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import gym
import pandas as pd
import numpy as np
from collections import deque
import random
from gym.spaces import Tuple, Box
random.seed(42)
np.random.seed(42)
class SolarSys(gym.Env):
def __init__(
self,
data_path="DATA/training/25houses_152days_TRAIN.csv",
state="", # Select from 'oklahoma', 'colorado', 'pennsylvania'
time_freq="15T",
house_ids_in_cluster=None,
preloaded_data=None
):
super().__init__() # initialize parent gym.Env
self.state = state.lower()
# --- Centralized Pricing Configuration ---
self._pricing_info = {
"oklahoma": {
"max_grid_price": 0.2112,
"feed_in_tariff": 0.04,
"price_function": self._get_oklahoma_price
},
"colorado": {
"max_grid_price": 0.32,
"feed_in_tariff": 0.055,
"price_function": self._get_colorado_price
},
"pennsylvania": {
"max_grid_price": 0.5505,
"feed_in_tariff": 0.06,
"price_function": self._get_pennsylvania_price
}
}
if self.state not in self._pricing_info:
raise ValueError(f"State '{self.state}' is not supported. Available states: {list(self._pricing_info.keys())}")
state_config = self._pricing_info[self.state]
self.max_grid_price = state_config["max_grid_price"]
self.feed_in_tariff = state_config["feed_in_tariff"]
self._get_price_function = state_config["price_function"]
self.data_path = data_path
self.time_freq = time_freq
if preloaded_data is not None:
all_data = preloaded_data
if house_ids_in_cluster:
print(f"Using pre-loaded data for cluster with {len(house_ids_in_cluster)} houses.")
else:
print(f"Loading data from {data_path}...")
try:
all_data = pd.read_csv(data_path)
all_data["local_15min"] = pd.to_datetime(all_data["local_15min"], utc=True)
all_data.set_index("local_15min", inplace=True)
except FileNotFoundError:
raise FileNotFoundError(f"Data file {data_path} not found.")
except pd.errors.EmptyDataError:
raise ValueError(f"Data file {data_path} is empty.")
except Exception as e:
raise ValueError(f"Error loading data: {e}")
# Compute global maxima for normalization
grid_cols = [c for c in all_data.columns if c.startswith("grid_")]
solar_cols = [c for c in all_data.columns if c.startswith("total_solar_")]
all_grid = all_data[grid_cols].values
all_solar = all_data[solar_cols].values
# max total demand = max(grid + solar) over all time & agents
self.global_max_demand = float((all_grid + all_solar).max()) + 1e-8
# max solar generation alone
self.global_max_solar = float(all_solar.max()) + 1e-8
# Store the resampled dataset
self.all_data = all_data
all_house_ids_in_file = [
col.split("_")[1] for col in self.all_data.columns
if col.startswith("grid_")
]
if house_ids_in_cluster:
self.house_ids = [hid for hid in house_ids_in_cluster if hid in all_house_ids_in_file]
else:
self.house_ids = all_house_ids_in_file
if not self.house_ids:
raise ValueError("No valid house_ids found for this environment instance.")
self.env_log_infos = []
self.time_freq = time_freq
freq_offset = pd.tseries.frequencies.to_offset(time_freq)
minutes_per_step = freq_offset.nanos / 1e9 / 60.0
self.steps_per_day = int(24 * 60 // minutes_per_step)
total_rows = len(self.all_data)
self.total_days = total_rows // self.steps_per_day
if self.total_days < 1:
raise ValueError(
f"After resampling, dataset has {total_rows} rows, which is "
f"less than a single day of {self.steps_per_day} steps."
