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This paper considers two pertinent research inquiries: 'Can an AI-based predictive framework be utilised for the optimisation of solar energy management?' and 'What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?' The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and $ R^2 $ R 2 . A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency. [ABSTRACT FROM AUTHOR]

Hydrogen hubs are emerging as strategic solutions to meet governmental emission reduction targets. A critical aspect of hydrogen hub design involves diversifying end uses, encompassing mobility, backup/storage, heating and cooling provision, onsite electricity generation, and grid energy supply. This paper introduces a comprehensive hydrogen hub model catering to these varied applications. Additionally, a novel multi-stage planning framework is proposed to assist hub operators in making informed decisions regarding the integration of diverse renewable energy sources and infrastructure. This framework addresses varying demands efficiently and economically. Moreover, to align with governmental emission reduction policies leading to net-zero targets, a proactive strategy is suggested. Through zoning for commercial, residential, educational, and healthcare sectors, the Sydney hydrogen hub is envisioned under two scenarios: business-as-usual and towards-net-zero design. Notably, CO 2 emissions are projected to decrease from 18.3 ktonnes in the former to 4.7 ktonnes in the latter scenario. [Display omitted] • Introduces a comprehensive hydrogen hub model catering to varied applications. • Multi-stage planning framework to assist hub operators in making informed decisions. • A proactive strategy aligning with governmental emission reduction/net-zero policy. • Determines optimal sizing, scheduling, and timing for a mix of technologies. • Designing Sydney H 2 hub zoned for commercial, residential, and educational tenants. [ABSTRACT FROM AUTHOR]

This paper presents a novel and effective approach for calculating Levelized Cost of Hydrogen production across the globe, without requiring timely resolved modelling or data. We select characteristic production patterns of photovoltaic and wind turbine power generation based on location qualities indicated by full load hours. These representative patterns are applied worldwide on similar locations to evaluate the proportion of electricity that electrolyzers can use if they have a smaller capacity than the renewable electricity source. Hence, the ratio of capacities is optimized to minimize hydrogen cost. The findings from various worldwide locations reveal that decreasing electrolyzer capacity is economically beneficial across all locations for boosting full load hours and curbing investments, particularly in locations with lower electricity generation. Validation using precise electricity production timeseries in specific locations revealed minimal errors in the methodology, though the quality of the results is impacted by the seasonality index differences between representative and exact location. Levelized cost of hydrogen from wind electricity for different countries depending on the ratio between wind turbine capacity and electrolysis capacity. [Display omitted] [ABSTRACT FROM AUTHOR]

Electrical vehicles are finding wide acceptance in the Jordanian transportation market; this has caused an accelerating shift in the emissions of greenhouse gases from the direct burning of fossil fuels consumed by the transportation sector towards the power generation sector. On the other hand, as electric vehicles gain more popularity, an extra load is added to the electrical power generation systems, raising essential concerns such as the capability of the power network to support this massive extra load and the increased emission of greenhouse gases caused by power plants. Studies show that Jordan's weather is known for being bright, sunny, and very suitable for the generation of electric power from solar energy sources. Therefore, with an infrastructure that can support convenient off-grid charging, a huge burden will be taken off the national grid and the environment. Therefore, this work proposes a basic design for an off-grid PV-covered carport and presents a study of the economics and effectiveness of using such a system to charge electric vehicles owned by the students and employees of the Applied Science Private University. The study is based on actual solar irradiance data collected on-site during university working hours (8 a.m.–5 p.m.) to allow students and employees to charge their electric vehicles from an off-grid carport system while on campus. Space limitations for carport design, initial design cost, return on investment, and annual electricity consumption are discussed to demonstrate the benefits of such a system for both the consumer (convenience and low charging cost) and the power company provider (less load to maintain). [ABSTRACT FROM AUTHOR]

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

The instability of the increasingly important photovoltaic (PV) electricity generation in Korea due to a dependence on meteorological conditions is an emerging problem. In this study, we examined the influence of various meteorological conditions on the prediction accuracy of PV generation and module temperature. The predictions were based on weather data acquired from the Korea Meteorological Administration and compared with actual PV generation data from the Dangjin coal yard PV power facility. The results were analyzed on the basis of meteorological conditions, insolation, ambient temperature, and wind velocity. [ABSTRACT FROM AUTHOR]

Solar photovoltaics have emerged as an economic source of renewable electricity generation for regions with high solar irradiation. Still, the installation of utility-scale PV systems is often disrupted due to the unavailability of land. Sparsely populated areas with a hot, dry climate can mitigate the land-energy conflict as they experience higher solar irradiation. In arid areas, the PV systems incur more losses due to dust deposition on PV modules and reduced energy conversion efficiency at higher temperatures. The shortage of water makes the module cleaning procedures cost-prohibitive. Floating Solar photovoltaic (FSPV) systems resolve these problems due to their reduced land footprint, higher efficiency, and reduce evaporative water loss. This study estimates the energy generation of a 4.8 MW FSPV-based microgrid in a hot, semi-arid urban area. By utilizing 5% area of the waterbody, the FSPV system yields 8.876 GWh of electricity annually, to meet 41.7% electricity demand of the study area. The system generates 16.7% more power compared to the land-based PV system. The performance ratio of 82%, and a DC capacity utilization factor of 20.3% is achieved. The system saves 52,810.32 m3 of water per year, compensating for the water requirement of 786.99 m3. for cleaning and dust removal of PV modules. The weekly cleaning procedure limits the soiling loss to 2.79%, compared to 8.47% after a month without cleaning. The FSPV-based microgrid reduces annual CO2 emissions by 2.93 tonnes. This study shall enable engineers, urban planners, and policymakers to develop clean energy-based, sustainable microgrid solutions for urban areas. [ABSTRACT FROM AUTHOR]

