update the csv file output code and add a progress bar in the code

update electricity data

EnergySystem.py

@@ -5,21 +5,74 @@ class EnergySystem:
         self.pv = pv_type
         self.ess = ess_type
         self.grid = grid_type
+        self.day_generated = []
+        self.generated = 0
+        self.stored = 0
+        self.hour_stored = []
+        self.hour_stored_2 = []
+        self.afford = True
+        self.cost = self.ess.get_cost() + self.pv.get_cost()
+        self.overload_cnt = 0
+        self.spring_week_gen = []
+        self.summer_week_gen = []
+        self.autumn_week_gen = []
+        self.winter_week_gen = []
+        self.spring_week_soc = []
+        self.summer_week_soc = []
+        self.autumn_week_soc = []
+        self.winter_week_soc = []
+        self.factory_demand = []
+        self.buy_price_kWh = []
+        self.sell_price_kWh = []
+        self.pv_generated_kWh = []
+        self.grid_need_power_kW = []
+        self.time = []
+        self.ess_rest = 0
+        self.granularity = 4
+        self.season_step = self.granularity * 24 * 7 * 12
+        self.season_start= self.granularity * 24 * 7 * 2
+        self.week_length = self.granularity * 24 * 7
+        self.unmet = []
+        
+    def get_cost(self):
+        return self.ess.get_cost()+self.pv.get_cost()
 
     # 优先使用PV供电给工厂 - 如果PV输出能满足工厂的需求，则直接供电，多余的电能用来给ESS充电。
     # PV不足时使用ESS补充 - 如果PV输出不足以满足工厂需求，首先从ESS获取所需电量。
     # 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。
     def simulate(self, data, time_interval):
         total_benefit = 0
+        total_netto_benefit = 0
+        total_gen = 0
+        net_grid = 0.
         for index, row in data.iterrows():
             time = row['time']
-            sunlight_intensity = row['sunlight']
+            self.time.append(time)
+            # sunlight_intensity = row['sunlight']
+            pv_yield = row['PV yield[kW/kWp]']
             factory_demand = row['demand']
+            electricity_price = row['buy']
+            sell_price = row['sell']
             # electricity_price = self.grid.get_price_for_time(time)
-            electricity_price = row['price']
 
