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AlphaSearch: Agentic AI for Price Arbitrage in Energy Systems

Primary research

#1491

T1new
Topic
unassigned (set during synthesis)
First seen
2026-08-09 07:16:03
Last seen
2026-08-09 07:16:03

Source raw items (1)

  • Semantic Scholar2026-08-09 07:15:26
    AlphaSearch: Agentic AI for Price Arbitrage in Energy Systems

    Wholesale electricity prices are highly volatile due to fluctuations in weather, renewable generation, demand, and grid conditions. These fluctuations create arbitrage opportunities for energy storage, but realizing this value requires dispatch decisions that account for price uncertainty, battery constraints, and deployment complexity. Existing approaches often require practitioners to separately manage forecasting, optimization, feasibility constraints, and evaluation, making deployment fragmented and hard to interpret. We propose ?search, an agentic AI framework for battery arbitrage that accounts for price uncertainty in wholesale electricity markets. ?search supports and benchmarks heuristic, optimization-based, reinforcement learning, and LLM-assisted decision-making approaches across multiple Independent System Operator (ISO) datasets. We further conduct a series of stress tests to assess the effectiveness of these methods in realistic market settings.