)
self.num_agents = len(self.house_ids)
self.original_no_p2p_import = {}
for hid in self.house_ids:
col_grid = f"grid_{hid}"
self.original_no_p2p_import[hid] = self.all_data[col_grid].clip(lower=0.0).values
solar_cols = [f"total_solar_{hid}" for hid in self.house_ids]
solar_sums = self.all_data[solar_cols].sum(axis=0).to_dict()
self.agent_groups = [
1 if solar_sums[f"total_solar_{hid}"] > 0 else 0
for hid in self.house_ids
]
self.group_counts = {
0: self.agent_groups.count(0),
1: self.agent_groups.count(1)
}
print(f"Number of houses in each group: {self.group_counts}")
#battery logic
self.battery_options = {
"teslapowerwall": {"max_capacity": 13.5, "charge_efficiency": 0.95, "discharge_efficiency": 0.90, "max_charge_rate": 5.0, "max_discharge_rate": 5.0, "degradation_cost_per_kwh": 0.005},
"enphase": {"max_capacity": 5.0, "charge_efficiency": 0.95, "discharge_efficiency": 0.90, "max_charge_rate": 2.0, "max_discharge_rate": 2.0, "degradation_cost_per_kwh": 0.005},
"franklin": {"max_capacity": 15.0, "charge_efficiency": 0.95, "discharge_efficiency": 0.90, "max_charge_rate": 6.0, "max_discharge_rate": 6.0, "degradation_cost_per_kwh": 0.005},
}
self.solar_houses = [
hid for hid in self.house_ids
if (self.all_data[f"total_solar_{hid}"] > 0).any()
]
self.batteries = {}
for hid in self.solar_houses:
choice = random.choice(list(self.battery_options))
specs = self.battery_options[choice]
self.batteries[hid] = {"soc": 0.0, **specs}
self.battery_charge_history = {hid: [] for hid in self.batteries}
self.battery_discharge_history = {hid: [] for hid in self.batteries}
self.battery_capacity = sum(b["max_capacity"] for b in self.batteries.values())
self.battery_level = sum(b["soc"] for b in self.batteries.values())
self.current_solar = 0.0
self.has_battery = np.array([1 if hid in self.batteries else 0 for hid in self.house_ids], dtype=np.float32)
# Initialize arrays for all agents, with zeros for non-battery agents
self.battery_soc = np.zeros(self.num_agents, dtype=np.float32)
self.battery_max_capacity = np.zeros(self.num_agents, dtype=np.float32)
self.battery_charge_efficiency = np.zeros(self.num_agents, dtype=np.float32)
self.battery_discharge_efficiency = np.zeros(self.num_agents, dtype=np.float32)
self.battery_max_charge_rate = np.zeros(self.num_agents, dtype=np.float32)
self.battery_max_discharge_rate = np.zeros(self.num_agents, dtype=np.float32)
self.battery_degradation_cost = np.zeros(self.num_agents, dtype=np.float32)
# Populate the arrays using the created battery dictionary
for i, hid in enumerate(self.house_ids):
if hid in self.batteries:
batt = self.batteries[hid]
self.battery_max_capacity[i] = batt["max_capacity"]
self.battery_charge_efficiency[i] = batt["charge_efficiency"]
self.battery_discharge_efficiency[i] = batt["discharge_efficiency"]
self.battery_max_charge_rate[i] = batt["max_charge_rate"]
self.battery_max_discharge_rate[i] = batt["max_discharge_rate"]
self.battery_degradation_cost[i] = batt["degradation_cost_per_kwh"]
# ========== SPACES (Observation & Action) ===================================
self.observation_space = gym.spaces.Box(
low=-np.inf, high=np.inf,
shape=(self.num_agents, 8),
dtype=np.float32
)
self.action_space = Tuple((
Box(low=0.0, high=1.0, shape=(self.num_agents, 6), dtype=np.float32),
Box(low=-np.inf, high=np.inf, shape=(1,), dtype=np.float32),
Box(low=-1.0, high=np.inf, shape=(1,), dtype=np.float32)
))
# ========== REWARD FUNCTION PARAMETERS ======================================
self.data = None
self.env_log = []
self.day_index = -1
self.current_step = 0
self.num_steps = self.steps_per_day
self.demands = {}
self.solars = {}
self.previous_actions = {
hid: np.zeros(6) for hid in self.house_ids
}
self._initialize_episode_metrics()
def get_grid_price(self, step_idx):
"""
Returns the grid price for the current step based on the selected state.
