nothing

agent.py

@@ -19,7 +19,7 @@ class AgentBase:
         self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None
 
     def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, _if_per_or_gae=False, gpu_id=0):
-        # explict call self.init() for multiprocessing
+        # 显式调用self.init()进行多进程
         self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
         self.action_dim = action_dim
 
@@ -81,7 +81,7 @@ class AgentBase:
 class AgentDDPG(AgentBase):
     def __init__(self):
         super().__init__()
-        self.explore_noise = 0.1  # explore noise of action
+        self.explore_noise = 0.1
         self.if_use_cri_target = self.if_use_act_target = True
         self.ClassCri = Critic
         self.ClassAct = Actor
@@ -163,7 +163,7 @@ class AgentSAC(AgentBase):
         super().init(net_dim, state_dim, action_dim, learning_rate, _if_use_per, gpu_id)
 
         self.alpha_log = torch.tensor((-np.log(action_dim) * np.e,), dtype=torch.float32,
-                                      requires_grad=True, device=self.device)  # trainable parameter
+                                      requires_grad=True, device=self.device)
         self.alpha_optim = torch.optim.Adam((self.alpha_log,), lr=learning_rate)
         self.target_entropy = np.log(action_dim)
 
@@ -250,10 +250,9 @@ class AgentPPO(AgentBase):
         with torch.no_grad():
             buf_len = buffer[0].shape[0]
             buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]
-            # (ten_state, ten_action, ten_noise, ten_reward, ten_mask) = buffer
 
             '''get buf_r_sum, buf_logprob'''
-            bs = 2 ** 10  # set a smaller 'BatchSize' when out of GPU memory.
+            bs = 2 ** 10
             buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]
             buf_value = torch.cat(buf_value, dim=0)
             buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)
@@ -273,7 +272,7 @@ class AgentPPO(AgentBase):
             logprob = buf_logprob[indices]
             advantage = buf_advantage[indices]
 
-            new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action)  # it is obj_actor
+            new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action)  # it's obj_actor
             ratio = (new_logprob - logprob.detach()).exp()
             surrogate1 = advantage * ratio
             surrogate2 = advantage * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)
@@ -309,6 +308,6 @@ class AgentPPO(AgentBase):
         for i in range(buf_len - 1, -1, -1):
             buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum
             pre_r_sum = buf_r_sum[i]
-            buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])  # fix a bug here
+            buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])
             pre_advantage = ten_value[i] + buf_advantage[i] * self.lambda_gae_adv
         return buf_r_sum, buf_advantage

environment.py

@@ -28,8 +28,8 @@ class ESSEnv(gym.Env):
         self.dg_parameters = kwargs.get('dg_parameters', dg_parameters)
         self.solar_parameters = kwargs.get('solar_parameters', solar_parameters)
         self.wind_parameters = kwargs.get('wind_parameters', wind_parameters)
-        self.penalty_coefficient = 50  # control soft penalty constrain
-        self.sell_coefficient = 0.5  # control sell benefits
+        self.penalty_coefficient = 50  # 约束惩罚系数
+        self.sell_coefficient = 0.5  # 售出利润系数
 
         self.grid = Grid()
         self.battery = Battery(self.battery_parameters)
@@ -81,12 +81,12 @@ class ESSEnv(gym.Env):
         return obs
 
