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	2.5	5.3	2011	17.8 Days	CHF 2400
Buildings buildings	3.1	3.4	2011	17.2 Days	CHF 2600
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400
Urban Science urbansci	2.1	4.3	2017	24.7 Days	CHF 1600

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Published Papers (3 papers)

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All Applied Sciences Buildings Energies Sustainability Urban Science

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Supplementary material:
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24 pages, 6415 KiB  

Open AccessArticle

Impact of Energy-Related Properties of Cities on Optimal Urban Energy System Design

by Joel Bertilsson, Lisa Göransson and Filip Johnsson

Energies 2024, 17(15), 3813; https://doi.org/10.3390/en17153813 - 2 Aug 2024

Viewed by 1177

Abstract

This study investigates how differences in energy-related properties of cities influence the composition of a cost-efficient urban energy system, assuming electrification of the transport and industry sectors and zero-emission of CO₂. These differences are evaluated for two scenarios regarding the capacities [...] Read more.

This study investigates how differences in energy-related properties of cities influence the composition of a cost-efficient urban energy system, assuming electrification of the transport and industry sectors and zero-emission of CO₂. These differences are evaluated for two scenarios regarding the capacities of the modeled cities to import electricity. A linear optimization model that encompasses the electricity, heating, industry, and transport sectors, using measured data from six cities in Sweden, is applied. Results show that when strict constraints on electricity imports are enforced, cities with a lower ratio of annual electricity demand for heat encourage the implementation of power-to-heat solutions in the heating sector. This study also reveals that under such stringent electricity import conditions, cities with a high level of flexibility in electricity demand favor a combination of batteries and solar photovoltaics as opposed to biomass-based electricity production. Conversely, when electricity importation is less restricted and biomass prices surpass 20 EUR/MWh, local electricity generation is outcompeted by imports, and large-scale heat pumps working in tandem with thermal energy storage dominate the heating sector in all modeled cities. This assertion holds true when the maximum electricity import capacity is utilized up to 5000 h annually. Full article

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

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24 pages, 1270 KiB  

Open AccessSystematic Review

Computational Optimisation of Urban Design Models: A Systematic Literature Review

by JingZhi Tay, Frederick Peter Ortner, Thomas Wortmann and Elif Esra Aydin

Urban Sci. 2024, 8(3), 93; https://doi.org/10.3390/urbansci8030093 - 22 Jul 2024

Viewed by 992

Abstract

The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a [...] Read more.

The densification of urban spaces globally has contributed to a need for design tools supporting the planning of more sustainable, efficient, and liveable cities. Urban Design Optimisation (UDO) responds to this challenge by providing a means to explore many design solutions for a district, evaluate multiple objectives, and make informed selections from many Pareto-efficient solutions. UDO distinguishes itself from other forms of design optimisation by addressing the challenges of incorporating a wide range of planning goals, managing the complex interactions among various urban datasets, and considering the social–technical aspects of urban planning involving multiple stakeholders. Previous reviews focusing on specific topics within UDO do not sufficiently address these challenges. This PRISMA systematic literature review provides an overview of research on topics related to UDO from 2012 to 2022, with articles analysed across seven descriptive categories. This paper presents a discussion on the state-of-the-art and identified gaps present in each of the seven categories. Finally, this paper argues that additional research to improve the socio-technical understanding and usability of UDO would require: (i) methods of optimisation across multiple models, (ii) interfaces that address a multiplicity of stakeholders, (iii) exploration of frameworks for scenario building and backcasting, and (iv) advancing AI applications for UDO, including generalizable surrogates and user preference learning. Full article

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

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24 pages, 624 KiB  

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

Cited by 2 | Viewed by 1840

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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