From c7af3daa45ea53de8532752c9cfd60e2d497b35a Mon Sep 17 00:00:00 2001 From: Abderrahman AIT SAID Date: Fri, 24 Jul 2026 13:59:04 +0000 Subject: [PATCH 01/10] removed unused simulator from backend --- .../CONFIG_POWERGRID.toml | 11 - .../XD_silly_repo/submission/__init__.py | 1 - .../XD_silly_repo/submission/combos.txt | 12696 ---------------- .../XD_silly_repo/submission/my_agent.py | 825 - .../submission/redispatch_action.pkl | Bin 166260 -> 0 bytes .../submission/unitary_action.npz | Bin 6196 -> 0 bytes .../submission/unitary_action_network.npz | Bin 5790064 -> 0 bytes .../submission/unitary_action_scalar.npz | Bin 2388 -> 0 bytes .../__init__.py | 0 .../assistant_manager.py | 382 - .../alerts_info.json | 15 - .../apr_42_1/_seeds_info_chronix2grid.json | 1 - .../chronics/apr_42_1/load_p.csv.bz2 | Bin 98592 -> 0 bytes .../apr_42_1/load_p_forecasted.csv.bz2 | Bin 91645 -> 0 bytes .../chronics/apr_42_1/load_q.csv.bz2 | Bin 80378 -> 0 bytes .../apr_42_1/load_q_forecasted.csv.bz2 | Bin 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backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/chronics/split_and_save_meta_params_1.json delete mode 100755 backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/chronics/split_and_save_meta_params_2.json delete mode 100755 backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/config.py delete mode 100755 backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/difficulty_levels.json delete mode 100755 backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/grid.json delete mode 100755 backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/grid_layout.json delete mode 100755 backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/env_icaps_input_data_test/prods_charac.csv diff --git a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/CONFIG_POWERGRID.toml b/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/CONFIG_POWERGRID.toml deleted file mode 100644 index d1b53993..00000000 --- a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/CONFIG_POWERGRID.toml +++ /dev/null @@ -1,11 +0,0 @@ -# Fichier de configuration du simulateur -titre = "Simulator configuration" - -env_name = "env_icaps_input_data_test" -env_seed = 2118338672 -scenario_name = 'jan_28_1' # the scenario to play from env_name repository - -assistant_name = "XD_silly_repo" -assistant_seed = 1227139268 - - \ No newline at end of file diff --git a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/__init__.py b/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/__init__.py deleted file mode 100644 index 41df1172..00000000 --- a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .my_agent import make_agent diff --git a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/combos.txt b/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/combos.txt deleted file mode 100644 index d1793fb1..00000000 --- a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/combos.txt +++ /dev/null @@ -1,12696 +0,0 @@ -0 5 19 -0 5 110 -0 5 118 -0 23 184 -0 25 19 -0 25 110 -0 25 118 -0 36 71 -0 41 90 -0 54 19 -0 54 110 -0 54 118 -0 71 36 -0 71 52 -0 71 81 -0 71 107 -0 71 109 -0 71 120 -0 71 140 -0 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b/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/my_agent.py deleted file mode 100644 index 15bed842..00000000 --- a/backend/recommendation-service/resources/PowerGrid/PowerGridgrid2op_poc_simulator/XD_silly_repo/submission/my_agent.py +++ /dev/null @@ -1,825 +0,0 @@ -import _pickle as cPickle -import copy -import functools -import os -from copy import deepcopy -from typing import Tuple, List - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from grid2op.Agent import BaseAgent -from loguru import logger - - -class unitary_action_network(nn.Module): - def __init__(self, params_dict, scalar_dict): - super(unitary_action_network, self).