"""
return self._get_price_function(step_idx)
def _get_oklahoma_price(self, step_idx):
minutes_per_step = 24 * 60 / self.steps_per_day
hour = int((step_idx * minutes_per_step) // 60) % 24
if 14 <= hour < 19:
return 0.2112
else:
return 0.0434
def _get_colorado_price(self, step_idx):
minutes_per_step = 24 * 60 / self.steps_per_day
hour = int((step_idx * minutes_per_step) // 60) % 24
if 15 <= hour < 19:
return 0.32
elif 13 <= hour < 15:
return 0.22
else:
return 0.12
def _get_pennsylvania_price(self, step_idx):
minutes_per_step = 24 * 60 / self.steps_per_day
hour = int((step_idx * minutes_per_step) // 60) % 24
if 13 <= hour < 21:
return 0.125048
elif hour >= 23 or hour < 6:
return 0.057014
else:
return 0.079085
def get_peer_price(self, step_idx, total_surplus, total_shortfall):
grid_price = self.get_grid_price(step_idx)
feed_in_tariff = self.feed_in_tariff
# Parameters for arctangent-log pricing
p_balance = (grid_price * 0.80) + (feed_in_tariff * 0.20)
p_con = (grid_price - feed_in_tariff) / (1.5 * np.pi)
k = 1.5
epsilon = 1e-6
supply = total_surplus + epsilon
demand = total_shortfall + epsilon
ratio = demand / supply
log_ratio = np.log(ratio)
if log_ratio < 0:
power_term = - (np.abs(log_ratio) ** k)
else:
power_term = log_ratio ** k
price_offset = 2 * np.pi * p_con * np.arctan(power_term)
peer_price = p_balance + price_offset
final_price = float(np.clip(peer_price, feed_in_tariff, grid_price))
return final_price
def _initialize_episode_metrics(self):
"""Initializes or resets all metrics tracked over a single episode (day)."""
self.cumulative_grid_reduction = 0.0
self.cumulative_grid_reduction_peak = 0.0
self.cumulative_degradation_cost = 0.0
self.agent_cost_savings = np.zeros(self.num_agents)
self.degradation_cost_timeseries = []
self.cost_savings_timeseries = []
self.grid_reduction_timeseries = []
def get_episode_metrics(self):
"""
Returns a dictionary of performance metrics for the last completed episode.
"""
return self.episode_metrics
##########################################################################
# Gym Required Methods
def reset(self):
if self.current_step > 0:
positive_savings = self.agent_cost_savings[self.agent_cost_savings > 0]
if len(positive_savings) > 1:
fairness_on_savings = self._compute_jains_index(positive_savings)
else:
fairness_on_savings = 0.0
self.episode_metrics = {
"grid_reduction_entire_day": self.cumulative_grid_reduction,
"grid_reduction_peak_hours": self.cumulative_grid_reduction_peak,
"total_cost_savings": np.sum(self.agent_cost_savings),
"fairness_on_cost_savings": fairness_on_savings,
"battery_degradation_cost_total": self.cumulative_degradation_cost,
"degradation_cost_over_time": self.degradation_cost_timeseries,
"cost_savings_over_time": self.cost_savings_timeseries,
"grid_reduction_over_time": self.grid_reduction_timeseries,
}
self.day_index = np.random.randint(0, self.total_days)
start_row = self.day_index * self.steps_per_day
end_row = start_row + self.steps_per_day
day_data = self.all_data.iloc[start_row:end_row].copy()
self.data = day_data
self.no_p2p_import_day = {}
for hid in self.house_ids:
self.no_p2p_import_day[hid] = self.original_no_p2p_import[hid][start_row:end_row]
demand_list = []
solar_list = []
for hid in self.house_ids:
col_grid = f"grid_{hid}"
col_solar = f"total_solar_{hid}"
grid_series = day_data[col_grid].fillna(0.0)
solar_series = day_data[col_solar].fillna(0.0).clip(lower=0.0)
demand_array = grid_series.values + solar_series.values
demand_array = np.clip(demand_array, 0.0, None)
demand_list.append(demand_array)
solar_list.append(solar_series.values)
self.demands_day = np.stack(demand_list, axis=1).astype(np.float32)
self.solars_day = np.stack(solar_list, axis=1).astype(np.float32)
