from fleetrl.fleet_env.fleet_environment import FleetEnv
from fleetrl.benchmarking.benchmark import Benchmark
from stable_baselines3.common.vec_env import SubprocVecEnv, VecNormalize
from stable_baselines3.common.env_util import make_vec_env
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
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class Uncontrolled(Benchmark):
def __init__(self,
n_steps: int,
n_evs: int,
n_episodes: int = 1,
n_envs: int = 1,
time_steps_per_hour: int = 4):
self.n_steps = n_steps
self.n_evs = n_evs
self.n_episodes = n_episodes
self.n_envs = n_envs
self.time_steps_per_hour = time_steps_per_hour
self.env_config = None
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def run_benchmark(self,
use_case: str,
env_kwargs: dict,
seed: int = None
) -> pd.DataFrame:
dumb_vec_env = make_vec_env(FleetEnv,
env_kwargs=env_kwargs,
n_envs=self.n_envs,
vec_env_cls=SubprocVecEnv,
seed=seed)
dumb_norm_vec_env = VecNormalize(venv=dumb_vec_env,
norm_obs=True,
norm_reward=True,
training=True,
clip_reward=10.0)
episode_length = self.n_steps
n_episodes = self.n_episodes
dumb_norm_vec_env.reset()
self.env_config = env_kwargs["env_config"]
for i in range(episode_length * self.time_steps_per_hour * n_episodes):
if dumb_norm_vec_env.env_method("is_done")[0]:
dumb_norm_vec_env.reset()
dumb_norm_vec_env.step([np.ones(self.n_evs)])
dumb_log: pd.DataFrame = dumb_norm_vec_env.env_method("get_log")[0]
dumb_log.reset_index(drop=True, inplace=True)
dumb_log = dumb_log.iloc[0:-2]
return dumb_log
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def plot_benchmark(self,
dumb_log: pd.DataFrame,
) -> None:
dumb_log["hour_id"] = (dumb_log["Time"].dt.hour + dumb_log["Time"].dt.minute / 60)
mean_per_hid_dumb = dumb_log.groupby("hour_id").mean()["Charging energy"].reset_index(drop=True)
mean_all_dumb = []
for i in range(mean_per_hid_dumb.__len__()):
mean_all_dumb.append(np.mean(mean_per_hid_dumb[i]))
mean = pd.DataFrame()
mean["Dumb charging"] = np.multiply(mean_all_dumb, 4)
mean.plot()
plt.xticks([0, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88]
, ["00:00", "02:00", "04:00", "06:00", "08:00", "10:00", "12:00", "14:00", "16:00", "18:00", "20:00",
"22:00"],
rotation=45)
plt.legend()
plt.grid(alpha=0.2)
plt.ylabel("Charging power in kW")
price_lookahead = self.env_config["price_lookahead"] * int(self.env_config["include_price"])
bl_pv_lookahead = self.env_config["bl_pv_lookahead"]
number_of_lookaheads = sum([int(self.env_config["include_pv"]), int(self.env_config["include_building"])])
# check observer module for building of observation list
power_index = self.n_evs * 6 + 2 * (price_lookahead+1) + number_of_lookaheads * (bl_pv_lookahead+1) + 1
max_val = dumb_log.loc[0, "Observation"][power_index]
plt.ylim([-max_val * 1.2, max_val * 1.2])
plt.show()