from optimizer.ev.scheduler import EVChargingScheduler, ChargingSession
from optimizer.base import OptimizationConfig
from coordinator import (
    DispatchEngine, EventBus, EventType,
    OCPPAdapter, AdapterRegistry,
    create_ev_charger_state_machine
)
from datetime import datetime, timezone

# --- Layer 3: Optimize ---
config = OptimizationConfig(horizon="24h", resolution="1h")
scheduler = EVChargingScheduler(config=config, site_capacity_kw=200.0, charger_capacity_kw=22.0)

sessions = [
    ChargingSession(
        vehicle_id="ev_001",
        arrival_time=datetime(2025, 1, 15, 8, 0, tzinfo=timezone.utc),
        departure_time=datetime(2025, 1, 15, 17, 0, tzinfo=timezone.utc),
        energy_needed_kwh=30.0,
        max_charge_rate_kw=22.0,
    ),
]

tariff = {
    "energy_rates": [
        {"name": "off_peak", "rate": 0.08, "schedule": {"start_time": "00:00", "end_time": "06:59"}},
        {"name": "peak", "rate": 0.25, "schedule": {"start_time": "07:00", "end_time": "22:59"}},
        {"name": "off_peak_night", "rate": 0.08, "schedule": {"start_time": "23:00", "end_time": "23:59"}}
    ]
}

result = scheduler.optimize(
    sessions=sessions, tariff=tariff,
    start_time=datetime(2025, 1, 15, 0, 0, tzinfo=timezone.utc)
)

print(f"Optimization: {result.status}, cost=${result.total_cost:.2f}")

# --- Layer 4: Coordinate ---
bus = EventBus()
registry = AdapterRegistry()
registry.register("ast_ev_001", OCPPAdapter("ocpp_site"))

engine = DispatchEngine(event_bus=bus, adapter_registry=registry)

# Convert schedule to commands
commands = engine.schedule_to_commands(
    result.schedule,
    asset_map={"ev_001": "ast_ev_001"}
)
print(f"Generated {len(commands)} commands")

# Dispatch pending commands
now = datetime(2025, 1, 15, 9, 0, tzinfo=timezone.utc)
for cmd in engine.get_pending_commands(now):
    adapter = registry.get(cmd.asset_id)
    engine.dispatch_command(cmd, adapter)
    print(f"  Dispatched: {cmd.command_type.value} → {cmd.asset_id} "
          f"({cmd.parameters['power_kw']:.0f} kW)")

from optimizer.peak_shaving.controller import PeakShavingController, BatterySpec
from optimizer.base import OptimizationConfig
from coordinator import (
    DispatchEngine, EventBus,
    ModbusAdapter, AdapterRegistry,
    create_battery_state_machine
)
from datetime import datetime, timezone
import numpy as np

# --- Layer 3: Optimize ---
config = OptimizationConfig(horizon="24h", resolution="1h")
controller = PeakShavingController(config=config, peak_target_kw=150.0)

battery_spec = BatterySpec(
    asset_id="ast_batt_001",
    capacity_kwh=200.0,
    max_charge_kw=50.0,
    max_discharge_kw=50.0,
)

hours = np.arange(24)
load = 80 + 100 * np.exp(-((hours - 9) ** 2) / 4) + 120 * np.exp(-((hours - 18) ** 2) / 4)

result = controller.optimize(
    battery=battery_spec, load_forecast=load,
    start_time=datetime(2025, 1, 15, 0, 0, tzinfo=timezone.utc)
)

print(f"Peak before: {load.max():.0f} kW → after: {result.peak_demand_kw:.0f} kW")

# --- Layer 4: Coordinate ---
bus = EventBus()
registry = AdapterRegistry()
registry.register("ast_batt_001", ModbusAdapter("modbus_bms"))

engine = DispatchEngine(event_bus=bus, adapter_registry=registry)

commands = engine.schedule_to_commands(
    result.schedule,
    asset_map={"battery": "ast_batt_001"}
)

print(f"\nGenerated {len(commands)} battery commands:")
for cmd in commands:
    print(f"  {cmd.scheduled_at.strftime('%H:%M')} | "
          f"{cmd.command_type.value} | {cmd.parameters['power_kw']:.1f} kW")