154 lines
7.3 KiB
Python
154 lines
7.3 KiB
Python
import gym
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import pandas as pd
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from data_manager import *
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from module import *
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from parameters import *
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class WgzGym(gym.Env):
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def __init__(self, **kwargs):
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super(WgzGym, self).__init__()
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self.excess = None
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self.shedding = None
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self.unbalance = None
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self.real_unbalance = None
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self.operation_cost = None
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self.current_output = None
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self.final_step_outputs = None
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self.data_manager = DataManager()
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self._load_year_data()
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self.month = 1
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self.day = 1
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self.TRAIN = True
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self.current_time = None
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self.episode_length = 24
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self.penalty_coefficient = 50 # 约束惩罚系数
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self.sell_coefficient = 0.1 # 售出利润系数
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self.EC_parameters = kwargs.get('EC_parameters', EC_parameters) # 电解水制氢器
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self.HST_parameters = kwargs.get('dg_parameters', dg_parameters) # 储氢罐
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self.grid = Grid()
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self.EC = EC(self.EC_parameters)
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self.HST = HST(self.HST_parameters)
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self.action_space = gym.spaces.Box(low=-1, high=1, shape=(3,), dtype=np.float32)
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'''
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时间 光伏 温度（湿度暂未考虑） 电需 热需（转化为对应热水所需瓦数） 人数 电价 7
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电解水制氢功率 市电功率 储氢罐容量占比 3
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'''
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self.state_space = gym.spaces.Box(low=0, high=1, shape=(10,), dtype=np.float32)
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def reset(self, *args):
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self.month = np.random.randint(1, 13) # choose 12 month
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if self.TRAIN:
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self.day = np.random.randint(1, 20)
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else:
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self.day = np.random.randint(20, Constant.MONTHS_LEN[self.month - 1])
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self.current_time = 0
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self.EC.reset()
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self.HST.reset()
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return self._build_state()
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def _build_state(self):
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soc = self.HST.SOC()
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ec_output = self.EC.current_output
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time_step = self.current_time
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price = self.data_manager.get_price_data(self.month, self.day, self.current_time)
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temper = self.data_manager.get_temperature_data(self.month, self.day, self.current_time)
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solar = self.data_manager.get_solar_data(self.month, self.day, self.current_time)
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load = self.data_manager.get_load_data(self.month, self.day, self.current_time)
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heat = self.data_manager.get_heat_data(self.month, self.day, self.current_time)
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people = self.data_manager.get_people_data(self.month, self.day, self.current_time)
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obs = np.concatenate((np.float32(time_step), np.float32(soc), np.float32(price), np.float32(netload),
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np.float32(dg1_output), np.float32(dg2_output), np.float32(dg3_output),
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np.float32(temperature), np.float32(irradiance), np.float32(windspeed)), axis=None)
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return obs
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def step(self, action): # state transition: current_obs->take_action->get_reward->get_finish->next_obs
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# 在每个组件中添加动作
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current_obs = self._build_state()
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temperature = current_obs[7]
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irradiance = current_obs[8]
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self.wind.current_power = current_obs[9]
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self.battery.step(action[0]) # 执行状态转换，电池当前容量也改变
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self.dg1.step(action[1])
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self.dg2.step(action[2])
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self.dg3.step(action[3])
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self.solar.step(temperature, irradiance, action[4])
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self.current_output = np.array((self.dg1.current_output, self.dg2.current_output, self.dg3.current_output,
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-self.battery.energy_change, self.solar.current_power, self.wind.current_power))
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actual_production = sum(self.current_output)
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price = current_obs[1]
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netload = current_obs[3] - self.solar.output_change
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unbalance = actual_production - netload
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# reward = 0.0
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excess_penalty = 0
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deficient_penalty = 0
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sell_benefit, buy_cost = 0, 0
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self.excess, self.shedding = 0, 0
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if unbalance >= 0: # 过剩
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if unbalance <= self.grid.exchange_ability:
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sell_benefit = self.grid.get_cost(price, unbalance) * self.sell_coefficient
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else:
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sell_benefit = self.grid.get_cost(price, self.grid.exchange_ability) * self.sell_coefficient
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# real unbalance：超电网限值
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self.excess = unbalance - self.grid.exchange_ability
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excess_penalty = self.excess * self.penalty_coefficient
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else: # unbalance <0, 缺少惩罚
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if abs(unbalance) <= self.grid.exchange_ability:
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buy_cost = self.grid.get_cost(price, abs(unbalance))
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else:
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buy_cost = self.grid.get_cost(price, self.grid.exchange_ability)
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self.shedding = abs(unbalance) - self.grid.exchange_ability
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deficient_penalty = self.shedding * self.penalty_coefficient
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battery_cost = self.battery.get_cost(self.battery.energy_change)
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dg1_cost = self.dg1.get_cost(self.dg1.current_output)
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dg2_cost = self.dg2.get_cost(self.dg2.current_output)
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dg3_cost = self.dg3.get_cost(self.dg3.current_output)
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solar_cost = self.solar.get_cost(self.solar.current_power)
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wind_cost = self.wind.gen_cost(self.wind.current_power)
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self.operation_cost = (battery_cost + dg1_cost + dg2_cost + dg3_cost + solar_cost + wind_cost
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+ excess_penalty + deficient_penalty - sell_benefit + buy_cost)
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reward = - self.operation_cost / 1e3
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self.unbalance = unbalance
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self.real_unbalance = self.shedding + self.excess
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final_step_outputs = [self.dg1.current_output, self.dg2.current_output, self.dg3.current_output,
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self.battery.current_capacity, self.solar.current_power, self.wind.current_power]
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self.current_time += 1
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finish = (self.current_time == self.episode_length)
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if finish:
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self.final_step_outputs = final_step_outputs
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self.current_time = 0
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next_obs = self.reset()
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else:
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next_obs = self._build_state()
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return current_obs, next_obs, float(reward), finish
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def _load_year_data(self):
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data_df = pd.read_csv('data/all_data.csv', sep=',')
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solar = data_df['solar_power'].to_numpy(dtype=float)
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temper = data_df['temper'].to_numpy(dtype=float)
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energy = data_df['energy_demand'].to_numpy(dtype=float)
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water = data_df['water_demand'].to_numpy(dtype=float)
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people = data_df['people_count'].to_numpy(dtype=float)
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price = data_df['price'].to_numpy(dtype=float)
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'''可根据需求重新设计训练数据大小'''
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def process_elements(elements, transform_function, add_function):
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for e in elements:
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transformed_e = transform_function(e)
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add_function(transformed_e)
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process_elements(solar, lambda x: x, self.data_manager.add_load_element)
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process_elements(temper, lambda x: x, self.data_manager.add_load_element)
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process_elements(energy, lambda x: x, self.data_manager.add_irradiance_element)
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process_elements(water, lambda x: x, self.data_manager.add_temperature_element)
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process_elements(people, lambda x: x, self.data_manager.add_wind_element)
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process_elements(price, lambda x: x, self.data_manager.add_price_element)
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