self.hours_day = (self.data.index.hour + self.data.index.minute / 60.0).values
self.current_step = 0
self.env_log = []
for hid in self.house_ids:
self.previous_actions[hid] = np.zeros(6)
lows = 0.30 * self.battery_max_capacity
highs = 0.70 * self.battery_max_capacity
self.battery_soc = np.random.uniform(low=lows, high=highs)
self.battery_soc *= self.has_battery
initial_demands = self.demands_day[0]
initial_solars = self.solars_day[0]
initial_surplus = np.maximum(initial_solars - initial_demands, 0.0).sum()
initial_shortfall = np.maximum(initial_demands - initial_solars, 0.0).sum()
initial_peer_price = self.get_peer_price(0, initial_surplus, initial_shortfall)
obs = self._get_obs(peer_price=initial_peer_price)
self._initialize_episode_metrics()
return obs, {}
def step(self, packed_action):
actions, transfer_kwh_arr, peer_price_arr = packed_action
inter_cluster_transfer_kwh = float(transfer_kwh_arr[0])
override_peer_price_val = float(peer_price_arr[0])
override_peer_price = override_peer_price_val if override_peer_price_val >= 0 else None
actions = np.array(actions, dtype=np.float32)
if actions.shape != (self.num_agents, 6):
raise ValueError(f"Actions shape mismatch: got {actions.shape}, expected {(self.num_agents, 6)}")
actions = np.clip(actions, 0.0, 1.0)
a_sellGrid = actions[:, 0]
a_buyGrid = actions[:, 1]
a_sellPeers = actions[:, 2]
a_buyPeers = actions[:, 3]
a_chargeBatt = actions[:, 4]
a_dischargeBatt = actions[:, 5]
demands = self.demands_day[self.current_step]
solars = self.solars_day[self.current_step]
total_surplus = np.maximum(solars - demands, 0.0).sum()
total_shortfall = np.maximum(demands - solars, 0.0).sum()
self.current_solar = total_surplus
if override_peer_price is not None:
peer_price = override_peer_price
else:
peer_price = self.get_peer_price(
self.current_step,
total_surplus,
total_shortfall
)
grid_price = self.get_grid_price(self.current_step)
shortfall = np.maximum(demands - solars, 0.0)
surplus = np.maximum(solars - demands, 0.0)
final_shortfall = shortfall.copy()
final_surplus = surplus.copy()
grid_import = np.zeros(self.num_agents, dtype=np.float32)
grid_export = np.zeros(self.num_agents, dtype=np.float32)
# ### VECTORIZED BATTERY DISCHARGE ###
available_from_batt = self.battery_soc * self.battery_discharge_efficiency
desired_discharge = a_dischargeBatt * self.battery_max_discharge_rate
discharge_amount = np.minimum.reduce([desired_discharge, available_from_batt, final_shortfall])
discharge_amount *= self.has_battery # Ensure only batteries discharge
# Update SOC (energy drawn from battery before efficiency loss)
self.battery_soc -= (discharge_amount / (self.battery_discharge_efficiency + 1e-9)) * self.has_battery
self.battery_soc = np.maximum(0.0, self.battery_soc)
final_shortfall -= discharge_amount
cap_left = self.battery_max_capacity - self.battery_soc
desired_charge = a_chargeBatt * self.battery_max_charge_rate
charge_amount = np.minimum.reduce([
desired_charge,
cap_left / (self.battery_charge_efficiency + 1e-9),
final_surplus
])
charge_amount *= self.has_battery
# Update SOC
self.battery_soc += charge_amount * self.battery_charge_efficiency
final_surplus -= charge_amount
# ### VECTORIZED P2P TRADING ###
battery_offer = (self.battery_soc * self.battery_discharge_efficiency) * self.has_battery
effective_surplus = final_surplus + battery_offer
netPeer = a_buyPeers - a_sellPeers
p2p_buy_request = np.maximum(0, netPeer) * final_shortfall
p2p_sell_offer = np.maximum(0, -netPeer) * effective_surplus
total_sell = np.sum(p2p_sell_offer)
total_buy = np.sum(p2p_buy_request)
matched = min(total_sell, total_buy)
if matched > 1e-9:
sell_fraction = p2p_sell_offer / (total_sell + 1e-12)
buy_fraction = p2p_buy_request / ( total_buy + 1e-12)
actual_sold = matched * sell_fraction