-            generated_pv_power = self.pv.capacity * sunlight_intensity  # 生成的功率，单位 kW
+            if time == '00:00':
+                self.day_generated.append(self.generated)
+                self.generated = 0
+            if time.endswith('14:00'):
+                soc = self.ess.storage / self.ess.capacity
+                self.hour_stored.append(soc)
+            if time.endswith('08:00'):
+                soc = self.ess.storage / self.ess.capacity
+                self.hour_stored_2.append(soc)
+
+            generated_pv_power = self.pv.capacity * pv_yield# 生成的功率，单位 kW
             generated_pv_energy = generated_pv_power * time_interval * self.pv.loss  # 生成的能量，单位 kWh
+            self.pv_generated_kWh.append(generated_pv_energy)
+            self.factory_demand.append(factory_demand)
+            self.buy_price_kWh.append(electricity_price)
+            self.sell_price_kWh.append(sell_price)
+
+            self.generated += generated_pv_energy
             # pv生成的能量如果比工厂的需求要大
             if generated_pv_energy >= factory_demand * time_interval:
                 # 剩余的能量(kwh) = pv生成的能量 - 工厂需求的功率 * 时间间隔 
@@ -31,32 +84,80 @@ class EnergySystem:
                 # 如果还有电量盈余,且pv功率大于ess的充电功率+工厂的需求功率则准备卖电
                 if surplus_after_ess > 0 and generated_pv_power > self.ess.charge_power + factory_demand:
                     sold_to_grid = surplus_after_ess
-                    sell_income = sold_to_grid * self.grid.sell_price
+                    sell_income = sold_to_grid * sell_price
                     total_benefit += sell_income
                 # 节省的能量 = 工厂需求的能量 * 时间段
-                total_energy = factory_demand * time_interval
+                # total_energy = factory_demand * time_interval
+                saved_energy = factory_demand * time_interval
+                self.grid_need_power_kW.append(0)
             # pv比工厂的需求小
             else:
                 # 从ess中需要的电量 = 工厂需要的电量 - pv中的电量
                 needed_from_ess = factory_demand * time_interval - generated_pv_energy
-                # 如果ess中村的电量比需要的多
-                if self.ess.storage >= needed_from_ess:
+                # 如果ess中存的电量比需要的多
+                if self.ess.storage * self.ess.loss >= needed_from_ess:
                     # 取出电量
-                    discharging_power = min(self.ess.discharge_power * time_interval, needed_from_ess)
+                    if self.ess.discharge_power * time_interval * self.ess.loss < needed_from_ess:
+                        discharging_power = self.ess.discharge_power * time_interval 
+                    else:
+                        discharging_power = needed_from_ess / self.ess.loss
+
                     self.ess.storage -= discharging_power
-                    # 生下来的能量 = pv的能量 + 放出来的能量
-                    total_energy = generated_pv_energy + discharging_power
+                    # 节省下来的能量 = pv的能量 + 放出来的能量
+                    saved_energy = generated_pv_energy + discharging_power * self.ess.loss
+                    self.grid_need_power_kW.append(0)
                 else:
-                    total_energy = generated_pv_energy + self.ess.storage
+                    # 如果存的电量不够
+                    # 需要把ess中的所有电量释放出来
+                    if self.grid.capacity * time_interval + generated_pv_energy + self.ess.storage * self.ess.loss < factory_demand * time_interval:
+                        self.afford = False
+                        self.overload_cnt+=1
+                        log = f"index: {index}, time: {time}, SoC:{self.ess.storage / self.ess.capacity}%, storage: {self.ess.storage}, pv_gen:{generated_pv_power}, power_demand: {factory_demand}, overload_cnt:{self.overload_cnt}, day:{int(index/96) + 1}"
+                        self.unmet.append((index,time,factory_demand,generated_pv_power))
+                        # with open(f'plots/summary/ess-{self.ess.capacity}-pv-{self.pv.capacity}', 'a') as f:
+                            # f.write(log)
+                        # print(log)
+                        # self.unmet.append(log)
+                    saved_energy = generated_pv_energy + self.ess.storage * self.ess.loss
                     self.ess.storage = 0
-                    needed_from_grid = factory_demand * time_interval - total_energy
-                    net_grid = min(self.grid.capacity * time_interval, needed_from_grid) *  self.grid.loss
+                    needed_from_grid = factory_demand * time_interval - saved_energy
+                    net_grid = min(self.grid.capacity * time_interval, needed_from_grid) * self.grid.loss
+                    self.grid_need_power_kW.append(needed_from_grid * 4)
+                    # grid_energy += net_grid
                     # total_energy += net_grid
             # print(total_energy)
             # 工厂需求量-总能量
             # unmet_demand = max(0, factory_demand * time_interval - total_energy)
             # benefit = (total_energy - unmet_demand) * electricity_price
-            benefit = (total_energy) * electricity_price
-            total_benefit += benefit
+            total_gen += saved_energy
+            benefit = (saved_energy) * electricity_price
+            cost = net_grid * electricity_price
+            # print(f"time:{time} benefit: {benefit}, cost: {cost}")
+            total_netto_benefit += benefit
+            total_benefit += benefit - cost
+            # # spring
+            week_start = self.season_start
+            week_end = self.week_length + week_start
+            if index in range(week_start, week_end):
+                self.spring_week_gen.append(generated_pv_power)
+                self.spring_week_soc.append(self.ess.storage / self.ess.capacity)
+            self.ess_rest = self.ess.storage
+            # summer
+            # week_start += self.season_step
+            # week_end += self.season_step
+            # if index in range(week_start, week_end):
+            #     self.summer_week_gen.append(generated_pv_power)
+            #     self.summer_week_soc.append(self.ess.storage / self.ess.capacity)
+            # # autumn
+            # week_start += self.season_step
+            # week_end += self.season_step
+            # if index in range(week_start, week_end):
+            #     self.autumn_week_gen.append(generated_pv_power)
+            #     self.autumn_week_soc.append(self.ess.storage / self.ess.capacity)
+            # week_start += self.season_step
+            # week_end += self.season_step
+            # if index in range(week_start, week_end):
+            #     self.winter_week_gen.append(generated_pv_power)
+            #     self.winter_week_soc.append(self.ess.storage / self.ess.capacity)
 
-        return total_benefit
+        return (total_benefit, total_netto_benefit, total_gen)

README.md

@@ -2,11 +2,13 @@
 
 Feature list:
 
-- []Draw the entire year's electricity consumption figure.
-- []Draw the entire year's energy generation figure.
-- []Draw a heatmap of the system profit in different configurations.
-- []Calculate the probability of a successful run.
-- []Read the configs from the file, including `time granularity`, 
+- [] Draw the entire year's electricity consumption figure.
+- [] Draw the entire year's energy generation figure.
+- [] Draw a heatmap of the system profit in different configurations.
+~~- []Calculate the probability of a successful run.~~
+- [] Determine whether the system can run successfully
+- [] Calculate the ROI.
+- [] Read the configs from the file, including `time granularity`, 
   - ess: `capacity`, `cost per kW`, `charge power`, `discharge power`, `loss`
   - pv: `capacity`, `cost per kW`, `loss`
   - grid: `capacity`, `sell price`
@@ -14,4 +16,4 @@ Feature list:
     - `lightintensity.xlsx`: record the light intensity. Value in the file should be between 0 and 1
     - `factory_power.xlsx`: record the power consumption in the factory. Default time granularity is `15min`
     - `combined_data.csv`: This file is generated by the Python script, including the `light` `intensity`, `factory_power`, `time`. 
-- []GUI.
+- [] GUI.

