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Grid Power & Water Demand Co-Simulation with CrewAI & Fixie

Grid capacity limits and community opposition present key hurdles for new data center proposals. Build a collaborative multi-agent simulation using CrewAI and Fixie interfaces to model real-time electrical grid strain and water usage against regional utility capacity. Achieve sub-minute simulation accuracy across peak load scenarios.

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What you are building

Develop a multi-agent utility impact simulator using CrewAI agents and Fixie voice/text interfaces to model data center power and water footprint.

How work is evaluated

Evaluation

Evaluates power and water load projections calculated by CrewAI agents against standard energy physics benchmarks.

Datasets

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

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Capacity Overload Identification

Correctly flags overload state when peak MW exceeds grid capacity

Binary check

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

Dimension 2

MW Calculation Accuracy

Percentage variance from theoretical thermodynamic baseline • target: 1 • range: 0-5

Binary check

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

Learning goals

  • Formulate CrewAI agent roles for Electrical Grid Analysis and Hydrological Modeling

  • Integrate Fixie interface endpoints for dynamic natural language scenario queries

  • Simulate hourly resource draw for 100MW+ data center configurations

  • Produce utility feasibility reports with actionable peak-shaving recommendations

Resources

Dataset notes

Hourly grid load patterns and regional aquifer extraction limits simulated for high-density compute regions.

Challenge setup

Evaluates power and water load projections calculated by CrewAI agents against standard energy physics benchmarks.

Challenge input

JSON with facility_capacity_mw, cooling_type, ambient_temp_c

CrewAI

Enables multi-role agent collaboration

Fixie

Provides structured interface for conversational agent deployment

Evaluated output

JSON with projected_peak_mw, daily_water_gallons, grid_status

Evaluation checks
  • Correctly flags overload state when peak MW exceeds grid capacity
  • Percentage variance from theoretical thermodynamic baseline • target: 1 • range: 0-5
Configured evaluation evidence
  • Benchmark: GridSimBench
  • MW Calculation Accuracy target: 1
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

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

View technical recipe

Configured tools

Action Space
  • Fixie · Required
  • CrewAI · Optional
  • Adept · Optional

Evaluation contract

  • Capacity Overload Identification · Weight 1
  • MW Calculation Accuracy · Weight 1

Metrics

  • MW Calculation Accuracy · Target 1

Benchmarks

  • GridSimBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with facility_capacity_mw, cooling_type, ambient_temp_c

    Output: JSON with projected_peak_mw, daily_water_gallons, grid_status

    Execution harness: Python

Evaluation tools

  • CrewAI · Multi-agent framework
  • Fixie · Agent UI & API Layer

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Grid Power & Water Demand Co-Simulation with CrewAI & Fixie