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Processes
Special Issues
Process Safety and Control Strategies for Urban Clean Energy Systems
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Process Safety and Control Strategies for Urban Clean Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2744

Special Issue Editors

Dr. Zhilei Wang

E-Mail Website
Guest Editor
College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
Interests: gas self-ignition; gas explositon; jet flame; fire protection; public safety
Special Issues, Collections and Topics in MDPI journals
Dr. Qingxin Ba

E-Mail Website
Guest Editor
Institute of Thermal Science and Technology (Institute for Advanced Technology), Shandong University, Jinan 250061, China
Interests: hydrogen safety; hydrogen jet; jet flame; quantitative risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Congliang Ye

E-Mail Website
Guest Editor
1. Energetic Materials Safety Review and Certification Center, Beijing Institute of Technology, Beijing 100081, China
2. School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: explosion dynamics; battery thermal safety; firefighting technology; safety science; CFD simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the most critical challenges for sustainable urban development is ensuring the safe integration and operation of clean energy technologies in densely populated environments. Advances in clean energy safety are being pursued at multiple scales—from interconnected city-wide smart grids and district heating/cooling networks, through industrial-scale renewable generation facilities and storage hubs, down to distributed rooftop solar, EV charging infrastructure, and microgrids within neighborhoods and individual buildings. The technologies under scrutiny vary widely, including solar photovoltaics, wind energy, advanced battery storage (stationary and mobile), hydrogen production, storage and fueling, geothermal systems, smart grid controls, and the integration interfaces with existing urban infrastructure. Ensuring the inherent and operational safety of these systems is paramount for their successful urban adoption. Modeling, simulation, and data-driven analysis play an indispensable role in understanding and enhancing safety as they provide powerful, cost-effective tools for predicting hazards, evaluating risks, designing inherently safer systems, and developing robust emergency response strategies.

This Special Issue on “Process Safety and Control Strategies for Urban Clean Energy Systems” will curate novel advances in research that either apply modeling, simulation, or advanced data analytics as core components for analyzing the safety aspects of urban clean energy systems, or present the development of new and improved safety assessment methodologies, models, or digital tools.

Topics include, but are not limited to, the following:

  • Research on fires, explosions, or toxic release scenarios involving urban renewables or storage (e.g., battery thermal runaway or hydrogen leaks).
  • Risk assessment frameworks and models for integrating diverse clean energy sources within complex city grids and existing infrastructure.
  • The development and application of computational tools for safety monitoring, early warning systems, and emergency response planning for urban energy networks.
  • Life cycle safety analysis (LCSA) or environmental risk assessment (ERA) specific to clean energy technologies in urban settings.
  • The safety implications and modeling of cyber–physical security for smart city energy systems.
  • Reliability modeling and failure prediction for clean energy components deployed in diverse urban environments.
  • Safety optimization in the design, planning, and operation of city-scale clean energy systems.

Thanks for your contributions, and I hope you consider participating in this timely Special Issue.

You may choose our Joint Special Issue in Sustainability.

Dr. Zhilei Wang
Dr. Qingxin Ba
Dr. Congliang Ye
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban clean energy safety
  • renewable energy risk assessment
  • battery storage hazards
  • hydrogen infrastructure safety
  • safety simulation frameworks
  • failure mode prediction

