envs package

Submodules

envs.bat_env_fwd_view module

class envs.bat_env_fwd_view.BatteryEnvFwd(env_config)[source]

Bases: Env

CO2_footprint(dc_load, ci, a_t, discharge_energy)[source]

Calculates carbon footprint

Parameters:

  • dc_load (float) – Total energy consumption of the DC.

  • ci (float) – Carbon intensity at current time step.

  • a_t (string) – Agent’s action.

  • discharge_energy (float) – Amount of energy to be discharged

Returns:

Carbon footprint produced at the current time step (gCO2e)

Return type:

CO2_footprint (float)

charging_rate_modifier(battery)[source]

Calculates the battery state depending on the charging rate

Parameters:

battery (batt.Battery2) – Battery model

Returns:

Battery charging rate

Return type:

charging_rate (float)

discharging_rate_modifier(battery)[source]

Calculates the battery state depending on the discharging rate

Parameters:

battery (batt.Battery2) – Battery model

Returns:

Battery discharging rate

Return type:

discharging_rate (float)

reset(*, seed=None, options=None)[source]

Reset BatteryEnvFwd to initial state.

Parameters:

  • seed (int, optional) – Random seed.

  • options (dict, optional) – Environment options.

Returns:

Current state of the environmment info (dict): A dictionary that containing additional information about the environment state

Return type:

temp_state (List[float])

set_dcload(dc_load)[source]

Set the current DC energy consumption

Parameters:

float (dc_load) – DC energy consumption.

sigmoid(x)[source]
step(action_id)[source]

Step function :param action_id: the action id :type action_id: int

Returns:

Current state of the environmment reward (float): reward value. done (bool): A boolean value signaling the if the episode has ended. info (dict): A dictionary that containing additional information about the environment state

Return type:

obs (list)

update_ci(ci, ci_n)[source]

Sets internal CIs values.

update_state()[source]

Updates obsevation with current DC energy consumption

Returns:

normalized_observations (string)

envs.battery_model module

class envs.battery_model.Battery2(capacity, current_load=0, eff_c=1, eff_d=1, c_lim=0.1, d_lim=1, upper_u=-0.04, upper_v=1, lower_u=0.01, lower_v=0)[source]

Bases: object

c_lim = 3
calc_max_charge(T_u)[source]
calc_max_discharge(T_u, dc_load)[source]
capacity = 0
charge(input_load, T_u)[source]
current_load = 0
d_lim = 3
discharge(output_load, T_u, dc_load)[source]
eff_c = 1
eff_d = 1
find_and_init_capacity(input_load)[source]
is_full()[source]
lower_lim_u = 0
lower_lim_v = 0
reset()[source]
upper_lim_u = 0
upper_lim_v = 1

envs.carbon_ls module

class envs.carbon_ls.CarbonLoadEnv(env_config={}, future=True, n_vars_ci=4, flexible_workload_ratio=0.2, n_vars_energy=0, n_vars_battery=1, test_mode=False, queue_max_len=500)[source]

Bases: Env

reset(*, seed=None, options=None)[source]

Reset CarbonLoadEnv to initial state.

Returns:

Current state of the environmment info (dict): A dictionary that containing additional information about the environment state

Return type:

observations (List[float])

step(action)[source]

Makes an environment step in`CarbonLoadEnv.

Parameters:

action (int) – Action to take.

Returns:

Current state of the environmment reward (float): reward value. done (bool): A boolean value signaling the if the episode has ended. info (dict): A dictionary that containing additional information about the environment state

Return type:

observations (List[float])

update_workload(workload)[source]

Makes an environment step in`BatteryEnvFwd.

Parameters:

workload (float) – Workload assigned at the current time step

envs.datacenter module

class envs.datacenter.CPU(full_load_pwr=None, idle_pwr=None, cpu_config=None)[source]

Bases: object

cpu_curve1()[source]

initialize the cpu power ratio curve at different IT workload ratios as a function of inlet temperatures [3]

itfan_curve2()[source]

initialize the itfan velocity ratio curve at different IT workload ratios as a function of inlet temperatures [3]

class envs.datacenter.DataCenter_ITModel(num_racks, rack_supply_approach_temp_list, rack_CPU_config, max_W_per_rack=10000, DC_ITModel_config=None, chiller_sizing=False)[source]

Bases: object

calculate_cooling_tower_water_usage()[source]

Calculate the estimated water usage of the cooling tower.