config.json

@@ -0,0 +1,53 @@
+{
+    "pv":{ 
+        "loss": 0.98, 
+        "cost_per_kW": 200,
+        "lifetime": 15
+    },
+    "ess":{
+        "loss": 0.98,
+        "cost_per_kW": 300,
+        "lifetime": 8
+    },
+    "grid":{
+        "loss": 0.98,
+        "sell_price": 0.2 ,
+        "capacity": 5000
+    },
+    "pv_capacities":{
+        "begin": 0,
+        "end": 50000,
+        "groups":  3
+    },
+    "ess_capacities":{
+        "begin": 0,
+        "end": 100000,
+        "groups":  3
+    },
+    "time_interval":{
+        "numerator": 15,
+        "denominator": 60
+    },
+    "annotated": {
+        "unmet_prob": false,
+        "benefit": false,
+        "cost": false,
+        "roi": false 
+    },
+    "figure_size":{
+        "height": 9,
+        "length": 10
+    },
+    "plot_title":{
+        "unmet_prob": "Coverage Rate of Factory Electrical Demands",
+        "cost": "Costs of Microgrid system [m-EUR]",
+        "benefit": "Financial Profit Based on Py & Ess Configuration (k-EUR / year)",
+        "roi": "ROI"
+    },
+    "data_path": {
+        "pv_yield": "read_data/Serbia.csv",
+        "demand": "read_data/factory_power1.csv",
+        "sell": "read_data/electricity_price_data_sell.csv",
+        "buy": "read_data/electricity_price_data.csv"
+    }
+}

config.py

@@ -5,15 +5,23 @@ class pv_config:
         self.cost_per_kW = cost_per_kW
         self.lifetime = lifetime
         self.loss = loss
+    def get_cost(self):
+        return self.capacity * self.cost_per_kW
+    def get_cost_per_year(self):
+        return self.capacity * self.cost_per_kW / self.lifetime
 class ess_config:
-    def __init__(self, capacity, cost_per_kW, lifetime, loss, charge_power, discharge_power):
+    def __init__(self, capacity, cost_per_kW, lifetime, loss, charge_power, discharge_power, storage=0):
         self.capacity = capacity
         self.cost_per_kW = cost_per_kW
         self.lifetime = lifetime
         self.loss = loss
-        self.storage = 0
+        self.storage = storage
         self.charge_power = charge_power
         self.discharge_power = discharge_power
+    def get_cost(self):
+        return self.capacity * self.cost_per_kW
+    def get_cost_per_year(self):
+        return self.capacity * self.cost_per_kW / self.lifetime
 
 class grid_config:
     def __init__(self, capacity, grid_loss, sell_price):

convert.py

@@ -0,0 +1,11 @@
+import nbformat
+from nbconvert import PythonExporter
+
+with open('main.ipynb', "r", encoding="utf-8") as f:
+    notebook = nbformat.read(f, as_version = 4)
+
+exporter = PythonExporter()
+python_code, _ = exporter.from_notebook_node(notebook)
+
+with open('main.py', "w", encoding="utf-8") as f:
+    f.write(python_code)

draw.py

@@ -0,0 +1,209 @@
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.ticker import FuncFormatter
+import numpy as np
+import pandas as pd
+import os
+import seaborn as sns
+import json
+from matplotlib.colors import LinearSegmentedColormap
+
+def read_data(file_name: str):
+    with open(file_name, 'r') as f:
+        data = json.load(f)
+    for key, value in data.items():
+        for subkey, subvalue in value.items():
+            data[key][subkey] = float(subvalue)
+    df = pd.DataFrame.from_dict(data, orient='index')
+    df = df.T
+    df.index = pd.to_numeric(df.index)
+    df.columns = pd.to_numeric(df.columns)
+    return df
+
+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)
+
+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)
+
+
+def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10)):
+    df = overload_cnt
+    df = (4 * 24 * 365 - df) / (4 * 24 * 365)
+    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()
+
+
+    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, 0.25, 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%}'))
+    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)
+
+with open('config.json', 'r') as f:
+    js_data = json.load(f)
+
+data = pd.read_csv('combined_data.csv')
+time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"]
+
+pv_loss = js_data["pv"]["loss"]
+pv_cost_per_kW = js_data["pv"]["cost_per_kW"]
+pv_lifetime = js_data["pv"]["lifetime"]
+
+ess_loss = js_data["ess"]["loss"]
+ess_cost_per_kW = js_data["ess"]["cost_per_kW"]
+ess_lifetime = js_data["ess"]["lifetime"]
+
+grid_loss = js_data["grid"]["loss"]
+sell_price = js_data["grid"]["sell_price"]
+grid_capacity = js_data["grid"]["capacity"]
+
+pv_begin = js_data["pv_capacities"]["begin"]
+pv_end = js_data["pv_capacities"]["end"]
+pv_groups = js_data["pv_capacities"]["groups"]
+
+ess_begin = js_data["ess_capacities"]["begin"]
+ess_end = js_data["ess_capacities"]["end"]
+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"]
+
+title_unmet = js_data["plot_title"]["unmet_prob"]
+title_cost = js_data["plot_title"]["cost"]
+title_benefit = js_data["plot_title"]["benefit"]
+
+figure_size = (js_data["figure_size"]["length"], js_data["figure_size"]["height"])
+
+directory = 'data/'
+
+file_list = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
+
+
+split_files = [f.split('-') for f in file_list]
+
+costs_files = [f for f in split_files if f[-1].endswith('costs.json')]
+print(f'find costs files: {costs_files}')
+overload_files = [f for f in split_files if f[-1].endswith('overload_cnt.json')]
+print(f'find coverage/unmet files: {overload_files}')
+results_files = [f for f in split_files if f[-1].endswith('results.json')]
+print(f'find profit/benefit files: {results_files}')
+
+costs_dfs = [read_data(directory + '-'.join(f)) for f in costs_files]
+overload_dfs = [read_data(directory + '-'.join(f)) for f in overload_files]
+results_dfs = [read_data(directory + '-'.join(f)) for f in results_files]
+
+for costs_df, overload_df, results_df in zip(costs_dfs, overload_dfs, results_dfs):
+
+    draw_cost(costs_df, 
+              f'plots/costs-ess-{int(costs_df.columns[0])}-{int(costs_df.columns[-1])}-pv-{int(costs_df.index[0])}-{int(costs_df.index[-1])}.png', 
+              title_cost=title_cost, 
+              annot_cost=annot_cost)
+
+    draw_overload(overload_df, 
+                  f'plots/overload-ess-{overload_df.columns[0]}-{overload_df.columns[-1]}-pv-{overload_df.index[0]}-{overload_df.index[-1]}.png', 
+                  title_unmet=title_unmet, 
+                  annot_unmet=False)
+
+    draw_results(results_df, 
+                 f'plots/results-ess-{results_df.columns[0]}-{results_df.columns[-1]}-pv-{results_df.index[0]}-{results_df.index[-1]}.png', 
+                 title_benefit=title_benefit,
+                 annot_benefit=False)
+
+
+
+
+
+
+
+

