random_generator_battery.py

# ------------------------------------------------------------------------
# Energy management environment for reinforcement learning agents developed by
# Hou Shengren, TU Delft, h.shengren@tudelft.nl
import random
import numpy as np
import pandas as pd 
import gym
from gym import spaces 

from Parameters import battery_parameters, dg_parameters

class Constant:
	MONTHS_LEN = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
	MAX_STEP_HOURS = 24 * 30            # TODO: ??? How and where is it used ?

class DataManager():
    def __init__(self) -> None:
        self.PV_Generation = []
        self.Prices = []
        self.Electricity_Consumption = []
    def add_pv_element(self, element):
        self.PV_Generation.append(element)
    def add_price_element(self,element):
        self.Prices.append(element)
    def add_electricity_element(self,element):
        self.Electricity_Consumption.append(element)

    # get current time data based on given month day, and day_time
    def get_pv_data(self, month, day, day_time):
        return self.PV_Generation[(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24+day_time]
    def get_price_data(self,month,day,day_time):
        return self.Prices[(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24+day_time]
    def get_electricity_cons_data(self,month,day,day_time):
        return self.Electricity_Consumption[(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24+day_time]

    # get series data for one episode
    def get_series_pv_data(self,month,day):
        return self.PV_Generation[(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24:(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24+24]
    def get_series_price_data(self,month,day):
        return self.Prices[(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24:(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24+24]
    def get_series_electricity_cons_data(self,month,day):
        return self.Electricity_Consumption[(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24:(sum(Constant.MONTHS_LEN[:month-1])+day-1)*24+24]

class DG():
    ''' simulate a simple diesel generator here '''
    def __init__(self, parameters):
        self.name = parameters.keys()
        self.a_factor = parameters['a']
        self.b_factor = parameters['b']
        self.c_factor = parameters['c']
        self.power_output_max = parameters['power_output_max']
        self.power_output_min = parameters['power_output_min']
        self.ramping_up = parameters['ramping_up']
        self.ramping_down = parameters['ramping_down']
        self.last_step_output = None

    def step(self, action_gen):
        output_change = action_gen*self.ramping_up      # constrain the output_change with ramping up boundary
        output = self.current_output+output_change
        if output > 0:
            output = max(self.power_output_min, min(self.power_output_max, output))     # meet the constraint
        else:
            output = 0
        self.current_output = output

    def _get_cost(self, output):
        if output <= 0:
            cost = 0
        else:
            cost = (self.a_factor*pow(output, 2) + self.b_factor*output + self.c_factor)
        return cost 

    def reset(self):
        self.current_output = 0

class Battery():
    '''simulate a simple battery here'''
    def __init__(self, parameters):
        self.capacity = parameters['capacity']
        self.max_soc = parameters['max_soc']
        self.initial_capacity = parameters['initial_capacity']
        self.min_soc = parameters['min_soc']            # 0.2
        self.degradation = parameters['degradation']    # degradation cost 1.2
        self.max_charge = parameters['max_charge']      # max charge ability
        self.max_discharge = parameters['max_discharge']
        self.efficiency = parameters['efficiency']

    def step(self, action_battery):
        energy = action_battery*self.max_charge
        updated_capacity = max(self.min_soc, min(self.max_soc, (self.current_capacity*self.capacity+energy)/self.capacity))
        self.energy_change = (updated_capacity-self.current_capacity)*self.capacity # if charge, positive, if discharge, negative
        self.current_capacity = updated_capacity        # update capacity to current codition

    def _get_cost(self, energy):    # calculate the cost depends on the energy change
        cost = energy**2*self.degradation
        return cost

    def SOC(self):
        return self.current_capacity

    def reset(self):
        self.current_capacity = np.random.uniform(0.2, 0.8)
class Grid():
    def __init__(self):
        self.on = True
        if self.on:
            self.exchange_ability = 100
        else:
            self.exchange_ability = 0
    def _get_cost(self, current_price, energy_exchange):
        return current_price*energy_exchange

