Managing complex Cyber-Physical Energy Systems (CPES) requires solving various optimization problems with multiple objectives and constraints. As distributed control architectures are becoming more popular in CPES for certain tasks due to their flexibility, robustness, and privacy protection, multi-objective optimization must also be distributed. For this purpose, we present MO-COHDA, a fully distributed, agent-based algorithm, for solving multi-objective optimization problems of CPES. MO-COHDA allows an easy and flexible adaptation to different use cases and integration of custom functionality. To evaluate the effectiveness of MO-COHDA, we compare it to a central NSGA-2 algorithm using multi-objective benchmark functions from the ZDT problem suite. The results show that MO-COHDA can approximate the reference front of the benchmark problems well and is suitable for solving multi-objective optimization problems. In addition, an example use case of scheduling a group of generation units while optimizing three different objectives was evaluated to show how MO-COHDA can be easily applied to real-world optimization problems in CPES.

翻译：管理复杂的分布式信息物理能源系统需解决涉及多目标和约束条件的各类优化问题。随着分布式控制架构因其灵活性、鲁棒性和隐私保护优势在特定任务中日益普及，多目标优化也须采用分布式方法。为此，我们提出MO-COHDA——一种完全分布式的智能体算法，用于求解CPES中的多目标优化问题。该算法可灵活适配不同应用场景并集成定制化功能。通过采用ZDT基准问题套件中的多目标测试函数，我们将MO-COHDA与集中式NSGA-2算法进行对比评估。结果表明，MO-COHDA能够有效逼近基准问题的参考前沿，适用于求解多目标优化问题。此外，我们以某发电机组群调度场景为例，验证了该算法在CPES实际优化问题中同时优化三个不同目标的便捷应用能力。