Several papers explore optimal bidding algorithms on the electricity market when bids influence the clearing price, i.e. the market player is a price-maker. Some relevant examples include the following: Oren et al. computed the optimal bidding strategy with dynamic programming by estimating other market players.
The current work explores the use of adaptive control for optimizing the bidding strategy of a price-maker agent participating in a regular wholesale market. Several papers explore optimal bidding algorithms on the electricity market when bids influence the clearing price, i.e. the market player is a price-maker.
The combination of the market state and the battery state is sent back to the battery’s bidding agent to compute a new bid at the next step. Batteries generally have a larger impact on ancillary service markets and especially on frequency control mar-kets.
Throughout this literature, a common method to solve the optimal bidding strategy for a price-maker is used. A bi-level optimization program where the first layer maximizes the player’s revenue and the second layer solves a dispatch problem to maximize the social welfare.
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We''re constructing a simple operational trading strategy to maximize revenue from hypothetical battery by Buying and selling electricity during the hold-out period located at the …
Battery storage operators in day-ahead electricity markets rely heavily on price forecasting to guide their charge–discharge decisions. …
We compare the supervised Actor-Critic algorithm with the MPC algorithm as a supervisor, finding that the former reaps higher profits via learning.
We are often asked how the financial optimization (or: arbitrage) of a battery across the different market places of the spot market works. We show this x-market …
First, we model consumers with batteries, generators with batteries, and accumulators, all of whom strive to maximize their own profit. An optimal pricing algorithm based on dual …
Abstract. There is growing interest in the use of grid{level storage to smooth variations in supply that are likely to arise with increased use of wind and solar energy. Energy …
Battery storage operators in day-ahead electricity markets rely heavily on price forecasting to guide their charge–discharge decisions. Accurate forecasts enable an arbitrage …
Introduction In this tutorial, we extend the reinforcement learning (RL) framework to storage units, such as batteries, which face a unique decision structure: they must buy …
To fill these gaps, we implement an online Supervised Actor-Critic (SAC) algorithm, supervised with a model-based controller – Model Predictive Control (MPC). The energy …
In this paper, we present a trading-oriented battery energy storage system (BESS) planning model for a distribution market. The proposed planning model is formulated as a …
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