A study was carried out to evaluate phytodiversity along with the metal accumulation potential of native plants growing in the vicinity of a thermal power plant (TPP). We documented 26 tree species, six shrubs, and 35 herbs. Importance value index (IVI), which measures the extent to which a species dominates in an area, was found highest for Senna siamea (95.7) followed by Tectona grandis (56.5), and Pithecellobium dulce (19.6). Soil was acidic (pH 5.4) in nature with higher concentrations of Al and Fe. The pH of ground water was found acidic while pH of nearby river was found slightly alkaline. Values of PM2.5 and PM10 were slightly higher than NAAQS standards for industrial areas. The concentration of metals was found higher in aquatic plants than in terrestrial plants. In general, herbs and shrubs showed more metal accumulation potential than trees. Our results suggest that Senna siamea could be used for revegetation purposes in FA landfills. Further, terrestrial and aquatic plants such as Ageratina adenophora and Stuckenia pectinata could be used for reclamation of Mn, Zn, Al, and Fe from contaminated soils. Hydrilla verticillata (Ni and Mn), Nelumbo nucifera, and Ipomoea aquatica (Cr) can be used for metal removal from contaminated water. Novelty statement: The study focuses on the assessment of phytodiversity, soil and water analysis, ambient air quality, and bioaccumulation of heavy metals in plants growing in and around a thermal power plant. The study assumes significance as more than 65% of India's electricity generation is still by coal-fired power plants, having major implications for air, soil, and water pollution. By selecting native plant species adapted to the region, we can enhance biodiversity, restore habitats, and contribute to the overall ecological health of the area surrounding the power plant. [ABSTRACT FROM AUTHOR]

Summary: According to recent research work, increasing electric power generation is one of the significant advantages of the dual‐rotor wind turbine (DRWT) compared to the other types for the same wind speed. In this research work, a modified super‐twisting sliding mode control (STSMC) based on the neural network (NN) is suggested to regulate the stator powers of a DRWT‐based doubly‐fed induction generator (DFIG) in normal and unbalanced grid fault modes. The design of this strategy involves replacing the gains of conventional STSMC with the NN algorithm to enhance robustness, mitigate the impact of unbalanced grid voltage, and consequently improve the quality of the generated power of DRWT‐based DFIG. This forms the primary contribution of this work. The suggested strategy is compared with vector control (VC) and conventional STSMC in terms of reference tracking, power ripples, response dynamics, harmonic distortion of stator current, and the effect of an unbalanced grid fault. Finally, the utility and effectiveness of the designed controller are confirmed through computer simulations. Furthermore, when the grid is subjected to a 20% voltage drop, the results demonstrate that the suggested strategy reduced the total harmonic distortion (THD) value of the stator current by 12.92% compared to VC and by 9.29% compared to conventional STSMC. [ABSTRACT FROM AUTHOR]

Summary: LLC resonant converters have attracted much attention for its characteristics of high efficiency and high‐power density in recent years. Yet, with the diversity of application needs such as photovoltaic power generation and electric vehicles, traditional control solutions have gradually been unable to meet the increasing requirements, such as fast response to parameters perturbations. Besides, LLC converter is a high‐order nonlinear system, and it adds complexity to controller design. In order to solve these issues, taking the HB‐LLC converter as an example, this paper presents a simplified model‐based nonlinear control with fast response and simple design. The proposed controller is based on a second‐order model of LLC circuit, and the reverse design idea of the backstepping method is used to simplify the controller design. An experimental platform is built, and the results show that compared with PID control and linear active disturbance rejection control (LADRC), the proposed method has much smaller voltage undershoot (approximately 53.1% of PID and 68.4% of LADRC) and shorter settling time (approximately 27.8% of PID and 39.3% of LADRC) under input voltage change. [ABSTRACT FROM AUTHOR]

Load shifting for existing electric resistance water heaters (EWHs) presents a potential solution to utility challenges caused by the temporal mismatch between solar generation and electricity consumption. A case study in the Florida Power & Light (FPL) territory for both an individual and a fleet of EWHs evaluates this potential. Two optimization problems are solved using mixed-integer linear programming to maximize solar self-consumption and minimize electricity cost for an individual EWH. Compared to default operation, the solar maximization strategy results in nearly twice as much heating electricity from solar (80% vs. 42%) and 14% increased electricity cost based on FPL rates. An energy balance approach shows the aggregate impact of all FPL EWHs on days of peak and minimal load, June 25th and December 3rd. Publicized goals of increased solar capacity by 2030 will cause changes to the FPL net load, total load minus solar generation. The four million EWHs in the territory can stabilize the net load in 2030 as shifting their load causes a 24% decrease in the standard deviation of the predicted load on both dates. These results encourage widespread adoption of retrofit devices that control EWHs based on solar generation profiles and water consumption schedules. [ABSTRACT FROM AUTHOR]

