Build a Heat-Resilient District Cooling Chiller Dispatcher
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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
District cooling plant superintendent
Extract weather forecast metrics and cooling load profiles for the district network.
Model chiller lift and cooling tower approach temperatures using EnergyPlus chiller plant simulation formulations.
Determine thermal energy storage tank charge and discharge cycles to shift electric load from peak tariff hours.
Produce an optimized chiller staging sequence and power demand schedule for plant control operators.
Chiller plant load-staging matrix showing thermal storage state-of-charge, power draw, and wet-bulb temperatures.
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.
EnergyPlus Applies EnergyPlus plant loop simulation logic and cooling tower performance curves to model chiller lift variations.
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.
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.
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.
```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 } } ] ```
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.
Participation guide
All dispatch setpoints are operational suggestions; plant emergency thermal trip interlocks remain under hardwired safety relays. Use mock external writes and human review.
How work is evaluated
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.
Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Complete workflow
Extract weather forecast metrics and cooling load profiles for the district network. Model chiller lift and cooling tower approach temperatures using EnergyPlus chiller plant simulation formulations. Determine thermal energy storage tank charge and discharge cycles to shift electric load from peak tariff hours. Produce an optimized chiller staging sequence and power demand schedule for plant control operators.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Correctness and evidence
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.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Review interface
Chiller plant load-staging matrix showing thermal storage state-of-charge, power draw, and wet-bulb temperatures.
Missing or incorrect.
Partly complete, with documented gaps.
Complete, with reproducible evidence.
Safe execution
All dispatch setpoints are operational suggestions; plant emergency thermal trip interlocks remain under hardwired safety relays.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Extract weather forecast metrics and cooling load profiles for the district network.
Model chiller lift and cooling tower approach temperatures using EnergyPlus chiller plant simulation formulations.
Determine thermal energy storage tank charge and discharge cycles to shift electric load from peak tariff hours.
Produce an optimized chiller staging sequence and power demand schedule for plant control operators.
Reference links and supporting material
Synthetic hourly cooling load profiles and wet-bulb weather records formatted according to EnergyPlus plant schema. Supplied starter examples are synthetic, not source extracts.
Challenge setup
Configuration summary for this challenge.
Challenge input
Input details are not configured.
Pydantic AI
Typed Python agent framework.
Evaluated output
No output checks are configured.
- Extract weather forecast metrics and cooling load profiles for the district network. Model chiller lift and...
- Computes simulated chiller efficiency variations across changing ambient wet-bulb temperatures. Schedules t...
- Chiller plant load-staging matrix showing thermal storage state-of-charge, power draw, and wet-bulb tempera...
- 2 public reference cases
- 2 configured tools
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Pydantic AI · Optional
- Pydantic AI · Optional
Evaluation contract
- Complete workflow · Weight 40
- Correctness and evidence · Weight 30
- Review interface · Weight 20
- Safe execution · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.