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A3C_plusplus.py

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+# A3C++ a modified version of Asynchronous Advantage actor critic algorithm
+# -----------------------------------
+#
+# A3C paper: https://arxiv.org/abs/1602.01783
+#
+# The A3C implementation is available at:
+# https://jaromiru.com/2017/02/16/lets-make-an-a3c-theory/
+# by: Jaromir Janisch, 2017
+
+# Two variations are implemented: A memory replay and a deterministic search following argmax(pi) instead of pi as a probability distribution
+# Every action selection is made following the action with the highest probability pi
+
+# Author: Taha Nakabi
+
+# Args: 'train' for training the model anything else will skip the training and try to use already saved models
+
+import tensorflow as tf
+import numpy as np
+import gym, time, random, threading
+from keras.callbacks import TensorBoard
+from keras.models import *
+from keras.layers import *
+from keras import backend as K
+
+from tcl_env_dqn_1 import *
+print("after import")
+import os
+
+
+
+# This is where the models are saved and retrieved from
+path = os.getcwd()
+# path = r'E:\member\XiongC\Sci Project\01-Reinforcement Learning\Github\DRL-for-microgrid-energy-management-master\DRL-for-microgrid-energy-management-master'
+MODELS_DIRECTORY = path + '/success1'
+
+
+# For tensor board
+NAME= "A3C++logs/A3C++{}".format(int(time.time()))
+# -- constants
+# Threading parameters
+RUN_TIME = 5000
+THREADS = 16
+OPTIMIZERS = 2
+THREAD_DELAY = 0.000001
+# Reinforcement learning parameters
+N_STEP_RETURN = 15
+GAMMA = 1.0
+GAMMA_N = GAMMA ** N_STEP_RETURN
+# Epsilon greedy strategy parameters
+EPS_START = .5
+EPS_STOP = .001
+EPS_DECAY = 5e-6
+# Memory replay parameters
+MIN_BATCH = 200
+TR_FREQ = 100
+# Advantage actor-critic parameters
+LOSS_V = 0.4  # v loss coefficient
+LOSS_ENTROPY = 1.0  # entropy coefficient
+# Initializing max rewards for models' saving purposes
+max_reward = -100.0
+# Training iterations and learning rate
+TRAINING_ITERATIONS = 1
+LEARNING_RATE = 1e-3
+# ---------
+# The brain class will handle building the neural network, sampling experiences for training and preparing and running the training process.
+# ---------
+class Brain:
+    # Memory
+    train_queue = [[], [], [], [], []]  # s, a, r, s', s' terminal mask
+    train_queue_copy = [[], [], [], [], []]  # s, a, r, s', s' terminal mask
+    lock_queue = threading.Lock()
+
+    def __init__(self, **kwargs):
+        self.env = kwargs.get("environment")
+        self.learning_rate = kwargs.get('learning_rate', LEARNING_RATE)
+        self.tr_freq = kwargs.get('training_frequency', TR_FREQ)
+        self.min_batch = kwargs.get('min_batch', MIN_BATCH)
+        self.gamman = kwargs.get('gamma_n', GAMMA_N)
+        self.models_directory = kwargs.get('models_directory', MODELS_DIRECTORY)
+        self.num_state = self.env.env.observation_space.shape[0]
+        self.num_tcl =self.env.env.num_tcls
+        self.num_actions= self.env.env.action_space.n
+        self.none_state=np.zeros(self.num_state)
+        tf.compat.v1.disable_eager_execution()
+        # self.session = tf.compat.v1.Session()
+        # K.set_session(self.session)
+        K.manual_variable_initialization(True)
+
+        self.model = self._build_model(num_state=self.num_state, num_tcls=self.num_tcl)
+        self.graph = self._build_graph(self.model)
+        # self.session.run(tf.compat.v1.global_variables_initializer())
+        # self.default_graph = tf.compat.v1.get_default_graph()
+        # We keep track of the best rewards achieved so far for each day
+        self.max_reward = max_reward
+        self.rewards = {}
+        for i in range(self.env.env.day0, self.env.env.dayn):
+            self.rewards[i] = self.max_reward
+
+        # self.default_graph.finalize()  # avoid modifications
+
+    def _build_model(self, num_state, num_tcls):
+
+        l_input = Input(batch_shape=(None,num_state))
+        print('input shape')
+        print(format(l_input.shape.as_list()))
+        # The TCLs states are fed individually to the neural network but they are simply being averaged
+        l_input1 = Lambda(lambda x: x[:, 0:num_tcls])(l_input)
+        l_input2 = Lambda(lambda x: x[:, num_tcls:])(l_input)
+        print(self.env.env.num_tcls)
+        l_input1 = Reshape((num_tcls, 1))(l_input1)
+        l_Pool = AveragePooling1D(pool_size=num_tcls)(l_input1)
+        l_Pool = Reshape([1])(l_Pool)
+        l_dense = Concatenate()([l_Pool, l_input2])
+        l_dense = Dense(100, activation='relu')(l_dense)
+        l_dense = Dropout(0.3)(l_dense)
+        out = Dense(self.num_actions, activation='softmax')(l_dense)
+        out_value = Dense(1, activation='linear')(l_dense)
+        model = Model(inputs=l_input, outputs=[out, out_value])
+        model._make_predict_function()  # have to initialize before threading
+        return model
+
+    def _build_graph(self, model):
+        s_t = tf.compat.v1.placeholder(tf.float32, shape=(None, self.num_state))
+        a_t = tf.compat.v1.placeholder(tf.float32, shape=(None, self.num_actions))
+        r_t = tf.compat.v1.placeholder(tf.float32, shape=(None, 1))  # not immediate, but discounted n step reward
+        p, v = model(s_t)
+        log_prob = tf.math.log(tf.reduce_sum(input_tensor=p * a_t, axis=1, keepdims=True) + 1e-10)
+        advantage = r_t - v
+        loss_policy =  -log_prob * tf.stop_gradient(advantage)  # maximize policy
+        loss_value = LOSS_V * tf.square(advantage)  # minimize value error
+        entropy = LOSS_ENTROPY * (tf.reduce_sum(input_tensor=p * tf.math.log(p + 1e-10), axis=1, keepdims=True))
+        loss_total = tf.reduce_mean(input_tensor=loss_policy + loss_value + entropy)
+        optimizer = tf.compat.v1.train.RMSPropOptimizer(self.learning_rate)
+        minimize = optimizer.minimize(loss_total)
+        return s_t, a_t, r_t, minimize, loss_total
+
+    def optimize(self):
+        # self.train_queue_copy serves as a counter of the number of observations we make between training sessions
+        if len(self.train_queue_copy[0])<self.tr_freq or len(self.train_queue_copy[0])<self.min_batch :
+            time.sleep(0)  # yield
+            return
+
+        with self.lock_queue:
+            if len(self.train_queue_copy[0])<self.tr_freq:  # more thread could have passed without lock
+                return  # we can't yield inside lock
+            # We take a fraction from the memory and throw away the rest, the following experiences are added on top of the sampled experiences.