This work reports a novel TiO2/g-C3N4 photoanode-based photocatalytic fuel cell (PFC) designed to convert chemical energy from simulated wastewater. The g-C3N4 modified TiO2 nanorod was successfully synthesized by a facile hydrothermal method. The results indicated that the maximum photocurrent density reached 2.44 mA cm-2 at 1.23 V vs. RHE by 1.167 g L-1 g-C3N4 loaded TiO2 composite. On the basis of analysis, the photoelectrochemical mechanism of the composite photoanode was proposed. This mainly demonstrated that the composite photoanode increases the electron donor density and boosts charge separation efficiency. In addition, the power density and hydrogen production of the proposed PFC were enhanced by 5.37 and 1.49 times compared to TiO2 photoanode-based PFC. To find the origins of the excellent performance of PFC, the influence of the organic compounds were investigated. The ESR measurement results indicated that the organic matter was captured by the photoexcited holes directly to facilitate the charge separation. The achieved power density and hydrogen production of 0.14 mW cm-2 and 21.60 μmol h-1 cm-2 were measured using RhB as the model pollutant, which was 2.42 and 1.23 times higher than the experiments with PBS electrolyte. This study proposed a novel PFC system converts the organic pollutant to the hydrogen and the electricity. [ABSTRACT FROM AUTHOR]

Accurate prediction of electric power generation in combined cycle power plants is challenging yet crucial, especially when employing machine learning techniques like artificial neural networks. This research presents an advanced forecasting model based on the robust adaptive neuro‐fuzzy inference system to estimate electric power generation under full operating conditions. The research dataset comprises 9568 data points featuring four input parameters, including ambient temperature, ambient pressure, exhaust vacuum, and relative humidity, spanning 6 years of the publicly available UCI Machine Learning Repository. These data were partitioned into 70% for training, 30% for validation, and 0% for testing to ensure robustness. A hybrid approach is implemented for optimization, combining the least squares method and gradient descent. The first‐order Sugeno fuzzy model was adopted to defuzzification the entire fuzzy set, achieving optimal results with three membership functions assigned to each input variable. This configuration minimizes the training's root mean square error values and the checking error of 3.8395 and 3.7849 regarding the generalized bell‐shaped membership functions, improving computational efficiency. These values are optimum when employing the root mean square error performance metric of identical studies. The validation of the adaptive neuro‐fuzzy inference system method and an optimal data selection strategy for training should be considered for optimum outcomes in the energy field. [ABSTRACT FROM AUTHOR]

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The electricity system and the industrial sector interrelate on the path to decarbonization. The study addresses the drivers and environmental pressure within the industrial sector on the National Electricity System (NES). This article aims to characterize the metabolic pattern of the NES and the industrial sector using the Multiscale Integrated Analysis of Social and Ecological Metabolism (MuSIASEM) from a bioeconomic perspective to identify fields of opportunity in the regulatory policy instruments. A set of extensive and intensive variables (2019) on energy, production factors, and emissions was used at different hierarchical levels based on both subsectors. Our results show that the NES used primary energy sources (PES) and secondary energy carriers to fulfill its functions, of which 72% were domestic sources and 28% were imported. México imported 79.5% of the natural gas (NG) for electricity generation. However, there are favorable conditions for renewable PES to increase the installed capacity of solar plants between 3 and 4 orders of magnitude and 2–3 in wind power from the current capacity. NES's energy consumption per hour of human time is 17,388 MJ/h, with 65% being the heat equivalent to a total energy input consumed of 2139 PJ/y. Public plants contributed 43.7% to the generation, and the independent and self-supply producers the rest. End uses needed the supply of 69.3% of electricity from baseload plants. Sixty-three percent of the metabolic pattern of the industrial sector is based on heat and fuel processes, which depend on NG imports. The NES is reorganizing and recovering its energy autonomy. [ABSTRACT FROM AUTHOR]

Small biomass piece, huge power—a fulcrum to lift two global lifelines, where water and energy meet. Initiative of new-to-nature electron–ion nexus and inter-functional synergy showcased sustainable integration between over-limit solar-evaporative desalination, even in backlit environments, and robust long-term all-day clean electricity co-generation. Sparks were created when intersecting with engineering. [Display omitted] The water–energy nexus has garnered worldwide interest. Current dual-functional research aimed at co-producing freshwater and electricity faces significant challenges, including sub-optimal capacities ("1 + 1 < 2"), poor inter-functional coordination, high carbon footprints, and large costs. Mainstream water-to-electricity conversions are often compromised owing to functionality separation and erratic gradients. Herein, we present a sustainable strategy based on renewable biomass that addresses these issues by jointly achieving competitive solar-evaporative desalination and robust clean electricity generation. Using hydrothermally activated basswood, our solar desalination exceeded the 100% efficiency bottleneck even under reduced solar illumination. Through simple size-tuning, we achieved a high evaporation rate of 3.56 kg h−1 m−2 and an efficiency of 149.1%, representing 128%–251% of recent values without sophisticated surface engineering. By incorporating an electron–ion nexus with interfacial Faradaic electron circulation and co-ion-predominated micro-tunnel hydrodynamic flow, we leveraged free energy from evaporation to generate long-term electricity (0.38 W m−3 for over 14 d), approximately 322% of peer performance levels. This inter-functional nexus strengthened dual functionalities and validated general engineering practices. Our presented strategy holds significant promise for global human–society–environment sustainability. [ABSTRACT FROM AUTHOR]

There has been a global expansion of solar electricity during the past decades. Solar photovoltaic (PV) electricity generation is not only simple but also environmentally benign and cost-effective. This is the most affordable and suitable power source for rural areas. The PV power output is unavoidably suppressed when there is partial shade. There were numerous maximum power point tracking (MPPT), arrays, and reconfiguration approaches that were available earlier. The authors have previously described an L-shaped propagating array arrangement. The L-shaped configuration creates the PV rows based on the chess game coin. The knight coin of this game will move in the shape of "L," which inspired new array configurations. This work discusses the hybridization of the L-shaped configuration with the conventional array configurations. The proposed hybrid L-shaped configuration has been developed in MATLAB/Simulink, where the conventional configuration and the hybrid configurations are validated under different test conditions. The test conditions are created based on the all possible real-word shading patterns. The power generation, mismatch losses, and characteristic curves were attained for each method, and this work presents all these validations with the inferences. In addition, the justification for the suitable array configuration with. [ABSTRACT FROM AUTHOR]

