fix the output format
This commit is contained in:
Hanzhang ma
parent
202d313684
commit
8d040e64a0
@ -11,6 +11,7 @@ class EnergySystem:
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self.hour_stored = []
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self.hour_stored_2 = []
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self.afford = True
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self.cost = self.ess.get_cost() + self.pv.get_cost()
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self.overload_cnt = 0
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self.spring_week_gen = []
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self.summer_week_gen = []
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@ -22,9 +23,12 @@ class EnergySystem:
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self.winter_week_soc = []
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self.granularity = 4
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self.season_step = self.granularity * 24 * 7 * 12
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self.season_start= self.granularity * 24 * 7 * 6
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self.season_start= self.granularity * 24 * 7 * 2
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self.week_length = self.granularity * 24 * 7
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self.unmet = []
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def get_cost(self):
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return self.ess.get_cost()+self.pv.get_cost()
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# 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。
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# 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。
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@ -89,9 +93,11 @@ class EnergySystem:
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self.afford = False
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self.overload_cnt+=1
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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}"
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self.unmet.append((index,time,factory_demand,generated_pv_power))
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# with open(f'plots/summary/ess-{self.ess.capacity}-pv-{self.pv.capacity}', 'a') as f:
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# f.write(log)
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# print(log)
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print(log)
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# self.unmet.append(log)
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saved_energy = generated_pv_energy + self.ess.storage * self.ess.loss
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self.ess.storage = 0
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needed_from_grid = factory_demand * time_interval - saved_energy
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@ -106,30 +112,28 @@ class EnergySystem:
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cost = net_grid * electricity_price
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# print(f"time:{time} benefit: {benefit}, cost: {cost}")
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total_benefit += benefit - cost
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# spring
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# # spring
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week_start = self.season_start
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week_end = self.week_length + week_start
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# print(index)
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# print(week_start, week_end)
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if index in range(week_start, week_end):
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self.spring_week_gen.append(generated_pv_power)
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self.spring_week_soc.append(self.ess.storage / self.ess.capacity)
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# summer
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week_start += self.season_step
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week_end += self.season_step
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if index in range(week_start, week_end):
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self.summer_week_gen.append(generated_pv_power)
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self.summer_week_soc.append(self.ess.storage / self.ess.capacity)
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# autumn
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week_start += self.season_step
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week_end += self.season_step
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if index in range(week_start, week_end):
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self.autumn_week_gen.append(generated_pv_power)
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self.autumn_week_soc.append(self.ess.storage / self.ess.capacity)
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week_start += self.season_step
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week_end += self.season_step
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if index in range(week_start, week_end):
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self.winter_week_gen.append(generated_pv_power)
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self.winter_week_soc.append(self.ess.storage / self.ess.capacity)
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# week_start += self.season_step
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# week_end += self.season_step
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# if index in range(week_start, week_end):
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# self.summer_week_gen.append(generated_pv_power)
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# self.summer_week_soc.append(self.ess.storage / self.ess.capacity)
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# # autumn
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# week_start += self.season_step
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# week_end += self.season_step
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# if index in range(week_start, week_end):
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# self.autumn_week_gen.append(generated_pv_power)
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# self.autumn_week_soc.append(self.ess.storage / self.ess.capacity)
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# week_start += self.season_step
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# week_end += self.season_step
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# if index in range(week_start, week_end):
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# self.winter_week_gen.append(generated_pv_power)
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# self.winter_week_soc.append(self.ess.storage / self.ess.capacity)
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return total_benefit
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14
README.md
14
README.md
@ -2,11 +2,13 @@
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Feature list:
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- []Draw the entire year's electricity consumption figure.
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- []Draw the entire year's energy generation figure.
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- []Draw a heatmap of the system profit in different configurations.
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- []Calculate the probability of a successful run.
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- []Read the configs from the file, including `time granularity`,
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- [] Draw the entire year's electricity consumption figure.
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- [] Draw the entire year's energy generation figure.
