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Found 9 papers, 1 papers with code
Economic Capacity Withholding Bounds of Competitive Energy Storage Bidders
This paper derives a theoretical framework to study the economic capacity withholding behavior of storage participating in competitive electricity markets and validate our results in simulations based on the ISO New England system.
Equitable Time-Varying Pricing Tariff Design: A Joint Learning and Optimization Approach
Simulation using real-world consumer data shows that our equitable tariffs protect low-income consumers from price surges while effectively motivating consumers to reduce peak demand.
Predicting Strategic Energy Storage Behaviors
Energy storage are strategic participants in electricity markets to arbitrage price differences.
Vehicle-to-Grid Fleet Service Provision considering Nonlinear Battery Behaviors
We incorporate nonlinear battery models and price uncertainty into the V2G management design to provide a realistic estimation of cost savings from different V2G options.
Transferable Energy Storage Bidder
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.
Energy Storage Price Arbitrage via Opportunity Value Function Prediction
This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming.
Energy Storage State-of-Charge Market Model
Combined with an optimal bidding design algorithm using dynamic programming, our paper shows that the SoC segment market model provides more accurate representations of the opportunity costs of energy storage compared to existing power-based bidding models.
End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals.
Bounding Regression Errors in Data-driven Power Grid Steady-state Models
Results identify how the bounds decrease with additional power grid physical knowledge or more training data.
