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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 7406

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Special Issue Editors

Prof. Dr. Michael Short

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School of Computing, Engineering, and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: control and protection; optimization; embedded systems; real-time systems; industry 4.0; industrial digitization and smart factories; smart grids; smart energy systems; advanced robotics/process control
Special Issues, Collections and Topics in MDPI journals

Dr. Sean Williams

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Guest Editor

School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK
Interests: control theory; MATLAB simulation; advanced control theory; system modeling; embedded systems; control systems engine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The design and operation of cities is under increasing pressure due to changes in population, climate, and congestion. In this Special Issue, the core theme will be digital engineering and how it can provide a platform for smarter, cleaner, and more sustainable cities. Specific themes of interest include (but are not limited to): Technologies for Smart Cities (IoT, Web 3.0, AI, and Machine Learning, Robotics/Domotics); Decarbonisation of Industry and Transportation; Smart Buildings, Smart Infrastructure, and Smart Energy; eMobility and Smart Logistics. Submissions are invited from researchers and practitioners working in related areas, with the aim of promoting a venue for cutting-edge fundamental and applied research related to digital engineering for future smart cities.

Prof. Dr. Michael Short
Dr. Sean Williams
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. Energies 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 2600 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

smart buildings
smart cities
smart energy

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Published Papers (7 papers)

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Research

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30 pages, 7017 KB

Open AccessArticle

A Deep Reinforcement Learning Approach for Multi-Unit Combined Heat and Power Scheduling with Preventive Maintenance Under Demand Uncertainty

by Sangjun Lee, Iljun Kwon, In-Beom Park and Kwanho Kim

Energies 2026, 19(8), 1849; https://doi.org/10.3390/en19081849 - 9 Apr 2026

Viewed by 366

Abstract

Operating multi-unit combined heat and power (MUCHP) plants involves determining unit commitment (UC) and coupled heat and power dispatch under demand uncertainty and progressive equipment degradation. This paper proposes a reinforcement learning approach to jointly optimize UC, dispatch, and preventive maintenance (PM). Specifically, we develop a Proximal Policy Optimization (PPO)-based policy that shifts the computational burden to offline training, enabling near-real-time decisions during operation. The trained agent is evaluated on an hourly five-unit CHP system model based on operational data from a district heating plant in the Republic of Korea, using a full-year simulation. The robustness of the proposed method is assessed against demand forecast noise and structural system shifts covering reduced, expanded, homogeneous, and heterogeneous unit configurations. The experiments indicate that the proposed approach reduced the total operating cost by 4.69 to 8.35 percent compared to three heuristic baselines across the evaluated scenarios. Moreover, it mitigates supply shortages during high-volatility seasons through proactive pre-commitment and preserves asset health by distributing production loads evenly. These results indicate that integrating PM into operational planning improves both the economic efficiency and operational stability of MUCHP systems. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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34 pages, 4793 KB

Open AccessArticle

Freezers in Residential Buildings as a Source of Power Grid Frequency Regulation in Response to the Demand for Innovation Within the Smart City Concept: Thermal–Electric Modeling, Technical Potential and Operational Challenges

by Wojciech Lewicki, Hasan Huseyin Coban, Federico Minelli and Panagiotis Michailidis

Energies 2026, 19(7), 1608; https://doi.org/10.3390/en19071608 - 25 Mar 2026

Viewed by 543

Abstract

This study assesses the technical feasibility of utilizing aggregated domestic freezers in Turkey as a distributed resource for frequency regulation. A dynamic thermal–electrical model was developed to simulate freezer responses under frequency deviation scenarios representative of real-world grid conditions. The modeled sample of 100,000 deep freezers (80 W each) can deliver approximately 3.2 MW of instantaneous down-regulation under a 40% initial duty cycle. Extrapolating to the estimated 4.7 million eligible freezers nationwide yields a total potential headroom of roughly 150–225 MW, depending on duty-cycle assumptions. The compressor duty cycle and allowable temperature range were identified as key factors influencing both regulation capacity and endurance. Although linear reference temperature control enabled effective participation in FCR-N within the simulated timeframes, it also led to cycle synchronization and peak loads following disturbances. Implementing strategies such as randomized reconnection delays could mitigate these effects. The wide availability of domestic freezers, minimal consumer impact, and broad geographic distribution suggest that this resource represents a promising complement to existing frequency regulation assets, particularly in enhancing grid stability amid increasing renewable energy penetration. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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24 pages, 3621 KB