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- [] Draw a heatmap of the system profit in different configurations.
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~~- []Calculate the probability of a successful run.~~
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- [] Determine whether the system can run successfully
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- [] Calculate the ROI.
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- [] Read the configs from the file, including `time granularity`,
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- ess: `capacity`, `cost per kW`, `charge power`, `discharge power`, `loss`
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- pv: `capacity`, `cost per kW`, `loss`
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- grid: `capacity`, `sell price`
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@ -14,4 +16,4 @@ Feature list:
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- `lightintensity.xlsx`: record the light intensity. Value in the file should be between 0 and 1
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- `factory_power.xlsx`: record the power consumption in the factory. Default time granularity is `15min`
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- `combined_data.csv`: This file is generated by the Python script, including the `light` `intensity`, `factory_power`, `time`.
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- []GUI.
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- [] GUI.
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@ -15,7 +15,7 @@ class ess_config:
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self.cost_per_kW = cost_per_kW
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self.lifetime = lifetime
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self.loss = loss
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self.storage = 0
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self.storage = 100
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self.charge_power = charge_power
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self.discharge_power = discharge_power
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def get_cost(self):
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119157
main.ipynb
119157
main.ipynb
File diff suppressed because one or more lines are too long
497
main.py
497
main.py
@ -1,37 +1,284 @@
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#!/usr/bin/env python
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# coding: utf-8
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# In[40]:
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import pandas as pd
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class pv_config:
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def __init__(self, capacity, cost_per_kW, lifetime, loss):
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self.capacity = capacity
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self.cost_per_kW = cost_per_kW
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self.lifetime = lifetime
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self.loss = loss
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def get_cost(self):
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return self.capacity * self.cost_per_kW
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def get_cost_per_year(self):
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return self.capacity * self.cost_per_kW / self.lifetime
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class ess_config:
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def __init__(self, capacity, cost_per_kW, lifetime, loss, charge_power, discharge_power):
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self.capacity = capacity
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self.cost_per_kW = cost_per_kW
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self.lifetime = lifetime
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self.loss = loss
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self.storage = 100
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self.charge_power = charge_power
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self.discharge_power = discharge_power
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def get_cost(self):
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return self.capacity * self.cost_per_kW
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def get_cost_per_year(self):
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return self.capacity * self.cost_per_kW / self.lifetime
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class grid_config:
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def __init__(self, capacity, grid_loss, sell_price):
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# self.price_schedule = price_schedule
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self.loss = grid_loss
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self.sell_price = sell_price
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self.capacity = capacity
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def get_price_for_time(self, time):
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hour, minute = map(int, time.split(':'))
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total_minutes = hour * 60 + minute
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for _, row in self.price_schedule.iterrows():
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start_hour, start_minute = map(int, row['time_start'].split(':'))
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end_hour, end_minute = map(int, row['time_end'].split(':'))
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start_total_minutes = start_hour * 60 + start_minute
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end_total_minutes = end_hour * 60 + end_minute
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if start_total_minutes <= total_minutes < end_total_minutes:
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return row['price']
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return 0.1 # 默认电价，以防万一没有匹配的时间段
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# In[41]:
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class EnergySystem:
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def __init__(self, pv_type: pv_config, ess_type: ess_config, grid_type: grid_config):
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self.pv = pv_type
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self.ess = ess_type
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self.grid = grid_type
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self.day_generated = []
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self.generated = 0
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self.stored = 0
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self.hour_stored = []
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self.hour_stored_2 = []
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self.afford = True
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self.cost = self.ess.get_cost() + self.pv.get_cost()
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self.overload_cnt = 0
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self.spring_week_gen = []
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self.summer_week_gen = []
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self.autumn_week_gen = []
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self.winter_week_gen = []
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self.spring_week_soc = []
