Price-Matching-Based Regional Energy Market With Hierarchical Reinforcement Learning Algorithm

This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation. With the ongoing advancements in energy distribution and marketization, the energy Internet necessitates improved applicability and efficiency for personalized energy responses. To address these requirements, a multienergy trading market model is proposed, which enables the avoidance of user information disclosure and guarantees user trading autonomy. In addition, a joint trading mechanism is designed that accounts for multiple time scales and energy types, consequently reducing trading failures caused by overlooking energy transmission processes. By performing the proposed trading mechanism, the market operator can match various energy types using conversion devices, thereby augmenting matching efficiency. An income mechanism is also established to deter the operator from purposefully evading potential trading opportunities for personal gain. To address the proposed model, an improved hierarchical reinforcement learning algorithm is employed, which effectively overcomes challenges associated with large state action spaces and sparse rewards. Numerical examples are provided to confirm the efficacy of the proposed approach.