Integrated energy-water systems for community-level flexibility: A hybrid deep Q-network and multi-objective optimization framework

As urban populations grow and cities become increasingly complex, the demand for efficient management of energy and water resources in smart cities has never been greater. The challenge lies in integrating diverse and fluctuating energy sources, such as renewable energy, while maintaining sustainability and cost-effectiveness. To address these challenges, this paper presents the Smart Energy-Water Systems (SEWS) framework, which integrates Deep Q-Networks (DQN) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to optimize resource management under conditions of uncertainty and competing objectives. The SEWS framework combines DQN's ability to make dynamic, real-time decisions with MOEA/D's capacity to handle multi-objective optimization. This integration ensures that the system can adjust to changing energy and water demands, while simultaneously minimizing operational costs and maximizing social engagement in peer-to-peer resource trading. Through simulations, we demonstrate that the SEWS framework leads to a 20 % reduction in operational costs and a 30 % increase in community participation in resource trading. These results highlight the potential of SEWS to improve the flexibility, sustainability, and resilience of urban resource systems. The paper provides a promising solution for managing the complex interplay of energy, water, and social factors in the context of smart cities, contributing to the development of more efficient, low-carbon, and inclusive urban environments.