actual_bought = matched * buy_fraction
else:
actual_sold = np.zeros(self.num_agents, dtype=np.float32)
actual_bought = np.zeros(self.num_agents, dtype=np.float32)
from_batt = np.minimum(actual_sold, battery_offer)
from_solar = actual_sold - from_batt
final_surplus -= from_solar
final_shortfall -= actual_bought
soc_reduction = (from_batt / (self.battery_discharge_efficiency + 1e-9)) * self.has_battery
self.battery_soc -= soc_reduction
self.battery_soc = np.maximum(0.0, self.battery_soc)
if inter_cluster_transfer_kwh > 0:
amount_received = inter_cluster_transfer_kwh
total_shortfall_in_cluster = np.sum(final_shortfall)
if total_shortfall_in_cluster > 1e-6:
to_cover_shortfall = min(amount_received, total_shortfall_in_cluster)
distribution_ratio = final_shortfall / total_shortfall_in_cluster
shortfall_reduction = distribution_ratio * to_cover_shortfall
final_shortfall -= shortfall_reduction
amount_received -= to_cover_shortfall
if amount_received > 1e-6:
cap_left = self.battery_max_capacity - self.battery_soc
storable_energy = cap_left / (self.battery_charge_efficiency + 1e-9)
total_storable_in_cluster = np.sum(storable_energy * self.has_battery)
if total_storable_in_cluster > 1e-6:
to_store = min(amount_received, total_storable_in_cluster)
storage_ratio = storable_energy / total_storable_in_cluster
energy_to_store_per_batt = storage_ratio * to_store
self.battery_soc += (energy_to_store_per_batt * self.battery_charge_efficiency) * self.has_battery
elif inter_cluster_transfer_kwh < 0:
amount_to_send = abs(inter_cluster_transfer_kwh)
total_surplus_in_cluster = np.sum(final_surplus)
if total_surplus_in_cluster > 1e-6:
sent_from_surplus = min(amount_to_send, total_surplus_in_cluster)
draw_ratio = final_surplus / total_surplus_in_cluster
surplus_reduction = draw_ratio * sent_from_surplus
final_surplus -= surplus_reduction
amount_to_send -= sent_from_surplus
if amount_to_send > 1e-6:
available_from_batt = (self.battery_soc * self.battery_discharge_efficiency) * self.has_battery
total_available_from_batt = np.sum(available_from_batt)
if total_available_from_batt > 1e-6:
# Discharge a maximum of 'amount_to_send' from batteries
to_discharge = min(amount_to_send, total_available_from_batt)
# Draw this amount proportionally from each available battery
discharge_ratio = available_from_batt / total_available_from_batt
discharged_per_batt = discharge_ratio * to_discharge # This is effective energy
# Update SoC (energy drawn from battery before efficiency loss)
soc_reduction = (discharged_per_batt / (self.battery_discharge_efficiency + 1e-9))
self.battery_soc -= soc_reduction * self.has_battery
self.battery_soc = np.maximum(0.0, self.battery_soc)
# =======================================================================
netGrid = a_buyGrid - a_sellGrid
grid_import = np.maximum(0, netGrid) * final_shortfall
grid_export = np.maximum(0, -netGrid) * final_surplus
forced = np.maximum(final_shortfall - grid_import, 0.0)
grid_import += forced
final_shortfall -= forced
feed_in_tariff = self.feed_in_tariff
costs = (
(grid_import * grid_price)
- (grid_export * feed_in_tariff)
+ (actual_bought * peer_price)
- (actual_sold * peer_price)
)
final_rewards = self._compute_rewards(
grid_import=grid_import, grid_export=grid_export, actual_sold=actual_sold,
actual_bought=actual_bought, charge_amount=charge_amount, discharge_amount=discharge_amount,
costs=costs, grid_price=grid_price, peer_price=peer_price
)
no_p2p_import_this_step = np.array([
self.no_p2p_import_day[hid][self.current_step]
for hid in self.house_ids
], dtype=np.float32)
# --- Metric 1 & 2: Grid Reduction (Entire Day & Peak Hours) ---
step_grid_reduction = np.sum(no_p2p_import_this_step - grid_import)
self.cumulative_grid_reduction += step_grid_reduction