+            # This sampling process makes the current memory include old and new experiences. After many sampling iterations the very old experiences will slowly fade and the newest will remain.
+            self.train_queue = random.sample(np.array(self.train_queue).T.tolist(), self.min_batch)
+            self.train_queue = np.array(self.train_queue).T.tolist()
+            s, a, r, s_, s_mask = self.train_queue_copy
+            self.train_queue_copy = [[], [], [], [], []]
+
+            s = np.vstack(s)
+            a = np.vstack(a)
+            r = np.vstack(r)
+            s_ = np.vstack(s_)
+            s_mask = np.vstack(s_mask)
+
+        if len(s) > 5 * self.min_batch: print("Optimizer alert! Minimizing batch of %d" % len(s))
+
+        v = self.predict_v(s_)
+        r = r + self.gamman * v * s_mask  # set v to 0 where s_ is terminal state
+
+        s_t, a_t, r_t, minimize, loss = self.graph
+        print("Training...")
+        # for _ in range(TRAINING_ITERATIONS):
+        minimize(s,a,r)
+        # self.session.run([minimize,loss], feed_dict={s_t: s, a_t: a, r_t: r})
+        print("Done...")
+
+
+    # pushing experiences into the memory
+    def train_push(self, s, a, r, s_):
+        with self.lock_queue:
+            self.train_queue[0].append(s)
+            self.train_queue[1].append(a)
+            self.train_queue[2].append(r)
+
+            self.train_queue_copy[0].append(s)
+            self.train_queue_copy[1].append(a)
+            self.train_queue_copy[2].append(r)
+
+            if s_ is None:
+                self.train_queue[3].append(self.none_state)
+                self.train_queue[4].append(0.)
+
+                self.train_queue_copy[3].append(self.none_state)
+                self.train_queue_copy[4].append(0.)
+            else:
+                self.train_queue[3].append(s_)
+                self.train_queue[4].append(1.)
+
+                self.train_queue_copy[3].append(s_)
+                self.train_queue_copy[4].append(1.)
+
+    def predict(self, s):
+        # with self.default_graph.as_default():
+        p, v = self.model.predict(s)
+        return p, v
+
+    def predict_p(self, s):
+        # with self.default_graph.as_default():
+        p, v = self.model.predict(s)
+        return p
+
+    def predict_p_vote(self, s):
+        # Boost learning. Several versions of the successfull models are voting for the best action
+        votes=[]
+        # print('retreiving models from {}'.format(self.models_directory))
+        for filename in os.listdir(self.models_directory):
+            if filename.endswith(".h5"):
+                # print(filename)
+                # with self.default_graph.as_default():
+                try:
+                    # print('trying to load weights')
+                    self.model.load_weights(self.models_directory+"/"+filename)
+                    # print('weights loaded')
+                    p = self.model.predict(s)[0][0]
+                    # print('probability predicted')
+                    # votes.append(p)
+                    votes.append(ACTIONS[np.argmax(p)])
+                except :
+                    print(filename+"didn't vote!")
+                    pass
+        boosted_p = np.average(np.array(votes),axis=0)
+        return  np.rint(boosted_p).astype(int)
+        # return ACTIONS[np.argmax(boosted_p)]
+
+    def predict_v(self, s):
+        # with self.default_graph.as_default():
+        p, v = self.model.predict(s)
+        return v
+
+# ---------
+# The agent handles the interactions with the environment and the selection of actions, stocking and retreiving experiences from the memory.
+# ---------
+frames = 0
+
+class Agent:
+    def __init__(self, eps_start, eps_end, eps_decay, num_actions):
+        self.eps_start = eps_start
+        self.eps_end = eps_end
+        self.eps_decay = eps_decay
+        self.memory = []  # used for n_step return
+        self.R = 0.
