png')
|
||||
plt.close()
|
||||
plt.figure(figsize=(10,8));
|
||||
plt.plot(ess_generated_2)
|
||||
plt.xlabel('day #')
|
||||
plt.ylabel('SoC%')
|
||||
plt.title(f'08:00 ESS SoC \n PV cap:{pv_capacity}, ESS cap:{ess_capacity}')
|
||||
plt.savefig(f'plots/ess/0800-{pv_capacity}-{ess_capacity}.png')
|
||||
plt.close()
|
||||
# print(energySystem.unmet)
|
||||
# spring_week_start = energySystem.season_start
|
||||
# spring_week_end = spring_week_start + energySystem.week_length
|
||||
# summer_week_start = energySystem.season_start + 1 * energySystem.season_step
|
||||
# summer_week_end = summer_week_start + energySystem.week_length
|
||||
# autumn_week_start = energySystem.season_start + 2 * energySystem.season_step
|
||||
# autumn_week_end = autumn_week_start + energySystem.week_length
|
||||
# winter_week_start = energySystem.season_start + 3 * energySystem.season_step
|
||||
# winter_week_end = winter_week_start+ energySystem.week_length
|
||||
for ess_capacity in ess_capacities:
|
||||
(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])
|
||||
results.loc[pv_capacity,ess_capacity] = result
|
||||
overload_cnt.loc[pv_capacity,ess_capacity] = overload
|
||||
costs.loc[pv_capacity,ess_capacity] = cost
|
||||
nettos.loc[pv_capacity,ess_capacity] = netto
|
||||
gen_energies.loc[pv_capacity, ess_capacity] = gen_energy
|
||||
gen_energies2.loc[pv_capacity, ess_capacity] = gen_energy2
|
||||
months_results.append(results)
|
||||
months_costs.append(costs)
|
||||
months_overload.append(overload_cnt)
|
||||
months_nettos.append(nettos)
|
||||
months_gen_energy.append(gen_energies)
|
||||
months_gen_energy2.append(gen_energies2)
|
||||
draw_results(results=results,
|
||||
filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-benefit.png',
|
||||
title_benefit=title_benefit,
|
||||
annot_benefit=annot_benefit,
|
||||
figure_size=figure_size)
|
||||
draw_overload(overload_cnt=overload_cnt,
|
||||
filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-unmet.png',
|
||||
title_unmet=title_unmet,
|
||||
annot_unmet=annot_unmet,
|
||||
figure_size=figure_size,
|
||||
days=months_days[index],
|
||||
granularity=granularity)
|
||||
|
||||
# spring_consume_data = []
|
||||
# summer_consume_data = []
|
||||
# autumn_consume_data = []
|
||||
# winter_consume_data = []
|
||||
# for index, row in data.iterrows():
|
||||
# if index in range(spring_week_start, spring_week_end):
|
||||
# spring_consume_data.append(row['demand'])
|
||||
# elif index in range(summer_week_start, summer_week_end):
|
||||
# summer_consume_data.append(row['demand'])
|
||||
# elif index in range(autumn_week_start, autumn_week_end):
|
||||
# autumn_consume_data.append(row['demand'])
|
||||
# elif index in range(winter_week_start, winter_week_end):
|
||||
# winter_consume_data.append(row['demand'])
|
||||
|
||||
# spring_week_time = list(range(spring_week_start, spring_week_end))
|
||||
# summer_week_time = list(range(summer_week_start, summer_week_end))
|
||||
# autumn_week_time = list(range(autumn_week_start, autumn_week_end))
|
||||
# winter_week_time = list(range(winter_week_start, winter_week_end))
|
||||
|
||||
# spring_pv_generated = energySystem.spring_week_gen
|
||||
# summer_pv_generated = energySystem.summer_week_gen
|
||||
# autumn_pv_generated = energySystem.autumn_week_gen
|
||||
# winter_pv_generated = energySystem.winter_week_gen
|
||||
|
||||
# spring_soc = energySystem.spring_week_soc
|
||||
# summer_soc = energySystem.summer_week_soc
|
||||
# autumn_soc = energySystem.autumn_week_soc
|
||||
# winter_soc = energySystem.winter_week_soc
|
||||
annual_result = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
annual_costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
annual_overload = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
annual_nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
annual_gen = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
annual_gen2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
|
||||
|
||||
|
||||
# fig, ax1 = plt.subplots()
|
||||
|
||||
# plt.plot(spring_week_time, spring_pv_generated, label = 'pv generation')
|
||||
# plt.plot(spring_week_time, spring_consume_data, label = 'factory consume')
|
||||
# plt.ylabel('Power / kW')
|
||||
# plt.xlabel('15 min #')
|
||||
# plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW spring week generate condition')
|
||||
# plt.legend()
|
||||
# plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-spring.png')
|
||||
# plt.close()
|
||||
# get the yearly results
|
||||
for pv_capacity in pv_capacities:
|
||||
for ess_capacity in ess_capacities:
|
||||
results = 0
|
||||
costs = 0
|
||||
overload_cnt = 0
|
||||
nettos = 0
|
||||
gen = 0
|
||||
gen2 = 0
|
||||