__init__() - - mean = scalar_dict["mean"] - std = scalar_dict["std"] - - self.zero_std = np.where(std == 0.0)[0] - self.mean = np.array([mean[i] for i in range(len(mean)) if i not in self.zero_std]) - self.std = np.array([std[i] for i in range(len(std)) if i not in self.zero_std]) - - self.month_embedding = nn.Embedding(12, 64, _weight=torch.Tensor(params_dict['emb_month.w_0'])) - self.hour_embedding = nn.Embedding(24, 64, _weight=torch.Tensor(params_dict['emb_hour.w_0'])) - - self.fc1 = nn.Linear(267, 512) - self.fc1.weight.data = torch.Tensor(params_dict['fc1.w_0']).T - self.fc1.bias.data = torch.Tensor(params_dict['fc1.b_0']) - - self.fc2 = nn.Linear(512, 512) - self.fc2.weight.data = torch.Tensor(params_dict['fc2.w_0']).T - self.fc2.bias.data = torch.Tensor(params_dict['fc2.b_0']) - - self.fc3 = nn.Linear(512, 512) - self.fc3.weight.data = torch.Tensor(params_dict['fc3.w_0']).T - self.fc3.bias.data = torch.Tensor(params_dict['fc3.b_0']) - - self.fc4 = nn.Linear(512, 512) - self.fc4.weight.data = torch.Tensor(params_dict['fc4.w_0']).T - self.fc4.bias.data = torch.Tensor(params_dict['fc4.b_0']) - - self.fc5 = nn.Linear(512, 512) - self.fc5.weight.data = torch.Tensor(params_dict['fc5.w_0']).T - self.fc5.bias.data = torch.Tensor(params_dict['fc5.b_0']) - - self.fc6 = nn.Linear(512, 500) - self.fc6.weight.data = torch.Tensor(params_dict['fc6.w_0']).T - self.fc6.bias.data = torch.Tensor(params_dict['fc6.b_0']) - - def feature_process(self, raw_obs): - obs = raw_obs.to_dict() - - loads = [] - for key in ['q', 'v']: - loads.append(obs['loads'][key]) - loads = np.concatenate(loads) - - prods = [] - for key in ['q', 'v']: - prods.append(obs['prods'][key]) - prods = np.concatenate(prods) - - features = np.concatenate([loads, prods]) - features = np.array([features[i] for i in range(len(features)) if i not in self.zero_std]) - norm_features = (features - self.mean) / self.std - - rho = obs['rho'] - - time_info = np.array([raw_obs.month - 1, raw_obs.hour_of_day]) - - return np.concatenate([norm_features, rho, time_info]).tolist() - - def forward(self, raw_obs): - - obs_vec = np.array(self.feature_process(raw_obs)).reshape([1, -1]) - dense_input = torch.tensor(obs_vec[:, :-2], dtype=torch.float32) - month = torch.tensor(obs_vec[:, -2], dtype=torch.int32) - hour = torch.tensor(obs_vec[:, -1], dtype=torch.int32) - - month_emb = self.month_embedding(month) - hour_emb = self.hour_embedding(hour) - obs_emb = torch.cat([dense_input, month_emb, hour_emb], axis=1) - - x = F.relu(self.fc1(obs_emb)) - x = F.relu(self.fc2(x)) - x = F.relu(self.fc3(x)) - x = F.relu(self.fc4(x)) - x = F.relu(self.fc5(x)) - predicts = self.fc6(x) - - return predicts.detach().cpu().numpy() - - -class PowerNetAgent(BaseAgent): - def __init__(self, env, submission_path, n_actions=1, take_action_index=0): - BaseAgent.__init__(self, action_space=env.action_space) - # global action - self.sub_topo_dict = {} - self.simulate_times = 0 - self.alive_step = 0 - self.action_space = env.action_space - self.n_actions = n_actions - self.take_action_index = take_action_index - logger.info(f"Pooling {n_actions} actions from our Reinforcement core") - # construct action_to_sub_topo - offset = 59 - self.action_to_sub_topo = {} - for sub_id, sub_elem_num in enumerate(self.action_space.sub_info): - self.action_to_sub_topo[sub_id] = (offset, offset + sub_elem_num) - offset += sub_elem_num - - # ============================= load baidu unitary action 500 ======================= - unitary_actions_vec = np.load(os.path.join(submission_path, "unitary_action.npz"), 'r')["actions"] - self.unitary_actions = [self.action_space.from_vect(action) for action in list(unitary_actions_vec)] - - params_dict = np.load(os.path.join(submission_path, 'unitary_action_network.npz')) - scalar_dict = np.load(os.path.join(submission_path, 'unitary_action_scalar.npz')) - self.unitary_es_agent = unitary_action_network(params_dict, scalar_dict) - - # construct action_to_sub_id - self.action_to_sub_id = {} - for act_idx, action in enumerate(self.unitary_actions): - act_dict = action.impact_on_objects() - if act_dict["redispatch"]['changed']: - self.action_to_sub_id[act_idx] = "redispatch" - elif act_dict["topology"]['changed']: - self.action_to_sub_id[act_idx] = act_dict["topology"]["assigned_bus"][0]['substation'] - - # ============================= load alarm action ======================= - self.alarms_lines_area = env.alarms_lines_area - self.alarms_area_names = env.alarms_area_names - self.alarm_overflow_flag = False - self.alarm_cool_down = False - self.alarm_cool_time = 3 # needed to be finetuned - self.alarm_count = 0 - - # ============================= load redispatch action 80 ======================= - self.redispatch_cnt = 0 - self.max_redispatch_cnt = 3 # needed to be finetuned - - act_matrix = cPickle.load(open(os.path.join(submission_path, 'redispatch_action.pkl'), 'rb'))['act_matrix'] - self.redispatch_actions = [self.action_space.from_vect(act_matrix[i, :]) for i in range(len(act_matrix))] - - # def act_without_alarm(self, observation): - # return recommandation(observation, n_actions=1) - def act_without_alarm(self, observation): # recommandation(self, observation, n_actions) - # if there is a disconnected line, then try to reconnect it - action = self.reconnect_action(observation) - if action is not None: - return action - - # if there is no disconnected line, try to reset topology and redispatch - if np.all(observation.topo_vect != -1): - self.redispatch_cnt = 0 - - self.sub_topo_dict = self.calc_sub_topo_dict(observation) - # try to reset the topology to the orginal state - action = self.reset_topology(observation) - if action is not None: - return action - - # # try to reset the redispatch to the orginal state - # action = self.reset_redispatch(observation) - # if action is not None: - # return action - - # if there is a overflow line, then try to fix it - if np.any(observation.rho > 1.0): - # if (observation.line_status[45] == False or observation.line_status[56] == False): - # action, least_overflow_1 = self.unitary_actions_simulate_seq(observation) - # else: - # action, least_overflow_1 = self.unitary_actions_simulate(observation) - if self.n_actions == 1: - action, least_overflow_1 = self.build_unitary_actions_with_seq_simulation(observation) - else: - actions = self.make_recommandations(observation, n_actions=self.n_actions) - # Simulate everything - logger.debug(f'== Pooled {len(actions)} actions from our Reinforcment core ==') - if got_redispatching([a[0] for a in actions]): - logger.debug('Got redispatching !') - for a, max_rho in actions: - logger.debug(f"Max rho (simulated): {max_rho}") - logger.debug(a) - idx = self.take_action_index if self.take_action_index < len(actions) else len(actions)-1 - action, least_overflow_1 = actions[idx] # Get first action, theoretically the most efficient - # if the action has not solved the overflow - if least_overflow_1 > 1.0: - self.alarm_overflow_flag = True - else: - action = self.action_space({}) - # If lines 45 or 56 are disconnected, add redispatching to the action - if (observation.line_status[45] == False or observation.line_status[56] == False): - - if action != self.action_space({}) \ - and self.redispatch_cnt < self.max_redispatch_cnt \ - and action.impact_on_objects()['topology']['changed']: - action, least_overflow_2 = self.add_redispatching_to_action_if_possible(observation, action) - - if observation.attention_budget[0] >= 2: - self.alarm_overflow_flag = True - - return action - - def act(self, observation, reward, done): - # if not observation.month == 12: - # return self.action_space({}) - - self.alive_step += 1 - self.alarm_overflow_flag = False - - if not self.alarm_cool_down: - self.alarm_count += 1 - if self.alarm_count >= self.alarm_cool_time: - self.alarm_cool_down = True - self.alarm_count = 0 - - action = self.act_without_alarm(observation) - - if np.any(observation.rho > 1.0) or np.any(observation.line_status == False): - - if observation.attention_budget[0] >= 2: - self.alarm_overflow_flag = True - - if self.alarm_overflow_flag and not observation.is_alarm_illegal and self.alarm_cool_down: - zones_alert = self.get_region_alert(observation) - action.raise_alarm = zones_alert - self.alarm_cool_down = False - self.alarm_count = 0 - - # if this action will cause game over, the try disconnected action - sim_obs, sim_reward, sim_done, sim_inf = observation.simulate(action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - - if sim_done: - for dis_line in range(59): # from the left to the right - try: - dis_action = self.action_space.disconnect_powerline(dis_line) - dis_obs, dis_reward, dis_done, dis_inf = observation.simulate(dis_action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - - if not dis_done: - return dis_action - - except BaseException: - print('disconnect_action error') - continue - - return action - - def calc_sub_topo_dict(self, observation): - offset = 0 - sub_topo_dict = {} - - for sub_id, sub_elem_num in enumerate(observation.sub_info): - sub_topo = observation.topo_vect[offset:offset + sub_elem_num] - offset += sub_elem_num - sub_topo_dict[sub_id] = sub_topo - - return sub_topo_dict - - def reconnect_action(self, observation): - # reconnect the line if the line will not cause overflow - # return a reconnect action or None - disconnected_lines = np.where(observation.line_status == False)[0].tolist() - - for line_id in disconnected_lines: # from the left to the right - if observation.time_before_cooldown_line[line_id] == 0: - action = self.action_space({"set_line_status": [(line_id, +1)]}) - - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - if np.max(observation.rho) < 1.0 and np.max(obs_simulate.rho) >= 1.0: - continue - - except BaseException: - print('reconnect_action error') - continue - - finally: - self.simulate_times += 1 - - return action - - return None - - def reset_topology(self, observation): - # if there is no overflow then try to reset the topology - if np.max(observation.rho) < 0.95: - - for sub_id, sub_elem_num in enumerate(observation.sub_info): - sub_topo = self.sub_topo_dict[sub_id] - - if sub_id == 28: - sub_28_topo = np.array([2.0, 1.0, 2.0, 1.0, 1.0]).astype(np.int32) - - if not np.all(sub_topo.astype(np.int32) == sub_28_topo.astype(np.int32)) \ - and observation.time_before_cooldown_sub[sub_id] == 0: - - act = self.action_space({ - "set_bus": { - "substations_id": [(sub_id, sub_28_topo.astype(np.int32).tolist())] - } - }) - - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(act, time_step = 0) - #observation._obs_env._reset_to_orig_state() - if info_simulate['is_illegal'] or info_simulate['is_ambiguous']: - return None - if not done_simulate and obs_simulate is not None and not any(np.isnan(obs_simulate.rho)): - if np.max(obs_simulate.rho) < 0.95: - return act - - except BaseException: - print('reset_topology error') - continue - - finally: - self.simulate_times += 1 - - continue - - if np.any(sub_topo == 2) and observation.time_before_cooldown_sub[sub_id] == 0: - sub_topo = np.where(sub_topo == 2, 1, sub_topo) # bus 2 to bus 1 - sub_topo = np.where(sub_topo == -1, 0, sub_topo) # don't do action in bus=-1 - reconfig_sub = self.action_space({ - "set_bus": { - "substations_id": [(sub_id, sub_topo.astype(np.int32).tolist())] - } - }) - - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(reconfig_sub, time_step = 0) - #observation._obs_env._reset_to_orig_state() - if info_simulate['is_illegal'] or info_simulate['is_ambiguous']: - return None - if not done_simulate: - if not np.any(obs_simulate.topo_vect != observation.topo_vect): # have some impact - return None - if not done_simulate and obs_simulate is not None and not any(np.isnan(obs_simulate.rho)): - if np.max(obs_simulate.rho) < 0.95: - return