main.py

@@ -1,3 +1,36 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[83]:
+
+
+import os
+import glob
+import shutil
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+from matplotlib.ticker import FuncFormatter
+import numpy as np
+import pandas as pd
+import os
+import seaborn as sns
+import json
+from matplotlib.colors import LinearSegmentedColormap
+
+def clear_folder_make_ess_pv(folder_path):
+    if os.path.isdir(folder_path):
+        shutil.rmtree(folder_path)
+    os.makedirs(folder_path)
+    os.makedirs(os.path.join(folder_path,'ess'))
+    os.makedirs(os.path.join(folder_path,'pv'))
+
+folder_path = 'plots'
+clear_folder_make_ess_pv(folder_path)
+
+
+# In[84]:
+
+
 import matplotlib.pyplot as plt
 import seaborn as sns
 import numpy as np
@@ -6,66 +39,533 @@ from EnergySystem import EnergySystem
 from config import pv_config, grid_config, ess_config
 
 
-if __name__ == '__main__':
-    data = pd.read_csv('combined_data.csv')
-    time_interval = 15 / 60
-
-    pv_loss = 0.95
-    pv_cost_per_kW = 200
-    pv_base = 50000
-    pv_lifetime = 25
-
-    ess_loss = 0.95
-    ess_cost_per_kW = 300
-    ess_base = 50000
-    ess_lifetime = 25
-
-    grid_loss = 0.95
-    sell_price = 0.4 #kWh
-    grid_capacity = 5000 #kWh
+# In[85]:
 