+        self.num_actions = num_actions
+
+    def getEpsilon(self):
+        return max(self.eps_start -  frames * self.eps_decay,self.eps_end)  # linearly interpolate
+
+    def act(self, s,render=False, br=None):
+        global frames, brain
+        if br != None:
+            brain = br
+        eps = self.getEpsilon()
+        frames = frames + 1
+        # Epsilon-greedy strategy:
+        if random.random() < eps:
+            p = np.random.dirichlet(np.ones(self.num_actions), size=1)
+        else:
+            s = np.array([s])
+            if render:
+                print('starting the vote')
+                a = brain.predict_p_vote(s)
+                p= np.random.dirichlet(np.ones(self.num_actions), size=1)
+                print(a)
+                return list(a),p
+            p = brain.predict_p(s)
+        # In the original version, the action selection follows a stochasic policy as follows:
+        # a = np.random.choice(NUM_ACTIONS, p=p.reshape(NUM_ACTIONS,))
+        # We follow a deterministic policy as follow:
+        a = np.argmax(p.reshape(self.num_actions,))
+        return a,p
+
+    def train(self, s, a, r, s_):
+        def get_sample(memory, n):
+            s, a, _, _ = memory[0]
+            _, _, _, s_ = memory[n - 1]
+
+            return s, a, self.R, s_
+
+        a_cats = a
+        # a_cats[a] = 1
+
+        self.memory.append((s, a_cats, r, s_))
+
+        self.R = (self.R + r * GAMMA_N) / GAMMA
+        if s_ is None:
+            while len(self.memory) > 0:
+                n = len(self.memory)
+                s, a, r, s_ = get_sample(self.memory, n)
+                brain.train_push(s, a, r, s_)
+                self.R = (self.R - self.memory[0][2]) / GAMMA
+                self.memory.pop(0)
+            self.R = 0
+
+        if len(self.memory) >= N_STEP_RETURN:
+            s, a, r, s_ = get_sample(self.memory, N_STEP_RETURN)
+            brain.train_push(s, a, r, s_)
+            self.R = self.R - self.memory[0][2]
+            self.memory.pop(0)
+
+
+# possible edge case - if an episode ends in <N steps, the computation is incorrect
+
+
+# ---------
+# The environment here is defined as a thread so that we can run the algorithm as a multi-thread process
+# ---------
+class Environment(threading.Thread):
+    stop_signal = False
+
+    def __init__(self, render=False, eps_start=EPS_START, eps_end=EPS_STOP, eps_decay=EPS_DECAY, **kwargs):
+        threading.Thread.__init__(self)
+
+        self.render = render
+        self.env = MicroGridEnv(**kwargs)
+        self.agent = Agent(eps_start, eps_end, eps_decay,num_actions=self.env.action_space.n)
+        self.brain = None
+
+
+    def runEpisode(self,day=None, pplt=True, web = False):
+        # print('resetting the environment')
+        if web==False:
+            s = self.env.reset_all(day=day)
+        else:
+            s = self.env.reset(day=day)
+        R = 0
+        while True:
+            time.sleep(THREAD_DELAY)  # yield
+            # print('Acting')
+            a, p = self.agent.act(s,self.render, self.brain)
+            # print('stepping')
+            s_, r, done, _ = self.env.step(a)
+            R += r
+            # print('rendering')
+            if self.render:
+                self.env.render(R)
+
+            if done:  # terminal state
+                s_ = None
+
+            if not self.render:
+                aa = np.zeros(shape=(NUM_ACTIONS,))
+                aa[a] = 1
+                self.agent.train(s, aa, r, s_)
+            s = s_
+
+            if done:
+                break
+        print("episode has been ran")
+        print(R)
+        if web==False:
+            REWARDS[self.env.day].append(R)
+
+        if self.render:
+            return R
+        if R > brain.rewards[self.env.day] and  self.agent.getEpsilon()<0.2:
+            print('new max found: '+str(R))
+            print("-------------------------------------------------------------------------------------------------")
+            try:
+                # Uncomment the following line for tensorboard
+                writer = tf.compat.v1.summary.FileWriter(NAME, brain.session.graph)
+                brain.model.save(MODELS_DIRECTORY+"/A3C++" + str(self.env.day) + ".h5")
+                print("Model saved")
+            except:
+                pass
+            brain.rewards[self.env.day] = R
+
+
+    def run(self):
+        while not self.stop_signal:
+            self.runEpisode()
+
+    def stop(self):
+        self.stop_signal = True
+
+
+# ---------
+class Optimizer(threading.Thread):
+    stop_signal = False
+
+    def __init__(self):
+        threading.Thread.__init__(self)
+
+    def run(self):
+        while not self.stop_signal:
+            brain.optimize()
+
+    def stop(self):
+        self.stop_signal = True
+
+
+
+
+if __name__ =="__main__":
+    import sys
+    TRAIN=False
+
+    # #
+    # if str(sys.argv[1]) == 'train':
+    #     TRAIN = True
+
+    DAY0 = 0
+    DAYN = 10
+
+    REWARDS = {}
+    for i in range(DAY0,DAYN):
+        REWARDS[i]=[]
+
+    env_test = Environment(render=True, eps_start=0., eps_end=0., day0=DAY0, dayn=DAYN, iterations=24)
+    NUM_STATE = env_test.env.observation_space.shape[0]
+    NUM_ACTIONS = env_test.env.action_space.n
+    NONE_STATE = np.zeros(NUM_STATE)
+
+    brain = Brain(environment=env_test)  # brain is global in A3C
+
+    if TRAIN:
+
+        envs = [Environment(day0=DAY0, dayn=DAYN) for i in range(THREADS)]
+        opts = [Optimizer() for i in range(OPTIMIZERS)]
+        t0=time.time()
+
+        for o in opts:
+            o.start()
+
+        for e in envs:
+            e.start()
+
+        time.sleep(RUN_TIME)
+
+        for e in envs:
+            e.stop()
+        for e in envs:
+            e.join()
+
+        for o in opts:
+            o.stop()
+        for o in opts:
+            o.join()
+        brain.model.save("success00/A3C++"  + ".h5")
+        print("Training finished")
+        print('training_time:', time.time()-t0)
+        # Save the rewards' list for each day
+        import pickle
+        with open("REWARDS_A3C++train.pkl", 'wb') as f:
+            pickle.dump(REWARDS, f, pickle.HIGHEST_PROTOCOL)
+
+
+    try:
+        for day in range(DAY0,DAYN):
+            env_test.runEpisode(day)
+        print("average reward: ",np.average([list(REWARDS[i])[-1] for i in range(DAY0,DAYN)]))
+        import pickle
+        # with open("REWARDS_A3C++test.pkl", 'wb') as f:
+        #     pickle.dump(REWARDS, f, pickle.HIGHEST_PROTOCOL)
+    except NameError:
+        print(NameError)
+
+
+

app.py

@@ -0,0 +1,54 @@
+# A3C++ a modified version of Asynchronous Advantage actor critic algorithm
+# -----------------------------------
+#
+# A3C paper: https://arxiv.org/abs/1602.01783
+#
+# The A3C implementation is available at:
+# https://jaromiru.com/2017/02/16/lets-make-an-a3c-theory/
+# by: Jaromir Janisch, 2017
+
+# Two variations are implemented: A memory replay and a deterministic search following argmax(pi) instead of pi as a probability distribution
+# Every action selection is made following the action with the highest probability pi
+
+# Author: Taha Nakabi
+
+# Args: 'train' for training the model anything else will skip the training and try to use already saved models
+
+
+import gradio as gr
+from gradio.components import *
+
+import subprocess
+
+def main(use_default, file):
+
+    subprocess.run(['python', './A3C_plusplus.py'])
+
+
+    base = './RESULT/'
+    img_path = []
+    for i in range(1, 11):
+        img_path.append(base + 'DAY' + str(i) + '.png')
+    # else:
+    # 根据上传的文件执行相应的逻辑
+    # 请根据您的实际需求自行编写代码
+
+    return [img for img in img_path]
+
+# 创建一个复选框来表示是否选择默认文件
+default_checkbox = gr.inputs.Checkbox(label="使用默认文件", default=False)
+
+inputs = [
+    default_checkbox,
+    File(label="上传文件", optional=True)
+]
+outputs = [
+    Image(label="DAY" + str(day + 1), type='filepath') for day in range(10)
+]
+
+iface = gr.Interface(fn=main, inputs=inputs, outputs=outputs)
+
+iface.launch()
+
+
+

day_ahead_prices2018.csv

@@ -0,0 +1,8762 @@
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tcl_env_dqn_1.py

@@ -0,0 +1,722 @@
+#!/usr/bin/env python3
+#
+#  tcl_env.py
+#  TCL environment for RL algorithms
+#
+# Author: Taha Nakabi
+import os
+import random
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+import gym
+# Trying out if this works for others. from gym import spaces had some issues
+import gym.spaces as spaces
+import threading
+import math
+
+# Default parameters for
+# default TCL environment.
