Integrated Modeling and Analytics for Sustainable Urban Energy Systems

Dear Colleagues,

We would like to invite submissions of original research papers on the Topic of Integrated Modeling and Analytics for Sustainable Urban Energy Systems.

The world is rapidly urbanizing. In order to facilitate energy transition and achieve the carbon neutrality goal, there is an emerging trend regarding the adoption of integrated urban energy systems to potentially drive a paradigm shift in energy production and consumption patterns. An increasing number of cities require integrated energy solutions in campuses, factories, industrial parks and urban districts. However, urban energy systems are becoming more complicated and interconnected. Changes in one system can usually have substantial (non-linear) impacts on another. The potential value (environment, engineering, social) of integrated energy solutions, including building energy efficiency, district heating and cooling systems, EV charging infrastructures, distributed energy resources (e.g., rooftop solar PV, storage), and electric heat pumps, has not yet been fully utilized. More importantly, integrated energy and microgrid systems are complex and are associated with multiple stakeholders. It is particularly challenging to balance overall system stability, environmental impacts, and performance optimization.

Integrated urban energy systems call for integrated modeling and analytics solutions to account for the interactions and interdependencies. This topic, Integrated Modeling and Analytics for Sustainable Urban Energy Systems, aims to include papers address the research gaps in advanced analytics and integrated modeling for improving urban energy system sustainability, intelligence, efficiency, and resilience, including (but not limited to) the following:

• Co-simulation of interactions between urban energy systems;
• Hybrid forecasting of interconnected urban energy demands and supplies;
• Machine learning, deep learning, and reinforcement learning for urban energy systems;
• Streamlined data engineering for heterogeneous urban data collection, cleaning, transformation, management, computation, etc.;
• Data-driven decision support and policy recommendations for energy system performance benchmarking and planning;
• Massive data mining and knowledge extraction for interdependencies of urban energy systems;
• Optimization techniques for urban energy system planning, design, and operation;
• Distributed computing, cloud computing, and edge computing for urban energy system data analytics;
• Database, datawarehouse, knowledge base, and ontology for interconnected urban energy data;
• Multi-scale urban spatial and temporal modeling and analytics;
• Digital twin and physical informed neural network (PINN) for urban energy system modeling;
• Econometrics of integrated urban energy market and business model innovation.

Dr. Zheng Yang
Dr. Lingqi Su
Dr. Yilong Han
Topic Editors