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Sensing, Analytics and Predictive Modelling to Aid Planning and Optimisation for the Urban Habitat (Electrical Grid)

This project forms part of a broader research programme addressing the ageing infrastructure of Housing and Development Board (HDB) housing estates in Singapore, which has left them increasingly susceptible to faults and breakdowns. The programme is a collaboration between the Chemical Engineering and Civil Engineering departments at ÌìÃÀ´«Ã½ and Singapore's Agency for Science, Technology and Research (A*STAR), spanning six applications: pneumatic waste conveyance systems, water pipelines, lifts, urban water management, electrical grid, and communication networks, and is funded by the Land and Liveability National Innovation Challenge (L2NIC) Research Programme, Singapore. Details of the other five applications are available on the respective project partners' . Our team's contribution focused on the electrical grid application.

This project investigated the potential of vehicle-to-grid (V2G) technology to support Singapore's growing electric vehicle (EV) fleet and its 2030 target to phase out internal combustion engine vehicles. V2G allows EVs to act as mobile batteries, both drawing power from and returning power to the grid, helping to balance electricity demand and integrate renewable energy. While V2G trials have begun in Singapore, its costs and benefits at the community scale had not yet been rigorously modelled, largely due to a lack of real-world data linking household energy use and EV charging behaviour.

Working with a residential community in Punggol, comprising seven HDB blocks and a multi-storey car park equipped with solar panels, the team built an optimisation model of demand-side management (DSM) grounded in real smart meter records, solar generation data, electricity tariffs, and car parking data. This approach preserved the link between residents' daily routines, their EV charging patterns, and household electricity consumption, an important connection missed by prior studies that relied on separate, simulated datasets.

The analysis found that lifts account for the largest share of common-area electricity use, followed by pumps and lighting. Under a scenario of 100% EV penetration by 2040, V2G was shown to reduce combined electricity costs for common areas and EVs by up to 56%, by optimising charging and discharging schedules alongside renewable energy use. Sensitivity analysis further identified EV penetration rate as the most critical factor in V2G performance, informing when a trial deployment becomes cost-effective.

The resulting framework is transferable to other residential communities and can be refined as more granular data on solar generation, EV charging, and household loads becomes available. It gives planners and policymakers an evidence-based way to weigh V2G's cost savings against the investment required, supporting Singapore's transition to smarter, more resilient electricity infrastructure.