Predicting Strategic Energy Storage Behaviors

Energy storage are strategic participants in electricity markets to arbitrage price differences. Future power system operators must understand and predict strategic storage arbitrage behaviors for market power monitoring and capacity adequacy planning. This paper proposes a novel data-driven approac...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2024-03, Vol.15 (2), p.1608-1619
Main Authors: Bian, Yuexin, Zheng, Ningkun, Zheng, Yang, Xu, Bolun, Shi, Yuanyuan
Format: Article
Language:English
Subjects:
Adequacy
Behavioral sciences
Costs
Data models
Degradation
Differentiable optimization
electricity markets
Energy storage
Mathematical models
Model accuracy
Optimization
Parameter identification
Predictive models
Storage systems
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Summary:Energy storage are strategic participants in electricity markets to arbitrage price differences. Future power system operators must understand and predict strategic storage arbitrage behaviors for market power monitoring and capacity adequacy planning. This paper proposes a novel data-driven approach that incorporates prior model knowledge for predicting the strategic behaviors of price-taker energy storage systems. We propose a gradient-descent method to find the storage model parameters given the historical price signals and observations. We prove that the identified model parameters will converge to the true user parameters under a class of quadratic objective and linear equality-constrained storage models. We demonstrate the effectiveness of our approach through numerical experiments with synthetic and real-world storage behavior data. The proposed approach significantly improves the accuracy of storage model identification and behavior forecasting compared to previous blackbox data-driven approaches.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2023.3303469