Search Results (107)

The digitalization of sustainable energy systems increasingly relies on containerized Internet of Things services deployed across cloud–edge architectures. These services introduce software supply-chain risks associated with public container images and their dependencies. This study evaluates 22 container images representing 13 official or vendor-maintained technologies used for data storage and processing, communication, proxy and API services, and application runtime environments. Each image was analysed using Trivy and Grype, resulting in 44 vulnerability scans performed using vulnerability databases available on 14 June 2026. The effect of image minimization was assessed using five strictly matched standard–minimized pairs, while scanner agreement was evaluated for all images using unique CVE sets, the Jaccard coefficient, and symmetrical and directional Tversky indices. Across the complete sample, Trivy reported 7006 vulnerability findings and Grype reported 2299. Within the strictly matched sample, findings decreased from 5181 to 193 for Trivy and from 982 to 290 for Grype. However, these reductions were strongly influenced by the Ruby image, and the exact Wilcoxon signed-rank test did not confirm a statistically significant general minimization effect ($p=0.250$). Redis, HAProxy, and Ruby showed substantial reductions, whereas Caddy remained unchanged and both .NET SDK variants produced zero findings. The set-based analysis revealed incomplete and asymmetric agreement between the scanners, demonstrating that similar aggregate counts may represent different CVE profiles. Operational prioritization of six selected image variants further showed differences in remediation availability, EPSS scores, and CISA KEV inclusion. The results indicate that image minimization can reduce scanner findings but does not independently confirm container security. A multi-tool DevSecOps process combining immutable digest verification, Software Bills of Materials, vulnerability prioritization, image rebuilding, and continuous rescanning is therefore recommended. Full article

We propose a three-functional-layer decomposition framework for underwater acoustic (UWA) integrated sensing and communication (ISAC) preambles, instantiated as P5. Two spectrally separated chirp layers—hyperbolic frequency modulation (HFM) for wideband Doppler invariance and linear frequency modulation (LFM) for sub-meter ranging—are carried under a common constant-amplitude zero-autocorrelation (CAZAC) envelope that supplies cell identification and despreading against a root-blind attacker. Closed-form screening conditions constrain the layers to a near-orthogonal subspace, and direct cross-ambiguity measurement confirms the realized separation. In matched-filter Monte Carlo simulation, P5 meets the continuous-sensing target (range root mean square error ${\sigma }_R\le 1$ m at 10 dB signal-to-noise ratio) and has the smallest normalized matched-filter peak loss across twelve modeled UWA environments among four tested waveforms. Against four classical structure-aware attackers it stays below the strict $P_d\le 0.1$ low-probability-of-intercept target at 0 dB attacker-input SNR. A 10-seed, 11.2-million-parameter spectrogram ResNet-18 reaches $P_d=0.5$ against P5 at mean $+21.24$ dB total-energy SNR (95% CI $[+21.06,+21.42]$ dB) and $P_d=0.1$ at $+18.72$ dB ($[+18.45,+19.00]$ dB); these crossings are lower bounds on adversary capability, not a security guarantee. The integration also has explicit costs: composite peak-sidelobe level ($-7.60$ dB default, $-11.29$ dB optimized) remains inferior to equal-aperture single-waveform baselines, and sixteen-cell identification falls to ≤$0.07$ under a $+2$ dB near–far interferer. All-60-sounding WATERMARK replay further gives adverse P5^def–B5 losses of $-0.816$ dB on NOF1 (sounding-cluster 95% CI $[-1.021,-0.621]$ dB) and $-0.950$ dB on NCS1 ($[-0.977,-0.923]$ dB) after all waveforms are scaled into the same measured 8-kHz band. The evidence is therefore simulation dominant and supplemented by measured-channel replay of band-scaled variants; native-band transducer, pool, and sea-trial validation remain future work. Full article