Wearable technology is experiencing remarkable progress, prompting the need for sustainable power sources like triboelectric nanogenerators (TENGs). However, integrating TENGs into fabrics and insufficient power outputs that allows for comfortable wear without obstructing user's movements presents a significant challenge. In this study, a novel kind of diamond‐structured fabric‐based triboelectric nanogenerators (DSF‐TENGs) is introduced utilizing an easy, economical, and scalable weaving method without any chemical modification. Owing to its 3D diamond pattern, surface interactions are enhanced for greater charge generation together with strengthened mechanical engagement for more effective charge transfer. The DSF‐TENG, with its unique self‐resilient structure, achieves impressive electric performance, including output voltage of ≈763 V, short‐circuit current of ≈20.4 µA, and power density of 2862.78 mW m−2, which is multiple times higher than most existing fabric‐based TENGs. It also offers excellent air permeability of 560 mm s−1, consistent electricity generation and sensing even after ten washing cycles, and incredible durability, withstanding over 30 000 cycles. Furthermore, DSF‐TENG is included in an insole that is capable of sensing gait patterns, walking speed, and fall detections of patients with Parkinson's disease. The remarkable power generation capabilities of DSF‐TENG indicate a strong potential for future developments in wearable electronics and healthcare applications. [ABSTRACT FROM AUTHOR]

Electric power generation safety incidents can lead to severe consequences, including casualties and widespread power outages. Previous research has mainly focused on the mechanisms and causal relationships of accidents. However, these incidents result from multiple factors working together, lacking systematic analysis. This study examines 161 electric power generation safety incidents from 2015 to 2022, utilizing Grounded Theory for coding to construct a causal model. The derived model is used as a conditional variable for fuzzy set qualitative comparative analysis (fsQCA), with accident severity as the outcome variable. Forty-five cases are selected for assigning values, and R language and fsQCA software are integrated for univariate necessary condition analysis, followed by configurational analysis. Results show the Grounded Theory-derived causal model includes six factors: human unsafe behavior, equipment factors, enterprise safety management, on-site safety management, safety qualifications of personnel, and environmental factors. Necessary condition analysis indicates incidents result from multiple conditions. Configurational analysis identifies seven paths condensed into three types: management deficiency, low safety qualifications, and unsafe behavior. Recommendations are proposed for each type, discussing intrinsic connections between variables based on conditional variables in configurational paths. The aim is to reduce electric power generation safety incidents, ensure personnel safety, and guarantee continuous electricity supply. [ABSTRACT FROM AUTHOR]

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production ( P p v ). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the P p v estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence P p v , the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in P p v between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m2. The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences. [ABSTRACT FROM AUTHOR]

This document explores the challenges and solutions related to the sustainable development of new power systems under dual carbon goals. The integration of renewable energy sources has impacted the stability of power systems, requiring measures such as grid control, optimization, and accurate forecasting of renewable energy generation. The accurate prediction of photovoltaic and wind power is crucial, and deep learning-based models have shown superior forecasting accuracy. Control and optimization strategies, such as voltage feedforward control and adaptive controllers, enhance system stability. Effective control of the power grid also has economic benefits, and planning and scheduling play a significant role in power system reliability and stability. System state management and intelligent question-answering systems contribute to efficient system operation. The advancement of artificial intelligence technology and big data analytics is crucial for enhancing power system stability and efficiency. Overall, these measures are essential for the sustainable development of power systems. [Extracted from the article]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

Urban expansion and fossil fuel dependence have led to energy and environmental concerns, highlighting the need for sustainable solutions. Rooftop photovoltaic (RPV) systems offer a viable solution for urban energy transition by utilizing idle rooftop space and meeting decentralized energy needs. However, due to limited information on building function attributes, detailed assessments of RPV potential at the city scale are still complicated. This study introduces a cost-effective framework that combines big Earth data and deep learning to evaluate RPV potential for various investment entities. We introduced a sample construction strategy that considers built environment and location awareness to improve the effectiveness and generalizability of the framework. Applied to Shanghai, our building function recognition model achieved 88.67%, 88.51%, and 67.18% for accuracy, weighted-F1, and kappa, respectively. We identified a potential installed capacity of 42 GW with annual electricity generation of 17 TWh for industrial and commercial, 16 TWh for residential, and 10 TWh for public RPVs. The levelized cost of electricity ranges from 0.32 to 0.41 CNY/kWh, demonstrating that both user-side and plant-side grid parity was achieved. This study supports sustainable development by providing detailed urban energy assessments and guiding local energy planning. The methods and findings may offer insights for similar studies globally. [ABSTRACT FROM AUTHOR]

