gym_power_res.envs.GYM_ENV_restoration_distribution

Module Contents

Classes

RestorationDisEnvRL	Restoration env for StableBaseline3 (reinforcement learning algorithm collections)
RestorationDisEnv	Restoration env for imitation learning
RestorationDisEnv119	Restoration env for imitation learning
RestorationDisVarConEnv	Restoration env with tieline and reactive power dispatch control

class gym_power_res.envs.GYM_ENV_restoration_distribution.RestorationDisEnvRL(max_disturbance, min_disturbance)

Bases: gym.Env

Restoration env for StableBaseline3 (reinforcement learning algorithm collections)

metadata

seed(self, seed=None)

reset(self, disturbance=None)

Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation

step(self, action)

Apply the given actions to the environment for one step

dict2list(self, dict)

convert an order dict to a list

render(self, mode='human')

close(self)

class gym_power_res.envs.GYM_ENV_restoration_distribution.RestorationDisEnv(max_disturbance, min_disturbance)

Bases: gym.Env

Restoration env for imitation learning

metadata

seed(self, seed=None)

reset(self, disturbance=None)

Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation

step(self, action)

Apply the given actions to the environment for one step

dict2list(self, dict)

convert an order dict to a list

render(self, mode='human')

close(self)

class gym_power_res.envs.GYM_ENV_restoration_distribution.RestorationDisEnv119(max_disturbance, min_disturbance)

Bases: gym.Env

Restoration env for imitation learning

metadata

seed(self, seed=None)

reset(self, disturbance=None)

Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation

step(self, action)

Apply the given actions to the environment for one step

dict2list(self, dict)

convert an order dict to a list

render(self, mode='human')

close(self)

class gym_power_res.envs.GYM_ENV_restoration_distribution.RestorationDisVarConEnv(max_disturbance, min_disturbance)

Bases: gym.Env

Restoration env with tieline and reactive power dispatch control

metadata

seed(self, seed=None)

reset(self, disturbance=None)

Here we should setup the episode control for the total time horizon, which will be independent of individual segment simulation

step(self, action, logger=None)

Apply the given actions to the environment for one step Action here is a dictionary with “tieline” and “varcon”

view_grid(self)

render(self, mode='human')

close(self)