2024-05-07 10:27:45 +02:00
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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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2024-05-04 09:58:05 +02:00
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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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2024-05-03 15:36:02 +02:00
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import pandas as pd
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from config import pv_config, grid_config, ess_config
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2024-05-07 10:27:45 +02:00
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figure_size = (10,8)
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# In[43]:
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import json
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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"]
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pv_end = js_data["pv_capacities"]["end"]
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pv_groups = js_data["pv_capacities"]["groups"]
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ess_begin = js_data["ess_capacities"]["begin"]
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ess_end = js_data["ess_capacities"]["end"]
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ess_groups = js_data["ess_capacities"]["groups"]
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pv_capacities = np.linspace(pv_begin, pv_end, pv_groups)
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ess_capacities = np.linspace(ess_begin, ess_end, ess_groups)
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results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
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affords = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
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costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
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overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)
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# In[44]:
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hour_demand = []
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for index, row in data.iterrows():
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time = row['time']
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demand = row['demand']
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if time.endswith('00'):
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hour_demand.append(demand)
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plt.figure(figsize=(10,8))
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plt.plot(hour_demand)
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plt.ylabel('Demand Power / kW')
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plt.savefig('plots/demand.png')
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plt.close()
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# In[45]:
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def cal_profit(es: EnergySystem, saved_money):
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profit = saved_money - es.ess.get_cost_per_year() - es.pv.get_cost_per_year()
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return profit
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# In[46]:
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for pv_capacity in pv_capacities:
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print(f"pv_capacity:{pv_capacity}")
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for ess_capacity in ess_capacities:
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print(f"ess_capacity:{ess_capacity}")
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pv = pv_config(capacity=pv_capacity,
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cost_per_kW=pv_cost_per_kW,
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lifetime=pv_lifetime,
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loss=pv_loss)
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ess = ess_config(capacity=ess_capacity,
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cost_per_kW=ess_cost_per_kW,
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lifetime=ess_lifetime,
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loss=ess_loss,
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charge_power=ess_capacity,
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discharge_power=ess_capacity)
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grid = grid_config(capacity=grid_capacity,
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grid_loss=grid_loss,
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sell_price= sell_price)
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energySystem = EnergySystem(pv_type=pv,
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ess_type=ess,
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grid_type= grid)
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benefit = energySystem.simulate(data, time_interval)
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results.loc[pv_capacity,ess_capacity] = cal_profit(energySystem, benefit)
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affords.loc[pv_capacity,ess_capacity] = energySystem.afford
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overload_cnt.loc[pv_capacity,ess_capacity] = energySystem.overload_cnt
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costs.loc[pv_capacity,ess_capacity] = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW
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pv_generated = energySystem.day_generated
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ess_generated = energySystem.hour_stored
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ess_generated_2 = energySystem.hour_stored_2
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plt.figure(figsize=(10,8));
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plt.plot(ess_generated)
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plt.xlabel('day #')
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plt.ylabel('SoC %')
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plt.title(f'14:00 ESS SoC \n PV cap:{pv_capacity}, ESS cap:{ess_capacity}')
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plt.savefig(f'plots/ess/1400-{pv_capacity}-{ess_capacity}.png')
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plt.close()
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plt.figure(figsize=(10,8));
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plt.plot(ess_generated_2)
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plt.xlabel('day #')
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plt.ylabel('SoC%')
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plt.title(f'08:00 ESS SoC \n PV cap:{pv_capacity}, ESS cap:{ess_capacity}')
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plt.savefig(f'plots/ess/0800-{pv_capacity}-{ess_capacity}.png')
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plt.close()
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print(energySystem.unmet)
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spring_week_start = energySystem.season_start
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spring_week_end = spring_week_start + energySystem.week_length
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# summer_week_start = energySystem.season_start + 1 * energySystem.season_step
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# summer_week_end = summer_week_start + energySystem.week_length
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# autumn_week_start = energySystem.season_start + 2 * energySystem.season_step
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# autumn_week_end = autumn_week_start + energySystem.week_length
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# winter_week_start = energySystem.season_start + 3 * energySystem.season_step
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# winter_week_end = winter_week_start+ energySystem.week_length
|
2024-05-03 15:36:02 +02:00
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2024-05-07 10:27:45 +02:00
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spring_consume_data = []
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# summer_consume_data = []
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# autumn_consume_data = []
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# winter_consume_data = []
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for index, row in data.iterrows():
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if index in range(spring_week_start, spring_week_end):
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|
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spring_consume_data.append(row['demand'])
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# elif index in range(summer_week_start, summer_week_end):
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|
|
# summer_consume_data.append(row['demand'])
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|
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# elif index in range(autumn_week_start, autumn_week_end):
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|
|
# autumn_consume_data.append(row['demand'])
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|
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# elif index in range(winter_week_start, winter_week_end):
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|
|
# winter_consume_data.append(row['demand'])
|
2024-05-03 15:36:02 +02:00
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|
2024-05-07 10:27:45 +02:00
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spring_week_time = list(range(spring_week_start, spring_week_end))
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|
# summer_week_time = list(range(summer_week_start, summer_week_end))
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# autumn_week_time = list(range(autumn_week_start, autumn_week_end))
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# winter_week_time = list(range(winter_week_start, winter_week_end))
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|
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spring_pv_generated = energySystem.spring_week_gen
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|
|
# summer_pv_generated = energySystem.summer_week_gen
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|
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# autumn_pv_generated = energySystem.autumn_week_gen
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|
|
# winter_pv_generated = energySystem.winter_week_gen
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|
|
# spring_soc = energySystem.spring_week_soc
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|
|
# summer_soc = energySystem.summer_week_soc
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|
|
# autumn_soc = energySystem.autumn_week_soc
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|
|
# winter_soc = energySystem.winter_week_soc
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|
|
# fig, ax1 = plt.subplots()
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|
|
plt.plot(spring_week_time, spring_pv_generated, label = 'pv generation')
|
|
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|
|
plt.plot(spring_week_time, spring_consume_data, label = 'factory consume')
|
|
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|
|
|
plt.ylabel('Power / kW')
|
|
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|
|
|
plt.xlabel('15 min #')
|
|
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|
|
|
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')
|
|
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|
|
# 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()
|
|
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|
|
# results = results.astype(float)
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# 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]:
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
energySystem.unmet
|
|
|
|
|
|
|
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|
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|
|
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|
|
# 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')
|
|
|
|
|
|
|
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|
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|
|
# 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')
|
2024-05-04 09:58:05 +02:00
|
|
|
|
|
|
|
|
|
|
# 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)
|
2024-05-03 15:36:02 +02:00
|
|
|
|
|
|
|
|
|
|
|
2024-05-07 10:27:45 +02:00
|
|
|
|
# print(benefit)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# In[ ]:
|
|
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|
|
# 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')
|
|
|
|
|
|
|