    def retrive_past_price(self):
        result = []
        if self.day < 1:
            past_price = self.past_price
        else:
            past_price = self.price[24*(self.day-1):24*self.day]
            # print(past_price)
        for item in past_price[(self.time-24)::]:
            result.append(item)
        for item in self.price[24*self.day:(24*self.day+self.time)]:
            result.append(item)
        return result 
class ESSEnv(gym.Env):
    def __init__(self, **kwargs):
        super(ESSEnv,self).__init__()
        #parameters 
        self.data_manager = DataManager()
        self._load_year_data()
        self.episode_length = kwargs.get('episode_length', 24)
        self.month = None
        self.day = None
        self.current_time = None
        self.TRAIN = True
        self.battery_parameters = kwargs.get('battery_parameters', battery_parameters)
        self.dg_parameters = kwargs.get('dg_parameters', dg_parameters)
        self.penalty_coefficient = 50     # control soft penalty constrain
        self.sell_coefficient = 0.5       # control sell benefits

        self.grid = Grid()
        self.battery = Battery(self.battery_parameters)
        self.dg1 = DG(self.dg_parameters['gen_1'])
        self.dg2 = DG(self.dg_parameters['gen_2'])
        self.dg3 = DG(self.dg_parameters['gen_3'])

        self.action_space = spaces.Box(low=-1, high=1, shape=(4,), dtype=np.float32)
        self.state_space = spaces.Box(low=0, high=1, shape=(7,), dtype=np.float32)

    # @property
    # def netload(self):
    #     return self.demand - self.grid.wp_gen - self.grid.pv_gen       # TODO: ??? what is wp_gen and pv_gen
        
    def reset(self,):
        self.month = np.random.randint(1, 13)        # here we choose 12 month
        if self.TRAIN:
            self.day = np.random.randint(1, 20)       # TODO: ??? why 20
        else:
            self.day = np.random.randint(20, Constant.MONTHS_LEN[self.month]-1)
        self.current_time = 0
        self.battery.reset()
        self.dg1.reset()
        self.dg2.reset()
        self.dg3.reset()
        return self._build_state()

    def _build_state(self):
        soc = self.battery.SOC()
        dg1_output = self.dg1.current_output
        dg2_output = self.dg2.current_output
        dg3_output = self.dg3.current_output
        time_step = self.current_time
        electricity_demand = self.data_manager.get_electricity_cons_data(self.month, self.day, self.current_time)
        pv_generation = self.data_manager.get_pv_data(self.month, self.day, self.current_time)
        price = self.data_manager.get_price_data(self.month, self.day, self.current_time)
        net_load = electricity_demand - pv_generation
        obs = np.concatenate((np.float32(time_step),
                              np.float32(price),
                              np.float32(soc),
                              np.float32(net_load),
                              np.float32(dg1_output),
                              np.float32(dg2_output),
                              np.float32(dg3_output)), axis=None)
        return obs

    def step(self, action):     # state transition here current_obs-->take_action-->get reward-->get_finish-->next_obs
        ## here we want to put take action into each components
        current_obs = self._build_state()
        self.battery.step(action[0])    # here execute the state-transition part, battery.current_capacity also changed
        self.dg1.step(action[1])
        self.dg2.step(action[2])
        self.dg3.step(action[3])
        current_output = np.array((self.dg1.current_output,
                                   self.dg2.current_output,
                                   self.dg3.current_output,
                                   -self.battery.energy_change))    # truely corresonding to the result
        self.current_output = current_output
        actual_production = sum(current_output)
        netload = current_obs[3]
        price = current_obs[1]

        unbalance = actual_production - netload

        reward = 0
        excess_penalty = 0
        deficient_penalty = 0
        sell_benefit = 0
        buy_cost = 0
        self.excess = 0
        self.shedding = 0
        if unbalance >= 0:     # it is now in excess condition
            if unbalance <= self.grid.exchange_ability:
                sell_benefit = self.grid._get_cost(price, unbalance)*self.sell_coefficient  # sell money to grid is little [0.029,0.1]
            else:
                sell_benefit = self.grid._get_cost(price, self.grid.exchange_ability)*self.sell_coefficient
                # real unbalance that even grid could not meet
                self.excess = unbalance - self.grid.exchange_ability
                excess_penalty = self.excess*self.penalty_coefficient
        else:   # unbalance <0, its load shedding model, in this case, deficient penalty is used
            if abs(unbalance) <= self.grid.exchange_ability:
                buy_cost = self.grid._get_cost(price, abs(unbalance))
            else:
                buy_cost = self.grid._get_cost(price, self.grid.exchange_ability)
                self.shedding = abs(unbalance) - self.grid.exchange_ability
                deficient_penalty = self.shedding*self.penalty_coefficient