Energy waste from power plants, typically emitted into the atmosphere, contributes to climate change and resource depletion. Integrating heat recovery systems into power plants can improve overall efficiency. This study focused on utilizing waste heat from a 500-MWe coal-based supercritical standalone plant through the organic Rankine cycle. The power plant uses Indian coal as a fuel input, and five distinct working fluids, R245fa, methanol, acetone, ethanol, and benzene, are considered working fluids for the ORC system. Thermodynamic analysis indicates that the standalone plant exhibits energy and exergy efficiencies of 27.33% and 25.01%, respectively. Following the integration of ORC, an overall efficiency improvement is observed. The increment in efficiency is because of the waste heat utilization, where the ORC generates additional electricity generation with outputs of 9.91 MWe for R245fa, 13.71 MWe for methanol, 13.97 MWe for acetone, 14.04 MWe for ethanol, and 14.11 MWe for benzene. Additionally, the study reveals a substantial reduction in CO2 emissions compared to the coal-based power plant with the same production of power, amounting to approximately 216.43 tons for R245fa, 299.43 tons for methanol, 305.10 tons for acetone, 306.63 tons for ethanol, and 308.16 tons for benzene. The thermodynamic investigation identifies the superior performance of the benzene-based ORC among the chosen fluids, and the economic study concludes that the ethanol-based ORC stands out as the most favorable option among the considered alternatives. [ABSTRACT FROM AUTHOR]

Solar thermal energy-based electricity is capital-intensive, and complex while technically evolving to achieve an optimal design. Global commitments and timelines to increase renewable energy share in the grid have promoted technically mature solar photovoltaics and wind. However, the potential of solar thermal energy for industrial applications, cogeneration and polygeneration is on the back foot for lack of policy, complexity, technical readiness level, cost of components and workable business models. Solar collectors that can generate high-quality steam for power generation is complex and costly; while, modified version of linear Fresnel reflector's (LFR) are prospective for various requirements. The current work looks into the possibility of an LFR and heliostat combination to generate steam at 45 bar, 400C temperature to produce electricity. The levelized cost of electricity (LCOE) and levelized cost of water (LCOW) is found to be INR 5.11 kWh−1 and INR 43.03 m−3, in a 70:30 debt–equity scenario. An IRR of 8.55% is achieved with a payback of 12.5 years, with the sale of electricity at INR 10.11 kWh−1 and water at INR 45 m−3. To reduce the LCOE, LCOW and have an attractive IRR, the viability gap funding (VGF) as an option is tried out. The impact of VGF at, 0%, 10%, 20% and 30% on water generation, electricity generation, IRR, NPV and payback are found. It is found that as the VGF increase, there is a better generation cost for electricity and water, with an improved IRR. However, with the VGF trend and corresponding reduction in electricity and water production cost, it will be ideal to have the VGF close to 15% to keep the electricity sale price at approximately INR 8.5 kWh−1 and water pricing at INR 46 m−3. [ABSTRACT FROM AUTHOR]

Power to Methane (PtM) systems are considered an attractive alternative for power generation, renewable sources' potential harnessing, and atmospheric carbon dioxide (CO 2) utilization. This study analyzes the potential use of Synthetic Natural Gas (SNG) for electrical power generation or its direct injection into the currently available Natural Gas Transportation infrastructure. A simulation approach using Aspen Plus v14 software was employed to assess various PtM configurations. Six different systems were analyzed for methanation processes, utilizing three types of electrolysis systems: Alkaline (AE), Proton Exchange Membrane (PEME), and Solid Oxide (SOE). Two primary methane applications were considered: integrated into a combined cycle for power generation and a standalone gas treatment stage for grid injection. As a result, the PEME-based system showed the highest generated-to-fed power ratio, larger than SOE (1.45% higher) and AE (20.66% higher). In addition, PEME technology reports the largest generation of SNG per power supply, exceeding 3.4% and 16.4% of those of SOE and AE, respectively. However, the SOE technology showed a larger efficiency than PEME technology by 8.2% and a PtM efficiency larger than PEME by 12.4%. Fixed capital investment for the PtM systems is around 8.6 and 13.9 million USD$, and their total earnings are between −8.2 and 20.9 thousand USD$ a year, depending on the electrolysis technology, methane application, and carbon credits scenario. According to these results, the PEME-based system is the most suitable option regarding technical and economic criteria. • PEME demonstrates superior efficiency and cost-effectiveness for producing Synthetic Natural Gas (SNG). • The primary application for this SNG is in electric power generation because its simplicity and potential to storage energy. • Integrating SNG into the national grid presents an important opportunity in energy transition, but its challenges are related to the high costs to meet regulatory standards. • Hydrogen-SNG blends can be interesting to be evaluated to meet regulatory standards. • SNG is an interesting route for contributing to a more sustainable and efficient energy landscape. [ABSTRACT FROM AUTHOR]

The droop control strategy is popularly employed in DC microgrids. However, its virtual resistance will cause voltage deviation and reduce transient response. The DOB‐based method is proven to improve transient response in literature. However, it is analyzed in this study that this method will negatively influence the current sharing when employed in droop control. A weakened disturbance observation (WDOB) is proposed in this work to improve the drawbacks. To employ the proposed method, the equivalent models of the droop controller and the physical system are separately established, and several transformations are conducted. An auxiliary compensation is added and the current loop is considered as a whole to be transmitted into the control plant, making the traditional DOB method successfully adopted. It is obvious that the dynamic performance is improved, but it disabled virtual resistance in the steady‐state. And the current sharing cannot be achieved in a multi‐converter parallel system. The reason for this problem is analyzed from the control process and transfer function, and the WDOB solution is finally proposed. Through the proposed method, both aims of improving dynamic response and current sharing can be achieved, and the steady voltage deviation is much less than that of the traditional droop controller. [ABSTRACT FROM AUTHOR]

