Grid Digital Twin
A digital twin is a virtual model of physical infrastructure, processes and systems that can carry out various functions such as intelligent data analysis, computer modelling and simulation and machine learning to support users in improving planning and decision-making processes.
The Grid Digital Twin comprising the Asset Twin and Network Twin.
EMA, SP Group and the Science and Technology Policy and Plans Office (S&TPPO) under the Prime Minister's Office are developing a Grid Digital Twin which aims to enhance Singapore's grid resilience to ensure grid reliability and support the deployment of clean energy sources.
Key benefits of the Grid Digital Twin include:
- Enhanced condition monitoring of assets and prioritisation of asset renewal, by having a decision tool that can identify risks and optimise grid asset renewal plans. The tool will take into account health, utilisation and failure history of the grid assets.
- Improvement in carrying out network planning analysis with better network utilisation when balancing new or peak electricity loads.
- Optimisation of asset investment, by identifying potential synergies between asset renewal and upgrades for load growth without compromising grid resilience.
More information on the Grid Digital Twin can be found in the
Media Release.
The Grid Digital Twin comprises two key models:
- Network Twin for impact assessment on grid. This uses modelling and simulations to determine the impact of additional loads (such as charging of electric vehicles) and distributed energy resources (such as solar photovoltaic and energy storage systems) on the grid. Details of the awarded project can be found here.
- Asset Twin to optimise the planning, operation and maintenance of SP's grid assets (such as transformers, switchgears and cables). The Asset Twin is able to remotely monitor and analyse the condition and performance of assets, and identify potential risks in grid operations early. Five research projects were awarded by EMA to SP and Nanyang Technological University (NTU) under the SP Group - NTU Joint Laboratory.
| Project Title |
Project Description |
Project Team |
| Risk-based Asset Investment and Planning |
The project will develop a new Asset Health and Criticality Index which could be used to assess the risk of assets and make forward projections of asset lifespan, enabling optimised decision-making for asset planning. |
Principal Investigator:
Prof Xu Yan, NTU
Co-Investigator(s):
SP Power Assets Ltd (SPPA), a business unit under SP Group |
| Distribution Switchgear Degradation Study |
The project will develop a novel virtual 3D multi-physics finite element model that can accurately replicate degradation and lifespan of distribution switchgear materials and components. |
Principal Investigator:
Prof Chen Zhong, NTU
Co-Investigator(s):
SPPA; NTU |
| Failure Mode Analysis and Mitigation Optimisation |
The project will develop a holistic failure mitigation platform that integrates survival analysis using multiple statistical models to adjust maintenance regime and replacement criteria to balance risk and cost. |
Principal Investigator:
Assoc Prof Hu Guoqiang, NTU
Co-Investigator(s):
SPPA; NTU |
| Online Condition Monitoring System for Distribution Switchgear |
Together with trend analysis to predict potential failures and prevent future network problems before they occur, project will develop an online partial discharge monitoring system that could be deployed across the distribution switchgear population. |
Principal Investigator:
Assoc Prof Zheng Yuanjin, NTU
Co-Investigator(s):
SPPA; NTU; SP Digital Pte Ltd, the digital arm of SP Group
|
| Distribution Cable Insulation Health Assessment |
Integrating both data analytics and trending historical cable IR test results, the project will develop an integrated tool to pre-empt impending failures of cable insulation based on historical and new test results, enabling effective health screening of the entire population of network distribution cables and prioritisation for further testing and mitigation, which would improve the robustness of network reliability. |
Principal Investigator:
Asst Prof Amer Ghias, NTU
Co-Investigator(s):
SPPA; NTU |
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