Adaptive nested robust multi-objective optimisation for integrated precinct-scale energy–water system planning

This paper proposes an adaptive nested robust multi-objective optimisation approach to support integrated energy–water system planning decision-making under uncertainty for sustainable urban precinct-scale infrastructure development. The proposed framework aims to enhance overall energy–water system performance by attaining desirable conflicting technoeconomic and environmental objectives, while strictly satisfying hard coupling nodal and operational constraints. Subject to these imposed constraints, the defined objectives ensure system reliability, balance cost-effectiveness, as well as promote resource sustainability. By exploiting the problem structure with nested energy–water system decisions, the proposed methodology derives robust counterparts of separable deterministic uncertain problems as mixed-integer disciplined convex programs. These optimisation programs address resource assignment and asset allocation as well as unit commitment, thereby tackling them for energy–water system capacity sizing and operation scheduling over the planning horizon, while incorporating adaptive recourse actions taken to handle uncertainties in realistic prospective scenarios. To validate the applicability of this approach, a decentralised energy–water distribution network was examined within a community microgrid as a precinct-scale case study. This evaluation determines integrated energy–water system plans by comparing the proposed method with existing related linear models and usual conservative formulations. The numerical results indicate that this mathematical optimisation modelling approach for integrated system planning can significantly enhance overall performance of using distributed energy–water resources, supporting decision intelligence to more beneficial investment strategies adopted by facility custodians or site managers in urban precincts. The findings demonstrate substantial energy–water resource savings (up to 18.2%), reduced overall system costs (up to 21.25%), and lower carbon emissions (up to 10.0%). Furthermore, the applied operations research methods facilitate greater penetration of renewable energy sources while harvesting utilisable nonpotable water sources. These methodological and practical advances contribute to the digital multi-utility transformation, fostering long-term and short-term economic as well as environmental benefits for sustainable development of resilient urban precinct-scale infrastructure.