+# From Taha's code
+# days range
+DEFAULT_DAY0=0
+DEFAULT_DAYN=1
+# Power generated in the microgrid
+path = os.getcwd()
+# path = r'E:\member\XiongC\Sci Project\02-强化学习优化算法-熊灿\Reinforcement Learning\Github\DRL-for-microgrid-energy-management-master\DRL-for-microgrid-energy-management-master'
+# r'E:\member\XiongC\Sci Project\01-Reinforcement Learning\Github\DRL-for-microgrid-energy-management-master\DRL-for-microgrid-energy-management-master'
+DEFAULT_POWER_GENERATED = np.genfromtxt(path +'/wind_generation_fortum.csv', delimiter=',', skip_header=0, usecols=[-1]) / 100
+DEFAULT_WIND_POWER_COST = 3.2
+# Balancing market prices
+DEFAULT_DOWN_REG = np.genfromtxt(path + '/down_regulation.csv', delimiter=',', skip_header=1, usecols=[-1]) / 10
+DEFAULT_UP_REG = np.genfromtxt(path + '/up_regulation.csv', delimiter=',', skip_header=1, usecols=[-1]) / 10
+DEFAULT_TRANSFER_PRICE_IMPORT = 0.97
+DEFAULT_TRANSFER_PRICE_EXPORT = 0.09
+# Length of one episode
+DEFAULT_ITERATIONS = 24
+# TCLs
+DEFAULT_NUM_TCLS = 100
+DEFAULT_AVGTCLPOWER = 1.5
+DEFAULT_TEMPERATURS = np.genfromtxt(path + '/temperatures.csv',usecols=[5],skip_header=1,delimiter=',')
+DEFAULT_TCL_SALE_PRICE = 3.2
+DEFAULT_TCL_TMIN = 19
+DEFAULT_TCL_TMAX = 24
+# Price responsive loads
+DEFAULT_NUM_LOADS = 150
+DEFAULT_BASE_LOAD = np.array(
+    [.4, .3,.2,.2,.2,.2,.3,.5,.6,.6,.5,.5,.5,.4,.4,.6,.8,1.4,1.2,.9,.8,.6,.5,.4])
+DEFAULT_MARKET_PRICE = 5.48
+DEFAULT_PRICE_TIERS = np.array([-3.0, -1.5, 0.0, 1.5, 3.0])
+# Battery characteristics (kwh)
+DEFAULT_BAT_CAPACITY=500
+DEFAULT_MAX_CHARGE=250
+DEFAULT_MAX_DISCHARGE=250
+
+
+MAX_R = 100
+
+# Rendering lists
+SOCS_RENDER = []
+LOADS_RENDER = []
+BATTERY_RENDER = []
+PRICE_RENDER = []
+ENERGY_SOLD_RENDER = []
+ENERGY_BOUGHT_RENDER = []
+GRID_PRICES_BUY_RENDER = []
+GRID_PRICES_SELL_RENDER = []
+ENERGY_GENERATED_RENDER = []
+TCL_CONTROL_RENDER = []
+TCL_CONSUMPTION_RENDER = []
+TOTAL_CONSUMPTION_RENDER=[]
+TEMP_RENDER=[]
+
+ACTIONS = [[i, j, k, l] for i in range(4) for j in range(5) for k in range(2) for l in range(2)]
+
+
+class TCL:
+    """
+    Simulates an invidual TCL
+    """
+
+    def __init__(self, ca, cm, q, P, Tmin=DEFAULT_TCL_TMIN, Tmax=DEFAULT_TCL_TMAX):
+        self.ca = ca
+        self.cm = cm
+        self.q = q
+        self.P = P
+        self.Tmin = Tmin
+        self.Tmax = Tmax
+
+        # Added for clarity
+        self.u = 0
+
+    def set_T(self, T, Tm):
+        self.T = T
+        self.Tm = Tm
+
+    def control(self, ui=0):
+        # control TCL using u with respect to the backup controller
+        if self.T < self.Tmin:
+            self.u = 1
+        elif self.Tmin <= self.T < self.Tmax:
+            self.u = ui
+        else:
+            self.u = 0
+
+    def update_state(self, T0):
+        # update the indoor and mass temperatures according to (22)
+        for _ in range(1):
+            self.T += self.ca * (T0 - self.T) + self.cm * (self.Tm - self.T) + self.P * self.u + self.q
+            self.Tm += self.cm * (self.T - self.Tm)
+            if self.T >= self.Tmax:
+                break
+
+    """ 
+    @property allows us to write "tcl.SoC", and it will
+    run this function to get the value
+    """
+
+    @property
+    def SoC(self):
+        return (self.T - self.Tmin) / (self.Tmax - self.Tmin)
+
+
+class Battery:
+    # Simulates the battery system of the microGrid
+    def __init__(self, capacity, useD, dissipation, rateC, maxDD, chargeE):
+        self.capacity = capacity  # full charge battery capacity
+        self.useD = useD  # useful discharge coefficient
+        self.dissipation = dissipation  # dissipation coefficient of the battery
+        self.rateC = rateC  # charging rate
+        self.maxDD = maxDD  # maximum power that the battery can deliver per timestep
+        self.chargeE = chargeE  # max Energy given to the battery to charge
+        self.RC = 0  # remaining capacity
+
+
+    def charge(self, E):
+        empty = self.capacity - self.RC
+        if empty <= 0:
+            return E
+        else:
+            self.RC += self.rateC * min(E,self.chargeE)
+            leftover = self.RC - self.capacity + max(E-self.chargeE,0)
+            self.RC = min(self.capacity, self.RC)
+            return max(leftover, 0)
+
+    def supply(self, E):
+        remaining = self.RC