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self.summer_week_soc = []
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self.autumn_week_soc = []
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self.winter_week_soc = []
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self.granularity = 4
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self.season_step = self.granularity * 24 * 7 * 12
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self.season_start= self.granularity * 24 * 7 * 2
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self.week_length = self.granularity * 24 * 7
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self.unmet = []
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def get_cost(self):
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return self.ess.get_cost()+self.pv.get_cost()
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# 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。
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# 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。
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# 如果ESS也不足以满足需求，再从电网获取 - 当ESS中的存储电量也不足以补充时，再从电网购买剩余所需电量。
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def simulate(self, data, time_interval):
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total_benefit = 0
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for index, row in data.iterrows():
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time = row['time']
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sunlight_intensity = row['sunlight']
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factory_demand = row['demand']
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electricity_price = row['price']
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# electricity_price = self.grid.get_price_for_time(time)
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if time == '00:00':
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self.day_generated.append(self.generated)
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self.generated = 0
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if time.endswith('14:00'):
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soc = self.ess.storage / self.ess.capacity
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self.hour_stored.append(soc)
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if time.endswith('08:00'):
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soc = self.ess.storage / self.ess.capacity
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self.hour_stored_2.append(soc)
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generated_pv_power = self.pv.capacity * sunlight_intensity # 生成的功率，单位 kW
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generated_pv_energy = generated_pv_power * time_interval * self.pv.loss # 生成的能量，单位 kWh
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self.generated += generated_pv_energy
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# pv生成的能量如果比工厂的需求要大
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if generated_pv_energy >= factory_demand * time_interval:
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# 剩余的能量(kwh) = pv生成的能量 - 工厂需求的功率 * 时间间隔
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surplus_energy = generated_pv_energy - factory_demand * time_interval
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# 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))
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charge_to_ess = min(surplus_energy, self.ess.charge_power * time_interval, self.ess.capacity - self.ess.storage)
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self.ess.storage += charge_to_ess
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surplus_after_ess = surplus_energy - charge_to_ess
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# 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
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if surplus_after_ess > 0 and generated_pv_power > self.ess.charge_power + factory_demand:
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sold_to_grid = surplus_after_ess
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sell_income = sold_to_grid * self.grid.sell_price
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total_benefit += sell_income
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# 节省的能量 = 工厂需求的能量 * 时间段
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# total_energy = factory_demand * time_interval
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saved_energy = factory_demand * time_interval
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# pv比工厂的需求小
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else:
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# 从ess中需要的电量 = 工厂需要的电量 - pv中的电量
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needed_from_ess = factory_demand * time_interval - generated_pv_energy
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# 如果ess中存的电量比需要的多
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if self.ess.storage * self.ess.loss >= needed_from_ess:
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# 取出电量
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if self.ess.discharge_power * time_interval * self.ess.loss < needed_from_ess:
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discharging_power = self.ess.discharge_power * time_interval
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else:
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discharging_power = needed_from_ess / self.ess.loss
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self.ess.storage -= discharging_power
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# 节省下来的能量 = pv的能量 + 放出来的能量
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saved_energy = generated_pv_energy + discharging_power * self.ess.loss
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else:
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# 如果存的电量不够
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# 需要把ess中的所有电量释放出来
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if self.grid.capacity * time_interval + generated_pv_energy + self.ess.storage * self.ess.loss < factory_demand * time_interval:
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self.afford = False
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self.overload_cnt+=1
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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}"
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self.unmet.append((index,time,factory_demand,generated_pv_power))
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# with open(f'plots/summary/ess-{self.ess.capacity}-pv-{self.pv.capacity}', 'a') as f:
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# f.write(log)
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print(log)
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# self.unmet.append(log)
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saved_energy = generated_pv_energy + self.ess.storage * self.ess.loss
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self.ess.storage = 0
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needed_from_grid = factory_demand * time_interval - saved_energy
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net_grid = min(self.grid.capacity * time_interval, needed_from_grid) * self.grid.loss
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# grid_energy += net_grid
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# total_energy += net_grid
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# print(total_energy)
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# 工厂需求量-总能量