Introduction: Sedimentary basins are naturally porous and permeable subsurface formations that underlie approximately half of the United States. In addition to being targets for geologic CO2 storage, these resources could supply geothermal power: sedimentary basin geothermal heat can be extracted with water or CO2 and used to generate electricity. The geothermal power potential of these basins and the accompanying implication for geologic CO2 storage are, however, understudied. Methods: Here, we use the Sequestration of CO2 Tool (SCO2TPRO) and the generalizable GEOthermal techno-economic simulator (genGEO) to address this gap by a) estimating the cost and capacity of sedimentary basin geothermal power plants across the United States and b) comparing those results to nationwide CO2 sequestration cost and storage potential estimates. Results and discussion: We find that across the United States, using CO2 as a geothermal heat extraction fluid reduces the cost of sedimentary basin power compared to using water, and some of the lowest cost capacity occurs in locations not typically considered for their geothermal resources (e.g., Louisiana, South Dakota). Additionally, using CO2 effectively doubles the sedimentary basin geothermal resource base, equating to hundreds of gigawatts of new capacity, by enabling electricity generation in geologies that are otherwise (with water) too impermeable, too thin, too cold, or not deep enough. We find there is competition for the best sedimentary basin resources between water- and CO2-based power, but no overlap between the lowest-cost resources for CO2 storage and CO2-based power. In this way, our results suggest that deploying CO2-based power may increase the cost of water based systems (by using the best resources) and the cost of CO2 storage (by storing CO2 in locations that otherwise may not be targeted). As such, our findings demonstrate that determining the best role for sedimentary basins within the energy transition may require balancing tradeoffs between competing priorities. [ABSTRACT FROM AUTHOR]

The Aotearoa New Zealand electricity sector is undergoing a transition to more distributed electricity generation, largely driven by the private sector. Local actors, however, are increasingly interested in playing a role in the energy transition, and addressing their resilience, with community renewable energy (CRE). These projects often have a wide range of impacts, dependent on the community’s motivations, as well as the design and operation of the project. However, there is a lack of tools that can accurately quantify the impacts derived from CRE projects, and guide communities to build a portfolio of projects. This study used system dynamics modelling to analyse the impacts of CRE projects over a 40-year time span. The Energise Ōtaki community group in Aotearoa New Zealand was used as a case study given their already established CRE project and goals to expand their project portfolio. The system dynamics model showed if the community reinvested 80% of the profits from the first project into a second project would provide the greatest long-term benefit for the community in terms of the impact areas important to them. System dynamics modelling proved a useful tool in evaluating the wide range of impacts, and how they interact. [ABSTRACT FROM AUTHOR]

This study develops a an optimization model focused on the layout and dispatch of a low-carbon hydrogen supply chain. The objective is to identify the lowest Levelized Cost of Hydrogen for a given demand. The model considers various elements, including electricity supply from the local grid and renewable sources (photovoltaic and wind), alongside hydrogen production, compression, storage, and transportation to end users. Applied to an industrial case study in Sweden, the findings indicate that the major cost components are linked to electricity generation and investment in electrolyzers, with the LCOH reaching 5.2 EUR/kgH2 under typical demand conditions. Under scenarios with higher peak demands and greater demand volatility, the LCOH increases to 6.8 EUR/kgH2 due to the need for additional renewable energy capacity. These results highlight the critical impact of electricity availability and demand fluctuations on the LCOH, emphasizing the complex interdependencies within the hydrogen supply chain. This study provides valuable insights into the feasibility and cost-effectiveness of adopting hydrogen as an energy carrier for renewable electricity in the context of decarbonizing industrial processes in the energy system. • MILP for optimizing the LCOH. • Identifying the optimum layout and dispatch for a hydrogen supply chain. • Hydrogen for decarbonizing industrial sector. • Identifying the primary cost drivers of LCOH. [ABSTRACT FROM AUTHOR]

The environmental effect of a manufacturing or service method is determined by the resource and energy inputs and outputs at each point of the product's life cycle. In Fused Deposition Modeling (FDM), generally, the material used for fabrication is plastic, and the raising of interest from different backgrounds of users could increase the issue of plastic pollution. Therefore, many scholars have proposed an initiative to employ bio-composite in FDM. In this study, an environmental assessment of global warming potential and fine particulate matter emission from the fabrication of bio-composite filament FDM was performed through its life cycle analysis using GaBi Software. Initially, data on resources and energy inputs and outputs were gathered. The functional unit in this study was the 1.0 kg wood/PLA composite filament extruded using a twin-screw extruder. All wastes were collected and recycled. The fabricated composite filaments were transported by container ship with a capacity of 5000 - 200 000 dwt gross weight for 100 km within Malaysia. Based on the results from the GaBi dashboard, the FDM process of bio-composite filament has contributed as much as 138.7 kg CO2 eq on the global warming potential and 1.71e-4 kg N eq. on fine particulate matter by the electricity power generation in extrusion and printing processes. The main factor for this issue is the consumption of coal in electric power generation, which is considered a non-renewable resource. Therefore, it is recommended that natural fibers such as wood fiber be employed in the filament of FDM to reduce the environmental impact. As shown in the study, the materials contribute less to the impact. Further study is suggested to compare the FDM technology with conventional technology using similar materials. [ABSTRACT FROM AUTHOR]

