Effective thermal management is essential for the reliable operation of lithium-ion batteries (LIBs) in electric vehicles (EVs) and other energy storage applications. In cylindrical cells, anisotropic thermal conductivity makes it difficult to maintain a uniform internal temperature, particularly under air-cooling conditions. This study investigates the effects of annular cooling fins with different radius distributions on the thermal performance and internal temperature uniformity of a cylindrical LIB. A three-dimensional transient electrochemical–thermal model was developed in COMSOL Multiphysics, coupling a one-dimensional pseudo-two-dimensional (P2D) electrochemical model with heat transfer and airflow simulations. Five annular fin configurations were evaluated against a finless design under forced-air cooling conditions. The numerical model was validated against published experimental measurements of cell voltage and surface temperature. The results showed that all fin configurations reduced the maximum cell temperature and internal temperature difference compared with the finless design. The uniform-radius configuration produced the lowest maximum temperature, whereas the increasing-radius configuration achieved the most uniform internal temperature distribution. Compared with the uniform-radius configuration, the increasing-radius design reduced the average maximum intra-cell temperature difference by 5.8%, fin material consumption by 37.5%, and airflow pressure drop by approximately 15%. These findings indicate that the distribution of annular fin radii plays an important role in balancing heat dissipation, internal temperature uniformity, and aerodynamic resistance. The study provides a practical basis for the design of air-cooled thermal management systems for cylindrical LIBs.
Distributed sensing in energy systems requires reliable power supplies where wiring and battery replacement are difficult. Micro-electro-mechanical systems (MEMS) vibration energy harvesters can supply local power, but their sustainability depends on usable energy delivery, operational reliability, and life-cycle burdens. This review examines the conditions under which MEMS vibration energy harvesting can support sustainable sensor operation. Foundational studies and journal literature available by 30 June 2026 were critically examined across piezoelectric, electrostatic, electromagnetic, and triboelectric conversion. Device evidence was assessed alongside fabrication, packaging, excitation conditions, power management, storage, and application validation. Reported outputs were distinguished by measurement boundary, and environmental assessments were examined for their functional units, assumptions, and exclusions. The comparison identified net energy delivery over complete sensing cycles as the basis for assessing autonomous operation. Low-frequency and broadband strategies extended operating ranges under specific excitation conditions, while interface losses, cold-start requirements, and intermittent inputs constrained usable energy. Reliability evidence remained dependent on test conditions, and environmental findings from materials or larger harvesters provided limited grounds for conclusions about complete MEMS sensor nodes. These findings indicate that sustainable sensor power requires coordinated design of the harvester, interface, storage, and sensing workload. The review establishes comparison criteria linking device performance to delivered energy, service continuity, and life-cycle assessment, and identifies the measurements needed to substantiate claims of battery replacement and environmental improvement.
Nitrogen oxide emissions from gas turbines at natural gas compressor stations pose a local environmental challenge to the sustainable operation of energy infrastructure. This study assesses nitrogen oxide dispersion around the Marand natural gas compressor station in northwestern Iran to identify candidate areas for ambient monitoring. Stack measurements from three natural gas-fired turbines were compiled to characterize nitric oxide (NO), nitrogen dioxide (NO$_2$), and total nitrogen oxides (NO$_x$). Meteorological data were processed using American Meteorological Society/Environmental Protection Agency Regulatory Model Improvement Committee Meteorological Preprocessor (AERMET), and dispersion was simulated using the American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD). Maximum 1-hour and 24-hour concentrations were compared together with their receptor coordinates and occurrence times. The predicted maximum 1-hour concentrations of NO, NO$_2$, and NO$_x$ were 90.50, 5.07, and 91.72 μg/m$^3$, respectively; the corresponding maximum 24-hour concentrations were 11.56, 0.88, and 11.71 μg/m$^3$. The hourly peak receptors were closely grouped, whereas the daily NO$_2$ and NO$_x$ peak receptors were approximately 0.70 km from their respective hourly peak locations. The NO peak receptor remained unchanged between averaging periods. These results indicate that selecting monitoring locations from an hourly maximum alone may overlook locations associated with the highest daily concentrations. Joint interpretation of peak magnitude, location, and timing identifies candidate monitoring areas and provides information relevant to the environmental management and sustainable operation of natural gas transmission infrastructure.
