This paper proposes a distributed real-time state estimation (RTSE) method for the combined heat and power systems (CHPSs). First, a difference-based model for the heat system is established considering the dynamics of heat systems. This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation. A cubature Kalman filter (CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information. Finally, a multi-time-scale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for large-scale systems. This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems. Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.

中文翻译：

---

热电联产系统的分布式实时状态估计

本文提出了一种针对热电联产系统的分布式实时状态估计（RTSE）方法。首先，考虑热系统的动力学，建立基于热系统的基于差异的模型。该热系统模型还与电力系统稳态模型一起用于整体CHPS状态估计。开发了一种基于库尔曼卡尔曼滤波器（CKF）的RTSE，以解决系统非线性问题，同时整合历史和当前测量信息。最后，设计了一种多时间尺度异步分布式计算方案，以增强所提出方法在大规模系统中的可扩展性。这种分布式实现只需要少量的信息交换，从而保护了不同能源系统的隐私。