Integrated Optimization of Urban Water–Energy Infrastructure Operations in a Metropolitan-Scale Network

In contemporary urban planning, infrastructure plays a decisive role in shaping the resilience, sustainability, and economic performance of cities. However, the urban planning discipline has conventionally lacked optimization-based frameworks for analyzing and operating these infrastructures. Instead, many studies have relied predominantly on AI or statistical pattern-recognition approaches that capture observable phenomena but often fail to reveal the underlying causal mechanisms. With the increasing diversity of operational data and the rapid evolution of computational capabilities, research that relies solely on empirical patterns is no longer sufficient for addressing the complexities of modern urban systems.

Water resource infrastructure is a prime example representing one of the largest electricity consumers in the public sector, yet much of the existing literature continues to depend on management of the conventional resources. In modern era, the scope of operable resources is far broader than pumps and treatment facilities alone. Renewable energy sources, battery energy storage, and even emerging hydrogen-based power systems increasingly interact with water infrastructure operations.

Given these expanded resource portfolios and the growing importance of electricity markets, urban infrastructure systems must be planned and operated through integrated, optimization-driven frameworks that recognize cross-sectoral coupling. Moreover, the scheduling horizon and operational logic of water and energy systems should be aligned with the temporal structures of electricity markets, enabling cities to capitalize on price signals, reduce operational costs, and enhance flexibility. Such a paradigm shift from isolated empirical decision-making to comprehensive optimization based on physics, economics, and system interactions, is essential for building next-generation climate-resilient and energy-efficient urban environments.

The study focuses on cost-optimization of a reconstructed water resource network of the city of Seongnam, developed using publicly available municipal data. The system serves a population of 943,676 and is supplied through an integrated metropolitan–local water network comprising 17 distribution reservoirs, 31 pumping stations, 140 pumps, 2 small hydropower generators, 2 photovoltaic generators, 1 battery energy storage system postulated, multiple intake stations and treatment facilities equipped with Oz-GAC and rapid filtration. Distances among facilities, stations and reservoirs were measured as straight-line distances using Google Earth, based on publicly provided GIS coordinates. The operational framework incorporates a MILP-based optimization model that explicitly accounts for operational delays through piecewise-linear flow representations, enabling time-dependent scheduling in the system.