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10 Commits

Author SHA1 Message Date
651833b521 try to plot a pic 2024-05-04 09:59:27 +02:00
3bc2478cd9 add generate price and read data 2024-05-04 09:59:14 +02:00
b79ffb2416 add data 2024-05-04 09:58:45 +02:00
c0a7b5beff update comment 2024-05-04 09:58:05 +02:00
ed58e34e7e add some old code 2024-05-04 09:57:37 +02:00
88240280ca update variable name 2024-05-03 15:36:02 +02:00
09ef44fc21 update variable name 2024-05-03 15:12:52 +02:00
f7afee2a64 write 2 random data scripts 2024-05-03 15:11:58 +02:00
90c96a512a update variable name 2024-05-03 14:08:18 +02:00
3cc208035a write alpha verison energy system 2024-05-03 14:07:57 +02:00
16 changed files with 123624 additions and 10 deletions

62
EnergySystem.py Normal file
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from config import pv_config, ess_config, grid_config
import pandas as pd
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
# 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 = 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
generated_pv_energy = generated_pv_power * time_interval * self.pv.loss # 生成的能量,单位 kWh
# 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
# 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:
# 取出电量
discharging_power = min(self.ess.discharge_power * time_interval, needed_from_ess)
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
else:
total_energy = generated_pv_energy + self.ess.storage
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
# 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
return total_benefit

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combined_data.csv Normal file

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import pandas as pd
class pv_config:
def __init__(self, capacity, cost_per_kW, pv_lifetime, pv_loss):
def __init__(self, capacity, cost_per_kW, lifetime, loss):
self.capacity = capacity
self.cost_per_kW = cost_per_kW
self.pv_lifetime = pv_lifetime
self.pv_loss = pv_loss
self.lifetime = lifetime
self.loss = loss
class ess_config:
def __init__(self, capacity, cost_per_kW, ess_lifetime, ess_loss, charge_power, discharge_power):
def __init__(self, capacity, cost_per_kW, lifetime, loss, charge_power, discharge_power):
self.capacity = capacity
self.cost_per_kW = cost_per_kW
self.ess_lifetime = ess_lifetime
self.ess_loss = ess_loss
self.ess_storage = 0
self.lifetime = lifetime
self.loss = loss
self.storage = 0
self.charge_power = charge_power
self.discharge_power = discharge_power
class grid_config:
def __init__(self, price_schedule, grid_loss):
self.price_schedule = price_schedule
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(':'))

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electricity_price_data.csv Normal file

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factory_power.xlsx Normal file

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generatedata.py Normal file
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import pandas as pd
import numpy as np
# 设置随机种子以重现结果
np.random.seed(43)
def simulate_sunlight(hour, month):
# 假设最大日照强度在正午,根据月份调整最大日照强度
max_intensity = 1.0 # 夏季最大日照强度
if month in [12, 1, 2]: # 冬季
max_intensity = 0.6
elif month in [3, 4, 10, 11]: # 春秋
max_intensity = 0.8
# 计算日照强度,模拟早晚日照弱,中午日照强
intensity = max_intensity * np.sin(np.pi * (hour - 6) / 12)**2 if 6 <= hour <= 18 else 0
return intensity
def simulate_factory_demand(hour, day_of_week):
# 周末工厂需求可能减少
if day_of_week in [5, 6]: # 周六和周日
base_demand = 3000
else:
base_demand = 6000
# 日常波动
if 8 <= hour <= 20:
return base_demand + np.random.randint(100, 200) # 白天需求量大
else:
return base_demand - np.random.randint(0, 100) # 夜间需求量小
def generate_data(days=10):
records = []
month_demand = 0
for day in range(days):
month = (day % 365) // 30 + 1
day_of_week = day % 7
day_demand = 0
for hour in range(24):
for minute in [0, 10, 20, 30, 40, 50]:
time = f'{hour:02d}:{minute:02d}'
sunlight = simulate_sunlight(hour, month)
demand = simulate_factory_demand(hour, day_of_week)
day_demand+=demand
records.append({'time': time, 'sunlight': sunlight, 'demand': demand})
print(f"day:{day}, day_demand: {day_demand}")
month_demand += day_demand
if day%30 == 0:
print(f"month:{month}, month_demand:{month_demand}")
month_demand = 0
return pd.DataFrame(records)
# 生成数据
data = generate_data(365) # 模拟一年的数据
data.to_csv('simulation_data.csv', index=False)
print("Data generated and saved to simulation_data.csv.")