The increasing worldwide demand for sustainable energy and effective waste management has heightened interest in solutions. Microbial fuel cells (MFCs) represent a potential category of bioelectrochemical systems that directly transform the chemical energy contained in organic waste into electrical energy via the metabolic processes of electroactive microorganisms. In the last twenty years, significant advancements have occurred in the comprehension of extracellular electron transfer (EET) mechanisms, biofilm formation, microbial community dynamics, electrode material engineering, and reactor design, resulting in marked enhancements in power density and wastewater treatment efficacy. Despite these breakthroughs, the extensive deployment and commercialization of MFC technology are constrained by various hurdles, including inadequate energy recovery, elevated material and fabrication expenses, operational instability, and the intricacies of system scale-up. This cutting-edge analysis offers a thorough evaluation of recent advancements in MFCs and their incorporation with sophisticated technology for waste management and energy generation. Focus is directed towards essential bioelectrochemical principles, microbial and biofilm engineering techniques, sophisticated electrode and membrane materials, reactor designs, and hybrid MFC systems integrated with anaerobic digestion, microbial electrolysis, and advanced oxidation methods. Ultimately, emerging trends, significant knowledge deficiencies, and future research goals are defined to inform the advancement of next-generation MFC systems that support circular economy and net-zero energy initiatives. Full article

Against the backdrop of the global aviation industry’s commitment to achieving the “Net Zero Carbon Emissions by 2050” goal, the issue of superimposed peak loads on distribution networks—arising from the large-scale transition from fossil-fueled to electric Ground Service Equipment (GSE) at civil airports—has become increasingly prominent, emerging as a critical constraint on green airport development. Focusing on the high-value airside area, this paper presents the first systematic review of how Vehicle-to-Grid (V2G) technology can transform electric Ground Service Equipment (e-GSE) from mere “charging loads” into “dispatchable energy storage resources.” The study proposes that, through bidirectional DC charging/discharging and intelligent aggregation technologies, e-GSE fleets operating on predictable schedules can be integrated as flexible regulation units within airport microgrids. To realize this pathway, the study comprehensively examines the core technological framework, encompassing wide-power-range bidirectional charging infrastructure, grid-forming power conversion topologies, standardized communication and grid interconnection interfaces, flight-schedule-based potential assessment and dispatch algorithms, and photovoltaic storage–charging hybrid system integration schemes. The review demonstrates that this technology can not only enhance grid resilience and promote renewable energy accommodation through peak shaving, valley filling, and ancillary services but also yields significant economic benefits. Finally, the study identifies the technical, standardization, and business model barriers hindering large-scale deployment, thereby providing a theoretical reference and a technology roadmap for the energy system planning and construction of future “zero-carbon smart airports”. Full article

Buildings are the primary energy consumption layer of Renewable Energy Communities (RECs) and a key target for net-zero policy under the EPBD recast. This scoping review applies the PRISMA-ScR framework to map Digital Twin (DT) architectures for building-scale and community-scale energy management in REC configurations. A Scopus search yielded a final analytical corpus of 102 studies, coded through an eight-dimensional thematic matrix covering lifecycle phases, digitalization objectives, enabling technologies, DT capability dimensions, and data realism. DT is the dominant enabling technology (55.9%), followed by IoT (23.5%) and machine learning (22.5%). Research is concentrated in the Planning and Design phase (77.5%) and markedly underrepresented in Implementation and Commissioning (16.7%). Notably, only 10.8% of studies integrate real-time operational data, exposing a significant gap between simulation-based research and the deployment conditions required under current EPBD mandates. The evidence base supports building energy monitoring, demand forecasting, and flexible grid operation but remains limited for retrofit verification, standardized net-zero KPIs, and operational workflows in existing stock. Critical DT capability gaps persist in Data Services (7.8%) and User Experience (18.6%). Overall, DT architectures show genuine potential for grid-interactive, net-zero building management, yet the field presents unresolved structural challenges for large-scale real-world deployment. Full article

Renewable energy is a central component of global sustainable energy development, with solar energy experiencing substantial growth over recent decades. Solar power is widely regarded as one of the most accessible routes to clean energy generation. However, high upfront costs remain a major barrier to adoption. Many potential users are reluctant to invest in solar photovoltaic (PV) systems because of the longer payback period. To address this financial constraint, a range of business models has been developed. This study used a systematic literature review to examine existing and emerging business models for promoting Solar PV solutions. The review included peer-reviewed journal articles published in English from 2020 to 2026. In total, 39 articles were critically evaluated considering their characteristics. Nine potential business models were identified, several of which are commonly used internationally and have shown positive results that could also be applied in the UK. Importantly, Community Energy Models have shown success in Europe, Sub-Saharan and Asian regions. This has been widely supported by the government due to sustainability and climate change targets. The UK has set their target to achieve net-zero in greenhouse gas emissions by 2050. Beyond financial barriers, reliance on weather conditions and the mismatch between energy demand and supply remain substantial barriers to wider solar PV deployment. Full article

