5 papers
A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
Saud Alghumayjan, Ming Yi, Bolun Xu
This paper proposes a few-shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real-time electricity prices. The…
A Decision-Focused Predict-then-Bid Framework for Strategic Energy Storage
Ming Yi, Yiqian Wu, Saud Alghumayjan +2
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…
Conformal Uncertainty Quantification of Electricity Price Predictions for Risk-Averse Storage Arbitrage
Saud Alghumayjan, Ming Yi, Bolun Xu
This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresse…
Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage
Ming Yi, Saud Alghumayjan, Bolun Xu
This paper presents a novel decision-focused framework integrating the physical energy storage model into machine learning pipelines. Motivated by the model predictive control for…
Energy Storage Arbitrage in Two-settlement Markets: A Transformer-Based Approach
Saud Alghumayjan, Jiajun Han, Ningkun Zheng +2
This paper presents an integrated model for bidding energy storage in day-ahead and real-time markets to maximize profits. We show that in integrated two-stage bidding, the real-ti…