 
-    pv_step=10000
-    ess_step=10000
+import json
 
-    pv_capacities = np.linspace(50000, 150000, 11)
-    ess_capacities = np.linspace(50000, 150000, 11)
-    results = pd.DataFrame(index=pv_capacities, columns = ess_capacities)
-    for pv_capacity in pv_capacities:
-        print(f"pv_capacity:{pv_capacity}")
+print("Version 0.0.5")
+
+with open('config.json', 'r') as f:
+    js_data = json.load(f)
+
+
+    
+
+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"]
+pv_lifetime = js_data["pv"]["lifetime"]
+
+ess_loss = js_data["ess"]["loss"]
+ess_cost_per_kW = js_data["ess"]["cost_per_kW"]
+ess_lifetime = js_data["ess"]["lifetime"]
+
+grid_loss = js_data["grid"]["loss"]
+sell_price = js_data["grid"]["sell_price"] #kWh
+grid_capacity = js_data["grid"]["capacity"] #kWh
+
+pv_begin = js_data["pv_capacities"]["begin"]
+pv_end = js_data["pv_capacities"]["end"]
+pv_groups = js_data["pv_capacities"]["groups"]
+
+ess_begin = js_data["ess_capacities"]["begin"]
+ess_end = js_data["ess_capacities"]["end"]
+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)
+
+
+# In[86]:
+
+
+hour_demand = []
+for index, row in data.iterrows():
+    time = row['time']
+    demand = row['demand']
+    if time.endswith('00'):
+        hour_demand.append(demand)
+plt.figure(figsize=(10,8))
+plt.plot(hour_demand)
+plt.ylabel('Demand Power / kW')
+plt.savefig('plots/demand.png')
+plt.close()
+
+
+# In[87]:
+
+
+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[88]:
+
+
+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)
+    plt.close()
+
+
+# In[89]:
+
+
+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)
+    plt.close()
+
+
+# In[90]:
+
+
+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)
+    plt.close()
+
+
+# In[91]:
+
+
+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[92]:
+
+
+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, storage=0):
+    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,
+                        storage=storage)
+    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,
+            energySystem.ess_rest,
+            energySystem.factory_demand,
+            energySystem.buy_price_kWh,
+            energySystem.sell_price_kWh,
+            energySystem.pv_generated_kWh,
+            energySystem.grid_need_power_kW,
+            energySystem.time)
+
+
+
+# In[93]:
+
+
+from tqdm import tqdm
+months_results = []
+months_costs = []
+months_overload = []
+months_nettos = []
+months_gen_energy = []
+months_gen_energy2 = []
+months_ess_rest = pd.DataFrame(30, index=pv_capacities, columns= ess_capacities)
+months_csv_data = {}
+for index, month_data in tqdm(enumerate(months_data), total=len(months_data), position=0, leave= True):
+    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)
+    factory_demands = {}
+    buy_prices= {}
+    sell_prices = {}
+    pv_generates = {}
+    grid_need_powers = {}
+    times = {}
+    for pv_capacity in tqdm(pv_capacities, total=len(pv_capacities), desc=f'generating pv for month {index + 1}',position=1, leave=False):
+        factory_demands[pv_capacity] = {}
+        buy_prices[pv_capacity] = {}
+        sell_prices[pv_capacity] = {}
+        pv_generates[pv_capacity] = {}
+        grid_need_powers[pv_capacity] = {}
+        times[pv_capacity] = {}
         for ess_capacity in ess_capacities:
-            print(f"ess_capacity:{ess_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] = benefit
-    results = results.astype(float)
-
-    plt.figure(figsize=(10, 8))  # 设置图形大小
-    sns.heatmap(results, annot=True, fmt=".1f", cmap='viridis')
-    plt.title('Benefit Heatmap Based on PV and ESS Capacities')
-    plt.xlabel('ESS Capacity (kWh)')
-    plt.ylabel('PV Capacity (kW)')
-    plt.show()
-
-    # 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)
+            (result, 
+             overload, 
+             cost, 
+             netto,
+             gen_energy,
+             gen_energy2,
+             ess_rest,
+             factory_demand,
+             buy_price,
+             sell_price,
+             pv_generate,
+             grid_need_power,
+             time) = 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],
+                        storage=months_ess_rest.loc[pv_capacity, ess_capacity])
+            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_ess_rest.loc[pv_capacity, ess_capacity] = ess_rest
+            factory_demands[pv_capacity][ess_capacity] = factory_demand
+            buy_prices[pv_capacity][ess_capacity] = buy_price
+            sell_prices[pv_capacity][ess_capacity] = sell_price
+            pv_generates[pv_capacity][ess_capacity] = pv_generate
+            grid_need_powers[pv_capacity][ess_capacity] = grid_need_power
+            times[pv_capacity][ess_capacity] = time
+    months_csv_data[index] = {"factory_demand": factory_demands, "buy_price": buy_prices, "sell_price": sell_prices, "pv_generate": pv_generates, "grid_need_power": grid_need_powers, "time": times}
+    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)
+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)
 
 
-    # print(benefit)
+# 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
+
+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)
+
+
+# In[94]:
+
+
+def collapse_months_csv_data(months_csv_data, column_name,pv_capacies, ess_capacities):
+    data = {}
+    for pv_capacity in pv_capacities:
+        data[pv_capacity] = {}
+        for ess_capacity in ess_capacities:
+            annual_data = []
+            for index, month_data in enumerate(months_data):
+                annual_data.extend(months_csv_data[index][column_name][pv_capacity][ess_capacity])
+                # months_csv_data[index][column_name][pv_capacity][ess_capacity] = months_csv_data[index][column_name][pv_capacity][ess_capacity].tolist()
+
+            data[pv_capacity][ess_capacity] = annual_data
+    return data 
+
+
+# In[102]:
+
+
+annual_pv_gen = collapse_months_csv_data(months_csv_data, "pv_generate", pv_capacities, ess_capacities)
+annual_time = collapse_months_csv_data(months_csv_data, "time", pv_capacities, ess_capacities)
+annual_buy_price = collapse_months_csv_data(months_csv_data, "buy_price",pv_capacities, ess_capacities)
+annual_sell_price = collapse_months_csv_data(months_csv_data, "sell_price", pv_capacities, ess_capacities)
+annual_factory_demand = collapse_months_csv_data(months_csv_data, "factory_demand", pv_capacities, ess_capacities)
+annual_grid_need_power = collapse_months_csv_data(months_csv_data, "grid_need_power", pv_capacities, ess_capacities)
+
+from datetime import datetime, timedelta
+
+for pv_capacity in pv_capacities:
+    for ess_capacity in ess_capacities:
+        with open(f'data/annual_data-pv-{pv_capacity}-ess-{ess_capacity}.csv', 'w') as f:
+            f.write("date, time,pv_generate (kW),factory_demand (kW),buy_price (USD/MWh),sell_price (USD/MWh),grid_need_power (kW)\n")
+            start_date = datetime(2023, 1, 1, 0, 0, 0)
+            for i in range(len(annual_time[pv_capacity][ess_capacity])):
+                current_date = start_date + timedelta(hours=i)
+                formate_date = current_date.strftime("%Y-%m-%d")
+                f.write(f"{formate_date},{annual_time[pv_capacity][ess_capacity][i]},{int(annual_pv_gen[pv_capacity][ess_capacity][i])},{int(annual_factory_demand[pv_capacity][ess_capacity][i])},{int(annual_buy_price[pv_capacity][ess_capacity][i]*1000)},{int(annual_sell_price[pv_capacity][ess_capacity][i]*1000)},{int(annual_grid_need_power[pv_capacity][ess_capacity][i])} \n")
+
+
+
+# In[96]:
+
+
+def save_data(data, filename):
+    data.to_csv(filename+'.csv')
+    data.to_json(filename + '.json')
+
+
+# In[97]:
+
+
+if not os.path.isdir('data'):
+    os.makedirs('data')
+
+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[98]:
+
+
+draw_results(annual_result, 'plots/test.png', 'test', False)
+
+
+# In[99]:
+
+
+draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png',  title_roi, 365, annot_benefit, figure_size)
+

