Generative AI for Battery Storage Performance

What to expect in this on-demand recording

In this session, Adam Sroka examines Gen AI use cases for battery storage, their feasibility, considerations, Azure-based tooling options, and recommended next steps for implementing an AI-first approach to BESS asset performance.

Access the recording below.

Overview

In 2023, the total battery storage capacity in the power sector reached over 85 GW. This trend is expected to continue, with significant new projects coming online in UK, Europe and the US. The increase is due to battery storage playing a crucial role in the energy transition, addressing several key challenges such as grid stability and balancing high peak demand. There are substantial investment costs in constructing and maintaining these new assets, and they rely on complex technology operating in an evolving marketplace. Generative AI (Gen AI) and large language models (LLMs) could be the answer to achieving operational efficiency and reducing optimisation complexity across battery storage, ensuring the highest revenue returns are achieved.

In this session, we covered:

  • Gen BI, combining business intelligence with Gen AI to enhance reporting
  • Potential of LLMs to enhance portfolio algorithms
  • Using LLMs to prepare constraints and inputs for optimisation engines
  • Processing and analysing contract data with Gen AI
  • Gen AI-powered market condition reports, personalised for energy traders.

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