Renovating hospital support buildings requires balancing reductions in fossil-fuel use against capital expenditure and operational constraints. This study examines the energy and investment trade-offs among renovation scenarios for an existing hospital support building. An annual energy model was reconstructed from available engineering estimates for the Technical-Economic Block (TEB) of the University Clinical Centre of the Republic of Srpska in Banja Luka. Envelope renovation, boiler replacement and solar thermal installation were combined into seven scenarios. Primary-energy savings, operational CO$_2$ reductions, investment and fuel-cost savings were compared. Preference Selection Index (PSI) rankings were calculated using four criteria and a reduced two-criterion formulation, followed by an analysis of alternative investment weights. The three-measure package achieved the largest estimated primary-energy savings, at 1.813 GWh/year, and ranked first under both PSI formulations. However, boiler replacement alone ranked first when energy savings and investment received equal weights. Envelope renovation combined with boiler replacement retained 98.1% of the three-measure package’s energy savings at 12.9% lower investment. Adding solar thermal required a further 322,337 Bosnia and Herzegovina convertible marks (BAMs) and produced estimated annual fuel-cost savings of 4,409 BAM, giving an incremental simple payback of 73.1 years. Estimated operational CO$_2$ reductions reached 454.5 tonnes/year using a conversion factor inferred from the source scenario data. These findings distinguish maximum energy savings from investment preference and marginal financial returns. The assessment supports preliminary renovation comparisons, while its absolute estimates remain conditional on verification of the baseline and engineering inputs.
Cold thermal energy storage supports sustainable cooling by allowing cold production and use to occur at different times, but slow solidification can restrict charging within available operating periods. This study investigates the effects of metal foam, hybrid nanoparticles, and radiative heat transfer on solidification in an enclosure with a circular cold wall and three radial fins, aiming to identify which measures most effectively shorten charging time. A transient finite element model was solved using the Galerkin method and implicit time discretization. The nanoparticle suspension was represented as a homogeneous medium, and variations in porosity, nanoparticle volume fraction, and the radiation parameter were examined through temperature distributions, solid fraction evolution, and complete solidification time. Reducing the porosity from 1 to 0.95 shortened the solidification time by approximately 81.9%. Nanoparticle addition and modeled radiative heat transfer yielded reductions of 6.54% and 8.94%, respectively, in their corresponding comparisons. Applying all three measures reduced the complete solidification time from 7742.8 to 1276.14 s, corresponding to a reduction of approximately 83.52%. Within the investigated comparisons, foam incorporation produced the largest reduction in complete solidification time, while nanoparticle addition and modeled radiative transfer produced smaller reductions. These findings identify the modifications that most strongly affect charging time in the studied enclosure and inform the selection of heat transfer measures for phase change storage in sustainable cooling applications.