read_data.py

@@ -1,52 +1,47 @@
 import pandas as pd
 import numpy as np
 import csv
+import json
 
-df_sunlight = pd.read_excel('lightintensity.xlsx', header=None, names=['SunlightIntensity'])
+with open('config.json', 'r') as f:
+    js_data = json.load(f)
 
-start_date = '2023-01-01 00:00:00'  # 根据数据的实际开始日期调整
-hours = pd.date_range(start=start_date, periods=len(df_sunlight), freq='h')
-df_sunlight['Time'] = hours
-df_sunlight.set_index('Time', inplace=True)
+pv_yield_file_name = js_data["data_path"]["pv_yield"]
+print(pv_yield_file_name)
+# factory_demand_file_name = 'factory_power1.xlsx'
+factory_demand_file_name = js_data["data_path"]["demand"]
+print(factory_demand_file_name)
+electricity_price_data = js_data["data_path"]["buy"]
+print(electricity_price_data)
+electricity_price_data_sell = js_data["data_path"]["sell"]
+print(electricity_price_data_sell)
 
-df_sunlight_resampled = df_sunlight.resample('15min').interpolate()
+pv_df = pd.read_csv(pv_yield_file_name, index_col='Time', usecols=['Time', 'PV yield[kW/kWp]'])
+pv_df.index = pd.to_datetime(pv_df.index)
 
-df_power = pd.read_excel('factory_power.xlsx', 
-                         header=None, 
-                         names=['FactoryPower'], 
-                         dtype={'FactoryPower': float})
-times = pd.date_range(start=start_date, periods=len(df_power), freq='15min')
-df_power['Time'] = times
-df_power.set_index('Time',inplace=True)
-print(df_power.head())
-
-df_combined = df_sunlight_resampled.join(df_power)
-
-df_combined.to_csv('combined_data.csv', index=True, index_label='Time')
-
-price_data = np.random.uniform(0.3, 0.3, len(times))
-
-# 创建DataFrame
-price_df = pd.DataFrame(data={'Time': times, 'ElectricityPrice': price_data})
-
-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_df.head())
-print("Electricity price data generated and saved.")
+df_power = pd.read_csv(factory_demand_file_name, index_col='Time', usecols=['Time', 'FactoryPower'])
+df_power.index = pd.to_datetime(df_power.index)
+df_combined = pv_df.join(df_power)
 
+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)
 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'])
-    for index, row in df_combined2.iterrows():
+    writer.writerow(['time', 'PV yield[kW/kWp]', 'demand','buy', 'sell'])
+    cnt = 0
+    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['PV yield[kW/kWp]'], row['FactoryPower'],row['ElectricityBuy'], row['ElectricitySell']])
+        
     print('The file is written to combined_data.csv')
 
-# combined_data.to_csv('updated_simulation_with_prices.csv', index=False)
 
 print("Simulation data with electricity prices has been updated and saved.")

