Distributed Observer-Based Resilient Optimization Control for DESSs Under False Data Injection Attacks

When designing cooperative control strategies for distributed energy storage systems (DESSs), traditional convex optimization algorithms often lead to sub-optimization, and overcurrent issues. To address these challenges and achieve power loss minimization across multiple DESSs, this paper proposes an automatic constraint convex optimization algorithm (ACCOA). ACCOA enhances traditional methods by incorporating a penalty function into the evaluation process, ensuring automatic satisfaction of inequality constraints during algorithm iterations. The proposed ACCOA facilitates communication solely between neighboring DESSs, offering scalability and robustness advantages. However, in the optimization process, distributed current allocation becomes susceptible to false data injection (FDI) attacks. To mitigate this vulnerability, a distributed extended observer (DESO) is developed to detect and counteract attack signals, thereby enhancing resilience against FDI attacks in secondary controllers. This research culminates in a resilient-enhanced control framework for cooperative optimization among DESSs, validated through simulations and OPAL-RT experiments to demonstrate the effectiveness of ACCOA-DESO in a multi-DESS system.