Public evaluation

Auction Bid Accuracy

The system will be evaluated on its ability to generate bids that maximize revenue without violating battery health constraints over a 30-day simulated period.

Evaluation type

task based

Challenge

Multi-Agent BESS Capacity Auction Strategy with AutoGen and Google Jules

Difficulty

Advanced

Rigor

Unspecified

Open linked challenge

Evaluation overview

How the linked challenge is judged: tasks, benchmarks, and criteria count.

Tasks

Benchmarks

Criteria

Task templates

Inputs and expected outputs.

Task 1

Auction Bid Accuracy

Evaluate if the bidding agent generates valid bid structures (Price, Volume, Duration) based on market signals.

Input format

JSON containing historical market prices and battery state

Output format

JSON bid object

Linked challenge

Multi-Agent BESS Capacity Auction Strategy with AutoGen and Google Jules

This challenge focuses on building a multi-agent simulation to optimize bidding strategies for Battery Energy Storage Systems (BESS) in the newly established German capacity market. Using Microsoft's AutoGen framework, you will design a system of collaborating agents: a 'Market Analyst' to forecast demand and prices, a 'BESS Operator' to calculate state-of-health and discharge costs, and a 'Bid Strategist' to synthesize these inputs into a winning auction bid. The implementation must incorporate Google Jules for AI-assisted engineering and code generation of the underlying physics-based battery models. Participants will tackle the complexities of energy arbitrage and capacity availability requirements. The goal is to maximize revenue while adhering to strict market regulations (such as those recently announced in Germany and Poland). You will need to implement complex logic for state-of-charge (SOC) management and cycling constraints, ensuring that the agents communicate effectively to reach a consensus on the optimal bid price and volume for a 200MW/800MWh BESS facility.

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