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

23 pages, 6131 KB  
Article
Carbon Flow Tracking and Optimal Scheduling of Distributed Integrated Energy Systems Embedding Biomass Combined Heat and Power
by Guang Tian and Pei Liu
Processes 2026, 14(7), 1128; https://doi.org/10.3390/pr14071128 - 31 Mar 2026
Viewed by 411
Abstract
Distributed integrated energy systems embedding biomass combined heat and power (BCHP) have the potential to enhance energy supply reliability in rural areas and to support the low-carbon transformation. However, the sources and transmission paths of car-bon emissions remain difficult to quantify due to [...] Read more.
Distributed integrated energy systems embedding biomass combined heat and power (BCHP) have the potential to enhance energy supply reliability in rural areas and to support the low-carbon transformation. However, the sources and transmission paths of car-bon emissions remain difficult to quantify due to the multi-energy coupling and diverse conversion processes. To address these issues, this study develops a carbon flow tracking and scheduling strategy for BCHP-integrated distributed energy systems. First, a bio-chemical reaction process model for BCHP is established to enable a life cycle-based carbon emission accounting. Second, the flexible heat-to-power ratio characteristics of BCHP are considered to more accurately reflect multi-energy coupling under varying operating conditions. Third, a dual-objective optimal scheduling model is constructed by combining node carbon potential with operating costs, enabling the system to simultaneously minimize operating costs and carbon emissions. A case study of an integrated energy system in Anping County, Hebei Province, demonstrates that the proposed method reduces total carbon emissions by over 9.8%, increases renewable energy utilization by 15.2%, and lowers operating costs by 7.5%. The results reveal the carbon flow characteristics and emission reduction potential of rural distributed integrated energy systems embedding BCHP, providing methodological support and empirical evidence for refined low-carbon governance. Full article
(This article belongs to the Special Issue Process Safety and Control Strategies for Urban Clean Energy Systems)
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24 pages, 4693 KB  
Article
A Short-Term Photovoltaic Power Prediction Based on Multidimensional Feature Fusion of Satellite Cloud Images
by Lingling Xie, Chunhui Li, Yanjing Luo and Long Li
Processes 2026, 14(5), 846; https://doi.org/10.3390/pr14050846 - 5 Mar 2026
Viewed by 460
Abstract
Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural [...] Read more.
Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. Full article
(This article belongs to the Special Issue Process Safety and Control Strategies for Urban Clean Energy Systems)
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32 pages, 7629 KB  
Article
Resilient Control Strategies for Urban Energy Transitions: A Robust HRES Sizing Typology for Nearly Zero Energy Ports
by Nikolaos Sifakis
Processes 2026, 14(3), 549; https://doi.org/10.3390/pr14030549 - 4 Feb 2026
Viewed by 460
Abstract
Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the [...] Read more.
Ports located within dense urban environments face a major challenge in achieving deep decarbonization without compromising the reliability and safety of critical maritime operations. This study develops and validates a resilience-oriented control and sizing typology for Hybrid Renewable Energy Systems (HRESs), supporting the transition of a medium-sized Mediterranean port toward a Nearly Zero Energy Port (nZEP). The framework integrates five years of measured electrical demand at 15 min resolution to capture stochastic load variability, seasonal effects, and safety-critical peak events. Thirty-five HRES configurations are simulated using HOMER Pro, assessing photovoltaic and wind generation combined with alternative Energy Storage System (ESS) technologies under two grid-interface control strategies: Net Metering (NM) and non-NM curtailment-based operation. Conventional Lead–Acid batteries are compared with inherently safer Vanadium Redox Flow Batteries (VRFBs), while autonomy constraints of 24 h and 48 h are imposed to represent operational resilience. System performance is evaluated through a multi-criteria framework encompassing economic viability (Levelized Cost of Energy), environmental impact (Lifecycle Assessment-based carbon footprint), and operational reliability. Results indicate that NM-enabled HRES architectures significantly outperform non-NM configurations by exploiting the external grid as an active balancing layer. The optimal NM configuration achieves a Levelized Cost of Energy of 0.063 €/kWh under a 24 h autonomy constraint, while reducing operational carbon intensity to approximately 70 gCO2,eq/kWh, corresponding to a reduction exceeding 90% relative to baseline grid-dependent operation. In contrast, non-NM systems require substantial storage and generation oversizing to maintain resilience, resulting in higher curtailment losses and Levelized Cost of Energy values of 0.12–0.15 €/kWh. Across both control regimes, VRFB-based systems consistently exhibit superior robustness and safety performance compared to Lead–Acid alternatives. The proposed typology provides a transferable framework for resilient and low-carbon port microgrid design under real-world operational constraints. Full article
(This article belongs to the Special Issue Process Safety and Control Strategies for Urban Clean Energy Systems)
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17 pages, 2768 KB  
Article
Modeling Analysis of Gas–Solid Reactor Performance in Gasifiers Based on Diverse Mechanisms
by Benfeng Zhang, Cuihong Hou, Xudong Li, Pei Wu and Sheng Yang
Processes 2026, 14(3), 522; https://doi.org/10.3390/pr14030522 - 2 Feb 2026
Viewed by 366
Abstract
To effectively cope with the impact of coal quality changes on the operation of coal gasification units and ensure the stable and efficient operation of coal gasification units, an entrained-flow gasifier was modeled and simulated in this paper. Equilibrium reaction models, reaction kinetics [...] Read more.
To effectively cope with the impact of coal quality changes on the operation of coal gasification units and ensure the stable and efficient operation of coal gasification units, an entrained-flow gasifier was modeled and simulated in this paper. Equilibrium reaction models, reaction kinetics models, and reaction zoning models based on various mechanisms were employed for the modeling. Simulation results for Xinjiang Zhundong Hongshaquan coal with different coal qualities were compared with the actual operation data of the plant using the root mean square deviation (RMSD) method. The deviations of the equilibrium reaction model, reaction kinetics model, and reaction zoning model were 1.14% and 1.09%, 0.65% and 0.61%, and 0.44% and 0.368%, respectively. The comparison results show that all three models can accurately simulate and predict the gasification process of Junggar coal when the coal quality changes. Then, the established models were used to study the influence of the changes in the main parameters of the gasification unit, such as coal slurry concentration and oxygen–coal ratio, on coal gasification. The results show that an increase in coal slurry concentration has a positive influence on the effective gas. The optimal oxygen–coal ratio in the coal studied in this paper is approximately 0.45, which provides strong theoretical support for the subsequent optimization of the coal gasification process and actual production operation. Full article
(This article belongs to the Special Issue Process Safety and Control Strategies for Urban Clean Energy Systems)
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14 pages, 844 KB  
Article
Knowledge-Enhanced Time Series Anomaly Detection for Lithium Battery Cell Screening
by Zhenjie Liu, Yudong Wang and Jianjun He
Processes 2026, 14(2), 371; https://doi.org/10.3390/pr14020371 - 21 Jan 2026
Viewed by 576
Abstract
The increasing application of lithium-ion batteries in manufacturing and energy storage systems necessitates high-precision screening of abnormal cells during manufacturing, so as to ensure safety and performance. Existing methods struggle to break down the barrier between prior knowledge and data, suffering from limitations [...] Read more.
The increasing application of lithium-ion batteries in manufacturing and energy storage systems necessitates high-precision screening of abnormal cells during manufacturing, so as to ensure safety and performance. Existing methods struggle to break down the barrier between prior knowledge and data, suffering from limitations such as insufficient detection accuracy and poor interpretability. This becomes even more prominent when facing distributional shifts in data. In this study, we propose a knowledge-enhanced anomaly detection framework for cell screening. This framework integrates domain knowledge, such as electrochemical principles, expert heuristic rules, and manufacturing constraints, into data-driven models. By combining features extracted from charging/discharging curves with rule-based prior knowledge, the proposed framework not only improves detection accuracy but also enables a traceable reasoning process behind anomaly identification. Experiments based on real-world battery production data demonstrate that the proposed framework outperforms baseline models in both precision and recall, making it a promising preferred solution for quality control in intelligent battery manufacturing. Full article
(This article belongs to the Special Issue Process Safety and Control Strategies for Urban Clean Energy Systems)
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