        battery_cost = self.battery._get_cost(self.battery.energy_change)   # we set it as 0 this time
        dg1_cost = self.dg1._get_cost(self.dg1.current_output)
        dg2_cost = self.dg2._get_cost(self.dg2.current_output)
        dg3_cost = self.dg3._get_cost(self.dg3.current_output)

        reward -= (battery_cost
                   + dg1_cost
                   + dg2_cost
                   + dg3_cost
                   + excess_penalty
                   + deficient_penalty
                   - sell_benefit
                   + buy_cost)/1e3      # TODO: ??? why divide by 1e3

        self.operation_cost = (battery_cost
                               + dg1_cost
                               + dg2_cost
                               + dg3_cost
                               + buy_cost
                               - sell_benefit
                               + excess_penalty
                               + deficient_penalty)

        self.unbalance = unbalance
        self.real_unbalance = self.shedding + self.excess
        final_step_outputs = [self.dg1.current_output,
                              self.dg2.current_output,
                              self.dg3.current_output,
                              self.battery.current_capacity]
        self.current_time += 1
        finish = (self.current_time == self.episode_length)
        if finish:
            self.final_step_outputs = final_step_outputs
            self.current_time = 0
            # self.day+=1
            # if self.day>Constant.MONTHS_LEN[self.month-1]:
            #     self.day=1
            #     self.month+=1
            # if self.month>12:
            #     self.month=1
            #     self.day=1
            next_obs = self.reset()
            
        else:
            next_obs = self._build_state()
        return current_obs, next_obs, float(reward), finish

    def render(self, current_obs, next_obs, reward, finish):
        print('day={}, hour={:2d}, state={}, next_state={}, reward={:.4f}, terminal={}\n'.format(self.day,
                                                                                                 self.current_time,
                                                                                                 current_obs,
                                                                                                 next_obs,
                                                                                                 reward,
                                                                                                 finish))

    def _load_year_data(self):
        # hourly PV data
        pv_df = pd.read_csv('data/PV.csv', sep=';')
        # hourly price data for a year
        price_df = pd.read_csv('data/Prices.csv', sep=';')
        # mins electricity consumption data for a year 
        electricity_df = pd.read_csv('data/H4.csv',sep=';')
        pv_data = pv_df['P_PV_'].apply(lambda x: x.replace(',','.')).to_numpy(dtype=float)
        price = price_df['Price'].apply(lambda x:x.replace(',','.')).to_numpy(dtype=float)
        electricity = electricity_df['Power'].apply(lambda x:x.replace(',','.')).to_numpy(dtype=float)

        # netload = electricity-pv_data
        '''we carefully redesign the magnitude for price and amount of generation as well as demand'''
        for element in pv_data:
            self.data_manager.add_pv_element(element*200)      # TODO: ??? why multiplied by 200
        for element in price:
            element/=10                                        # TODO: ??? why divided by 10
            if element <= 0.5:
                element = 0.5
            self.data_manager.add_price_element(element)
        for i in range(0, electricity.shape[0], 60):
            element = electricity[i:i+60]
            self.data_manager.add_electricity_element(sum(element)*300) # TODO: ??? why multiplied by 300

if __name__ == '__main__':
    env=ESSEnv()
    env.TRAIN = False
    rewards = []

    current_obs = env.reset()
    tem_action = [0.1, 0.1, 0.1, 0.1]
    for _ in range (2):   # 144
        print(f'current month is {env.month}, current day is {env.day}, current time is {env.current_time}')
        current_obs, next_obs, reward, finish = env.step(tem_action)
        env.render(current_obs, next_obs, reward, finish)
        current_obs = next_obs
        rewards.append(reward)