  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.

Machine Learning;Systems and Control;Systems and Control