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.
Implement the core optimization logic using a chosen LP/MIP solver, incorporating the forecasted data and degradation model. Construct your CrewAI agents, defining their tasks, tools, and collaboration patterns. Integrate Grok-2 via API to provide advanced analytical capabilities (e.g., interpreting complex market news for price anomaly detection or suggesting novel bidding strategies) to one or more agents. Ensure the agents can generate dispatch commands for a simulated BESS.
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 BESS Dispatch for Arbitrage & Ancillary Services with Degradation
The rapid deployment of large-scale Battery Energy Storage Systems (BESS), like the 800MWh Washington state project, necessitates sophisticated control strategies to maximize their economic value while preserving battery health. This challenge focuses on developing an advanced optimization framework to manage BESS charge and discharge cycles. Participants will design a system that dynamically allocates BESS capacity across multiple revenue streams, including energy arbitrage (buying low, selling high) and ancillary services (e.g., frequency regulation), while explicitly accounting for the non-linear effects of cycling on battery degradation. The solution will integrate real-time market data, forecast future prices and grid signals, and employ a physics-informed degradation model to make optimal dispatch decisions. The core task involves creating an intelligent, agent-based system that can adapt to changing market conditions and BESS state. This project emphasizes multi-objective optimization under real-world constraints, crucial for the long-term viability and profitability of grid-scale energy storage.
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.