def run_step(self, actions=None):
""" 按照给定actions运行一步。
:param actions: str. “random"，随机初始化，仅用于测试；
or dict. 动作集合，由load_action函数执行动作。
:return: (np.array, float, bool). 状态量、回报值、结束标志。
"""
self.step += 1
path = self.get_ep_path(step=self.step)
if actions == 'random': # just for test
distribute_generators_p(self.power.data['generator'], 1., sigma=0.1)
distribute_loads_p(self.power.data['load'], 1., p_sigma=0.1,
keep_factor=True, factor_sigma=0.1)
elif actions is not None:
self.load_action(actions)
shutil.rmtree(path, ignore_errors=True)
self.power.save_power(path, self.fmt, lf=True, lp=False, st=True)
shutil.copy(os.path.join(self.base_path, 'LF.L0'), path)
shutil.copy(os.path.join(self.base_path, 'ST.S0'), path)
call_wmlf(path)
if check_lfcal(path):
self.power.drop_data(self.fmt, 'lp')
self.power.load_power(path, self.fmt, lf=False, lp=True, st=False)
self.power.data['generator']['p0'] = self.power.data['generator']['p']
if self.step == 0:
self.load_init_info()
state = self.get_state()
if os.name != 'nt':
call_psa(path)
assess, done, _ = self.make_assessment(path, method='min', min_n=3)
else:
assess = np.random.rand()
done = (assess < 0.1)
else:
state = []
assess = PF_NOTCONV_REWARD
done = True
self.assessments.append(assess)
if self.step == 0:
reward = 0.
else:
reward = self.assessments[-1] - self.assessments[-2] + STEP_REWARD
# reward = assess
if not done:
# reward *= CCT_CHANGE_RATIO
loads = self.power.data['load']
load_p = np.sum(loads.loc[loads['mark'] == 1, 'p0'])
if abs(load_p - self.init_load_p) / self.init_load_p >= LOAD_CHANGE_THR:
reward += PF_LOADFULL_REWARD
done = True
else:
reward = assess
return state, reward, done
def run_tau(ppo, env, policy_d='ppo', policy_g='ppo', greedy=False,
lock_d=False, lock_g=False, def_actions_d=None):
tau = []
step = min(len(def_actions_d), TAU_LEN) if def_actions_d else TAU_LEN
for i in range(step):
# loads = env.power.data['load']
# generators = env.power.data['generator']
# L step
act_d = None
state = env.get_state()
if not lock_d:
valid = (env.power.data['load']['mark'] == 1)
load_p0 = np.sum(env.power.data['load'].loc[valid, 'p0'])
if policy_d == 'ppo':
act_d = ppo.choose_action(state, 'd', greedy=greedy)
elif policy_d == 'def':
act_d = def_actions_d[i]
env.load_action({'load_ratio_p': act_d})
if policy_g != 'nop':
delta = np.sum(env.power.data['load'].loc[valid, 'p0']) - load_p0
distribute_generators_p(env.power.data['generator'], delta)
next_state, r, done = env.run_step()
tau.append([state, ppo.get_v(state).numpy(), act_d, r, 0.])
if done:
break
# G step
state = next_state
act_g = None
if not lock_g:
valid = (env.power.data['generator']['mark'] == 1)
gen_p0 = np.sum(env.power.data['generator'].loc[valid, 'p0'])
if policy_g == 'ppo':
act_g = ppo.choose_action(state, 'g', greedy=greedy)
env.load_action({'generator_ratio_p': act_g})
delta = np.sum(env.power.data['generator'].loc[valid, 'p0']) - gen_p0
distribute_generators_p(env.power.data['generator'], -delta)
next_state, r, done = env.run_step()
tau.append([state, ppo.get_v(state).numpy(), act_g, r, 0.])
if done:
break
v_s_ = ppo.get_v(next_state.astype(np.float32)).numpy() if not done else 0.