+        self.RC -= min(E, remaining,self.maxDD)
+        self.RC = max(self.RC, 0)
+        return min(E, remaining,self.maxDD) * self.useD
+
+    def dissipate(self):
+        self.RC = self.RC * math.exp(- self.dissipation)
+
+    @property
+    def SoC(self):
+        return self.RC / self.capacity
+
+    def reset(self):
+        self.RC=0
+
+
+class Grid:
+    def __init__(self, down_reg,up_reg, exp_fees, imp_fees):
+        self.sell_prices = down_reg
+        self.buy_prices = up_reg
+        self.exp_fees=exp_fees
+        self.imp_fees = imp_fees
+        self.time = 0
+
+    def sell(self, E):
+        return (self.sell_prices[self.time] + self.exp_fees) * E
+
+    def buy(self, E):
+        return -(self.buy_prices[self.time] + self.imp_fees) * E
+
+    #
+    # def get_price(self,time):
+    #     return self.prices[time]
+
+    def set_time(self, time):
+        self.time = time
+
+    def total_cost(self,prices, energy):
+        return sum(prices * energy / 100 + self.imp_fees * energy)
+
+class Generation:
+    def __init__(self, generation):
+        self.power = generation
+
+
+    def current_generation(self, time):
+        # We consider that we have 2 sources of power a constant source and a variable source
+        return self.power[time]
+
+
+class Load:
+    def __init__(self, price_sens, base_load, max_v_load,patience):
+        self.price_sens = max(0,price_sens)
+        self.orig_price_sens = max(0,price_sens)
+        self.base_load = base_load
+        self.max_v_load = max_v_load
+        self.response = 0
+        self.shifted_loads={}
+        self.patience=max(patience,1)
+        self.dr_load=0
+
+    def react(self, price_tier , time_day):
+        self.dr_load=self.base_load[time_day]
+        response = self.price_sens * (price_tier - 2)
+        if response != 0 :
+            self.dr_load -= self.base_load[time_day] * response
+            self.shifted_loads[time_day] = self.base_load[time_day] * response
+        for k in list(self.shifted_loads):
+            probability_of_execution = -self.shifted_loads[k]*(price_tier - 2) + (time_day-k)/self.patience
+            if random.random()<=probability_of_execution:
+                self.dr_load+=self.shifted_loads[k]
+                del self.shifted_loads[k]
+
+    def load(self):
+        return max(self.dr_load, 0)
+
+
+class MicroGridEnv(gym.Env):
+    def __init__(self,**kwargs):
+
+        # Get number of iterations and TCLs from the
+        # parameters (we have to define it through kwargs because
+        # of how Gym works...)
+        self.iterations = kwargs.get("iterations", DEFAULT_ITERATIONS)
+        self.num_tcls = kwargs.get("num_tcls", DEFAULT_NUM_TCLS)
+        print(self.num_tcls)
+        self.avg_tcl_power = kwargs.get("tcl_power", DEFAULT_AVGTCLPOWER)
+        self.tcl_sale_price = kwargs.get("tcl_price", DEFAULT_TCL_SALE_PRICE)
+        self.num_loads = kwargs.get("num_loads", DEFAULT_NUM_LOADS)
+        self.typical_load = kwargs.get("base_load", DEFAULT_BASE_LOAD)
+        self.market_price = kwargs.get("normal_price", DEFAULT_MARKET_PRICE)
+        self.temperatures = kwargs.get("temperatures", DEFAULT_TEMPERATURS)
+        self.price_tiers = kwargs.get("price_tiers", DEFAULT_PRICE_TIERS)
+        self.day0 = kwargs.get("day0", DEFAULT_DAY0)
+        self.dayn = kwargs.get("dayn", self.day0+1)
+        self.power_cost = kwargs.get("power_cost", DEFAULT_WIND_POWER_COST)
+        self.down_reg = kwargs.get("down_reg", DEFAULT_DOWN_REG)
+        self.up_reg = kwargs.get("up_reg", DEFAULT_UP_REG)
+        self.imp_fees = kwargs.get("imp_fees", DEFAULT_TRANSFER_PRICE_IMPORT)
+        self.exp_fees = kwargs.get("exp_fees", DEFAULT_TRANSFER_PRICE_EXPORT)
+        self.bat_capacity = kwargs.get("battery_capacity", DEFAULT_BAT_CAPACITY)
+        self.max_discharge = kwargs.get("max_discharge", DEFAULT_MAX_DISCHARGE)
+        self.max_charge = kwargs.get("max_charge", DEFAULT_MAX_CHARGE)
+
+        # The current day: pick randomly
+        # self.day = random.randint(self.day0, self.dayn-1)
+        self.day = self.day0
+        # The current timestep
+        self.time_step = 0
+
+        # The cluster of TCLs to be controlled.
+        # These will be created in reset()
+        self.tcls_parameters = []
+        # The cluster of loads.
