Velazquez et al. base their bidding strategy on the study of the residual demand curve. The bidding of energy storage capacity on the electricity market adds a layer of complexity. The battery has a limited capacity and accumulates revenue by scheduling efficiently generation and load modes. J. Arteaga et al. develop price-taker.
Nevertheless, price endogeneity is rarely considered in storage bidding strategies and modeling the electricity market is a challenging task. Meanwhile, model-free reinforcement learning such as the Actor-Critic are becoming increasingly popular for designing energy system controllers.
The increase in storage capacity coupled with a unique position in the market has caused grid-scale energy stor-age to become a driver of the market price. In economic terms, energy storage is said to be a price-maker, or a monopolistic seller capable of influencing the market because no substitutes exist for their product.
The final case studies for the proposed models are implemented based on the real-world data and the results show the advantages of our developed innovative network-flow model for the battery energy storage bidding, through both one-time and rolling-horizon validations. References is not available for this document.
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