Higher education institutions (HEIs), engaged in education, research and community services, play an important role in promoting sustainable development. This study investigated the relationships between carbon emission-related variables—Knowledge, Attitude, and Practice (KAP)—and the amount of carbon emissions in HEIs. A cross-sectional design was adopted, and data were collected via an online questionnaire from September to December 2024. The participants were students from eight colleges and universities in central Taiwan, yielding 293 valid responses. The average daily per capita carbon emissions were calculated based on activity categories and emission coefficients. Carbon emissions from daily life contributed 87.7%, followed by transportation and academic activities (9.7% and 2.6%). The average carbon emission was 11.82 kg CO₂e/day/person. Statistical analysis showed that living arrangements and household size exhibited significant differences (p < 0.05). Regarding the KAP analysis, attitude and practice showed a significant positive correlation (r = 0.59, p < 0.01), while practice and individual-level Scope 3 emissions were negatively correlated (r = −0.12, p < 0.01), indicating that carbon reduction behaviors can effectively decrease individual carbon emissions. This study quantified the carbon emission in HEIs and addressed a research gap by linking individual-level energy behaviors with carbon emission estimates in Taiwan. The findings provide a basis for policy-making and promoting low-carbon behaviors. Future campus initiatives should focus on equipment upgrades, environmental education, and low-carbon actions to achieve sustainability and net-zero carbon goals. Full article

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all $\pm 1\sigma$ parameter variations and across two combined-scenario stress tests. Full article

The reduction in energy demand in buildings through the adaptation of energy-efficient strategies is attracting significant attention from the research community. In this context green building concepts can contribute towards achieving national sustainable development goals (SDGs) and NetZero targets. Given the substantial energy demand associated with heating and cooling in commercial and residential buildings, enhancing energy efficiency has become essential for achieving sustainable development, particularly amid ongoing global energy challenges. The Envelope Thermal Transfer Value (ETTV) model has been established as a simplified method of calculating building loads; however, its integration with green building elements remains limited, particularly in subtropical climates. Furthermore, the combined effects of living walls, green façades, and green roofs on building energy performance have not been comprehensively investigated. In this study, an extensive experimental investigation was conducted using prototype buildings under controlled conditions to evaluate the thermal performance of green elements. Modified ETTV formulations incorporating green envelope systems have been developed, and the thermodynamic effects of these green elements on the building energy performance have been analysed. The results demonstrate that integrating green elements significantly reduces thermal heat gain and cooling energy demand. Specifically, a combination of a living wall on a west facing wall and a green roof could reduce the thermal heat gain by up to 30%. Full article

Hydrogen produced with net zero CO₂ (H₂NZ) has a significant role to play in a sustainable energy transition. Often overlooked are the different means of producing H₂NZ with different trade-offs that will impact communities in diverse ways. Science and engineering need to be part of the dialogue so the nuances of these technologies can be understood and just solutions generated. However, there is little direct engagement with science and engineering in the energy justice literature. To address this gap, a workshop of expert engineers and social scientists is used to analyse four developing H₂NZ technologies with justice issues. The results propose a way forward to integrate engineering with the energy justice discourse and, at the same time, encourage social science to reach out to engineering. The outcome and novelty are a suite of questions that integrate disciplinary perspectives and offer a means of encouraging context and technologically sensitive outcomes. Full article

Automated demand response in residential sectors is critical for grid stability, but centralized control strategies fail to address the unique energy profiles of individual households. This limitation becomes more pronounced in districts where buildings differ in load demand, photovoltaic (PV) production and battery energy storage system (BESS) behavior, while electricity prices and grid carbon intensity vary hourly. Conventional rule-based controllers can exploit patterns, but they require tuning and do not generalize across heterogeneous buildings. Existing centralized reinforcement learning methods improve adaptivity, yet they often learn compromise policies and scale poorly as the number of buildings increases. To address these issues, this paper proposes an AttentionKAN-based multi-agent reinforcement learning controller for district-level BESS scheduling. The method uses centralized training with decentralized execution, where each building is controlled by its own actor and a centralized critic models cross-building interactions through a multi-head query-key-value attention mechanism. To improve approximation accuracy under nonlinear and constrained battery dynamics, multilayer perceptron (MLP) blocks in the actor and critic are replaced with Kolmogorov-Arnold Networks (KANs), whose spline-parameterized univariate functions capture saturation effects, tariff discontinuities and couplings among state of charge, PV availability and carbon intensity. Implemented in CityLearn and evaluated on a residential net-zero community dataset, the proposed controller is assessed using building-level and district-level indicators for cost, ${\mathrm{CO}}_2$ emissions, peak demand, ramping and load shape. The learned policy charges during solar-rich hours and discharges during evening peaks, achieving the strongest performance among benchmark controllers, including an approximately 50% cost reduction versus the reference case and emissions reduction. From a sustainability perspective, the results indicate that coordinated multi-building BESS control can support low-carbon residential electrification through emission reduction, lowering electricity expenditure and improving renewable-energy utilization and providing grid-supportive flexibility through reduced peaks and ramping. Full article