for index, month_data in enumerate(months_data):
|
||||
results += months_results[index].loc[pv_capacity,ess_capacity]
|
||||
costs += months_costs[index].loc[pv_capacity,ess_capacity]
|
||||
overload_cnt += months_overload[index].loc[pv_capacity, ess_capacity]
|
||||
nettos += months_nettos[index].loc[pv_capacity, ess_capacity]
|
||||
gen += months_gen_energy[index].loc[pv_capacity, ess_capacity]
|
||||
gen2 += months_gen_energy[index].loc[pv_capacity, ess_capacity]
|
||||
annual_result.loc[pv_capacity, ess_capacity] = results
|
||||
annual_costs.loc[pv_capacity, ess_capacity] = costs
|
||||
annual_overload.loc[pv_capacity, ess_capacity] = overload_cnt
|
||||
annual_nettos.loc[pv_capacity, ess_capacity] = nettos
|
||||
annual_gen.loc[pv_capacity, ess_capacity] = gen
|
||||
annual_gen2.loc[pv_capacity, ess_capacity] = gen2
|
||||
|
||||
# plt.plot(summer_week_time, summer_pv_generated, label = 'pv generation')
|
||||
# plt.plot(summer_week_time, summer_consume_data, label = 'factory consume')
|
||||
# plt.ylabel('Power / kW')
|
||||
# plt.xlabel('15 min #')
|
||||
# plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW summer week generate condition')
|
||||
# plt.legend()
|
||||
# plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-summer.png')
|
||||
# plt.close()
|
||||
|
||||
# plt.plot(autumn_week_time, autumn_pv_generated, label = 'pv generation')
|
||||
# plt.plot(autumn_week_time, autumn_consume_data, label = 'factory consume')
|
||||
# plt.ylabel('Power / kW')
|
||||
# plt.xlabel('15 min #')
|
||||
# plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW autumn week generate condition')
|
||||
# plt.legend()
|
||||
# plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-autumn.png')
|
||||
# plt.close()
|
||||
|
||||
# plt.plot(winter_week_time, winter_pv_generated, label = 'pv generation')
|
||||
# plt.plot(winter_week_time, winter_consume_data, label = 'factory consume')
|
||||
# plt.ylabel('Power / kW')
|
||||
# plt.xlabel('15 min #')
|
||||
# plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW winter week generate condition')
|
||||
# plt.legend()
|
||||
# plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-winter.png')
|
||||
# plt.close()
|
||||
|
||||
# plt.figure();
|
||||
# plt.plot(pv_generated)
|
||||
# plt.xlabel('day #')
|
||||
# plt.ylabel('Electricity kWh')
|
||||
# plt.title(f'PV generated pv cap:{pv_capacity}, ess cap:{ess_capacity}')
|
||||
# plt.savefig(f'plots/pv/{pv_capacity}-{ess_capacity}.png')
|
||||
# plt.close()
|
||||
draw_cost(costs=annual_costs,
|
||||
filename='plots/annual_cost.png',
|
||||
title_cost=title_cost,
|
||||
annot_cost=annot_cost,
|
||||
figure_size=figure_size)
|
||||
draw_results(results=annual_result,
|
||||
filename='plots/annual_benefit.png',
|
||||
title_benefit=title_benefit,
|
||||
annot_benefit=annot_benefit,
|
||||
figure_size=figure_size)
|
||||
draw_overload(overload_cnt=annual_overload,
|
||||
filename='plots/annual_unmet.png',
|
||||
title_unmet=title_unmet,
|
||||
annot_unmet=annot_unmet,
|
||||
figure_size=figure_size)
|
||||
|
||||
|
||||
# plt.show()
|
||||
|
||||
|
||||
|
||||
|
||||
# results = results.astype(float)
|
||||
|
||||
|
||||
# pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95)
|
||||
# ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000)
|
||||
# grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4)
|
||||
# grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4)
|
||||
|
||||
|
||||
# print(benefit)
|
||||
|
||||
|
||||
# In[20]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
def save_data(data, filename):
|
||||
@ -262,83 +442,25 @@ def save_data(data, filename):
|
||||
data.to_json(filename + '.json')
|
||||
|
||||
|
||||
# In[21]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
import matplotlib.ticker as ticker
|
||||
|
||||
if not os.path.isdir('data'):
|
||||
os.makedirs('data')
|
||||
|
||||
save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')
|
||||
save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')
|
||||
save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')
|
||||
df=results
|
||||
df = df.astype(float)
|
||||
df.index = df.index / 1000
|
||||
df.columns = df.columns / 1000
|
||||
min_value = df.min().min()
|
||||
max_value = df.max().max()
|
||||
max_scale = max(abs(min_value/1000), abs(max_value/1000))
|
||||
plt.figure(figsize=figure_size)
|
||||
cmap = sns.color_palette("coolwarm", as_cmap=True)
|
||||
ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
|
||||
# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
|
||||
plt.title(title_benefit)
|
||||
plt.gca().invert_yaxis()
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig('plots/benefit.png')
|
||||
