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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.

EngineeringHosted by Vera

What you are building

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.

Rules

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

Evaluation

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.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

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.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

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

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

All dispatch setpoints are operational suggestions; plant emergency thermal trip interlocks remain under hardwired safety relays.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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.

Resources

Dataset notes

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

Challenge input

Input details are not configured.

Pydantic AI

Typed Python agent framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • 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...
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 2 configured tools

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Optional
Orchestration
  • 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.

Frequently Asked Questions about Build a Heat-Resilient District Cooling Chiller Dispatcher