Hybrid Physics- and Data-Driven Dynamic State Estimation of Electricity-Heat Integrated Energy Systems

Chen, Long; Qin, Chun; Zhao, Jun; Wang, Wei

doi:10.1109/tim.2023.3338666

IEEE Trans. Instrum. Meas.

2024

DOI: 10.1109/tim.2023.3338666

|View full text |Cite

Hybrid Physics- and Data-Driven Dynamic State Estimation of Electricity-Heat Integrated Energy Systems

Long Chen,

Chun Qin,

Jun Zhao

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Physics and data‐driven approach for online joint state and parameter estimation of electricity and steam networks

Zhao,

Li,

Wang

et al. 2024

Energy Internet

View full text Add to dashboard Cite

The electricity and steam integrated energy systems, which can capture waste heat and improve the overall energy efficiency, have been widely utilised in industrial parks. However, intensive and frequent changes in demands would lead to model parameters with strong time‐varying characteristics. This paper proposes a hybrid physics and data‐driven framework for online joint state and parameter estimation of steam and electricity integrated energy system. Based on the physical non‐linear state space models for the electricity network (EN) and steam heating network (SHN), relevance vector machine is developed to learn parameters' dynamic characteristics with respect to model states, which is embedded with physical models. Then, the online joint state and parameter estimation based on unscented Kalman filter is proposed, which would be learnt recursively to capture the spatiotemporal transient characteristics between electricity and SHNs. The IEEE 39‐bus EN and the 29‐nodes SHN are employed to verify the effectiveness of the proposed method. The experimental results validate that the proposed method can provide a higher estimation accuracy than the state‐of‐the‐art approaches.

show abstract

Physics and data‐driven approach for online joint state and parameter estimation of electricity and steam networks

Zhao,

Li,

Wang

et al. 2024

Energy Internet

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Hybrid Physics- and Data-Driven Dynamic State Estimation of Electricity-Heat Integrated Energy Systems

Cited by 1 publication

References 40 publications

Physics and data‐driven approach for online joint state and parameter estimation of electricity and steam networks

Physics and data‐driven approach for online joint state and parameter estimation of electricity and steam networks

Contact Info

Product

Resources

About