+        # These will be created in reset()
+        self.loads_parameters = []
+
+        self.generation = Generation(kwargs.get("generation_data", DEFAULT_POWER_GENERATED))
+        self.grid = Grid(down_reg=self.down_reg,up_reg=self.up_reg, exp_fees=self.exp_fees, imp_fees=self.imp_fees)
+        self.battery = Battery(capacity=self.bat_capacity, useD=0.9, dissipation=0.001, rateC=0.9, maxDD=self.max_discharge, chargeE=self.max_charge)
+
+        self.tcls = [self._create_tcl(*self._create_tcl_parameters()) for _ in range(self.num_tcls)]
+        self.loads = [self._create_load(*self._create_load_parameters()) for _ in range(self.num_loads)]
+
+        self.action_space_sep = spaces.Box(low=0, high=1, dtype=np.float32,
+                                       shape=(13,))
+        self.action_space = spaces.Discrete(80)
+
+        # Observations: A vector of TCLs SoCs + loads +battery soc+ power generation + price + temperature + time of day
+        self.observation_space = spaces.Box(low=-100, high=100, dtype=np.float32,
+                                            shape=(self.num_tcls + 7,))
+
+    def _create_tcl_parameters(self):
+        """
+                Initialize one TCL randomly with given T_0,
+                and return it. Copy/paste from Taha's code
+                """
+        # Hardcoded initialization values to create
+        # bunch of different TCLs
+        ca = random.normalvariate(0.01, 0.003)
+        cm = random.normalvariate(0.3, 0.004)
+        q = random.normalvariate(0, 0.01)
+        P = random.normalvariate(self.avg_tcl_power, 0.01)
+        init_temp = random.uniform(15,24)
+        return [ca, cm, q, P,init_temp]
+
+    def _create_tcl(self, ca, cm, q, P, init_temp):
+        tcl = TCL(ca, cm, q, P)
+        tcl.set_T(init_temp, init_temp)
+        return tcl
+
+    def _create_load_parameters(self):
+        """
+        Initialize one load randomly,
+        and return it.
+        """
+        # Hardcoded initialization values to create
+        # bunch of different loads
+
+        price_sensitivity = random.normalvariate(0.4, 0.3)
+        max_v_load = random.normalvariate(0.4, 0.01)
+        patience= int(random.normalvariate(10,6))
+        return [price_sensitivity, max_v_load,patience]
+
+    def _create_load(self, price_sensitivity, max_v_load,patience):
+        load = Load(price_sensitivity, base_load=self.typical_load, max_v_load=max_v_load, patience=patience)
+        return load
+
+
+    def _build_state(self):
+        """
+        Return current state representation as one vector.
+        Returns:
+            state: 1D state vector, containing state-of-charges of all TCLs, Loads, current battery soc, current power generation,
+                   current temperature, current price and current time (hour) of day
+        """
+        # SoCs of all TCLs binned + current temperature + current price + time of day (hour)
+        socs = np.array([tcl.SoC for tcl in self.tcls])
+        # Scaling between 0 and 1
+        # We need to standardize the generation and the price
+        # Minimum soc is -1
+        socs = (socs+np.ones(shape=socs.shape))/2
+        loads = self.typical_load[(self.time_step) % 24]
+        loads = (loads - min(self.typical_load)) / (max(self.typical_load) - min(self.typical_load))
+
+        current_generation = self.generation.current_generation(self.day*self.iterations+self.time_step)
+        current_generation = (current_generation-
+                               np.average(self.generation.power[self.day*self.iterations:self.day*self.iterations+self.iterations]))\
+                              /np.std(self.generation.power[self.day*self.iterations:self.day*self.iterations+self.iterations])
+
+
+        temperature = self.temperatures[self.day*self.iterations+self.time_step]
+        temperature = (temperature-
+                       min(self.temperatures[self.day*self.iterations:self.day*self.iterations+self.iterations]))\
+                      /(max(self.temperatures[self.day*self.iterations:self.day*self.iterations+self.iterations])
+                        -min(self.temperatures[self.day*self.iterations:self.day*self.iterations+self.iterations]))
+
+        price = self.grid.buy_prices[self.day*self.iterations+self.time_step]
+        price = (price -
+                 np.average(self.grid.buy_prices[self.day*self.iterations:self.day*self.iterations+self.iterations])) \
+                / np.std(self.grid.buy_prices[self.day*self.iterations:self.day*self.iterations+self.iterations])
+
+        price_grid_sell = self.grid.sell_prices[self.day*self.iterations+self.time_step]
+        price_grid_sell = (price_grid_sell -
+                 np.average(self.grid.sell_prices[self.day*self.iterations:self.day*self.iterations + self.iterations])) \
+                / np.std(self.grid.sell_prices[self.day*self.iterations:self.day*self.iterations+self.iterations])
+
+        high_price = min(self.high_price/4,1)
+
+        time_step = (self.time_step)/(self.iterations-1)
+
+        state = np.concatenate((socs, [loads, high_price, time_step,self.battery.SoC, current_generation,
+                         price,
+                         price_grid_sell ]))
+        return state
+
+    def _build_info(self):
+        """
+        Return dictionary of misc. infos to be given per state.
+        Here this means providing forecasts of future
+        prices and temperatures (next 24h)
+        """
+        temp_forecast = np.array(self.temperatures[self.time_step + 1:self.time_step + self.iterations+1])
+        return {"temperature_forecast": temp_forecast,
+                "forecast_times": np.arange(0, self.iterations)}
+
+    def _compute_tcl_power(self):
+        """
+        Return the total power consumption of all TCLs
+        """
+        return sum([tcl.u * tcl.P for tcl in self.tcls])
+
+    def step(self, action):
+        """
+        Arguments:
+            action: A list.
+
+        Returns:
+            state: Current state
+            reward: How much reward was obtained on last action
+            terminal: Boolean on if the game ended (maximum number of iterations)
+            info: None (not used here)
+        """
+        if type(action) is not list:
+            action = ACTIONS[action]
+
+        self.grid.set_time(self.day*self.iterations + self.time_step)
+        reward = 0
+        # Update state of TCLs according to action
+
+        tcl_action = action[0]
+        price_action = action[1]
+        self.high_price += price_action - 2
+        if self.high_price > 4:
+            price_action = 2
+            self.high_price = 4
+
+        energy_deficiency_action = action[2]
+        energy_excess_action = action[3]
+        # Get the energy generated by the DER
+        available_energy = self.generation.current_generation(self.day*self.iterations + self.time_step)
+        # Calculate the cost of energy produced from wind turbines
+        reward-= available_energy * self.power_cost / 100
+        # We implement the pricing action and we calculate the total load in response to the price
+        for load in self.loads:
+            load.react(price_tier=price_action, time_day=self.time_step%24)
+
+        total_loads = sum([l.load() for l in self.loads])
+        # print("Total loads",total_loads)
+        # We fulfilled the load with the available energy.
