Thermo-Electrical state and demand response potential estimation for power systems with building thermostats

• Published in: International journal of electrical power & energy systems 2023-02, Vol.145, p.108588, Article 108588
• Main Authors: Song, Shaojian, Zhang, Wentao, Lin, Yuzhang, Wang, Cheng
• Format: Article
• Language: English
• Subjects: Building energy systems; Demand response; Flexible load; Heating and cooling; Situational awareness; State estimation
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2022.108588

•Tractable thermo-electrical state estimation/prediction for data fusion from buildings, power grid, and weather stations.•Tackling different time scales of thermo-electrical dynamics in power grids with building thermostats.•Noise filtering and bad data processing capabilities enabled for power grids and building energy systems.•Real-time demand response potential estimation providing situational awareness for reliable and efficient grid operation. Heating and cooling are major resources of Demand Response (DR) to enhance the flexibility and reliability of power grids. In order to maximize their potential, it is necessary to reliably keep track of their operating states in real-time operation. This paper presents a comprehensive state estimation framework for power systems with building thermostats, with a tractable thermodynamic building model and integration of multi-source information from weather, power grid, building systems. Based on the thermodynamic model and state of the buildings, the building temperature trajectories in the next few hours can be accurately predicted, such that the DR potential of the building can be precisely estimated. To jointly estimate the state variables of power grids and state variables in building thermostats with different time scales of dynamics, a holistic estimation framework is developed based on the partial equivalence between the Weighted Least Squares (WLS) estimation problem and the correction stage of the Iterative Extended-Kalman Filter (IEKF). Simulation results show that the proposed framework can accurately track the thermo-electrical states of the system and estimate the DR potential in the presence of measurement noise and bad data.