Build the application

## Build
Model district cooling plant chiller staging and thermal energy storage charging under extreme ambient wet-bulb conditions. Formulate optimal chiller dispatch sequences that reduce peak grid electrical stress.

## User
District cooling plant superintendent

## Workflow
1. Extract weather forecast metrics and cooling load profiles for the district network.
2. Model chiller lift and cooling tower approach temperatures using EnergyPlus chiller plant simulation formulations.
3. Determine thermal energy storage tank charge and discharge cycles to shift electric load from peak tariff hours.
4. Produce an optimized chiller staging sequence and power demand schedule for plant control operators.

## Interface
Chiller plant load-staging matrix showing thermal storage state-of-charge, power draw, and wet-bulb temperatures.

## Data
Synthetic hourly cooling load profiles and wet-bulb weather records formatted according to EnergyPlus plant schema.
Starter examples below are synthetic. They are not downloaded records.

## Source
[EnergyPlus](https://raw.githubusercontent.com/NREL/EnergyPlus/develop/README.md)
Applies EnergyPlus plant loop simulation logic and cooling tower performance curves to model chiller lift variations.

## Tools
pydantic-ai, pandas, numpy.
Validates complex multi-chiller electrical capacities and thermal storage bounds with strict type safety.
Fast hosted LLM with strong tabular evaluation capabilities via standard API.

## Deliverables
Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

## Acceptance
- Computes simulated chiller efficiency variations across changing ambient wet-bulb temperatures.
- Schedules thermal energy storage discharge during peak electrical tariff windows.
- Generates a plant operational sequence schedule requiring shift engineer confirmation.

## Starter Examples
```json
[
  {
    "input": {
      "plant_id": "DCP-MARINA-01",
      "ambient_wet_bulb_c": 31.5,
      "cooling_demand_tons": 18000,
      "chiller_available_capacity_tons": 24000,
      "tes_tank_capacity_ton_hours": 40000,
      "peak_tariff_window": true
    },
    "expected_output": {
      "dispatch_mode": "PEAK_SHAVING_TES_DISCHARGE",
      "tes_discharge_rate_tons": 6000,
      "chiller_load_tons": 12000,
      "estimated_power_reduction_kw": 4200,
      "operator_confirmation_required": true
    }
  },
  {
    "input": {
      "plant_id": "DCP-AIRPORT-02",
      "ambient_wet_bulb_c": 24,
      "cooling_demand_tons": 8000,
      "chiller_available_capacity_tons": 20000,
      "tes_tank_capacity_ton_hours": 40000,
      "peak_tariff_window": false
    },
    "expected_output": {
      "dispatch_mode": "OFF_PEAK_TES_CHARGE",
      "tes_discharge_rate_tons": -5000,
      "chiller_load_tons": 13000,
      "estimated_power_reduction_kw": -3500,
      "operator_confirmation_required": true
    }
  }
]
```

## Safety
All dispatch setpoints are operational suggestions; plant emergency thermal trip interlocks remain under hardwired safety relays.
Use mock external writes. Require human review before any real deployment.