reconfig_sub - - except BaseException: - print('reset_topology error') - return None - - finally: - self.simulate_times += 1 - - elif np.max(observation.rho) >= 1.0: - sub_id = 28 - sub_topo = self.sub_topo_dict[sub_id] - if np.any(sub_topo == 2) and observation.time_before_cooldown_sub[sub_id] == 0: - sub_28_topo = np.array([1.0, 1.0, 1.0, 1.0, 1.0]).astype(int) - act = self.action_space({ - "set_bus": { - "substations_id": [(sub_id, sub_28_topo.astype(int))] - } - }) - - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(act, time_step = 0) - #observation._obs_env._reset_to_orig_state() - if info_simulate['is_illegal'] or info_simulate['is_ambiguous']: - return None - if not done_simulate and obs_simulate is not None and not any(np.isnan(obs_simulate.rho)): - if np.max(obs_simulate.rho) < 0.99: - return act - - except BaseException: - print('reset_topology error') - return None - - return None - - def reset_redispatch(self, observation): - # if there is no overflow then try to reset the redispatch - if np.max(observation.rho) < 1.0: - # reset redispatch - if not np.all(observation.target_dispatch == 0.0): - gen_ids = np.where(observation.gen_redispatchable)[0] - gen_ramp = observation.gen_max_ramp_up[gen_ids] - changed_idx = np.where(observation.target_dispatch[gen_ids] != 0.0)[0] - redispatchs = [] - for idx in changed_idx: - target_value = observation.target_dispatch[gen_ids][idx] - value = min(abs(target_value), gen_ramp[idx]) - value = -1 * target_value / abs(target_value) * value - redispatchs.append((gen_ids[idx], value)) - action = self.action_space({"redispatch": redispatchs}) - - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - if info_simulate['is_illegal'] or info_simulate['is_ambiguous']: - return None - if not done_simulate and obs_simulate is not None and not any(np.isnan(obs_simulate.rho)): - if np.max(obs_simulate.rho) < 1.0: - return action - - except BaseException: - print('reset_redispatch error') - return None - - finally: - self.simulate_times += 1 - - def get_action_from_index(self, observation, idx): - # this function is used to get a legal action from the action space - action = self.unitary_actions[idx] - sub_id = self.action_to_sub_id[idx] - if sub_id != "redispatch": # topology change action - if observation.time_before_cooldown_sub[int(sub_id)] != 0: - return None - - legal_action_vec = np.array(action.to_vect()).copy() - sub_topo = self.sub_topo_dict[int(sub_id)] - - if np.any(sub_topo == -1): # line disconnected - start, end = self.action_to_sub_topo[int(sub_id)] - action_topo = legal_action_vec[start:end].astype(np.int32) # reference - action_topo[np.where(sub_topo == -1)[0]] = 0 # done't change bus=-1 - legal_action_vec[start:end] = action_topo - legal_action = self.action_space.from_vect(legal_action_vec) - - else: - legal_action = action - - else: - legal_action = action - - return legal_action - - def make_simulatable_obs(self, new_obs, old_obs): - # make the simulated obs simulatable - new_obs.action_helper = old_obs.action_helper - new_obs._obs_env = old_obs._obs_env - - # direct copy - new_obs._forecasted_inj = old_obs._forecasted_inj - - # if len(new_obs._forecasted_inj) > 2: - # new_obs._forecasted_inj = new_obs._forecasted_inj[1:] - - # linear extrapolation of the injection - # new_injection = {k : 2 * old_obs._forecasted_inj[1][1]['injection'][k] - old_obs._forecasted_inj[0][1]['injection'][k] for k in old_obs._forecasted_inj[1][1]['injection']} - # new_datetime = old_obs._forecasted_inj[1][0] + datetime.timedelta(minutes=5) - # new_obs._forecasted_inj = [old_obs._forecasted_inj[1], (new_datetime, {'injection' : new_injection})] - - return new_obs - - def build_unitary_actions_with_seq_simulation(self, observation): - # get a baseline by do nothing - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(self.action_space({}), time_step = 0) - #observation._obs_env._reset_to_orig_state() - - least_overflow = 2.0 - if obs_simulate is not None and not any(np.isnan(obs_simulate.rho)): - least_overflow = float(np.max(obs_simulate.rho)) - - except BaseException: - least_overflow = 2.0 - - finally: - self.simulate_times += 1 - - if least_overflow < 1.0: - return self.action_space({}), least_overflow - - # candidate in format (max_rho, action_sequence, simulatable_obs, expanded_flag) - line_overflow = observation.rho >= 1.0 - candidates = [(observation.rho.max(), [], observation, False)] - - def compare(a, b): - penalty_factor = 0.1 - - a_score = a[0] + penalty_factor * len(a[1]) - b_score = b[0] + penalty_factor * len(b[1]) - - a_solve = a[0] < 1.0 - b_solve = b[0] < 1.0 - - if not a_solve == b_solve: - return -1 if a_solve else 1 - - a_fix_line = np.all(np.logical_or(a[2].rho >= 1.0, line_overflow)) - b_fix_line = np.all(np.logical_or(b[2].rho >= 1.0, line_overflow)) - - if a_fix_line == b_fix_line: - if a_score == b_score: - return 0 - else: - return -1 if a_score < b_score else 1 - else: - return -1 if a_fix_line else 1 - - planning_horizon = 4 - observation.timestep_overflow.max() - for depth in range(planning_horizon): - new_candidates = [] - expand_num = 150 if depth == 0 else 50 - select_num = 50 if depth == 0 else 50 - - for candidate in candidates: - current_max_rho, current_actions, simulatable_obs, expanded_flag = candidate - - if current_max_rho < 1.0: # already in control, do not simulate further - new_candidates.append(candidate) - continue - - if expanded_flag == True: # the candidate is already expanded - new_candidates.append(candidate) - continue - else: - new_candidates.append((current_max_rho, current_actions, simulatable_obs, True)) # mark as expanded - - predicted_rho = self.unitary_es_agent(simulatable_obs)[0, :] # 500 - sorted_idx = np.argsort(predicted_rho).tolist() - top_idx = sorted_idx[:expand_num] - top_idx.sort() - - for idx in top_idx: - legal_action = self.get_action_from_index(simulatable_obs, idx) - if legal_action is None: continue - if legal_action in current_actions: continue - - obs_simulate, reward_simulate, done_simulate, info_simulate = simulatable_obs.simulate(legal_action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - - if info_simulate['is_illegal'] or info_simulate['is_ambiguous']: - continue - - if obs_simulate is not None and not any(np.isnan(obs_simulate.rho)) and not done_simulate: - overflow_value = float(np.max(obs_simulate.rho)) - action_sequence = deepcopy(current_actions) - action_sequence.append(legal_action) - new_candidates.append((overflow_value, action_sequence, - self.make_simulatable_obs(obs_simulate, simulatable_obs), False)) - - self.simulate_times += 1 - - # new_candidates = sorted(new_candidates, key=lambda x: x[0]) - new_candidates = sorted(new_candidates, key=functools.cmp_to_key(compare)) - candidates = new_candidates[:select_num] # only consider the top 30 - - if candidates[0][0] < 1.0: break - - if len(candidates) > 0: - best_candidate = candidates[0] - - if len(best_candidate[1]) == 0: - return self.action_space({}), least_overflow - - if best_candidate[0] >= least_overflow: - return self.action_space({}), least_overflow - - best_action = best_candidate[1][0] - simulated_obs, _, _, _ = observation.simulate(best_action, time_step = 0) - rho = simulated_obs.rho.max() - - return best_action, rho - else: - return self.action_space({}), least_overflow - - def make_recommandations(self, observation, n_actions=3) -> List[Tuple]: - """ - This function builds operational plans by ensuring a sufficient diversity of actions is retained. - It will try to provide amongst recommandations at least one redispatch action, alongside with - topological changes on distinct substations. - It is NOT designed to provide the `n_recommandations` best actions, but to provide human player with - interesting action choices. - :param observation: A Grid2oOp.Observation - :param n_actions: Number of actions to return - :return: A list of Grid2Op.Action with their simulated max_rho - """ - - try: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(self.action_space({}), time_step = 1) - #observation._obs_env._reset_to_orig_state() - - least_overflow = 2.0 - if obs_simulate is not None and not any(np.isnan(obs_simulate.rho)): - least_overflow = float(np.max(obs_simulate.rho)) - - except BaseException as e: - least_overflow = 2.0 - - - finally: - self.simulate_times += 1 - # No overflow: do nothing! - if least_overflow < 1.0: - return [(self.action_space({}), least_overflow)] - - self.sub_topo_dict = self.calc_sub_topo_dict(observation) - - # Candidate in format (max_rho, action_sequence, simulatable_obs, expanded_flag) - line_overflow = observation.rho >= 1.0 - candidates = [(observation.rho.max(), [], observation, False)] - - def compare(a, b): - penalty_factor = 0.1 - - a_score = a[0] + penalty_factor * len(a[1]) - b_score = b[0] + penalty_factor * len(b[1]) - - a_solve = a[0] < 1.0 - b_solve = b[0] < 1.0 - - if not a_solve == b_solve: - return -1 if a_solve else 1 - - a_fix_line = np.all(np.logical_or(a[2].rho >= 1.0, line_overflow)) - b_fix_line = np.all(np.logical_or(b[2].rho >= 1.0, line_overflow)) - - if a_fix_line == b_fix_line: - if a_score == b_score: - return 0 - else: - return -1 if a_score < b_score else 1 - else: - return -1 if a_fix_line else 1 - - planning_horizon = 4 - observation.timestep_overflow.max() - - # We'll build candidate action sequences up to planning_horizon depth - for depth in range(planning_horizon): - new_candidates = [] - expand_num = 150 if depth == 0 else 50 - select_num = 50 if depth == 0 else 50 - - for candidate in candidates: - # Get candidate (max_rho, action_sequence, simulatable_obs, expanded_flag) - current_max_rho, current_actions, simulatable_obs, expanded_flag = candidate - - if current_max_rho < 1.0: # already in control, do not simulate further - new_candidates.append(candidate) - continue - - if expanded_flag == True: # the candidate is already expanded - new_candidates.append(candidate) - continue - else: - new_candidates.append((current_max_rho, current_actions, simulatable_obs, True)) # mark as expanded - # Get forecasted rhos from our NeuralNet model - predicted_rho = self.unitary_es_agent(simulatable_obs)[0, :] # 500 - sorted_idx = np.argsort(predicted_rho).tolist() - # Keep only expand_num actions - top_idx = sorted_idx[:expand_num] - top_idx.sort() # WHY ?? - - # For action in top 50 to 150 actions - for idx in top_idx: - legal_action = self.get_action_from_index(simulatable_obs, idx) - if legal_action is None: continue - if legal_action in current_actions: continue - - obs_simulate, reward_simulate, done_simulate, info_simulate = simulatable_obs.simulate(legal_action, time_step = 1) - #observation._obs_env._reset_to_orig_state() - - # Drop invalid actions - if info_simulate['is_illegal'] or info_simulate['is_ambiguous']: - continue - # Add action seq to candidates - if obs_simulate is not None and not any(np.isnan(obs_simulate.rho)) and not done_simulate: - overflow_value = float(np.max(obs_simulate.rho)) - action_sequence = deepcopy(current_actions) - action_sequence.append(legal_action) - new_candidates.append((overflow_value, action_sequence, - self.make_simulatable_obs(obs_simulate, simulatable_obs), False)) - - self.simulate_times += 1 - - new_candidates = sorted(new_candidates, - key=functools.cmp_to_key(compare)) # sort action sequences by effectiveness - candidates = new_candidates[:select_num] # only consider the top candidates - - if candidates[0][0] < 1.0: break - - # Extract best candidate from all candidates. - if len(candidates) > 0: - return self.filter_candidates(candidates, observation, n_recommandations=n_actions) - - # No candidate... - else: - return [(self.action_space({}), least_overflow)] - - @staticmethod - def