fix the output format

EnergySystem.py

@@ -11,6 +11,7 @@ class EnergySystem:
         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 = []
@@ -22,9 +23,12 @@ class EnergySystem:
         self.winter_week_soc = []
         self.granularity = 4
         self.season_step = self.granularity * 24 * 7 * 12
-        self.season_start= self.granularity * 24 * 7 * 6
+        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。
@@ -89,9 +93,11 @@ class EnergySystem:
                         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)
+                        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 - saved_energy
@@ -106,30 +112,28 @@ class EnergySystem:
             cost = net_grid * electricity_price
             # print(f"time:{time} benefit: {benefit}, cost: {cost}")
             total_benefit += benefit - cost
-            # spring
+            # # spring
             week_start = self.season_start
             week_end = self.week_length + week_start
-            # print(index)
-            # print(week_start, week_end)
             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)
             # 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)
+            # 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

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 +1,31 @@
+{
+    "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": 10000,
+        "end": 50000,
+        "groups": 3
+    },
+    "ess_capacities":{
+        "begin": 10000,
+        "end": 50000,
+        "groups": 3
+    },
+    "time_interval":{
+        "numerator": 15,
+        "denominator": 60
+    }
+}

config.py

@@ -15,7 +15,7 @@ class ess_config:
         self.cost_per_kW = cost_per_kW
         self.lifetime = lifetime
         self.loss = loss
-        self.storage = 0
+        self.storage = 100
         self.charge_power = charge_power
         self.discharge_power = discharge_power
     def get_cost(self):

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)

main.py

@@ -1,66 +1,470 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[40]:
+
+import pandas as pd
+class pv_config:
+    def __init__(self, capacity, cost_per_kW, lifetime, loss):
+        self.capacity = capacity
+        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):
+        self.capacity = capacity
+        self.cost_per_kW = cost_per_kW
+        self.lifetime = lifetime
+        self.loss = loss
+        self.storage = 100
+        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):
+        # self.price_schedule = price_schedule
+        self.loss = grid_loss
+        self.sell_price = sell_price
+        self.capacity = capacity
+    
+    def get_price_for_time(self, time):
+        hour, minute = map(int, time.split(':'))
+        total_minutes = hour * 60 + minute
+        for _, row in self.price_schedule.iterrows():
+            start_hour, start_minute = map(int, row['time_start'].split(':'))
+            end_hour, end_minute = map(int, row['time_end'].split(':'))
+            start_total_minutes = start_hour * 60 + start_minute
+            end_total_minutes = end_hour * 60 + end_minute
+            if start_total_minutes <= total_minutes < end_total_minutes:
+                return row['price']
+        return 0.1  # 默认电价，以防万一没有匹配的时间段
+
+
+
+# In[41]:
+class EnergySystem:
+    def __init__(self, pv_type: pv_config, ess_type: ess_config, grid_type: grid_config):
+        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.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
+        for index, row in data.iterrows():
+            time = row['time']
+            sunlight_intensity = row['sunlight']
+            factory_demand = row['demand']
+            electricity_price = row['price']
+            # electricity_price = self.grid.get_price_for_time(time)
+
+            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 * sunlight_intensity  # 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.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 
+                surplus_energy = generated_pv_energy - factory_demand * time_interval
+                # 要充到ess中的能量 = min(剩余的能量,ess的充电功率*时间间隔(ess在时间间隔内能充进的电量),ess的容量-ess储存的能量(ess中能冲进去的电量))
+                charge_to_ess = min(surplus_energy, self.ess.charge_power * time_interval, self.ess.capacity - self.ess.storage)
+                self.ess.storage += charge_to_ess
+                surplus_after_ess = surplus_energy - charge_to_ess
+                # 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
+                    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
+                saved_energy = factory_demand * time_interval
+            # 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中存的电量比需要的多
+                if self.ess.storage * self.ess.loss >= needed_from_ess:
+                    # 取出电量
+                    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
+                    # 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
+                else:
+                    # 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 - saved_energy
+                    net_grid = min(self.grid.capacity * time_interval, needed_from_grid) * self.grid.loss
+                    # 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 = (saved_energy) * electricity_price
+            cost = net_grid * electricity_price
+            # print(f"time:{time} benefit: {benefit}, cost: {cost}")
+            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)
+            # 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
+
+import os
+import glob
+import shutil
+
+def clear_folder_make_ess_pv(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[42]:
+
+
 import matplotlib.pyplot as plt
 import seaborn as sns
 import numpy as np
 import pandas as pd
-from EnergySystem import EnergySystem
 from config import pv_config, grid_config, ess_config
+figure_size = (10,8)
 
