# Transferable Energy Storage Bidder

arXiv: https://arxiv.org/abs/2301.01233 · HTML: https://mlsanity.com/paper/2301.01233/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 11 of 11)

- [Exploiting Cyclic Symmetry in Convolutional Neural Networks](https://mlsanity.com/paper/1602.02660/) (2016)
- [Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning](https://mlsanity.com/paper/1711.03127/) (2017)
- [An Approximate Dynamic Programming Algorithm for Monotone Value Functions](https://mlsanity.com/paper/1401.1590/) (2014)
- [Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage using Approximate Dynamic Programming](https://mlsanity.com/paper/1402.3575/) (2014)
- [Reinforcement Learning Based Optimal Battery Control Under Cycle-based Degradation Cost](https://mlsanity.com/paper/2108.02374/) (2021)
- [Arbitraging Variable Efficiency Energy Storage using Analytical Stochastic Dynamic Programming](https://mlsanity.com/paper/2108.06000/) (2021)
- [Operational Valuation for Energy Storage under Multi-stage Price Uncertainties](https://mlsanity.com/paper/1910.09149/) (2019)
- [Impact of Bidding and Dispatch Models over Energy Storage Utilization in Bulk Power Systems](https://mlsanity.com/paper/2201.03421/) (2022)
- [Energy Storage State-of-Charge Market Model](https://mlsanity.com/paper/2207.07221/) (2022)
- [Energy Storage Price Arbitrage via Opportunity Value Function Prediction](https://mlsanity.com/paper/2211.07797/) (2022)
- [Convexifying Market Clearing of SoC-Dependent Bids from Merchant Storage Participants](https://mlsanity.com/paper/2209.02107/) (2022)

## Built on by (most cited first, 2 of 2)

- [Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach](https://mlsanity.com/paper/2404.17683/) (2024)
- [A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction](https://mlsanity.com/paper/2509.16743/) (2025)

## Abstract (arXiv)

Energy storage resources must consider both price uncertainties and their physical operating characteristics when participating in wholesale electricity markets. This is a challenging problem as electricity prices are highly volatile, and energy storage has efficiency losses, power, and energy constraints. This paper presents a novel, versatile, and transferable approach combining model-based optimization with a convolutional long short-term memory network for energy storage to respond to or bid into wholesale electricity markets. We test our proposed approach using historical prices from New York State, showing it achieves state-of-the-art results, achieving between 70% to near 90% profit ratio compared to perfect foresight cases, in both price response and wholesale market bidding setting with various energy storage durations. We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia. The result shows transfer learning achieves exceptional arbitrage profitability with as little as three days of local training data, demonstrating its significant advantage over training from scratch in scenarios with very limited data availability.