filter_candidates(candidates, observation, n_recommandations): - - # Candidates are sorted by efficiency - sub_ids = [] - actions = [] - best_action_to_add = None - for c in candidates: - # Empty actions ! - if len(c[1]) == 0: - continue - action = c[1][0] - - # Return when we have enough actions - if len(actions) == n_recommandations: - break - - # Try to fill the last free action slot with a redispatching action - if len(actions) == n_recommandations - 1 and not got_redispatching(actions) and not is_redispacthing( - action): - if best_action_to_add is None and get_sub_id_in_action(action) not in sub_ids: - best_action_to_add = action - continue - - # Filling actions with action - else: - # Redispatch - if is_redispacthing(action): - actions.append(action) - # Topological - else: - sub_id = get_sub_id_in_action(action) - # Already got an action on this sub_id - if sub_id in sub_ids: - continue - sub_ids.append(sub_id) - actions.append(action) - # Fill remaining actions with a saved action - if best_action_to_add is None: - if len(candidates[0][1])>0: - best_action_to_add = candidates[0][1][0] - else: - n_recommandations = 0 # pas trouve de recommandation a faire - if len(actions) < n_recommandations: - actions += [best_action_to_add] * (n_recommandations - len(actions)) - - out = [] - for action in actions: - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(action, time_step = 0) - rho = obs_simulate.rho.max() - out.append((action, rho)) - return out - - def add_redispatching_to_action_if_possible(self, observation, action): - """ - Add redispatching to a given action - """ - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - - origin_rho = 10.0 - if not done_simulate: - origin_rho = obs_simulate.rho.max() - - least_rho = origin_rho - best_action = None - for redispatch_action in self.redispatch_actions: - try: - redispatch_action = copy.deepcopy(redispatch_action) - redispatch_action._curtail = np.zeros(22) - action = copy.deepcopy(action) - action._redispatch = np.zeros(22) - - combine_action = self.action_space.from_vect(action.to_vect() + redispatch_action.to_vect()) - - obs_simulate, reward_simulate, done_simulate, info_simulate = observation.simulate(combine_action, time_step = 0) - #observation._obs_env._reset_to_orig_state() - - except BaseException: - print('redispatch_action error') - continue - - max_rho = 10.0 - if not done_simulate: - max_rho = obs_simulate.rho.max() - if max_rho < least_rho: - least_rho = max_rho - best_action = combine_action - - if least_rho < origin_rho: - action = best_action - self.redispatch_cnt += 1 - - return action, least_rho - - def get_region_alert(self, observation): - # extract the zones they belong too - zones_these_lines = set() - zone_for_each_lines = self.alarms_lines_area - - lines_overloaded = np.where(observation.rho >= 1)[0].tolist() # obs.rho>0.6 - # print(lines_overloaded) - for line_id in lines_overloaded: - line_name = observation.name_line[line_id] - for zone_name in zone_for_each_lines[line_name]: - zones_these_lines.add(zone_name) - - zones_these_lines = list(zones_these_lines) - zones_ids_these_lines = [self.alarms_area_names.index(zone) for zone in zones_these_lines] - return zones_ids_these_lines - - -def make_agent(env, submission_path, n_actions=3, take_action_index=2): - # my_agent = MyAgent(env.action_space, env.alarms_lines_area, env.alarms_area_names, submission_path) - my_agent = PowerNetAgent(env, submission_path, n_actions,take_action_index) - return my_agent - - -def is_redispacthing(action): - return action.impact_on_objects()["redispatch"]['changed'] - - -def got_redispatching(actions): - return any(is_redispacthing(a) for a in actions) - - -def get_sub_id_in_action(action): - impact = action.impact_on_objects() - if impact["topology"]['changed']: - return impact["topology"]["assigned_bus"][0]['substation'] - else: - return None diff --git 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