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.
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.
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.
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.
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.
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