Accurate electricity demand forecasting is important for power system planning, grid operation, and the integration of variable renewable energy sources. Although deep sequence-learning models are widely used for load forecasting, the relative performance of recurrent, gated, and hybrid architectures remains dependent on the characteristics of the available data and the forecasting procedure. This study investigates the performance of eight deep sequence-learning architectures for the simultaneous prediction of three electricity demand series in Great Britain. Daily demand records from 1 January 2018 to 30 November 2023 were used to develop and evaluate recurrent neural network (RNN), bidirectional RNN, long short-term memory (LSTM), bidirectional LSTM, gated recurrent unit (GRU), bidirectional GRU, convolutional neural network (CNN)-LSTM, and CNN-GRU models. All models used a 12-day input window and were evaluated using a chronological 80/20 training–test split. A separate 366-day recursive forecasting exercise was also conducted for the period from 1 December 2023 to 30 November 2024. The conventional RNN achieved the lowest mean absolute error (MAE) across National Demand, Transmission System Demand, and England and Wales Demand, with an average of approximately 1,246 MW. Its corresponding errors were 1,151 MW, 1,517 MW, and 1,069 MW, respectively. More complex gated, bidirectional, and convolutional-recurrent architectures produced higher errors under the implemented training and evaluation procedures. These findings indicate that greater architectural complexity does not necessarily translate into more accurate electricity demand predictions for daily-aggregated load data. The comparison also identifies the importance of model configuration and output handling when interpreting differences in forecasting performance. The study provides empirical evidence to inform model selection for electricity demand forecasting, an essential component of data-driven planning and operational decision-making in sustainable power systems.
Thermal management is essential for the efficient and reliable operation of energy systems, particularly where heat transfer must be regulated under varying thermal conditions. Magnetically responsive fluids offer a means of controlling convective transport without mechanically altering the flow domain. However, the combined effects of spatially non-uniform magnetic fields, porous-medium permeability, thermal radiation, and nanoparticle concentration on heat transfer in geometrically complex enclosures remain insufficiently understood. This study investigates the coupled thermal and hydrodynamic behavior of Fe$_3$O$_4$–water ferrofluid in a porous enclosure to examine its potential for controllable thermal management in energy-related applications. A two-dimensional numerical model was formulated for an enclosure with an inner heated elliptical boundary and an outer sinusoidal cooled wall. The governing equations were solved using the control volume finite element method (CVFEM), and the influences of the Hartmann number ($Ha$), Rayleigh number ($Ra$), Darcy number ($Da$), radiation parameter ($Rd$), and nanoparticle volume fraction ($\varphi$) were examined. The numerical predictions were compared with published results, and a mesh-independence assessment was performed. Increasing the magnetic field strength reduced the average Nusselt number by up to approximately 29.72%, indicating suppression of buoyancy-driven convection. In contrast, increasing the $Ra$ raised the Nusselt number by up to 150.22% in the absence of magnetic forcing. Higher porous-medium permeability increased the Nusselt number by up to 4.58%, while increasing the $Rd$ produced an increase of up to 104.75%. Raising the Fe$_3$O$_4$ $\varphi$ increased the Nusselt number by up to 5.98%. These results indicate that magnetic forcing provides a means of regulating convective heat transfer, while permeability, radiation, and nanoparticle loading influence the thermal response of the enclosure. The study identifies the competing physical mechanisms governing ferrofluid heat transfer and provides a basis for further investigation of magnetically controlled thermal components in sustainable energy systems.
Sustainable waste-to-energy (WtE) planning requires coordinated decisions on feedstock valorization, conversion technology, plant siting, operating strategy, and the utilization of recovered energy and by-products. These decisions are complicated by conflicting economic, environmental, technical, and social criteria, together with uncertainty and hesitation in operational and stakeholder information. This study develops an intelligent decision-support framework for integrated WtE planning and smart-energy infrastructure under uncertain information. Six sine-based similarity measures are extended from intuitionistic fuzzy sets to $q$-rung orthopair and $T$-spherical fuzzy environments, allowing support, opposition, hesitation, and stakeholder abstention to be represented within a common decision structure. The framework integrates data acquisition from supervisory control and data acquisition systems, smart meters, municipal information, and expert assessments with fuzzification, entropy-based weighting, similarity-based ranking, the Aggregate Separation Index (ASI), and Monte Carlo rank-stability analysis. Its performance is examined through four illustrative energy-planning cases involving waste-valorization pathway selection, energy-facility siting, conversion-route selection, operating-strategy selection for a biogas combined heat and power (CHP) unit, and industrial-symbiosis partner selection. Anaerobic digestion is consistently identified as the preferred valorization pathway, while biogas CHP is the leading conversion route. Under entropy-derived weights, Baseload is the preferred CHP operating strategy, although Hybrid-adaptive operation becomes more attractive when revenue and grid-support priorities receive greater emphasis. Under the reference setting, Monte Carlo analysis gives rank-1 probabilities of 0.948 for pathway selection and 0.999 for operating-strategy selection. By contrast, the two leading industrial-symbiosis partners differ by only 0.008, and the nominal leader retains first place with a probability of 0.632, so the decision is classified as contested. A machine-learning benchmark further shows that the similarity-based matcher remains within 0.6–3.9 percentage points of the best crisp prototype benchmark while retaining explicit information on similarity, hesitation, and aggregate separation. The results demonstrate how uncertainty-aware decision support can connect waste valorization, energy recovery, conversion technology, grid-responsive operation, and industrial symbiosis within an integrated framework for sustainable urban energy planning.
