Prescribed performance-based privacy-preserving quantized consensus for distributed energy dispatch optimization

To improve energy efficiency and optimize energy dispatching of combined heat and power (CHP) systems, a distributed economic optimization framework based on the equal incremental principle is established, and a distributed fixed-time quantized privacy-preserving consensus algorithm is adopted to achieve consistent incremental costs. First, a privacy mask with predetermined performance is designed to avoid the potential leakage of sensitive information in distributed communication. Based on this, fixed-time privacy-preserving leader–follower and weighted average consensus algorithms are proposed, where the setting time is independent of the initial state conditions. In contrast to existing works, the proposed algorithms can achieve complete consensus instead of converging within a small region, ensuring the global optimality of the solutions. Additionally, a quantization mechanism and time-varying gains are introduced into the algorithms, reducing transmission constraints and resource waste. Finally, the proposed algorithms are applied to the distributed economic dispatch problem, demonstrating precise convergence and excellent privacy protection effect.