def __init__(
self,
power_rating=2.0, # MW
capacity=4.0, # MWh
round_trip_eff=0.9, # %
initial_charge=0.5, # %
**kwargs
):
self.power_rating = float(power_rating)
self.capacity = float(capacity)
self.round_trip_eff = float(round_trip_eff)
self.initial_charge = initial_charge # can be 'random' or float
super().__init__(**kwargs)
"""
action space has a single dimension, ranging from max charge
to max discharge
for a 2 MW battery, a range of -2 to 2 MW
"""
self.action_space = GlobalSpace('action').from_spaces(
ContinuousSpace(-self.power_rating, self.power_rating),
'Rate [MW]'
)
self.action_space.no_op = np.array([0]).reshape(1, 1)
self.state_space.extend(ContinuousSpace(0, self.capacity),
'C_charge_level [MWh]')
self.observation_space.extend(ContinuousSpace(0, self.capacity),
'C_charge_level [MWh]')
def __init__(self):
env = gym.make('MountainCar-v0')
super(MountainCarEnv, self).__init__(env)
self.observation_space = self.env.observation_space
self.action_space = GlobalSpace('action').from_spaces(
DiscreteSpace(2), 'push_l_or_r')
def __init__(self):
env = gym.make('Pendulum-v0')
super(PendulumEnv, self).__init__(env)
self.observation_space = self.env.observation_space
self.action_space = GlobalSpace('action').from_spaces(
ContinuousSpace(low=-env.env.max_torque, high=env.env.max_torque),
'applied_torque')
def __init__(
self,
capacity=4.0, # MWh
supply_capacity=0.5, # MWh
release_time=12, # num 5 mins
supply_power=0.05, # MW
**kwargs):
self.capacity = float(capacity)
# this should look at the max of the data - TODO
self.supply_capacity = float(supply_capacity)
self.release_time = int(release_time)
super().__init__(**kwargs)
"""
action space has a single discrete dimension
0 = no op
1 = increase setpoint
2 = decrease setpoint
"""
self.action_space = GlobalSpace('action').from_spaces(
DiscreteSpace(3), 'setpoint')
self.action_space.no_op = np.array([0]).reshape(1, 1)
self.state_space.extend(
[
ContinuousSpace(0, self.episode_length),
ContinuousSpace(0, self.capacity),
ContinuousSpace(0, self.supply_capacity)
],
['Step', 'C_stored_demand [MWh]', 'C_stored_supply[MWh]'],
)
# let our agent see the stored demand
# let our agent see the stored supply
# see precool power?
# obs space is created during env init
self.observation_space.extend(
[
ContinuousSpace(0, self.capacity),
ContinuousSpace(0, self.supply_capacity)
],
['C_stored_demand [MWh]', 'C_stored_supply[MWh]'],
)
# supply = precooling
# i.e how much power we consume during precooling
self.supply_power = float(
max(float(supply_power),
self.state_space.data.loc[:, 'C_demand [MW]'].max()))