save_data(annual_result, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')
|
||||
save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')
|
||||
save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')
|
||||
|
||||
|
||||
# In[22]:
|
||||
# In[ ]:
|
||||
|
||||
|
||||
df = costs
|
||||
df = df.astype(int)
|
||||
df.index = df.index / 1000
|
||||
df.columns = df.columns / 1000
|
||||
|
||||
plt.figure(figsize=figure_size)
|
||||
sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost)
|
||||
plt.title(title_cost)
|
||||
plt.gca().invert_yaxis()
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig('plots/costs.png')
|
||||
|
||||
# pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95)
|
||||
# ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000)
|
||||
# grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4)
|
||||
# grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4)
|
||||
draw_results(annual_result, 'plots/test.png', 'test', False)
|
||||
|
||||
|
||||
# print(benefit)
|
||||
# In[ ]:
|
||||
|
||||
|
||||
# In[23]:
|
||||
|
||||
|
||||
from matplotlib.colors import LinearSegmentedColormap
|
||||
df = overload_cnt
|
||||
df = df.astype(int)
|
||||
df.index = df.index / 1000
|
||||
df.columns = df.columns / 1000
|
||||
min_value = df.min().min()
|
||||
max_value = df.max().max()
|
||||
max_scale = max(abs(min_value/1000), abs(max_value/1000))
|
||||
|
||||
plt.figure(figsize=figure_size)
|
||||
cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
|
||||
ax = sns.heatmap(df/(4*24*365), fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
|
||||
cbar = ax.collections[0].colorbar
|
||||
cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
|
||||
cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
|
||||
cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))
|
||||
|
||||
plt.title(title_unmet)
|
||||
plt.gca().invert_yaxis()
|
||||
plt.xlabel('ESS Capacity (MWh)')
|
||||
plt.ylabel('PV Capacity (MW)')
|
||||
plt.savefig('plots/unmet.png')
|
||||
draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png', title_roi, 365, annot_benefit, figure_size)
|
||||
|
||||
|
||||
38
read_data.py
38
read_data.py
@ -5,6 +5,7 @@ import csv
|
||||
sunlight_file_name = 'lightintensity.xlsx'
|
||||
factory_demand_file_name = 'factory_power1.xlsx'
|
||||
electricity_price_data = 'electricity_price_data.csv'
|
||||
electricity_price_data_sell = 'electricity_price_data_sell.csv'
|
||||
|
||||
df_sunlight = pd.read_excel(sunlight_file_name, header=None, names=['SunlightIntensity'])
|
||||
|
||||
@ -26,41 +27,28 @@ print(df_power.head())
|
||||
|
||||
df_combined = df_sunlight_resampled.join(df_power)
|
||||
|
||||
|
||||
|
||||
df_combined.to_csv('combined_data.csv', index=True, index_label='Time')
|
||||
|
||||
def read_csv(file_path):
|
||||
return pd.read_csv(file_path, index_col='Time', usecols=['Time', 'ElectricityPrice'])
|
||||
|
||||
# price_data = np.random.uniform(0.3, 0.3, len(times))
|
||||
|
||||
# 创建DataFrame
|
||||
price_df = read_csv(electricity_price_data)
|
||||
price_df = pd.read_csv(electricity_price_data, index_col='Time', usecols=['Time', 'ElectricityBuy'])
|
||||
price_df.index = pd.to_datetime(price_df.index)
|
||||
price_df = price_df.reindex(df_combined.index)
|
||||
|
||||
# price_df.set_index('Time', inplace=True)
|
||||
|
||||
# 保存到CSV文件
|
||||
# price_df.to_csv('electricity_price_data.csv', index=True)
|
||||
print("price____")
|
||||
print(price_df.index)
|
||||
print("df_combined____")
|
||||
print(df_combined.index)
|
||||
|
||||
print("Electricity price data generated and saved.")
|
||||
|
||||
|
||||
df_combined2 = df_combined.join(price_df)
|
||||
print(df_combined2.head())
|
||||
# 保存结果
|
||||
|
||||
sell_df = pd.read_csv(electricity_price_data_sell, index_col='Time', usecols=['Time', 'ElectricitySell'])
|
||||
sell_df.index = pd.to_datetime(sell_df.index)
|
||||
sell_df = sell_df.reindex(df_combined.index)
|
||||
|
||||
df_combined3 = df_combined2.join(sell_df)
|
||||
|
||||
with open('combined_data.csv', 'w', newline='') as file:
|
||||
writer = csv.writer(file)
|
||||
writer.writerow(['time', 'sunlight', 'demand','price'])
|
||||
writer.writerow(['time', 'sunlight', 'demand','buy', 'sell'])
|
||||
cnt = 0
|
||||
for index, row in df_combined2.iterrows():
|
||||
for index, row in df_combined3.iterrows():
|
||||
time_formatted = index.strftime('%H:%M')
|
||||
writer.writerow([time_formatted, row['SunlightIntensity'], row['FactoryPower'],row['ElectricityPrice']])
|
||||
writer.writerow([time_formatted, row['SunlightIntensity'], row['FactoryPower'],row['ElectricityBuy'], row['ElectricitySell']])
|
||||
|
||||
print('The file is written to combined_data.csv')
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user