Integrated Modeling and Analytics for Sustainable Urban Energy Systems

Topic Information

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

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.7	4.5	2011	16.9 Days	CHF 2400	Submit
Buildings buildings	3.8	3.1	2011	14.6 Days	CHF 2600	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400	Submit
Urban Science urbansci	2.0	4.5	2017	23.7 Days	CHF 1600	Submit

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Published Papers (1 paper)

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Open AccessArticle

Optimizing Renewable Injection in Integrated Natural Gas Pipeline Networks Using a Multi-Period Programming Approach

by Emmanuel Ogbe, Ali Almansoori, Michael Fowler and Ali Elkamel

Energies 2023, 16(6), 2631; https://doi.org/10.3390/en16062631 - 10 Mar 2023

Viewed by 1371

Abstract

In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where [...] Read more.

In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where carbon dioxide from different source locations is converted into renewable methane (also known as synthetic natural gas, SNG). The above pathways result in green hydrogen and methane, which can be injected into an existing natural gas pipeline network. Based on these pathways, a multi-period network optimization model that integrates the design and operation of hydrogen from PtH and renewable methane is proposed. The multi-period model is a mixed-integer non-linear programming (MINLP) model that determines (1) the optimal concentration of hydrogen and carbon dioxide in the natural gas pipelines, (2) the optimal location of PtH and carbon dioxide units, while minimizing the overall system cost. We show, using a case study in Ontario, the optimal network structure for injecting renewable hydrogen and methane within an integrated natural gas network system provides a $12M cost reduction. The optimal concentration of hydrogen ranges from 0.2 vol % to a maximum limit of 15.1 vol % across the network, while reaching a 2.5 vol % at the distribution point. This is well below the maximum limit of 5 vol % specification. Furthermore, the optimizer realized a CO₂ concentration ranging from 0.2 vol % to 0.7 vol %. This is well below the target of 1% specified in the model. The study is essential to understanding the practical implication of hydrogen penetration in natural gas systems in terms of constraints on hydrogen concentration and network system costs. Full article

(This article belongs to the Topic Integrated Modeling and Analytics for Sustainable Urban Energy Systems)

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