     def step(self, action):  # state transition: current_obs->take_action->get_reward->get_finish->next_obs
-        # put action into each component
+        # 在每个组件中添加动作
         current_obs = self._build_state()
         temperature = current_obs[7]
         irradiance = current_obs[8]
         wind_speed = current_obs[9]
-        self.battery.step(action[0])  # execute the state-transition part, battery.current_capacity also changed
+        self.battery.step(action[0])  # 执行状态转换，电池当前容量也改变
         self.dg1.step(action[1])
         self.dg2.step(action[2])
         self.dg3.step(action[3])
@@ -106,16 +106,16 @@ class ESSEnv(gym.Env):
         buy_cost = 0
         self.excess = 0
         self.shedding = 0
-        if unbalance >= 0:  # now in excess condition
+        if unbalance >= 0:  # 现在过剩
             if unbalance <= self.grid.exchange_ability:
                 # sell money to grid is little [0.029,0.1]
                 sell_benefit = self.grid.get_cost(price, unbalance) * self.sell_coefficient
             else:
                 sell_benefit = self.grid.get_cost(price, self.grid.exchange_ability) * self.sell_coefficient
-                # real unbalance that grid could not meet
+                # real unbalance：电网也无法满足
                 self.excess = unbalance - self.grid.exchange_ability
                 excess_penalty = self.excess * self.penalty_coefficient
-        else:  # unbalance <0, its load shedding model, deficient penalty is used
+        else:  # unbalance <0, 采用缺少惩罚
             if abs(unbalance) <= self.grid.exchange_ability:
                 buy_cost = self.grid.get_cost(price, abs(unbalance))
             else:
@@ -164,7 +164,7 @@ class ESSEnv(gym.Env):
         temperature = temperature_df['t2m'].to_numpy(dtype=float)
         wind = wind_df['wind_speed'].to_numpy(dtype=float)
 
-        '''redesign the magnitude for price and amount of generation as well as demand'''
+        '''重新设计价格和发电量以及需求的大小'''
         def process_elements(elements, transform_function, add_function):
             for element in elements:
                 transformed_element = transform_function(element)

module.py

@@ -2,7 +2,7 @@ import numpy as np
 
 
 class DG:
-    """simulate a simple diesel generator"""
+    """simulate a diesel generator"""
 
     def __init__(self, parameters):
         self.current_output = None
@@ -37,7 +37,7 @@ class DG:
 
 
 class Battery:
-    """simulate a simple battery"""
+    """simulate a battery"""
 
     def __init__(self, parameters):
         self.current_capacity = None
@@ -71,6 +71,7 @@ class Battery:
 
 
 class Solar:
+    """simulate a solar panel"""
     def __init__(self, parameters):
         self.current_power = None
         self.base_voltage = parameters['V_b']
@@ -106,6 +107,7 @@ class Solar:
 
 
 class Wind:
+    """simulate a wind turbine"""
     def __init__(self, parameters):
         self.current_power = None
         self.cutin_speed = parameters['cutin_speed']
@@ -137,6 +139,7 @@ class Wind:
 
 
 class Grid:
+    """simulate a grid"""
     def __init__(self):
         self.on = True
         self.delta = 1
@@ -158,15 +161,3 @@ class Grid:
         # current_day_price = self.price[24 * self.day:24 * self.day + self.time]
         # result.extend(current_day_price)
         return result
-
-    # 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]
-    #     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

plotDRL.py

@@ -17,6 +17,7 @@ def plot_optimization_result(datasource, directory):  # data source is dataframe
     plt.subplots_adjust(wspace=0.7, hspace=0.3)
     plt.autoscale(tight=True)
     T = np.array([i for i in range(24)])
+
     # plot step cost in ax[0]
     axs[0, 0].cla()
     axs[0, 0].set_ylabel('Costs')

tools.py

@@ -116,13 +116,11 @@ class Arguments:
     """revise here for our own purpose"""
 
     def __init__(self, agent=None, env=None):
-        self.agent = agent  # Deep Reinforcement Learning algorithm
-        self.env = env  # the environment for training
-        # self.plot_shadow_on = False  # control do we need to plot all shadow figures
-        self.cwd = None  # current work directory. None means set automatically
+        self.agent = agent
+        self.env = env
+        self.cwd = None  # current work directory, None means set automatically
         self.if_remove = False  # remove the cwd folder? (True, False, None:ask me)
         self.visible_gpu = '0,1,2,3'  # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
-        # self.worker_num = 2  # rollout workers number pre GPU (adjust it to get high GPU usage)
         self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
 
         '''Arguments for training'''
@@ -171,7 +169,7 @@ class Arguments:
         torch.set_num_threads(self.num_threads)
         torch.set_default_dtype(torch.float32)
 
-        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)  # control how many GPU is used 　
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)
 
 
 def test_one_episode(env, act, device):