Solar energy production is an intermittent process that is affected by weather and climate conditions. This can lead to unstable and fluctuating electricity generation, which can cause financial losses and damage to the power grid. To better control power production, it is important to predict solar energy production. Big data and machine learning algorithms have yielded excellent results in this regard. This study compares the performance of two different machine learning approaches to solar energy production prediction: regression and classification. The regression approach predicts the actual power output, while the classification approach predicts whether the power output will be above or below a certain threshold. The study found that the random forest regressor algorithm performed the best in terms of accuracy, with mean absolute errors and root mean square errors of 0.046 and 0.11, respectively. However, it did not predict peak power values effectively, which can lead to higher errors. The long short-term memory algorithm performed better in classifying peak power values. The study concluded that classification models may be better at generalizing than regression models. This proposed approach is valuable for interpreting model performance and improving prediction accuracy. [ABSTRACT FROM AUTHOR]

Wave energy conversion holds promise for renewable energy, but challenges like high initial costs hinder commercialization. Integrating wave-energy converters (WECs) into shore-protection structures creates dual-function structures for both electricity generation and coastal protection. Oscillating water columns (OWCs) have been well studied in the past with their simple generation mechanism and their out-of-water power take-off (PTO) system, which can minimize bio-fouling effects and maintenance costs compared to other submerged WECs. In addition, a slotted barrier allows for better circulation behind the breakwater while dissipating incoming wave energy through viscous damping. This study examines the performance of a new design which combines an OWC with a slotted breakwater. Small-scale (1:49) laboratory tests were performed with a piston-type wave generator. The performance is evaluated in terms of wave transmission, wave energy extraction, and wave loading under various wave conditions while focusing on the effects of the porosity of the slotted barrier and tide level changes. Results show that under larger waves, a decreasing wave transmission, increasing power extraction from the OWC, and energy dissipation from the slotted barrier are observed. On the other hand, under increasing wavelengths, wave transmission is observed to be constant; this is important for harbor design, which means that the breakwater is effective under a wider range of wavelengths. Porosity allows for more transmission while inducing less horizontal force on the structure. [ABSTRACT FROM AUTHOR]

This study investigates how differences in energy-related properties of cities influence the composition of a cost-efficient urban energy system, assuming electrification of the transport and industry sectors and zero-emission of CO2. These differences are evaluated for two scenarios regarding the capacities of the modeled cities to import electricity. A linear optimization model that encompasses the electricity, heating, industry, and transport sectors, using measured data from six cities in Sweden, is applied. Results show that when strict constraints on electricity imports are enforced, cities with a lower ratio of annual electricity demand for heat encourage the implementation of power-to-heat solutions in the heating sector. This study also reveals that under such stringent electricity import conditions, cities with a high level of flexibility in electricity demand favor a combination of batteries and solar photovoltaics as opposed to biomass-based electricity production. Conversely, when electricity importation is less restricted and biomass prices surpass 20 EUR/MWh, local electricity generation is outcompeted by imports, and large-scale heat pumps working in tandem with thermal energy storage dominate the heating sector in all modeled cities. This assertion holds true when the maximum electricity import capacity is utilized up to 5000 h annually. [ABSTRACT FROM AUTHOR]

This publication explores current and prospective methods for hydrogen production and purification, with a strong emphasis on membrane-based technologies for purification and separation. This focus is justified by the ongoing shift towards renewable energy sources (RESs) in electricity generation, necessitating strategic changes to increase hydrogen utilization, particularly in the automotive, heavy road, and rail sectors, by 2025–2030. The adoption of hydrogen from RESs in the construction, energy, and industrial sectors (e.g., for process heat or fertilizer production) is also under consideration, driving the need for innovative production, separation, and purification methods. Historically, industrial-scale hydrogen has been predominantly derived from fossil fuels, but renewable sources such as electrolysis, biological, and thermal processes now offer alternatives with varying production efficiencies (0.06–80%) and gas compositions. Therefore, selecting appropriate separation and purification methods is critical based on specific usage requirements and the gas composition. Industrial-scale hydrogen purification commonly employs pressure swing adsorption (PSA) technologies, capable of achieving up to 99.99% purity. Cryogenic distillation is suitable for applications needing up to 95% purity. Membrane technologies, including polymer, metallic, and electrolytic membranes, have traditionally been limited to moderate volumes of pure gas production but are crucial for hydrogen purification and separation. This publication critically evaluates the potential of membrane technology for hydrogen separation, particularly in response to the anticipated rise in demand for RES-derived hydrogen, including from renewable feedstocks. [ABSTRACT FROM AUTHOR]

The relationship between a region's dependency on variable renewable energy (VRE) and the viability of long-duration energy storage (LDES) technologies is recognised through various electricity grid modelling efforts in the contemporary literature. Numerous studies state a specific VRE penetration level in total electricity generation as an indicator of the emergence of an LDES market. However, there is considerable variability across studies when comparing VRE penetration levels in conjunction with LDES technology utilisation, and significant diversity exists in electricity grid modelling approaches. This review aims to highlight these inconsistencies by offering an overview of disparate findings and dissecting the influencing variables. Sixteen parameters are identified from reviewed studies, complemented by an additional five recognised through in-depth analysis. This comprehensive examination not only sheds light on critical aspects overlooked in previous reviews, requiring further investigation, but also provides novel insights into the complexity of this correlation, elevating the understanding of LDES market creation by unravelling the factors that influence the technology adoption across various contexts. Furthermore, it provides clarity in LDES research terminology by rectifying ambiguous language in the existing literature. Altogether, seven databases were explored to produce a trustworthy foundation for the study. [ABSTRACT FROM AUTHOR]

