Modelling and simulation for the optimal resilient resource infrastructure under uncertainty

Hydroelectric and thermal energy generation and transmission require water. Similarly, energy generation is used for extracting, treating and transporting water.

Next generation planning for sustainable resource management should consider energy and water resources simultaneously. It should also be able to suggest the right amount of new supply infrastructure at the right time to meet future water-energy demands cost-effectively. Planning solutions to such an interconnected system is a challenge in that it needs multidisciplinary research. This requires descriptive analytics, including system dynamic simulation and optimisation techniques, to help make informed decisions in this context.

The research used big data analysis, decision sciences under uncertainty, multi-objective evolutionary search, and agent-based modelling to identify the best possible approaches to improving water-energy system efficiency, increasing their infrastructure resilience and reducing the financial risk involved. Methods developed have been applied to real-world problems such as capacity expansion to balance London’s Thames Water supply and demand, pipe leakage detection and scheduling in South America, analysing the interaction of water, energy and food in the Nile basin, trading off damage resiliency, disruption and economic cost of UK flooding, and managing climate change extreme events and CO2 emissions in the Middle East.