self.grid_reduction_timeseries.append(step_grid_reduction)
if grid_price >= self.max_grid_price * 0.99:
self.cumulative_grid_reduction_peak += step_grid_reduction
# --- Metric 3: Total Cost Savings ---
cost_no_p2p = no_p2p_import_this_step * grid_price
step_cost_savings_per_agent = cost_no_p2p - costs
self.agent_cost_savings += step_cost_savings_per_agent
self.cost_savings_timeseries.append(np.sum(step_cost_savings_per_agent))
# --- Metric 5 & 6: Battery Degradation Cost (Total and Over Time) ---
degradation_cost_agent = (charge_amount + discharge_amount) * self.battery_degradation_cost
step_degradation_cost = np.sum(degradation_cost_agent)
self.cumulative_degradation_cost += step_degradation_cost
self.degradation_cost_timeseries.append(step_degradation_cost)
info = {
"p2p_buy": actual_bought,
"p2p_sell": actual_sold,
"grid_import_with_p2p": grid_import,
"grid_import_no_p2p": no_p2p_import_this_step,
"grid_export": grid_export,
"costs": costs,
"charge_amount": charge_amount,
"discharge_amount": discharge_amount,
"step": self.current_step,
"step_grid_reduction": step_grid_reduction,
"step_cost_savings": np.sum(step_cost_savings_per_agent),
"step_degradation_cost": step_degradation_cost,
}
self.env_log.append([
self.current_step, np.sum(grid_import), np.sum(grid_export),
np.sum(actual_bought), np.sum(actual_sold), np.sum(costs)
])
self.current_step += 1
terminated = False
truncated = (self.current_step >= self.num_steps)
obs_next = self._get_obs(peer_price=peer_price)
info['agent_rewards'] = final_rewards
self.last_info = info
self.env_log_infos.append(info)
return obs_next, final_rewards.sum(), terminated, truncated, info
def _get_obs(self, peer_price: float):
step = min(self.current_step, self.num_steps - 1)
demands = self.demands_day[step]
solars = self.solars_day[step]
grid_price = self.get_grid_price(step)
hour = self.hours_day[step]
soc_frac = self.battery_soc / (self.battery_max_capacity + 1e-9)
soc_frac = np.where(self.has_battery == 1, soc_frac, -1.0)
total_demand_others = demands.sum() - demands
total_solar_others = solars.sum() - solars
obs = np.stack([
demands,
solars,
soc_frac,
np.full(self.num_agents, grid_price),
np.full(self.num_agents, peer_price),
total_demand_others,
total_solar_others,
np.full(self.num_agents, hour)
], axis=1).astype(np.float32)
return obs
def _compute_jains_index(self, usage_array):
x = np.array(usage_array, dtype=np.float32)
numerator = (np.sum(x))**2
denominator = len(x) * np.sum(x**2) + 1e-8
return numerator / denominator
def _compute_rewards(
self, grid_import, grid_export, actual_sold, actual_bought,
charge_amount, discharge_amount, costs, grid_price, peer_price
):
w1 = 0.3; w2 = 0.5; w3 = 0.5; w4 = 0.1; w5 = 0.05; w6 = 0.4; w7 = 1.0
p_grid_norm = grid_price / self.max_grid_price
p_peer_norm = peer_price / self.max_grid_price
rewards = -costs * w7
rewards -= w1 * grid_import * p_grid_norm
rewards += w2 * actual_sold * p_peer_norm
buy_bonus = w3 * actual_bought * ((grid_price - peer_price) / self.max_grid_price)
rewards += np.where(peer_price < grid_price, buy_bonus, 0.0)
# ### VECTORIZED REWARD PENALTIES ###
soc_frac = self.battery_soc / (self.battery_max_capacity + 1e-9)
soc_penalties = w4 * ((soc_frac - 0.5) ** 2) * self.has_battery
degrad_penalties = w5 * (charge_amount + discharge_amount) * self.battery_degradation_cost
rewards -= soc_penalties
rewards -= degrad_penalties
jfi = self._compute_jains_index(actual_bought + actual_sold)
rewards += w6 * jfi
return rewards
def save_log(self, filename="env_log.csv"):
columns = [
"Step", "Total_Grid_Import", "Total_Grid_Export",
"Total_P2P_Buy", "Total_P2P_Sell", "Total_Cost",
]
df = pd.DataFrame(self.env_log, columns=columns)
df.to_csv(filename, index=False)
print(f"Environment log saved to {filename}") |