for i in reversed(range(len(tau))):
v_s_ = tau[i][3] + GAMMA * v_s_
tau[i][4] = v_s_
mem_d = [tau[i] for i in range(len(tau)) if i % 2 == 0]
mem_g = [tau[i] for i in range(len(tau)) if i % 2 == 1]
return mem_d, mem_g
def random_generate(base_path, fmt, size, out_path,
min_p=None, max_p=None, gl_ratio=0.9,
random_q0=True, random_open=False, open_prob=[0.8]):
power = Power(fmt=fmt)
power.load_power(base_path, fmt=fmt)
generators_bak = power.data['generator'].copy()
loads_bak = power.data['load'].copy()
if random_open:
aclines_bak = power.data['acline'].copy()
min_p = np.sum(generators_bak['pmin']) if not min_p else min_p
max_p = np.sum(generators_bak['pmax']) if not max_p else max_p
p0 = np.sum(generators_bak['p0'])
shutil.rmtree(out_path, ignore_errors=True)
os.mkdir(out_path)
conv_count = 0
for i in range(size):
generators = power.data['generator'] = generators_bak.copy()
loads = power.data['load'] = loads_bak.copy()
if random_open:
power.data['acline'] = aclines_bak.copy()
p = min_p + (max_p - min_p) * np.random.rand()
distribute_generators_p(generators, p - p0, sigma=0.2)
gen_p = np.sum(generators['p0'])
load_p = np.sum(loads['p0'])
distribute_loads_p(loads, gl_ratio * gen_p - load_p,
p_sigma=0.2, keep_factor=False)
if random_q0:
random_load_q0(loads, sigma=None)
if random_open:
open_num = np.sum(np.random.rand(1) > open_prob)
random_open_acline(power, num=open_num)
path = os.path.join(out_path, '%08d' % i)
power.save_power(path, fmt, lf=True, lp=False, st=True)
shutil.copy(os.path.join(base_path, 'LF.L0'), path)
shutil.copy(os.path.join(base_path, 'ST.S0'), path)
call_wmlf(path)
if check_lfcal(path):
conv_count += 1
print('Random generate done: %d / %d' % (conv_count, size))
def run_episode(ppo,
env,
policy_d='ppo',
policy_g='ppo',
greedy=False,
def_actions_d=None):
states_d, states_g, actions_d, actions_g, rewards = [], [], [], [], []
step = min(len(def_actions_d), EP_LEN) if def_actions_d else EP_LEN
for i in range(step):
loads = env.power.data['load']
generators = env.power.data['generator']
s_d = env.get_state()
load_p = np.sum(loads.loc[loads['mark'] == 1, 'p0'])
if policy_d == 'ppo':
act_d = ppo.choose_action(s_d, 'd', greedy=greedy)
actions_d.append(act_d)
env.load_action({'load_ratio_p': act_d})
elif policy_d == 'def':
env.load_action({'load_ratio_p': def_actions_d[i]})
s_g = env.get_state()
delta = np.sum(loads.loc[loads['mark'] == 1, 'p0']) - load_p
if policy_g == 'ppo':
act_g = ppo.choose_action(s_g, 'g', greedy=greedy)
actions_g.append(act_g)
act_g = {'generator_ratio_p': act_g}
else:
if policy_g == 'dis':
distribute_generators_p(generators, delta)
act_g = None
s_, r, done = env.run_step(act_g)
states_d.append(s_d)
states_g.append(s_g)
rewards.append(r)
if done:
break
return done, s_, states_d, states_g, actions_d, actions_g, rewards
def reset(self, random=True, load_path=None, error='raise'):
""" 重置潮流，并进行评估。
:param random: bool. 是否随机初始化潮流。
:param load_path: str. 初始断面目录；
or None. 用self.base_path作为初始断面。
:param errpr: str. 初始化失败则raise exception.
:return: bool. 是否重置成果(not done)
"""
load_path = self.base_path if load_path is None else load_path
self.episode += 1
self.min_max = None
self.init_load_p = 0.
path = self.get_ep_path()
shutil.rmtree(path, ignore_errors=True)
os.mkdir(path)
self.step = -1
self.assessments = []
self.power.load_power(load_path, fmt=self.fmt)
if random:
generators = self.power.data['generator']
loads = self.power.data['load']
generators['p0'] = generators['p']
# gl_rate = np.sum(generators['p']) / np.sum(loads['p'])
max_p, p0 = np.sum(generators[['pmax', 'p0']])
p = max_p * (0.4 + 0.5 * np.random.rand()) # 40% ~ 90%
distribute_generators_p(generators, p - p0, sigma=0.2)
# dp = np.sum(generators['p0']) - p0
gen_p = np.sum(generators['p0'])
load_p = np.sum(loads['p0'])
distribute_loads_p(loads, 0.9 * gen_p - load_p, p_sigma=0.1, keep_factor=False)
# distribute_loads_p(loads, dp / gl_rate, p_sigma=0.1, keep_factor=False)
random_load_q0(loads, sigma=None)
self.state0, _, done = self.run_step()
if done and error == 'raise':
raise ValueError
return not done
def reset(self, random=True, load_path=None):
""" 重置潮流，并进行评估。
:param random: bool. 是否随机初始化潮流。
:param load_path: str. 初始断面目录；
or None. 用self.base_path作为初始断面。
:return: bool. 是否重置成果(not done)
"""
if load_path is None:
self.power.load_power(self.base_path, fmt=self.fmt)
else:
self.power.load_power(load_path, fmt=self.fmt)
self.power.data['generator']['p0'] = self.power.data['generator']['p']
self.episode += 1
path = self.get_ep_path()
if os.path.exists(path):
shutil.rmtree(path)
os.mkdir(path)
self.step = -1
self.assessments = []
if random:
generators = self.power.data['generator']
loads = self.power.data['load']
max_p, gen_p = np.sum(generators[['pmax', 'p']])
p = max_p * 0.4 + max_p * 0.5 * np.random.rand() # 40% ~ 90%
distribute_generators_p(generators, p - gen_p, sigma=0.2)
generators['p0'] = np.clip(generators['p0'],
generators['pmin'], generators['pmax'])
gen_p = np.sum(generators['p0'])
load_p = np.sum(loads['p'])
distribute_loads_p(loads, 0.9 * gen_p - load_p, p_sigma=0.1, keep_factor=False)
random_load_q0(loads, sigma=None)
self.min_max = None
self.init_load_p = 0.
self.state0, _, done = self.run_step()
return not done