Learnings

Outcomes

The outcomes of the project can be summarised as: 

Developed testing system and models in PSCAD/EMTDC environment. 
Harmonic injection and measurements module which is applicable for power electronic devices. 
Impedance-based stability analysis using an automation toolbox for identifying potential interaction risks. 
Development and validation of the model reduction technique. The data-driven approach is adopted to build the reduced order model for the power system under multiple operating points to achieve a good balance between system accuracy and computational efforts. 
'D3' Technical report 1: Benchmark power system with HVDC and wind farm. 
'D3' Technical report 2: Automation on impedance measurements and stability analysis. 
'D3' Final project report: 'D3' - Data-driven Network Dynamic Representation for Derisking the HVDC and Offshore Wind. 

Following that: 
 

Internal Dissemination: 
WP1 Knowledge Dissemination Event on 30 September 2022; 
WP2 Knowledge Dissemination Event on 30 April 2023; 
Training session 1: Harmonic injection and impedance measurements on 28 September 2023; 
Training session 2: Data-driven based eigenvalue cluster and aggregation on 3 October 2023; 
Training session 3: Automation simulation and analysis on 5 October 2023; 
Final Knowledge Dissemination for WPs 1-3 on 10 November 2023. 
 
External Dissemination and Publications:  
A series of publications following knowledge and capability as built through this project has been published and/or are under development with acceptance of Abstract.  

S. Dai and X. -P. Zhang, "Advanced Identification Methods for Power System Oscillations based on Measurements," 2022 IEEE 16th International Conference on Compatibility, Power Electronics, and Power Engineering (CPE-POWERENG), Birmingham, United Kingdom, 2022, pp. 1-6, doi: 10.1109/CPE-POWERENG54966.2022.9880879. 
C. Wu, X. P. Zhang, X. Zhou and D. Kong, "Comparative research on DC braking choppers for VSC-HVDC with offshore wind farms," 19th International Conference on AC and DC Power Transmission (ACDC 2023), Glasgow, UK, 2023, pp. 45-51, doi: 10.1049/icp.2023.1306. 
D. Li and X. P. Zhang, "An online power system voltage stability index for a VSC HVDC using local measurements," 19th International Conference on AC and DC Power Transmission (ACDC 2023), Glasgow, UK, 2023, pp. 160-165, doi: 10.1049/icp.2023.1324. 
S. Dai, C. Wu, D. Li, D. Kong, X. Zhou, and X. P. Zhang, "A Methodology to Derisk HVDC and Offshore Wind Connections to a Network", CIGRE Paris Session 2024. (Synopsis was accepted) 
 
The Case Study of D3 project has also been captured into ESO’s Network Innovation Allowance – Annual Summary 2021/22.  
https://smarter.energynetworks.org/media/j0ik3ue5/ngeso_nia_annual_summary_2022.pdf 
https://reports.nationalgrideso.com/innovationannualsummary/ . 

Lessons Learnt

The D3 project has highlighted several critical lessons for managing control interaction risks and ensuring system stability. Through the development and application of advanced tools and methodologies, several key insights have emerged that are vital for future projects. 
 
First and foremost, the necessity of a robust screening methodology to identify and prioritise control interaction risks in complex, inverter-based resource (IBR) dominated networks was clearly demonstrated. Developing benchmark systems that accurately simulate these interactions provides a solid foundation for understanding the dynamics of power electronics-integrated networks. This preliminary step is crucial before delving into more sophisticated modelling and analysis. 
 
The adoption of a transfer function-based impedance methodology has proven invaluable for identifying potential risks between inverters. This method, enhanced by automation tools, allows for a detailed examination of system interactions and stability risks. By employing a frequency domain impedance model validated through various simulation techniques, future projects can more accurately predict and mitigate stability issues. This approach also underscores the importance of automating processes to enhance efficiency and accuracy, reducing the likelihood of human error and expediting the analysis. 
 
Another significant lesson is the effectiveness of data-driven methods for model reduction. These techniques enable the aggregation of large data sets across multiple operating points, resulting in a lower order, yet accurate, representation of the power system. Utilising open-source software and enhancing functionalities with programming tools such as Python can streamline this process, making it more accessible and efficient. This approach not only ensures robust modelling but also facilitates easier integration and analysis by external stakeholders. 
Future projects should also consider the broader implications of their methodologies and tools. Disseminating outcomes, obtaining feedback from stakeholders, and trialling models with manufacturers are essential steps for wider adoption and potential regulatory changes. The successful application of these advanced tools and techniques can significantly enhance the capability of system operators to manage control interactions and integrate HVDC and power electronics systems, supporting the transition to a zero-carbon energy system. 
 
In conclusion, the lessons learned from this project emphasize the importance of a structured, automated, and data-driven approach to managing the integration of renewable energy and HVDC systems. By adopting these strategies, future projects can achieve greater accuracy, efficiency, and stability in power system operations, paving the way for a more resilient and sustainable energy network.