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# unmet_demand = max(0, factory_demand * time_interval - total_energy)
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# benefit = (total_energy - unmet_demand) * electricity_price
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benefit = (saved_energy) * electricity_price
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cost = net_grid * electricity_price
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# print(f"time:{time} benefit: {benefit}, cost: {cost}")
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total_benefit += benefit - cost
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# # spring
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week_start = self.season_start
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week_end = self.week_length + week_start
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if index in range(week_start, week_end):
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self.spring_week_gen.append(generated_pv_power)
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self.spring_week_soc.append(self.ess.storage / self.ess.capacity)
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# summer
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# week_start += self.season_step
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# week_end += self.season_step
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# if index in range(week_start, week_end):
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# self.summer_week_gen.append(generated_pv_power)
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# self.summer_week_soc.append(self.ess.storage / self.ess.capacity)
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# # autumn
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# week_start += self.season_step
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# week_end += self.season_step
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# if index in range(week_start, week_end):
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# self.autumn_week_gen.append(generated_pv_power)
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# self.autumn_week_soc.append(self.ess.storage / self.ess.capacity)
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# week_start += self.season_step
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# week_end += self.season_step
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# if index in range(week_start, week_end):
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# self.winter_week_gen.append(generated_pv_power)
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# self.winter_week_soc.append(self.ess.storage / self.ess.capacity)
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return total_benefit
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import os
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import glob
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import shutil
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def clear_folder_make_ess_pv(folder_path):
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shutil.rmtree(folder_path)
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os.makedirs(folder_path)
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os.makedirs(os.path.join(folder_path,'ess'))
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os.makedirs(os.path.join(folder_path,'pv'))
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folder_path = 'plots'
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clear_folder_make_ess_pv(folder_path)
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# In[42]:
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import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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import pandas as pd
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from EnergySystem import EnergySystem
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from config import pv_config, grid_config, ess_config
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figure_size = (10,8)
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if __name__ == '__main__':
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data = pd.read_csv('combined_data.csv')
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time_interval = 15 / 60
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pv_loss = 0.95
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pv_cost_per_kW = 200
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pv_base = 50000
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pv_lifetime = 25
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ess_loss = 0.95
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ess_cost_per_kW = 300
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ess_base = 50000
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ess_lifetime = 25
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grid_loss = 0.95
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sell_price = 0.4 #kWh
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grid_capacity = 5000 #kWh
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# In[43]:
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pv_step=10000
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ess_step=10000
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import json
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pv_capacities = np.linspace(50000, 150000, 11)
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ess_capacities = np.linspace(50000, 150000, 11)
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results = pd.DataFrame(index=pv_capacities, columns = ess_capacities)
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for pv_capacity in pv_capacities:
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with open('config.json', 'r') as f:
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js_data = json.load(f)
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data = pd.read_csv('combined_data.csv')
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time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"]
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pv_loss = js_data["pv"]["loss"]
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pv_cost_per_kW = js_data["pv"]["cost_per_kW"]
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pv_lifetime = js_data["pv"]["lifetime"]
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ess_loss = js_data["ess"]["loss"]
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ess_cost_per_kW = js_data["ess"]["cost_per_kW"]
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ess_lifetime = js_data["ess"]["lifetime"]
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grid_loss = js_data["grid"]["loss"]
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sell_price = js_data["grid"]["sell_price"] #kWh
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grid_capacity = js_data["grid"]["capacity"] #kWh
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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}")
|
||||
@ -52,15 +299,172 @@ if __name__ == '__main__':
|
||||
ess_type=ess,
|
||||
grid_type= grid)
|
||||
benefit = energySystem.simulate(data, time_interval)
|
||||
results.loc[pv_capacity,ess_capacity] = benefit
|
||||
results = results.astype(float)
|
||||
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
|
||||
|
||||
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()
|
||||
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)
|
||||
@ -69,3 +473,32 @@ if __name__ == '__main__':
|
||||
|
||||
|
||||
# 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')
|
||||
|
||||
|
||||
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user