Open AccessArticle

Phase-Space Reconstruction and 2-D Fourier Descriptor Features for Appliance Classification in Non-Intrusive Load Monitoring

by Motaz Abu Sbeitan, Hussain Shareef, Madathodika Asna, Rachid Errouissi, Muhamad Zalani Daud, Radhika Guntupalli and Bala Bhaskar Duddeti

Energies 2026, 19(6), 1512; https://doi.org/10.3390/en19061512 - 18 Mar 2026

Viewed by 398

Abstract

Non-Intrusive Load Monitoring (NILM) enables appliance-level classification from aggregate electrical measurements and supports efficient energy management in smart buildings. However, the accuracy of existing NILM methods is often limited by the inability of conventional feature extraction techniques to capture nonlinear steady-state behavior. This study proposes a novel feature extraction framework for appliance classification, which integrates phase-space reconstruction (PSR) with 2-D Fourier series to derive geometry-based descriptors of appliance current waveforms. Unlike traditional signal-processing methods, the proposed approach utilizes the nonlinear geometric structure revealed by PSR and encodes it through Fourier descriptors, offering a discriminative, low-dimensional feature space suitable for classification using supervised machine learning algorithms. The method is evaluated on the high-resolution controlled single-appliance recordings from the COOLL dataset using the K-Nearest Neighbor (KNN) classifier. Extension to aggregated multi-appliance NILM scenarios would require additional stages such as event detection and load separation. Sensitivity analysis demonstrates that classification performance depends strongly on the choice of time delay and harmonic order, with optimal settings yielding an accuracy of up to 99.52% using KNN. The results confirm that larger time delays and a small number of harmonics effectively capture appliance-specific signatures. The findings highlight the effectiveness of PSR–Fourier-based geometric features as a robust alternative to conventional NILM feature extraction strategies. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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23 pages, 924 KB

Open AccessArticle

Energy and Water Management in Smart Buildings Using Spiking Neural Networks: A Low-Power, Event-Driven Approach for Adaptive Control and Anomaly Detection

by Malek Alrashidi, Sami Mnasri, Maha Alqabli, Mansoor Alghamdi, Michael Short, Sean Williams, Nashwan Dawood, Ibrahim S. Alkhazi and Majed Abdullah Alrowaily

Energies 2025, 18(19), 5089; https://doi.org/10.3390/en18195089 - 24 Sep 2025

Cited by 2 | Viewed by 1459

Abstract

The growing demand for energy efficiency and sustainability in smart buildings necessitates advanced AI-driven methods for adaptive control and predictive maintenance. This study explores the application of Spiking Neural Networks (SNNs) to event-driven processing, real-time anomaly detection, and edge computing-based optimization in building automation. In contrast to conventional deep learning models, SNNs provide low-power, high-efficiency computation by mimicking biological neural processes, making them particularly suitable for real-time, edge-deployed decision-making. The proposed SNN based on Reward-Modulated Spike-Timing-Dependent Plasticity (STDP) and Bayesian Optimization (BO) integrates occupancy and ambient condition monitoring to dynamically manage assets such as appliances while simultaneously identifying anomalies for predictive maintenance. Experimental evaluations show that our BO-STDP-SNN framework achieves notable reductions in both energy consumption by 27.8% and power requirements by 70%, while delivering superior accuracy in anomaly detection compared with CNN, RNN, and LSTM based baselines. These results demonstrate the potential of SNNs to enhance the efficiency and resilience of smart building systems, reduce operational costs, and support long-term sustainability through low-latency, event-driven intelligence. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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21 pages, 6897 KB

Open AccessArticle

Low-Power Energy-Efficient Hetero-Dielectric Gate-All-Around MOSFETs: Enablers for Sustainable Smart City Technology

by Ram Devi, Gurpurneet Kaur, Ameeta Seehra, Munish Rattan, Geetika Aggarwal and Michael Short