This function uses the attributes set in the class to estimate the water usage based [Sharma, R.K., Shah, A., Bash, C.E., Christian, T., & Patel, C.D. (2009). Water efficiency management in datacenters: Metrics and methodology. 2009 IEEE International Symposium on Sustainable Systems and Technology, 1-6.] [Mohammed Shublaq, Ahmad K. Sleiti., (2020). Experimental analysis of water evaporation losses in cooling towers using filters] https://spxcooling.com/water-calculator/

compute_datacenter_IT_load_outlet_temp(ITE_load_pct_list, CRAC_setpoint)[source]

Calculate the average outlet temperature of all the racks

Parameters:

  • ITE_load_pct_list (List[float]) – CPU load for each rack

  • CRAC_setpoint (float) – CRAC setpoint

Returns:

Rackwise CPU power usage rackwise_itfan_pwr (List[float]): Rackwise IT fan power usage rackwise_outlet_temp (List[float]): Rackwise outlet temperature

Return type:

rackwise_cpu_pwr (List[float])

total_datacenter_full_load()[source]

Calculate the total DC IT(IT CPU POWER + IT FAN POWER) power consumption

class envs.datacenter.Rack(CPU_config_list, max_W_per_rack=10000, rack_config=None)[source]

Bases: object

clamp_supply_approach_temp(supply_approach_temperature)[source]

Returns the clamped delta/ supply approach temperature between the range [3.8, 5.3]

Returns:

Supply approach temperature

Return type:

float

compute_instantaneous_pwr(inlet_temp, ITE_load_pct)[source]

Calculate the power consumption of the whole rack at the current step

Parameters:

  • inlet_temp (float) – Room temperature

  • ITE_load_pct (float) – Current CPU usage

Returns:

Current CPU power usage

Return type:

cpu_power (float)

compute_instantaneous_pwr_vecd(inlet_temp, ITE_load_pct)[source]
cpu_and_fan_init()[source]
get_average_rack_fan_v()[source]

Calculate the average fan velocity for each rack :returns: Average fan flow rate for the rack :rtype: (float)

get_current_rack_load()[source]

Returns the total power consumption of the rack

Returns:

Total power consumption of the rack

Return type:

float

get_total_rack_fan_v()[source]

Calculate the total fan velocity for each rack :returns: Total fan flow rate for the rack :rtype: (float)

envs.datacenter.calculate_HVAC_power(CRAC_setpoint, avg_CRAC_return_temp, ambient_temp, data_center_full_load, DC_Config, ctafr=None)[source]

Calculate the HVAV power attributes

Parameters:

  • CRAC_Setpoint (float) – The control action

  • avg_CRAC_return_temp (float) – The average of the temperatures from all the Racks + their corresponding return approach temperature (Delta)

  • ambient_temp (float) – outside air temperature

  • data_center_full_load (float) – total data center capacity

Returns:

CRAC fan power CT_Fan_pwr (float): Cooling tower fan power CRAC_cooling_load (float): CRAC cooling load Compressor_load (float): Chiller compressor load

Return type:

CRAC_Fan_load (float)

envs.datacenter.calculate_avg_CRAC_return_temp(rack_return_approach_temp_list, rackwise_outlet_temp)[source]

Calculate the CRAC return air temperature

Parameters:

  • rack_return_approach_temp_list (List[float]) – The delta change in temperature from each rack to the CRAC unit

  • rackwise_outlet_temp (float) – The outlet temperature of each rack

Returns:

CRAC return air temperature

Return type:

(float)

envs.datacenter.calculate_chiller_power(max_cooling_cap, load, ambient_temp)[source]

Calculate the chiller power consumption based on load and operating conditions. Obtained from:

Parameters:

  • load (float) – The heat load to be removed by the chiller (Watts).

  • ambient_temp (float) – Current ambient temperature (Celsius).

Returns:

Estimated power consumption of the chiller (Watts).

Return type:

float

envs.datacenter.chiller_sizing(DC_Config, min_CRAC_setpoint=16, max_CRAC_setpoint=22, max_ambient_temp=40.0)[source]

Calculates the chiller sizing for a data center based on the given configuration and parameters.

Parameters:

  • DC_Config (object) – The data center configuration object.

  • min_CRAC_setpoint (float) – The minimum CRAC setpoint temperature in degrees Celsius. Default is 16.

  • max_CRAC_setpoint (float) – The maximum CRAC setpoint temperature in degrees Celsius. Default is 22.

  • max_ambient_temp (float) – The maximum ambient temperature in degrees Celsius. Default is 40.0.

Returns:

A tuple containing the cooling tower reference air flow rate (ctafr) and the rated load of the cooling tower (CT_rated_load).

Return type:

tuple

envs.dc_gym module

class envs.dc_gym.dc_gymenv(observation_variables: list, observation_space: Box, action_variables: list, action_space: Discrete, action_mapping: dict, ranges: dict, add_cpu_usage: bool, min_temp: float, max_temp: float, action_definition: dict, DC_Config: dict, seed: int = 123, episode_length_in_time: Timedelta | None = None)[source]

Bases: Env

NormalizeObservation()[source]

Obtains the value for normalizing the observation.

get_obs()[source]

Returns the observation at the current time step.

