Together with industry partners and other government agencies, EMA is working to develop a digital twin for the power grid.
With growing demand and the need to integrate new energy sources into the power system, we are leveraging digital solutions to optimise our energy grid in order to maintain reliable and resilient supply of energy to consumers.
One of the initiatives is the Grid Digital Twin, which comprises the network twin and asset twin.
It uses modelling and simulations to determine how additional loads, like charging of electric vehicles and distributed energy resources such as solar photovoltaic and energy storage systems, affect the grid.
By using an advanced software framework called the Multi Energy System Modelling & Optimisation (MESMO), the Network Twin provides SP Group with a high-level assessment of the impact of demands on the grid and identifies any necessary upgrades for different scenarios.
It improves the planning, operation and maintenance of SP Group's grid assets such as transformers, switchgears and cables. It can monitor and analyse asset conditions remotely, thus identifying potential risks in grid operations.
Five research projects were awarded by EMA to SP Group and Nanyang Technological University (NTU) under the SP Group - NTU Joint Laboratory, as shown in the table below.
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.
Prof Xu Yan, NTU
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.
Prof Chen Zhong, 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.
Assoc Prof Hu Guoqiang, 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.
Assoc Prof Zheng Yuanjin, NTU
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.