To maximize performance and profitability, power markets may use a variety of trading methods and operational techniques for multi-type energy storage systems. In accordance with the capacity and structure of the market, these systems may take part in energy markets, supplementary services markets, along with local or P2P marketplaces. With the use of pricing signals and demand estimates, operational strategies aim to maximize income through intelligently charging and releasing the various storage kinds. Regional distribution networks that rely on distribution network operators can now quantitatively determine their energy storage supply and demand with the help of this study's suggested approach for determining Hybrid Golden Flower Pollination (HGFP) Method energy storage action deviations. This method will be crucial for their future participation in market trading. Second, taking into account the economic advantages for all parties involved, the study created a pricing mechanism that incorporates a valley compensation mechanism to encourage autonomous and active user participation using the SARSA Deep Learning technique. The trading mechanism relies on combinatorial auctions and accommodates various types of market participants. Taking into consideration the variations in HGFP energy storage action when different regional networks are involved, numerical simulations were run to confirm the trading mechanism's practicality and rationale.
Zhao et al. (Mon,) studied this question.