Returns:

Current state of the environmment.

Return type:

observation (List[float])

normalize(obs)[source]

Normalizes the observation.

reset(*, seed=None, options=None)[source]

Reset dc_gymenv to initial state.

Parameters:

  • seed (int, optional) – Random seed.

  • options (dict, optional) – Environment options.

Returns:

Current state of the environmment {} (dict): A dictionary that containing additional information about the environment state

Return type:

raw_curr_state (List[float])

set_ambient_temp(ambient_temp, wet_bulb)[source]

Updates the external temperature.

set_bat_SoC(bat_SoC)[source]

Updates the battery state of charge.

set_shifted_wklds(cpu_load)[source]

Updates the current CPU workload. fraction between 0.0 and 1.0

set_workload_hysterisis(power)[source]
step(action)[source]

Makes an environment step in`dc_gymenv.

Parameters:

action_id (int) – Action to take.

Returns:

Current state of the environmment reward (float): reward value. done (bool): A boolean value signaling the if the episode has ended. info (dict): A dictionary that containing additional information about the environment state

Return type:

observations (List[float])

envs.dcrl_env module

envs.dcrl_env_harl module

class envs.dcrl_env_harl.DCRL(env_config)[source]

Bases: Env

calculate_reward(params)[source]

Calculate the individual reward for each agent.

Parameters:

params (dict) – Dictionary of parameters to calculate the reward.

Returns:

Individual reward for the load shifting agent. dc_reward (float): Individual reward for the data center agent. bat_reward (float): Individual reward for the battery agent.

Return type:

ls_reward (float)

close()[source]

After the user has finished using the environment, close contains the code necessary to “clean up” the environment.

This is critical for closing rendering windows, database or HTTP connections.

get_avail_actions()[source]
get_avail_agent_actions(agent_id)[source]

Returns the available actions for agent_id

get_hierarchical_variables()[source]
render()[source]

Compute the render frames as specified by render_mode during the initialization of the environment.

The environment’s metadata render modes (env.metadata[“render_modes”]) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through gymnasium.make which automatically applies a wrapper to collect rendered frames.

Note

As the render_mode is known during __init__, the objects used to render the environment state should be initialised in __init__.

By convention, if the render_mode is:

  • None (default): no render is computed.

  • “human”: The environment is continuously rendered in the current display or terminal, usually for human consumption. This rendering should occur during step() and render() doesn’t need to be called. Returns None.

  • “rgb_array”: Return a single frame representing the current state of the environment. A frame is a np.ndarray with shape (x, y, 3) representing RGB values for an x-by-y pixel image.

  • “ansi”: Return a strings (str) or StringIO.StringIO containing a terminal-style text representation for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors).

  • “rgb_array_list” and “ansi_list”: List based version of render modes are possible (except Human) through the wrapper, gymnasium.wrappers.RenderCollection that is automatically applied during gymnasium.make(..., render_mode="rgb_array_list"). The frames collected are popped after render() is called or reset().

Note

Make sure that your class’s metadata "render_modes" key includes the list of supported modes.

Changed in version 0.25.0: The render function was changed to no longer accept parameters, rather these parameters should be specified in the environment initialised, i.e., gymnasium.make("CartPole-v1", render_mode="human")

reset()[source]

Reset the environment.

Parameters:

  • seed (int, optional) – Random seed.

  • options (dict, optional) – Environment options.

Returns:

Dictionary of states. infos (dict): Dictionary of infos.

Return type:

states (dict)

seed(seed=None)[source]
set_hierarchical_workload(workload)[source]
state()[source]
step(action_dict)[source]

Step the environment.

Parameters:

action_dict – Dictionary of actions of each agent defined in self.agents.

Returns:

Dictionary of observations/states. rews (dict): Dictionary of rewards. terminated (dict): Dictionary of terminated flags. truncated (dict): Dictionary of truncated flags. infos (dict): Dictionary of infos.

Return type:

obs (dict)

class envs.dcrl_env_harl.EnvConfig(raw_config)[source]

Bases: dict

DEFAULT_CONFIG = {'actions_are_logits': False, 'agents': ['agent_ls', 'agent_dc', 'agent_bat'], 'bat_reward': 'default_bat_reward', 'cintensity_file': 'NYIS_NG_&_avgCI.csv', 'datacenter_capacity_mw': 1, 'days_per_episode': 30, 'dc_config_file': 'dc_config.json', 'dc_reward': 'default_dc_reward', 'evaluation': False, 'flexible_load': 0.1, 'individual_reward_weight': 0.8, 'location': 'ny', 'ls_reward': 'default_ls_reward', 'max_bat_cap_Mw': 2, 'timezone_shift': 0, 'weather_file': 'USA_NY_New.York-Kennedy.epw', 'workload_file': 'Alibaba_CPU_Data_Hourly_1.csv'}

envs.dcrl_env_harl_partialobs module

class envs.dcrl_env_harl_partialobs.DCRL(env_config)[source]

Bases: Env

calculate_reward(params)[source]

Calculate the individual reward for each agent.

