160 lines
6.5 KiB
Python
160 lines
6.5 KiB
Python
import gym
|
|
import pandas as pd
|
|
|
|
from data_manager import *
|
|
from module import *
|
|
from parameters import *
|
|
|
|
|
|
class WgzGym(gym.Env):
|
|
def __init__(self, **kwargs):
|
|
super(WgzGym, self).__init__()
|
|
self.rec_data = None
|
|
self.unbalance = None
|
|
self.reward = None
|
|
self.current_output = None
|
|
self.final_step_outputs = None
|
|
self.data_manager = DataManager()
|
|
self._load_year_data()
|
|
self.month = 1
|
|
self.day = 1
|
|
self.TRAIN = True
|
|
self.current_time = None
|
|
self.episode_length = 24
|
|
self.penalty_coefficient = 10 # 约束惩罚系数
|
|
self.sell_coefficient = 0.5 # 售出利润系数
|
|
self.a = 0.5
|
|
self.b = 0.3
|
|
self.c = 0.2
|
|
self.heat_a = 0.6
|
|
self.power_a = 0.4
|
|
self.EC_parameters = kwargs.get('EC_parameters', EC_parameters) # 电解水制氢器
|
|
self.HST_parameters = kwargs.get('dg_parameters', dg_parameters) # 储氢罐
|
|
|
|
self.grid = Grid()
|
|
self.EC = EC(self.EC_parameters)
|
|
self.HST = HST(self.HST_parameters)
|
|
|
|
self.action_space = gym.spaces.Box(low=-1, high=1, shape=(3,), dtype=np.float32)
|
|
'''
|
|
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
|
|
电解水制氢功率 储氢罐容量占比 市电功率（注意标准化） 3
|
|
'''
|
|
self.state_space = gym.spaces.Box(low=0, high=1, shape=(10,), dtype=np.float32)
|
|
|
|
def reset(self, *args):
|
|
self.month = np.random.randint(1, 13) # choose 12 month
|
|
if self.TRAIN:
|
|
self.day = np.random.randint(1, 20)
|
|
else:
|
|
self.day = np.random.randint(20, Constant.MONTHS_LEN[self.month - 1])
|
|
self.current_time = 0
|
|
self.EC.reset()
|
|
self.HST.reset()
|
|
return self._build_state()
|
|
|
|
def _build_state(self):
|
|
hst_soc = self.HST.current_soc
|
|
ec_out = self.EC.get_hydrogen()
|
|
grid_ex = self.grid
|
|
time_step = self.current_time
|
|
|
|
if self.TRAIN:
|
|
price = self.data_manager.get_price_data(self.month, self.day, self.current_time)
|
|
temper = self.data_manager.get_temperature_data(self.month, self.day, self.current_time)
|
|
solar = self.data_manager.get_solar_data(self.month, self.day, self.current_time)
|
|
load = self.data_manager.get_load_data(self.month, self.day, self.current_time)
|
|
heat = self.data_manager.get_heat_data(self.month, self.day, self.current_time)
|
|
people = self.data_manager.get_people_data(self.month, self.day, self.current_time)
|
|
else:
|
|
price = self.rec_data[0]
|
|
temper = self.rec_data[1]
|
|
solar = self.rec_data[2]
|
|
load = self.rec_data[3]
|
|
heat = self.rec_data[4]
|
|
people = self.rec_data[5]
|
|
|
|
obs = np.concatenate((np.float32(time_step), np.float32(price), np.float32(temper),
|
|
np.float32(solar), np.float32(load), np.float32(heat),
|
|
np.float32(people), np.float32(ec_out), np.float32(hst_soc), np.float32(grid_ex)),
|
|
axis=None)
|
|
return obs
|
|
|
|
def step(self, action):
|
|
# 每个组件执行动作 one step
|
|
current_obs = self._build_state()
|
|
self.EC.step(action[0])
|
|
self.HST.step(action[1])
|
|
self.grid.step(action[2])
|
|
price = current_obs[1]
|
|
temper = current_obs[2] # 用途待补充
|
|
solar = current_obs[3]
|
|
load = current_obs[4]
|
|
heat = current_obs[5]
|
|
people = current_obs[6] # 用途待补充
|
|
|
|
power_gap = solar + self.HST.get_power() - self.EC.current_power - load
|
|
heat_gap = self.HST.get_heat() + self.EC.get_heat() - heat
|
|
|
|
# reward = 0.0
|
|
sell_benefit, buy_cost = 0, 0
|
|
if power_gap >= 0: # 过剩
|
|
sell_benefit = self.grid.get_cost(price, power_gap) * self.sell_coefficient
|
|
power_gap = 0
|
|
power_penalty = 0
|
|
else: # 缺少
|
|
power_gap = abs(power_gap)
|
|
buy_cost = self.grid.get_cost(price, power_gap)
|
|
power_penalty = power_gap * self.penalty_coefficient
|
|
|
|
if heat_gap >= 0:
|
|
heat_gap = 0
|
|
heat_penalty = 0
|
|
else:
|
|
heat_gap = abs(heat_gap)
|
|
heat_penalty = heat_gap * self.penalty_coefficient
|
|
|
|
hst_cost = self.HST.get_cost()
|
|
ec_cost = self.EC.get_cost(price)
|
|
solar_cost = solar # 待补充
|
|
|
|
economic_cost = hst_cost + ec_cost + solar_cost - sell_benefit + buy_cost
|
|
demand_cost = self.heat_a * heat_penalty + self.power_a * power_penalty
|
|
eco_benifit = self.EC.less_carbon() - self.grid.get_carbon()
|
|
reward = - self.a * demand_cost - self.b * economic_cost + self.c * eco_benifit
|
|
|
|
self.unbalance = power_gap + heat_gap
|
|
final_step_outputs = [self.HST.current_soc, self.EC.current_power, self.grid.current_power]
|
|
self.current_time += 1
|
|
finish = (self.current_time == self.episode_length)
|
|
if finish:
|
|
self.final_step_outputs = final_step_outputs
|
|
self.current_time = 0
|
|
next_obs = self.reset()
|
|
else:
|
|
next_obs = self._build_state()
|
|
return current_obs, next_obs, float(reward), finish
|
|
|
|
def _load_year_data(self):
|
|
data_df = pd.read_csv('data/all_data.csv', sep=',')
|
|
solar = data_df['solar_power'].to_numpy(dtype=float)
|
|
temper = data_df['temper'].to_numpy(dtype=float)
|
|
energy = data_df['energy_demand'].to_numpy(dtype=float)
|
|
water = data_df['water_demand'].to_numpy(dtype=float)
|
|
people = data_df['people_count'].to_numpy(dtype=float)
|
|
price = data_df['price'].to_numpy(dtype=float)
|
|
|
|
'''可根据需求重新设计训练数据大小'''
|
|
|
|
def process_elements(elements, transform_function, add_function):
|
|
for e in elements:
|
|
transformed_e = transform_function(e)
|
|
add_function(transformed_e)
|
|
|
|
process_elements(solar, lambda x: x, self.data_manager.add_load_element)
|
|
process_elements(temper, lambda x: x, self.data_manager.add_load_element)
|
|
process_elements(energy, lambda x: x, self.data_manager.add_irradiance_element)
|
|
process_elements(water, lambda x: x, self.data_manager.add_temperature_element)
|
|
process_elements(people, lambda x: x, self.data_manager.add_wind_element)
|
|
process_elements(price, lambda x: x, self.data_manager.add_price_element)
|