160 lines
6.5 KiB
Python
160 lines
6.5 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.rec_data = None
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self.unbalance = None
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self.reward = 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 = 10 # 约束惩罚系数
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self.sell_coefficient = 0.5 # 售出利润系数
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self.a = 0.5
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self.b = 0.3
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self.c = 0.2
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self.heat_a = 0.6
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self.power_a = 0.4
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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 储氢罐容量占比 市电功率（注意标准化） 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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hst_soc = self.HST.current_soc
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ec_out = self.EC.get_hydrogen()
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grid_ex = self.grid
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time_step = self.current_time
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if self.TRAIN:
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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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else:
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price = self.rec_data[0]
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temper = self.rec_data[1]
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solar = self.rec_data[2]
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load = self.rec_data[3]
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heat = self.rec_data[4]
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people = self.rec_data[5]
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obs = np.concatenate((np.float32(time_step), np.float32(price), np.float32(temper),
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np.float32(solar), np.float32(load), np.float32(heat),
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np.float32(people), np.float32(ec_out), np.float32(hst_soc), np.float32(grid_ex)),
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axis=None)
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return obs
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def step(self, action):
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# 每个组件执行动作 one step
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current_obs = self._build_state()
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self.EC.step(action[0])
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self.HST.step(action[1])
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self.grid.step(action[2])
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price = current_obs[1]
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temper = current_obs[2] # 用途待补充
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solar = current_obs[3]
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load = current_obs[4]
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heat = current_obs[5]
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people = current_obs[6] # 用途待补充
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power_gap = solar + self.HST.get_power() - self.EC.current_power - load
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heat_gap = self.HST.get_heat() + self.EC.get_heat() - heat
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# reward = 0.0
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sell_benefit, buy_cost = 0, 0
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if power_gap >= 0: # 过剩
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sell_benefit = self.grid.get_cost(price, power_gap) * self.sell_coefficient
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power_gap = 0
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power_penalty = 0
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else: # 缺少
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power_gap = abs(power_gap)
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buy_cost = self.grid.get_cost(price, power_gap)
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power_penalty = power_gap * self.penalty_coefficient
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if heat_gap >= 0:
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heat_gap = 0
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heat_penalty = 0
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else:
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heat_gap = abs(heat_gap)
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heat_penalty = heat_gap * self.penalty_coefficient
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hst_cost = self.HST.get_cost()
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ec_cost = self.EC.get_cost(price)
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solar_cost = solar # 待补充
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economic_cost = hst_cost + ec_cost + solar_cost - sell_benefit + buy_cost
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demand_cost = self.heat_a * heat_penalty + self.power_a * power_penalty
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eco_benifit = self.EC.less_carbon() - self.grid.get_carbon()
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reward = - self.a * demand_cost - self.b * economic_cost + self.c * eco_benifit
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self.unbalance = power_gap + heat_gap
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final_step_outputs = [self.HST.current_soc, self.HST.get_power(), self.EC.current_power, self.grid.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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