Parameters:

params (dict) – Dictionary of parameters to calculate the reward.

Returns:

Individual reward for the load shifting agent. dc_reward (float): Individual reward for the data center agent. bat_reward (float): Individual reward for the battery agent.

Return type:

ls_reward (float)

close()[source]

After the user has finished using the environment, close contains the code necessary to “clean up” the environment.

This is critical for closing rendering windows, database or HTTP connections.

get_avail_actions()[source]
get_avail_agent_actions(agent_id)[source]

Returns the available actions for agent_id

get_available_capacity(time_steps: int) → float[source]

Calculate the available capacity of the datacenter over the next time_steps.

Parameters:

time_steps (int) – Number of 15-minute time steps to consider.

Returns:

The available capacity in MW.

Return type:

float

get_hierarchical_variables()[source]
render()[source]

Compute the render frames as specified by render_mode during the initialization of the environment.

The environment’s metadata render modes (env.metadata[“render_modes”]) should contain the possible ways to implement the render modes. In addition, list versions for most render modes is achieved through gymnasium.make which automatically applies a wrapper to collect rendered frames.

Note

As the render_mode is known during __init__, the objects used to render the environment state should be initialised in __init__.

By convention, if the render_mode is:

  • None (default): no render is computed.

  • “human”: The environment is continuously rendered in the current display or terminal, usually for human consumption. This rendering should occur during step() and render() doesn’t need to be called. Returns None.

  • “rgb_array”: Return a single frame representing the current state of the environment. A frame is a np.ndarray with shape (x, y, 3) representing RGB values for an x-by-y pixel image.

  • “ansi”: Return a strings (str) or StringIO.StringIO containing a terminal-style text representation for each time step. The text can include newlines and ANSI escape sequences (e.g. for colors).

  • “rgb_array_list” and “ansi_list”: List based version of render modes are possible (except Human) through the wrapper, gymnasium.wrappers.RenderCollection that is automatically applied during gymnasium.make(..., render_mode="rgb_array_list"). The frames collected are popped after render() is called or reset().

Note

Make sure that your class’s metadata "render_modes" key includes the list of supported modes.

Changed in version 0.25.0: The render function was changed to no longer accept parameters, rather these parameters should be specified in the environment initialised, i.e., gymnasium.make("CartPole-v1", render_mode="human")

reset()[source]

Reset the environment.

Parameters:

  • seed (int, optional) – Random seed.

  • options (dict, optional) – Environment options.

Returns:

Dictionary of states. infos (dict): Dictionary of infos.

Return type:

states (dict)

seed(seed=None)[source]
set_hierarchical_workload(workload)[source]
state()[source]
step(action_dict)[source]

Step the environment.

Parameters:

action_dict – Dictionary of actions of each agent defined in self.agents.

Returns:

Dictionary of observations/states. rews (dict): Dictionary of rewards. terminated (dict): Dictionary of terminated flags. truncated (dict): Dictionary of truncated flags. infos (dict): Dictionary of infos.

Return type:

obs (dict)

class envs.dcrl_env_harl_partialobs.EnvConfig(raw_config)[source]

Bases: dict

DEFAULT_CONFIG = {'actions_are_logits': False, 'agents': ['agent_ls', 'agent_dc', 'agent_bat'], 'bat_reward': 'default_bat_reward', 'cintensity_file': 'NYIS_NG_&_avgCI.csv', 'datacenter_capacity_mw': 1, 'days_per_episode': 30, 'dc_config_file': 'dc_config.json', 'dc_reward': 'default_dc_reward', 'evaluation': False, 'flexible_load': 0.1, 'individual_reward_weight': 0.8, 'location': 'ny', 'ls_reward': 'default_ls_reward', 'max_bat_cap_Mw': 2, 'timezone_shift': 0, 'weather_file': 'USA_NY_New.York-Kennedy.epw', 'workload_file': 'Alibaba_CPU_Data_Hourly_1.csv'}

envs.geo_dcrl module

envs.heirarchical_env module

envs.hierarchical_env_harl module

envs.hierarchical_env_rllib module

envs.truly_heirarchical_env module

envs.truly_heirarchical_ms_env module

Module contents

Includes the environments.