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generatepriceschedule.py Normal file
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import pandas as pd
import numpy as np
def generate_price_schedule():
records = []
# 假设一天分为三个时段:谷时、平时、峰时
times = [('00:00', '06:00', 0.25),
('06:00', '18:00', 0.3),
('18:00', '24:00', 0.35)]
# 随机调整每天的电价以增加现实性
for time_start, time_end, base_price in times:
# 随机浮动5%以内
fluctuation = np.random.uniform(-0.005, 0.005)
price = round(base_price + fluctuation, 3)
records.append({'time_start': time_start, 'time_end': time_end, 'price': price})
return pd.DataFrame(records)
# 生成电价计划
price_schedule = generate_price_schedule()
price_schedule.to_csv('price_schedule.csv', index=False)
print("Price schedule generated and saved to price_schedule.csv.")
print(price_schedule)

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main.ipynb Normal file

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main.py
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import matplotlib
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
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
pv_step=10000
ess_step=10000
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)
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)
# print(benefit)

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import pandas as pd
import numpy as np
start_date = '2023-01-01'
end_date = '2024-01-01'
# 创建时间索引
time_index = pd.date_range(start=start_date, end=end_date, freq='15min')
# 生成电价数据假设电价在0.28到0.32欧元/kWh之间波动
price_data = np.random.uniform(0.28, 0.32, len(time_index))
# 创建DataFrame
price_df = pd.DataFrame(data={'Time': time_index, 'ElectricityPrice': price_data})
# 保存到CSV文件
price_df.to_csv('electricity_price_data.csv', index=False)
print("Electricity price data generated and saved.")

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old/generatedata.py Normal file
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import pandas as pd
import numpy as np
# 设置随机种子以重现结果
np.random.seed(43)
def simulate_sunlight(hour, month):
# 假设最大日照强度在正午,根据月份调整最大日照强度
max_intensity = 1.0 # 夏季最大日照强度
if month in [12, 1, 2]: # 冬季
max_intensity = 0.6
elif month in [3, 4, 10, 11]: # 春秋
max_intensity = 0.8
# 计算日照强度,模拟早晚日照弱,中午日照强
intensity = max_intensity * np.sin(np.pi * (hour - 6) / 12)**2 if 6 <= hour <= 18 else 0
return intensity
def simulate_factory_demand(hour, day_of_week):
# 周末工厂需求可能减少
if day_of_week in [5, 6]: # 周六和周日
base_demand = 3000
else:
base_demand = 6000
# 日常波动
if 8 <= hour <= 20:
return base_demand + np.random.randint(100, 200) # 白天需求量大
else:
return base_demand - np.random.randint(0, 100) # 夜间需求量小
def generate_data(days=10):
records = []
month_demand = 0
for day in range(days):
month = (day % 365) // 30 + 1
day_of_week = day % 7
day_demand = 0
for hour in range(24):
for minute in [0, 10, 20, 30, 40, 50]:
time = f'{hour:02d}:{minute:02d}'
sunlight = simulate_sunlight(hour, month)
demand = simulate_factory_demand(hour, day_of_week)
day_demand+=demand
records.append({'time': time, 'sunlight': sunlight, 'demand': demand})
print(f"day:{day}, day_demand: {day_demand}")
month_demand += day_demand
if day%30 == 0:
print(f"month:{month}, month_demand:{month_demand}")
month_demand = 0
return pd.DataFrame(records)
# 生成数据
data = generate_data(365) # 模拟一年的数据
data.to_csv('simulation_data.csv', index=False)
print("Data generated and saved to simulation_data.csv.")

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import pandas as pd
import numpy as np
def generate_price_schedule():
records = []
# 假设一天分为三个时段:谷时、平时、峰时
times = [('00:00', '06:00', 0.25),
('06:00', '18:00', 0.3),
('18:00', '24:00', 0.35)]
# 随机调整每天的电价以增加现实性
for time_start, time_end, base_price in times:
# 随机浮动5%以内
fluctuation = np.random.uniform(-0.005, 0.005)
price = round(base_price + fluctuation, 3)
records.append({'time_start': time_start, 'time_end': time_end, 'price': price})
return pd.DataFrame(records)
# 生成电价计划
price_schedule = generate_price_schedule()
price_schedule.to_csv('price_schedule.csv', index=False)
print("Price schedule generated and saved to price_schedule.csv.")
print(price_schedule)

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old/price_schedule.csv Normal file
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time_start,time_end,price
00:00,06:00,0.247
06:00,18:00,0.3
18:00,24:00,0.349
1 time_start time_end price
2 00:00 06:00 0.247
3 06:00 18:00 0.3
4 18:00 24:00 0.349

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old/simulation_data.csv Normal file

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read_data.py Normal file
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import pandas as pd
import numpy as np
import csv
df_sunlight = pd.read_excel('lightintensity.xlsx', 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')
df_sunlight['Time'] = hours
df_sunlight.set_index('Time', inplace=True)
df_sunlight_resampled = df_sunlight.resample('15min').interpolate()
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_combined2 = df_combined.join(price_df)
print(df_combined2.head())
# 保存结果
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():
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)
print("Simulation data with electricity prices has been updated and saved.")