Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Elaborate on how you would model specific grid ancillary services (e.g., frequency regulation, capacity firming) and thermal energy provision within your MILP. Use Semantic Kernel to integrate Llama 3.3 70B. Provide a prompt to Llama 3.3 that asks it to interpret a complex grid code excerpt (e.g., specific ramp rates, minimum/maximum dispatch duration for a grid service) or a thermal demand profile. Demonstrate how Llama's response can be used to generate or refine specific MILP constraints or parameters programmatically. Show how these derived constraints are incorporated into your Pyomo model.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Optimize Hybrid Multi-Storage & Heat System for Grid Services
Design and implement a sophisticated optimization model for a hybrid energy system comprising solar PV, a traditional Li-ion battery, and an advanced thermal energy storage (TES) system (e.g., a sand battery or Liquid Air Energy Storage as a proxy for complex TES with thermal output). The system must simultaneously satisfy local electrical and thermal loads, minimize operational costs, and provide ancillary grid services (e.g., frequency regulation, capacity firming) to the broader grid. This challenge requires formulating a mixed-integer linear programming (MILP) or similar optimization problem that captures the distinct operational characteristics, efficiencies, and constraints of each storage technology, as well as complex grid service requirements. Leverage an LLM to assist in interpreting nuanced grid codes and generating plausible operational scenarios for robustness testing.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.