The cavity geometry of a single-slope solar still directly influences the internal transport phenomena by altering natural convection, heat transfer, and vapor movement, as their alteration collectively determines the system’s freshwater productivity. Variations in the glass cover inclination modify the cavity geometry, thereby changing the natural convection flow pattern, evaporation, and condensation mechanisms, as well as the overall thermal behavior of the solar still. Despite its importance, the impact of cavity geometry on the internal transport phenomena has not yet been fully understood. Therefore, the present work conducted a systematic two-dimensional numerical investigation to assess the effects of five glass cover inclination angles (10°, 15°, 20°, 25°, and 30°) on the thermo-fluid behavior and freshwater productivity. To isolate the influence of cavity geometry, the basin length and front-wall height were kept constant, while the back-wall height was varied to produce the desired inclination angles without altering the remaining design parameters. The numerical results confirmed that modifying the glass cover inclination induced a substantial change in the natural convection flow regime within the still cavity, leading to significant nuances in heat and mass transfer characteristics. At steeper inclination angles, the airflow was dominated by a single large recirculating convection cell that confined convective heat transfer between the evaporating water surface and the glass cover, causing a uniform reduction in the effectiveness of condensation and freshwater production. In contrast, the shallowest inclination angle of 10° promoted the formation of multiple localized convection cells that enhanced fluid mixing, strengthened convective heat transfer, and improved vapor transport toward the condensing surface. Consequently, the 10° configuration exhibited the highest freshwater productivity among all investigated geometries.
The electricity grid is the physical backbone of the low-carbon transition, yet in most industrialised economies it was designed for a fossil-fuelled, unidirectional, and centralised system. This paper examines how the electricity grid of Great Britain has become the object of a comprehensive reform programme and asks what generalisable lessons its experience offers for other countries pursuing deep decarbonisation. A qualitative policy-document analysis, triangulated with publicly available quantitative indicators, is adopted: policy documents, regulatory determinations, and network operator business plans issued between 2020 and 2026 are analysed and triangulated with published quantitative indicators, including connection queue volumes, transmission constraint costs, and planned capital expenditure. The evidence shows that the pre-reform “first come, first served” model produced a connection queue of over 700 GW of generation and storage and more than 120 GW of demand by 2025, while transmission constraint costs reached approximately £1.9 billion in 2024/25. In response, three mutually reinforcing reform strands have been introduced: a redesigned connections process (Target Model Option 4+, TMO4+) that combines readiness with strategic alignment; a step change in transmission investment under the Accelerated Strategic Transmission Investment (ASTI) framework and the RIIO-T3 (Revenue = Incentives + Innovation + Outputs, transmission price control 2026–2031) price control; and a strengthened regulatory regime with enforceable delivery obligations. This policy synthesis concludes that reforming the grid is as much an institutional and regulatory task as a technical one, and that coordinated action across the system operator, the regulator, government and network companies is a necessary condition for delivering an affordable, secure and low-carbon electricity system.
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