read_data/convert_data.ipynb

@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_csv(filename):\n",
+    "    skip_rows = list(range(1, 17))\n",
+    "    data = pd.read_csv(filename, sep=';', skiprows=skip_rows)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3075037/3659192646.py:3: DtypeWarning: Columns (32,33,35) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  data = pd.read_csv(filename, sep=';', skiprows=skip_rows)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Time', 'Irradiance onto horizontal plane ',\n",
+       "       'Diffuse Irradiation onto Horizontal Plane ', 'Outside Temperature ',\n",
+       "       'Module Area 1: Height of Sun ',\n",
+       "       'Module Area 1: Irradiance onto tilted surface ',\n",
+       "       'Module Area 1: Module Temperature ', 'Grid Export ',\n",
+       "       'Energy from Grid ', 'Global radiation - horizontal ',\n",
+       "       'Deviation from standard spectrum ', 'Ground Reflection (Albedo) ',\n",
+       "       'Orientation and inclination of the module surface ', 'Shading ',\n",
+       "       'Reflection on the Module Surface ',\n",
+       "       'Irradiance on the rear side of the module ',\n",
+       "       'Global Radiation at the Module ',\n",
+       "       'Module Area 1: Reflection on the Module Surface ',\n",
+       "       'Module Area 1: Global Radiation at the Module ',\n",
+       "       'Global PV Radiation ', 'Bifaciality ', 'Soiling ',\n",
+       "       'STC Conversion (Rated Efficiency of Module) ', 'Rated PV Energy ',\n",
+       "       'Low-light performance ', 'Module-specific Partial Shading ',\n",
+       "       'Deviation from the nominal module temperature ', 'Diodes ',\n",
+       "       'Mismatch (Manufacturer Information) ',\n",
+       "       'Mismatch (Configuration/Shading) ',\n",
+       "       'Power optimizer (DC conversion/clipping) ',\n",
+       "       'PV Energy (DC) without inverter clipping ',\n",
+       "       'Failing to reach the DC start output ',\n",
+       "       'Clipping on account of the MPP Voltage Range ',\n",
+       "       'Clipping on account of the max. DC Current ',\n",
+       "       'Clipping on account of the max. DC Power ',\n",
+       "       'Clipping on account of the max. AC Power/cos phi ', 'MPP Matching ',\n",
+       "       'PV energy (DC) ',\n",
+       "       'Inverter 1 - MPP 1 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 1 - MPP 2 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 1 - MPP 3 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 1 - MPP 4 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 1 - MPP 5 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 1 - MPP 6 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 2 - MPP 1 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Inverter 2 - MPP 2 - to Module Area 1: PV energy (DC) ',\n",
+       "       'Energy at the Inverter Input ',\n",
+       "       'Input voltage deviates from rated voltage ', 'DC/AC Conversion ',\n",
+       "       'Own Consumption (Standby or Night) ', 'Total Cable Losses ',\n",
+       "       'PV energy (AC) minus standby use ', 'Feed-in energy ',\n",
+       "       'Inverter 1 to Module Area 1: Own Consumption (Standby or Night) ',\n",
+       "       'Inverter 1 to Module Area 1: PV energy (AC) minus standby use ',\n",
+       "       'Inverter 2 to Module Area 1: Own Consumption (Standby or Night) ',\n",
+       "       'Inverter 2 to Module Area 1: PV energy (AC) minus standby use ',\n",
+       "       'Unnamed: 58'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "file_name = 'Riyahd_raw.csv'\n",
+    "df = read_csv(file_name)\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "remain_column = ['Time','PV energy (AC) minus standby use ']\n",
+    "energy_row_name = remain_column[1]\n",
+    "\n",
+    "df = df[remain_column]\n",
+    "df[energy_row_name] = df[energy_row_name].str.replace(',','.').astype(float)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "770594.226863267"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum_energy = df[energy_row_name].sum()\n",
+    "sum_energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1975.882632982736"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum_energy / 390"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "group_size = 15\n",
+    "df['group_id'] = df.index // group_size\n",
+    "\n",
+    "sums = df.groupby('group_id')[energy_row_name].sum()\n",
+    "sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.head of        Energy\n",
+       "0         0.0\n",
+       "1         0.0\n",
+       "2         0.0\n",
+       "3         0.0\n",
+       "4         0.0\n",
+       "...       ...\n",
+       "35035     0.0\n",
+       "35036     0.0\n",
+       "35037     0.0\n",
+       "35038     0.0\n",
+       "35039     0.0\n",
+       "\n",
+       "[35040 rows x 1 columns]>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sums_df.head"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 Time\n",
+      "0 2023-01-01 00:00:00\n",
+      "1 2023-01-01 00:15:00\n",
+      "2 2023-01-01 00:30:00\n",
+      "3 2023-01-01 00:45:00\n",
+      "4 2023-01-01 01:00:00\n",
+      "                     Time\n",
+      "35035 2023-12-31 22:45:00\n",
+      "35036 2023-12-31 23:00:00\n",
+      "35037 2023-12-31 23:15:00\n",
+      "35038 2023-12-31 23:30:00\n",
+      "35039 2023-12-31 23:45:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "start_date = '2023-01-01'\n",
+    "end_date = '2023-12-31'\n",
+    "\n",
+    "# 生成每天的15分钟间隔时间\n",
+    "all_dates = pd.date_range(start=start_date, end=end_date, freq='D')\n",
+    "all_times = pd.timedelta_range(start='0 min', end='1435 min', freq='15 min')\n",
+    "\n",
+    "# 生成完整的时间标签\n",
+    "date_times = [pd.Timestamp(date) + time for date in all_dates for time in all_times]\n",
+    "\n",
+    "# 创建DataFrame\n",
+    "time_frame = pd.DataFrame({\n",
+    "    'Time': date_times\n",
+    "})\n",
+    "\n",
+    "# 查看生成的DataFrame\n",
+    "print(time_frame.head())\n",
+    "print(time_frame.tail())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(35040, 1)\n",
+      "(35040, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sums_df.shape)\n",
+    "print(time_frame.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sums_df['Time'] = time_frame['Time']\n",
+    "sums_df = pd.concat([time_frame, sums_df], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     Energy\n",
+      "Time                       \n",
+      "2023-01-01 00:00:00     0.0\n",
+      "2023-01-01 00:15:00     0.0\n",
+      "2023-01-01 00:30:00     0.0\n",
+      "2023-01-01 00:45:00     0.0\n",
+      "2023-01-01 01:00:00     0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "sums_df.set_index('Time', inplace=True)\n",
+    "print(sums_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_value = sums_df['Energy'].max()\n",
+    "sums_df['Energy'] = sums_df['Energy'] / max_value\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_csv(df, filename, columns):\n",
+    "    tmp_df = df.copy()\n",
+    "    tmp_df[columns[1]] = tmp_df[columns[1]].round(4)\n",
+    "    with open(filename, 'w', newline='') as file:\n",
+    "        writer = csv.writer(file)\n",
+    "        writer.writerow(columns)\n",
+    "        for index, row in tmp_df.iterrows():\n",
+    "            time_formatted = index.strftime('%H:%M')\n",
+    "            writer.writerow([time_formatted, row[columns[1]]])\n",
+    "            \n",
+    "        print(f'The file is written to {filename}')\n",
+    "        \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The file is written to Riyahd.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_csv(sums_df, 'Riyahd.csv', ['Time', 'Energy'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

