A Decision-Focused Predict-then-Bid Framework for Strategic Energy Storage

TL;DR

This paper presents a decision-focused predict-then-bid framework for energy storage arbitrage that integrates market prediction and optimization, significantly improving profit outcomes in electricity markets.

Contribution

It introduces a novel tri-layer end-to-end framework combining prediction and optimization for energy storage bidding, utilizing implicit function theorem and perturbation-based loss for differentiability.

Findings

Outperforms existing methods in profit maximization.

Effective integration of prediction and optimization layers.

Demonstrates success on New York electricity market data.

Abstract

This paper introduces a novel decision-focused framework for energy storage arbitrage bidding. Inspired by the bidding process for energy storage in electricity markets, we propose a predict-then-bid end-to-end method incorporating the storage arbitrage optimization and market clearing models. This is achieved through a tri-layer framework that combines a price prediction layer with a two-stage optimization problem: an energy storage optimization layer and a market-clearing optimization layer. We leverage the implicit function theorem for gradient computation in the first optimization layer and incorporate a perturbation-based approach into the decision-focused loss function to ensure differentiability in the market-clearing layer. Numerical experiments using electricity market data from New York demonstrate that our bidding design substantially outperforms existing methods, achieving the highest profits and showcasing the effectiveness of the proposed approach.