  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.
