add version number and remove some draw code

This commit is contained in:
Hanzhang ma 2024-05-08 09:39:56 +02:00
parent f85fcd58f2
commit c217b6309c
2 changed files with 157 additions and 322 deletions

File diff suppressed because one or more lines are too long

310
main.py
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@ -1,198 +1,15 @@
#!/usr/bin/env python
# coding: utf-8
# In[40]:
# In[14]:
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):
if os.path.isdir(folder_path):
shutil.rmtree(folder_path)
os.makedirs(folder_path)
os.makedirs(os.path.join(folder_path,'ess'))
@ -202,22 +19,24 @@ folder_path = 'plots'
clear_folder_make_ess_pv(folder_path)
# In[42]:
# In[15]:
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)
# In[43]:
# In[16]:
import json
print("Version 0.0.2")
with open('config.json', 'r') as f:
js_data = json.load(f)
@ -243,6 +62,17 @@ 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"])
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)
@ -251,7 +81,7 @@ costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
# In[44]:
# In[17]:
hour_demand = []
@ -267,7 +97,7 @@ plt.savefig('plots/demand.png')
plt.close()
# In[45]:
# In[18]:
def cal_profit(es: EnergySystem, saved_money):
@ -275,13 +105,13 @@ def cal_profit(es: EnergySystem, saved_money):
return profit
# In[46]:
# In[24]:
for pv_capacity in pv_capacities:
print(f"pv_capacity:{pv_capacity}")
for ess_capacity in ess_capacities:
for ess_capacity in ess_capacities:
print(f"ess_capacity:{ess_capacity}")
for pv_capacity in pv_capacities:
print(f"pv_capacity:{ess_capacity}")
pv = pv_config(capacity=pv_capacity,
cost_per_kW=pv_cost_per_kW,
lifetime=pv_lifetime,
@ -320,9 +150,9 @@ for pv_capacity in pv_capacities:
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
# 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
@ -330,13 +160,13 @@ for pv_capacity in pv_capacities:
# winter_week_start = energySystem.season_start + 3 * energySystem.season_step
# winter_week_end = winter_week_start+ energySystem.week_length
spring_consume_data = []
# 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'])
# 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):
@ -344,12 +174,12 @@ for pv_capacity in pv_capacities:
# 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))
# 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
# 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
@ -362,14 +192,14 @@ for pv_capacity in pv_capacities:
# fig, ax1 = plt.subplots()
plt.plot(spring_week_time, spring_pv_generated, label = 'pv generation')
plt.plot(spring_week_time, spring_consume_data, label = 'factory consume')
plt.ylabel('Power / kW')
plt.xlabel('15 min #')
plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW spring week generate condition')
plt.legend()
plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-spring.png')
plt.close()
# 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')
@ -398,13 +228,13 @@ for pv_capacity in pv_capacities:
# 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.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()
@ -424,15 +254,25 @@ for pv_capacity in pv_capacities:
# print(benefit)
# In[47]:
# In[20]:
energySystem.unmet
def save_data(data, filename):
data.to_csv(filename+'.csv')
data.to_json(filename + '.json')
# In[48]:
# In[21]:
import matplotlib.ticker as ticker
if not os.path.isdir('data'):
os.makedirs('data')
save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')
save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')
save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')
df=results
df = df.astype(float)
df.index = df.index / 1000
@ -442,15 +282,16 @@ 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)')
ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f'))
plt.title(title_benefit)
plt.gca().invert_yaxis()
plt.xlabel('ESS Capacity (MWh)')
plt.ylabel('PV Capacity (MW)')
plt.savefig('plots/benefit.png')
# In[49]:
# In[22]:
df = costs
@ -459,8 +300,8 @@ 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')
sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost)
plt.title(title_cost)
plt.gca().invert_yaxis()
plt.xlabel('ESS Capacity (MWh)')
plt.ylabel('PV Capacity (MW)')
@ -475,13 +316,7 @@ plt.savefig('plots/costs.png')
# print(benefit)
# In[ ]:
# In[50]:
# In[23]:
from matplotlib.colors import LinearSegmentedColormap
@ -495,8 +330,13 @@ 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')
ax = sns.heatmap(df/(4*24*365), fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
cbar = ax.collections[0].colorbar
cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))
plt.title(title_unmet)
plt.gca().invert_yaxis()
plt.xlabel('ESS Capacity (MWh)')
plt.ylabel('PV Capacity (MW)')