Distributed Real-time State Estimation for Combined Heat and Power Systems
| Autor: | Jian Chen, Wen Zhang, Junbo Zhao, Yaxin Du, Tingting Zhang, Qi Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
TK1001-1841
cubature Kalman filter (CKF) Renewable Energy Sustainability and the Environment Computer science 020209 energy Computation 020208 electrical & electronic engineering TJ807-830 Energy Engineering and Power Technology heat dynamics 02 engineering and technology real-time state estimation (RTSE) Renewable energy sources System model Nonlinear system Electric power system Production of electric energy or power. Powerplants. Central stations Control theory Asynchronous communication Scalability Combined heat and power system (CHPS) 0202 electrical engineering electronic engineering information engineering State (computer science) multi-time-scale asynchronous distributed scheme Energy (signal processing) |
| Zdroj: | Journal of Modern Power Systems and Clean Energy, Vol 9, Iss 2, Pp 316-327 (2021) |
| ISSN: | 2196-5625 |
| DOI: | 10.35833/mpce.2020.000052 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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