Optimization of market access and penalty mechanisms for shared energy storage

This study aims to optimize the market access and penalty mechanisms for shared energy storage to address issues such as fragmented market access standards, simplistic penalty mechanisms, and insufficient multi-agent coordination in the development of shared energy storage under the double carbon goals. Using methods such as literature research, mathematical modeling, and case analysis, dynamic adjustment models for access mechanisms, game analysis models for agent behavior, and quantitative design models for penalty mechanisms are constructed. A market dynamic simulation platform is built by integrating Agent-Based Modeling (ABM), system dynamics, and machine learning. The results show that innovative mechanism design and methodological applications can e ffectively regulate the behavior of market agents and improve resource utilization efficiency.The proposed 3D penalty coefficients (β₁, β₂, β₃) reduce regulatory costs by 56.7% while cutting violation rates by 53% over 10 years. This study develops a quantitative evaluation model for market access and penalties in shared energy storage, providing decision support for the National Energy Administration to formulate market rules, reducing new energy curtailment rates, and contributing significantly to the healthy development of the shared energy storage industry.