 
-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[43]:
 
 
-    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}")
-        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)
+with open('config.json', 'r') as f:
+    js_data = json.load(f)
 
-    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()
+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"] #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"]
+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[44]:
+
+
+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[45]:
+
+
+def cal_profit(es: EnergySystem, saved_money):
+    profit = saved_money - es.ess.get_cost_per_year() - es.pv.get_cost_per_year()
+    return profit
+
+
+# In[46]:
+
+
+for pv_capacity in pv_capacities:
+    print(f"pv_capacity:{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] = 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
+
+        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
+
+
+        # 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()
+
+        # 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()
+
+
+        # 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[47]:
+
+
+energySystem.unmet
+
+
+# In[48]:
+
+
+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)
+sns.heatmap(df/1000, annot=True, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale)
+plt.title('Benefit Heatmap Based on PV and ESS Capacities (kEUR/year)')
+plt.gca().invert_yaxis()
+plt.xlabel('ESS Capacity (MWh)')
+plt.ylabel('PV Capacity (MW)')
+plt.savefig('plots/benefit.png')
+
+
+# In[49]:
+
+
+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, annot=True, fmt=".1f", cmap='viridis')
+plt.title('Costs of the PV System/million Eur')
+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)
@@ -68,4 +472,33 @@ if __name__ == '__main__':
     # grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4)
 
 
-    # print(benefit)
+    # print(benefit)
+
+
+# In[ ]:
+
+
+
+
+
+# In[50]:
+
+
+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"])
+sns.heatmap(df/(4*24*365), annot=True, fmt=".1f", cmap=cmap, vmin=0, vmax=1)
+plt.title('Probability of unmet electricity demands')
+plt.gca().invert_yaxis()
+plt.xlabel('ESS Capacity (MWh)')
+plt.ylabel('PV Capacity (MW)')
+plt.savefig('plots/unmet.png')
+

read_data.py

@@ -2,7 +2,10 @@ import pandas as pd
 import numpy as np
 import csv
 
-df_sunlight = pd.read_excel('lightintensity.xlsx', header=None, names=['SunlightIntensity'])
+sunlight_file_name = 'lightintensity.xlsx'
+factory_demand_file_name = 'factory_power1.xlsx'
+
+df_sunlight = pd.read_excel(sunlight_file_name, header=None, names=['SunlightIntensity'])
 
 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')
@@ -11,7 +14,7 @@ df_sunlight.set_index('Time', inplace=True)
 
 df_sunlight_resampled = df_sunlight.resample('15min').interpolate()
 
-df_power = pd.read_excel('factory_power.xlsx', 
+df_power = pd.read_excel(factory_demand_file_name, 
                          header=None, 
                          names=['FactoryPower'], 
                          dtype={'FactoryPower': float})
@@ -42,9 +45,11 @@ print(df_combined2.head())
 with open('combined_data.csv', 'w', newline='') as file:
     writer = csv.writer(file)
     writer.writerow(['time', 'sunlight', 'demand','price'])
+    cnt = 0
     for index, row in df_combined2.iterrows():
         time_formatted = index.strftime('%H:%M')
         writer.writerow([time_formatted, row['SunlightIntensity'], row['FactoryPower'],row['ElectricityPrice']])
+        
     print('The file is written to combined_data.csv')
 
 # combined_data.to_csv('updated_simulation_with_prices.csv', index=False)