This simulation study explores the potential of a novel façade design with integrated control system comprising a dynamic photovoltaic (PV) facade integrated with dimming lighting control to enhance the work environment in office buildings and achieve energy-efficient solutions. Parametric modeling using the Grasshopper plug-in for Rhino software 7, coupled with energy simulation through the Honeybee environmental plug-in for the EnergyPlus program, are used in the methodology. The integrated control strategy was simulated to study in a single office space, utilizing the Daysim engine to assess indoor daylight quality and focusing on Daylight Factor (DF) and Daylight Glare Probability (DGP). Additionally, two artificial lighting control systems were examined for potential integration with the dynamic PV facade to minimize lighting load. The study employs the Galapagos evolutionary solver function embedded within Grasshopper to identify optimum solutions. The dynamic PV façade achieves substantial reductions in overall energy consumption, cutting it by 73% in June, 54% in July, 54.5% in August, and 52.55% in September. The results demonstrate substantial reductions in total energy consumption, with notable savings in heating and cooling due to the dynamic facade's ability to balance and control solar radiation during working hours. Moreover, the dynamic PV facade contributes to electricity generation, demonstrating its potential to improve visual comfort, decrease energy consumption, and generate electric energy through rotational adjustments and varying transparency levels. [ABSTRACT FROM AUTHOR]

Solar tower with thermal energy storage (ST-TES) represents a promising technology for large-scale exploitation of solar irradiation for electricity generation. A ST-TES has the potential to extend electricity generation to more favorable conditions, such as high electricity prices. The size of TES, however, constrains the flexibility of dispatching, especially when there is significant uncertainty in forecasts of solar irradiation and electricity prices. This study explores the impact of TES size when the plant uses model-predictive control (MPC) for dispatch planning. The performance of MPC is benchmarked against one perfect knowledge (PK) and two day-ahead strategies. The optimal achievable profit for each TES size is determined using the PK strategy. An analysis is conducted to evaluate the relative profit losses for all the other simulated strategies compared to the PK strategy. A case study is conducted for a hypothetical 115 MWe ST-TES in South Australia. For January and August, 100 tests are performed for each dispatch policy, with the TES size varying from 6 to 14 h. The revenue evaluation is conducted with both fixed and wholesale spot prices. The analysis shows that MPC-aided dispatching enables the adoption of a smaller TES compared to day-ahead policies while maintaining the same expected profit. The resulting TES size reduction from 14 to 10 h translates into approximately up to $45.4 million in capital cost savings. The findings of this study can inform the ST-TES plant's design procedures and facilitate negotiations for electricity sales contracts. [ABSTRACT FROM AUTHOR]

The escalating concern over global warming has garnered significant international attention, with carbon emission intensity emerging as a crucial barrier to sustainable economic development across various regions. While previous studies have largely focused on annual scales, this study introduces a novel examination of Tianjin's quarterly carbon emission intensity and its influencing factors from 2012 to 2022 using quarterly data and the Logarithmic Mean Divisia Index (LMDI) model. The analysis considers the carbon emission effects of thermal power generation, the power supply structure, power intensity effects, and economic activity intensity. The results indicate a general decline in Tianjin's carbon emission intensity from 2012 to 2020, followed by an increase in 2021 and 2022. This trend, exhibiting significant seasonal fluctuations, revealed the highest carbon emission intensity in the first quarter (an average of 1.4093) and the lowest in the second quarter (an average of 1.0019). Economic activity intensity emerged as the predominant factor influencing carbon emission intensity changes, particularly notable in the second quarter (an average of −0.0374). Thermal power generation and electricity intensity effects were significant in specific seasons, while the power supply structure's impact remained relatively minor yet stable. These findings provide essential insights for formulating targeted carbon reduction strategies, underscoring the need to optimize energy structures, enhance energy efficiency, and account for the seasonal impacts of economic activity patterns on carbon emissions. [ABSTRACT FROM AUTHOR]

The goal of securing almost 100 % of electricity generation in Germany with renewable energies (RE) and green energy imports is analyzed. The annual continuous load lines of the ways of generating electricity with RE demonstrate the actual supply capacity of RE as a function of the installed capacity. The increasing volatility resulting from the expansion of RE has a negative impact on the effectiveness of electricity generation and leads to high residual loads. It is not possible to compensate for this by storing the surplus electricity with hydrogen and regenerating electricity and importing green energy sources. A globally feasible concept of the complementary decarbonization of electricity generation from RE and fossil fuels via natural gas pyrolysis and the material‐energetic use of lignite is proposed. [ABSTRACT FROM AUTHOR]

Emergy analysis (EmA) is the quantitative sustainability method that analyses materials, energy resources, goods, or services under a common unit of solar emergy (seJ). Meanwhile, life cycle assessment (LCA) is one of the popular tools to evaluate environmental impacts. Both methods have been widely used in various applications, hence, the idea of co‐jointing these two methods has been increasing to optimize the assessment's inclusivity. However, the existing integrated LCA–EmA methods are applied segmentally with minimal data integration, which has limited the potential of co‐benefits in sustainability accounting. This research proposes a more comprehensive integration between LCA and EmA, known as life cycle emergy analysis (LCEmA). The process data is fully integrated, and the detailed methodological approach is presented to demonstrate the assessment process. Diaper manufacturing is selected as a case study to validate the functionality of the proposed LCEmA approach. The inclusion of foreground and background data offered in LCEmA approach considers backend processes such as electricity generation for manufacturing activities. This has shifted the sustainability hotspot from raw material extraction to the manufacturing process. This research demonstrates that the LCEmA approach can perform comprehensive analysis as a promising alternative to the existing integrated LCA–EmA methods. [ABSTRACT FROM AUTHOR]

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This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8%. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)