Energies 2025, 18(6), 1422; https://doi.org/10.3390/en18061422 - 13 Mar 2025

Cited by 3 | Viewed by 1807

Abstract

In the context of increasing digitalization and the emergence of applications such as smart cities, embedded devices are becoming ever more pervasive, mobile, and ubiquitous. Due to increasing concerns around energy efficiency, gate density, and scalability in the semiconductor industry, there has been much interest recently in the fabrication of viable low-power energy-efficient devices. The Hetero-Dielectric Gate-All-Around (HD-GAA) MOSFET represents a cutting-edge transistor architecture designed for superior sustainability and energy efficiency, improving the overall efficiency of the system by reducing leakage and enhancing gate control; therefore, as part of the transition to a sustainable future, several semiconductor industries, including Intel, Samsung, Texas Instruments, and IBM, are using this technology. In this study, Hetero-Dielectric Single-Metal Gate-All-Around MOSFET (HD-SM-GAA MOSFET) devices and circuits were designed using Schottky source/drain contacts and tunable high-k dielectric Hf_xTi_1−xO₂ in the TCAD simulator using the following specifications: N-Channel HD-SM-GAA MOSFET (‘Device-I’) with a 5 nm radius and a 21 nm channel length alongside two P-Channel HD-SM-GAA MOSFETs (‘Device-II’ and ‘Device-III’) with radii of 5 nm and 8 nm, respectively, maintaining the same channel length. Thereafter, the inverters were implemented using these devices in the COGENDA TCAD simulator. The results demonstrated significant reductions in short-channel effects: subthreshold swing (SS) (‘Device-I’ = 61.5 mV/dec, ‘Device-II’ = 61.8 mV/dec) and drain-induced barrier lowering (DIBL) (‘Device-I’ = 8.2 mV/V, ‘Device-II’ = 8.0 mV/V) in comparison to the existing literature. Furthermore, the optimized inverters demonstrated significant improvements in noise margin values such as Noise Margin High (NM_H) and Noise Margin Low (NM_L), with Inverter-1 showing 38% and 44% enhancements and Inverter-2 showing 40% and 37% enhancements, respectively, compared to the existing literature. The results achieved illustrate the potential of using this technology (e.g., for power inverters) in embedded power control applications where energy efficiency and scalability are important, such as sustainable smart cities. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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Review

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48 pages, 13773 KB

Open AccessReview

The Smart City from the Energy Perspective

by Florentin-Robert Drăgan, Lucian Toma and Irina-Ioana Picioroagă

Energies 2026, 19(8), 1993; https://doi.org/10.3390/en19081993 - 21 Apr 2026

Viewed by 707

Abstract

The accelerated development of Smart Cities globally, driven by rapid urbanization and urgent climate challenges, underscores the critical role of advanced energy infrastructures integrated with emerging digital technologies. This article explores the evolution of smart cities from an energy-centric viewpoint, emphasizing the interdependence among energy systems, digitalization and cutting-edge communication technologies. Adopting a system-of-systems perspective, we examine how different urban subsystems, including energy grids, transportation networks and data management systems, interact to improve overall urban functionality and long-term viability. Through a structured analysis of recent literature, we highlight the transformative potential of renewable energy integration, intelligent energy management systems and the crucial transition from 5G to 6G communication infrastructures, which collectively promise significant enhancements in urban sustainability, efficiency and resilience. Additionally, we address key challenges such as cybersecurity vulnerabilities, fragmented standardization frameworks and the need for comprehensive data governance. Viewing smart cities as a complex system of systems, this article argues for a holistic and interdisciplinary approach, emphasizing enhanced interoperability, robust cybersecurity protocols and inclusive participatory governance frameworks. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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19 pages, 1021 KB

Open AccessReview

Urban Building Energy Modelling: A Review on the Integration of Geographic Information Systems and Remote Sensing

by Sebastiano Anselmo and Piero Boccardo

Energies 2026, 19(7), 1667; https://doi.org/10.3390/en19071667 - 28 Mar 2026

Viewed by 515

Abstract

Decarbonising the building sector is an energy policy priority due to its major contribution to global energy consumption and related emissions. Accurate energy modelling is crucial, with significant scientific advancements being made in the last decade. As data gathering is a primary bottleneck, the potential of Geographic Information Systems and Remote Sensing for streamlining data acquisition and integrating data sources has gained specific interest. This study aims to identify prevailing trends in scales, inputs, and outputs of energy modelling, focusing on Remote Sensing and Geographic Information Systems applications. A structured literature review was conducted, encompassing screening, textual analysis, and findings synthesis to identify key research trends. The results highlight a predominance of the neighbourhood scale (54%) and the reliance on building geometries as principal input (91% of studies). Remote Sensing, used in 36% of cases, is employed for defining geometric (41%) and non-geometric (45%) attributes, while 17% of studies leverage it to determine climatic variables. EnergyPlus remains the most widespread simulation engine (37%), frequently coupled with construction archetypes (50% of cases) to address data gaps. The increasing integration of these technologies in energy modelling is expected to diversify the number of inputs, ultimately enhancing output accuracy, scalability, and generalisability. Full article

(This article belongs to the Special Issue Digital Engineering for Future Smart Cities)

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