+        available_energy -= total_loads
+        # Constraint of charging too high prices
+
+        # We calculate the return based on the sale price.
+        self.sale_price = self.price_tiers[price_action] + self.market_price
+        # We increment the reward by the amount of return
+        # Division by 100 to transform from cents to euros
+        reward += total_loads * (self.sale_price) / 100
+
+
+        # Distributing the energy according to priority
+        sortedTCLs = sorted(self.tcls, key=lambda x: x.SoC)
+        # print(tcl_action)
+        control = max(min(tcl_action * self.num_tcls * self.avg_tcl_power / 3, available_energy), 0)
+        self.control = control
+
+        for tcl in sortedTCLs:
+            if control > 0:
+                tcl.control(1)
+                control -= tcl.P * tcl.u
+            else:
+                tcl.control(0)
+            tcl.update_state(self.temperatures[self.day*self.iterations + self.time_step])
+
+
+        available_energy -= self._compute_tcl_power()
+        reward += self._compute_tcl_power() * self.tcl_sale_price / 100
+        # print("Available energy:", available_energy)
+        if available_energy > 0:
+            if energy_excess_action:
+                available_energy = self.battery.charge(available_energy)
+                # print("available energy after charging the battery", available_energy)
+                reward += self.grid.sell(available_energy) / 100
+            else:
+                reward += self.grid.sell(available_energy) / 100
+            self.energy_sold = available_energy
+            self.energy_bought = 0
+
+        else:
+            if energy_deficiency_action:
+                available_energy += self.battery.supply(-available_energy)
+                # print("after energy was taken from battery", available_energy)
+            self.energy_bought = -available_energy
+            reward += self.grid.buy(self.energy_bought) / 100
+            self.energy_sold = 0
+
+        # Proceed to next timestep.
+        self.time_step += 1
+        # Build up the representation of the current state (in the next timestep)
+        state = self._build_state()
+
+
+        terminal = self.time_step == self.iterations
+        # if terminal:
+
+        #     # # reward if battery is charged
+        #     # reward += abs(reward * self.battery.SoC / 2)
+        info = self._build_info()
+        return state, reward/MAX_R , terminal, info
+
+    def reset(self,day=None):
+        """
+        Create new TCLs, and return initial state.
+        Note: Overrides previous TCLs
+        """
+        if day == None:
+            self.day= random.randint(self.day0,self.dayn)
+        else:
+            self.day = day
+        print("Day:", self.day)
+        self.time_step = 0
+
+        self.high_price = 0
+
+        return self._build_state()
+
+    def reset_all(self,day=None):
+        """
+        Create new TCLs, and return initial state.
+        Note: Overrides previous TCLs
+        """
+        if day == None:
+            # self.day = random.randint(self.day0, self.dayn-1)
+            self.day= self.day0
+        else:
+            self.day = day
+        print("Day:", self.day)
+        self.time_step = 0
+        self.battery.reset()
+        self.high_price = 0
+        self.tcls.clear()
+        self.loads.clear()
+        self.tcls = [self._create_tcl(*self._create_tcl_parameters()) for _ in range(self.num_tcls)]
+        self.loads = [self._create_load(*self._create_load_parameters()) for _ in range(self.num_loads)]
+
+
+        return self._build_state()
+
+    def render(self,name=''):
+        SOCS_RENDER.append([tcl.SoC*100 for tcl in self.tcls])
+        LOADS_RENDER.append([l.load() for l in self.loads])
+        PRICE_RENDER.append(self.sale_price)
+        BATTERY_RENDER.append(self.battery.SoC)
+        ENERGY_GENERATED_RENDER.append(self.generation.current_generation(self.day*self.iterations+self.time_step-1))
+        ENERGY_SOLD_RENDER.append(self.energy_sold)
+        ENERGY_BOUGHT_RENDER.append(self.energy_bought)
+        GRID_PRICES_BUY_RENDER.append(self.grid.buy_prices[self.day * self.iterations + self.time_step-1])
+        GRID_PRICES_SELL_RENDER.append(self.grid.sell_prices[self.day * self.iterations + self.time_step-1])
+        TCL_CONTROL_RENDER.append(self.control)
+        TCL_CONSUMPTION_RENDER.append(self._compute_tcl_power())
+        TOTAL_CONSUMPTION_RENDER.append(self._compute_tcl_power()+np.sum([l.load() for l in self.loads]))
+        TEMP_RENDER.append(self.temperatures[self.day*self.iterations+self.time_step-1])
+        if self.time_step==self.iterations:
+            fig=plt.figure()
+            # ax = pyplot.axes()
+            ax = plt.subplot(2, 1, 1)
+            plt.axhspan(0, 24, facecolor='g', alpha=0.5)
+
+            ax.set_facecolor("silver")
+            ax.yaxis.grid(True)
+
+            ax.set_ylabel("TCLs state of charge %")
+
+            # ax.boxplot(SOCS_RENDER, positions=range(len(SOCS_RENDER)))
+            ax.boxplot(SOCS_RENDER, positions=range(24))
+
+            ax1 = ax.twinx()
+            ax1.set_ylabel("Temperatures °C")
+            ax1.plot(np.array(TEMP_RENDER), '--')
+            plt.title("TCLs state of charge and outdoor Temperatures")
+            plt.xlabel("Time (h)")
+            plt.legend(["Outdoor Temperatures"], loc='lower right')
+            # plt.show()
+
+            ax = plt.subplot(2, 1, 2)
+            ax.set_facecolor("silver")
+            ax.set_ylabel("kW")
+            ax.set_xlabel("Time (h)")
+            ax.yaxis.grid(True)
+            ax.plot(ENERGY_GENERATED_RENDER, color='k')
+            ax.bar(x=np.array(np.arange(self.iterations)) - 0.2, height=TCL_CONTROL_RENDER, width=0.2)
+            ax.bar(x=np.array(np.arange(self.iterations)), height=TCL_CONSUMPTION_RENDER, width=0.2)
+            plt.xticks( np.array(np.arange(self.iterations)) )