Remote communities in Canada face high electricity costs, energy insecurity, and significant greenhouse gas emissions due to heavy dependence on diesel generation. This study proposes and evaluates an AC-coupled hybrid distribution microgrid for remote off-grid communities, using Black Tickle, Newfoundland and Labrador as a representative case study. The system integrates two 200 kW wind turbines, a 200 kW diesel backup generator, a 16 MWh lithium-ion battery storage system, and a bidirectional converter, modeled and optimized in HOMER Pro 3.18.3 using local meteorological data, community load profiles, and a cycle-charging dispatch strategy. The optimized configuration achieves 86.7% wind penetration and 100% supply reliability with zero unmet load, yielding a total net present cost of USD 13.6 million and a levelized cost of energy of 0.999 USD/kWh over a 25-year horizon. Battery storage accounts for 73.5% of annualized costs, representing the primary economic challenge for wider deployment. Sensitivity analyses show that diesel price fluctuations exert approximately 4.1 times greater influence on system economics than equivalent carbon pricing changes, while the optimal configuration remains robust across all tested policy scenarios. These findings demonstrate that AC-coupled wind–diesel–battery microgrids offer a viable pathway for reducing fossil fuel dependence and supporting clean energy transition in remote, harsh-climate communities. Full article

This review systematically synthesizes technological synergies within a Community Energy System (CES), emphasizing cold-climate contexts where heating-dominant demand profiles and strong seasonality create distinct operational challenges. Drawing on 115 studies (2010–2024), the paper explores how integrated thermal, electrical, and digital infrastructures support net-zero and climate-resilient communities in regions with substantial heating requirements. Thermal–electrical coupling emerges as a foundational mechanism in cold climates, where heating loads dominate annual energy demand and drive winter peak constraints. Power-to-Heat (P2H) systems, cold-climate heat pumps, and hybrid configurations combining Thermal Energy Storage (TES) with Battery Energy Storage Systems (BESS) enable multi-timescale flexibility, allowing renewable energy to be shifted from hours to seasons. District Energy Systems (DES) act as a thermal backbone, enabling this integration across extended heating seasons and transforming thermal demand into a grid-balancing resource. Digital technologies further enhance system coordination under variable climatic conditions. Artificial Intelligence (AI), the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) support real-time optimization, demand response, and cross-vector control within Renewable Energy Communities (RECs) and Virtual Power Plants (VPPs). At the system level, decentralized architectures—including microgrids, Non-Wire Alternatives (NWAs), and peer-to-peer (P2P) trading—strengthen resilience by maintaining thermal and electrical continuity during grid disruptions. Building on these findings, the review synthesizes cross-cutting technological synergies and proposes deployment pathways tailored to cold-climate CES, supported by comparative case studies. Despite demonstrated benefits, widespread adoption remains constrained by high upfront costs, interoperability challenges, and fragmented regulatory frameworks. The review concludes with policy, governance, and research recommendations to enable scalable, equitable, and climate-responsive CES deployment in heating-dominated regions. Full article

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

The rise in energy demand of Electric Vehicles (EVs) is an increasing burden on the grid. Solutions proposed to reduce grid load, for example, storing surplus energy from EVs, are costly and do not address associated challenges such as communication reliability and the optimum number of charging stations. This paper proposes an optimized energy management by availing supply from EVs and renewable resources for achieving net zero. We consider that EVs sell their surplus energy via bidirectional Vehicle-to-Grid exchange. Demand and supply from EVs and energy output from renewables are intelligently predicted and shared with the grid through a 5G communication network. A cost minimization solution alters grid supply according to available EV supply. This paper analyzes the upper bounds of EV demand and supply, utilizes game theory to incentivize EVs, and discusses the optimum number of charging stations. Results show that the proposed solution reduces 38.21% of the grid load and 5.3% cost. Full article