read_data/convert_data.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import os
+import csv
+
+def generate_min_df(mins = 15):
+    end = 60/mins * 24
+    start_date = '2023-01-01'
+    end_date = '2023-12-31'
+
+    all_dates = pd.date_range(start=start_date, end=end_date, freq='D')
+    all_times = pd.timedelta_range(start='0 min', end=f'1435 min', freq=f'{mins} min')
+
+    date_times = [pd.Timestamp(date) + time for date in all_dates for time in all_times]
+
+    time_frame = pd.DataFrame({
+        'Time': date_times
+    })
+    return time_frame
+
+def save_csv(df, filename, columns):
+    with open(filename, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow(['Time', 'PV yield[kW/kWp]'])
+        for index, row in df.iterrows():
+            time_formatted = index.strftime('%H:%M')
+            writer.writerow([time_formatted, row[columns[1]]])
+            
+        print(f'The file is written to {filename}')
+
+def read_csv(filename):
+    skip_rows = list(range(1, 17))
+    data = pd.read_csv(filename, sep=';', skiprows=skip_rows)
+    return data
+
+def process(file_name):
+    df = read_csv(file_name)
+    city = file_name.split('_')[0]
+
+    remain_column = ['Time','PV energy (AC) minus standby use ']
+    energy_row_name = remain_column[1]
+
+    df = df[remain_column]
+    df[energy_row_name] = df[energy_row_name].str.replace(',','.').astype(float)
+
+    sum_energy = df[energy_row_name].sum()
+    group_size = 15
+    df['group_id'] = df.index // group_size
+
+    sums = df.groupby('group_id')[energy_row_name].sum()
+    sums_df = sums.reset_index(drop=True).to_frame(name = 'Energy')
+
+    pv_energy_column_name = 'PV yield[kW/kWp]'
+    sums_df = sums_df.rename(columns={'Energy': pv_energy_column_name})
+
+    time_frame = generate_min_df(15)
+    sums_df = pd.concat([time_frame, sums_df], axis=1)
+    # sums_df.set_index('Time', inplace=True)
+    # max_value = sums_df[pv_energy_column_name].max()
+    sums_df[pv_energy_column_name] = sums_df[pv_energy_column_name] / 390.
+    sums_df[pv_energy_column_name] = sums_df[pv_energy_column_name].round(4)
+    sums_df[pv_energy_column_name].replace(0.0, -0.0)
+
+    sums_df.to_csv(f'{city}.csv')
+    # save_csv(sums_df, f'{city}.csv', ['Time', 'Energy'])
+
+if __name__ == '__main__':
+    city_list = ['Riyahd', 'Cambodge', 'Berlin', 'Serbia']
+    for city in city_list:
+        print(f'Processing {city}')
+        file_name = f'{city}_raw.csv'
+        process(file_name)
+        print(f'Processing {city} is done\n')
+

xlsx2csv.py

@@ -0,0 +1,16 @@
+import pandas as pd
+
+excel_file = 'factory_power1.xlsx'
+sheet_name = 'Sheet1'
+
+df = pd.read_excel(excel_file, sheet_name=sheet_name)
+
+start_date = '2023-01-01'
+df_power = pd.read_excel(excel_file, 
+                         header=None, 
+                         names=['FactoryPower'], 
+                         dtype={'FactoryPower': float})
+times = pd.date_range(start=start_date, periods=len(df_power), freq='15min')
+df_power['Time'] = times
+df_power = df_power[['Time', 'FactoryPower']]
+df_power.to_csv('factory_power1.csv', index=True)