+            plt.title("Energy allocated to and consumed by TCLs and energy generated")
+            plt.legend(['Energy generated','Energy allocated for TCLs', 'Energy consumed by TCLs'])
+            plt.xlabel("Time (h)")
+            plt.ylabel("kW")
+
+            plt.savefig('./RESULT/Day'+str(self.day+1)+'.png')
+            # plt.show()
+
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.yaxis.grid(True)
+            # plt.plot(PRICE_RENDER,color='k')
+            # plt.title("SALE PRICES")
+            # plt.xlabel("Time (h)")
+            # plt.ylabel("€ cents")
+            # plt.show()
+            #
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.set_xlabel("Time (h)")
+            # ax.yaxis.grid(True)
+            # plt.plot(np.array(BATTERY_RENDER),color='k')
+            # plt.title("ESS SOC")
+            # plt.xlabel("Time (h)")
+            # # ax4.set_ylabel("BATTERY SOC")
+            # plt.show()
+            #
+            #
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.set_xlabel("Time (h)")
+            # ax.set_ylabel("kWh")
+            # ax.yaxis.grid(True)
+            # plt.plot(np.array(TOTAL_CONSUMPTION_RENDER), color='k')
+            # plt.title("Demand")
+            # plt.xlabel("Time (h)")
+            # plt.show()
+            #
+            #
+            #
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.set_xlabel("Time (h)")
+            # ax.yaxis.grid(True)
+            # plt.plot(np.array(self.typical_load), color='k')
+            # plt.title("Expected Individual basic load (L_b)")
+            # plt.xlabel("Time (h)")
+            # plt.ylabel("kWh")
+            # plt.show()
+            #
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.set_ylabel("kW")
+            # ax.set_xlabel("Time (h)")
+            # ax.yaxis.grid(True)
+            # plt.boxplot(np.array(LOADS_RENDER).T)
+            # plt.title("Hourly residential loads")
+            # plt.xlabel("Time (h)")
+            # plt.show()
+            #
+            #
+            #
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.yaxis.grid(True)
+            # plt.plot(np.array(ENERGY_GENERATED_RENDER),color='k')
+            # plt.title("ENERGY GENERATED")
+            # plt.xlabel("Time (h)")
+            # plt.ylabel("kW")
+            # plt.show()
+            #
+            # ax = plt.axes()
+            # ax.set_facecolor("silver")
+            # ax.yaxis.grid(True)
+            # # ax.axis(ymin=0,ymax=610)
+            # ax.bar(x=np.array(np.arange(self.iterations)),height=np.array(ENERGY_SOLD_RENDER),color='navy', width=0.8)
+            # ax.bar(x=np.array(np.arange(self.iterations)),height=np.array(ENERGY_BOUGHT_RENDER),color='darkred', width=0.8)
+            # ax.set_xlabel("Time (h)")
+            # ax.set_ylabel("Energy Exchanged kWh")
+            # ax.legend(['Energy sold', 'Energy purchased'],loc='upper left')
+            # # pyplot.show()
+            #
+            # ax1=ax.twinx()
+            # ax1.plot(np.array(GRID_PRICES_BUY_RENDER),color='red')
+            # ax1.plot(np.array(GRID_PRICES_SELL_RENDER), color='green')
+            # ax1.set_ylabel("GRID PRICES € cents")
+            # ax1.legend(['Buying prices','Selling prices'],loc='upper right')
+            # plt.show()
+
+
+
+
+
+
+            # np.save(name + 'Cost' + str(self.day) + '.npy', self.grid.total_cost(np.array(GRID_PRICES_RENDER),np.array(ENERGY_BOUGHT_RENDER)))
+            # np.save(name + 'Energy_bought_sold' + str(self.day) + '.npy', np.array(ENERGY_BOUGHT_RENDER)-np.array(ENERGY_SOLD_RENDER))
+            # np.save(name+'TOTAL_Consumption'+str(self.day)+'.npy' , TOTAL_CONSUMPTION_RENDER)
+            SOCS_RENDER.clear()
+            LOADS_RENDER.clear()
+            PRICE_RENDER.clear()
+            BATTERY_RENDER.clear()
+            GRID_PRICES_BUY_RENDER.clear()
+            GRID_PRICES_SELL_RENDER.clear()
+            ENERGY_BOUGHT_RENDER.clear()
+            ENERGY_SOLD_RENDER.clear()
+            ENERGY_GENERATED_RENDER.clear()
+            TCL_CONTROL_RENDER.clear()
+            TCL_CONSUMPTION_RENDER.clear()
+            TOTAL_CONSUMPTION_RENDER.clear()
+            TEMP_RENDER.clear()
+
+    def close(self):
+        """
+        Nothing to be done here, but has to be defined
+        """
+        return
+
+    def seedy(self, s):
+        """
+        Set the random seed for consistent experiments
+        """
+        random.seed(s)
+        np.random.seed(s)
+
+
+if __name__ == '__main__':
+    # Testing the environment
+    # Initialize the environment
+    env = MicroGridEnv()
+    env.seedy(1)
+    # Save the rewards in a list
+    rewards = []
+    # reset the environment to the initial state
+    state = env.reset()
+    # Call render to prepare the visualization
+
+    # Interact with the environment (here we choose random actions) until the terminal state is reached
+    while True:
+        # Pick an action from the action space (here we pick an index between 0 and 80)
+        # action = env.action_space.sample()
+        # action =[np.argmax(action[0:4]),np.argmax(action[4:9]),np.argmax(action[9:11]),np.argmax(action[11:])]
+        action=[1,2,0,0]
+        # Using the index we get the actual action that we will send to the environment
+        # print(ACTIONS[action])
+        print(action)
+        # Perform a step in the environment given the chosen action
+        # state, reward, terminal, _ = env.step(action)
+        state, reward, terminal, _ = env.step(list(action))
+        env.render()
+        print(reward)
+        rewards.append(reward)
+        if terminal:
+            break
+    print("Total Reward:", sum(rewards))
+
+    # Plot the TCL SoCs
+    states = np.array(rewards)
+    plt.plot(rewards)
+    plt.title("rewards")
+    plt.xlabel("Time")
+    plt.ylabel("rewards")
+    plt.show()
+    # plt.savefig('./result.png', format='png')
+