File size: 1,468 Bytes
a283aa6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import numpy as np
class PriceAwareAgent:
"""
Heuristic agent for EVChargeEnv.
- Charges more when price is low and grid load is safe.
- Charges less when price is high or grid load is high.
"""
def __init__(self,
low_price_threshold: float = 0.4,
high_price_threshold: float = 0.7,
high_load_threshold: float = 0.8):
self.low_price_threshold = low_price_threshold
self.high_price_threshold = high_price_threshold
self.high_load_threshold = high_load_threshold
def select_action(self, observation):
"""
observation = [charge, price, load, time_step_norm]
returns: np.array([action]) in [0, 1]
"""
charge, price, load, t = observation
# If almost full, stop charging.
if charge >= 0.98:
return np.array([0.0], dtype=np.float32)
# If grid is very stressed, back off.
if load >= self.high_load_threshold:
return np.array([0.1], dtype=np.float32)
# If price is low, charge aggressively.
if price <= self.low_price_threshold:
return np.array([0.9], dtype=np.float32)
# If price is very high, charge slowly, just enough to make progress.
if price >= self.high_price_threshold:
return np.array([0.2], dtype=np.float32)
# Medium case: moderate charging.
return np.array([0.5], dtype=np.float32)
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