Search Results (402)

The global climate crisis has driven unprecedented agreements among nations on carbon mitigation. With China’s commitment to carbon peaking and carbon neutrality targets, the building sector has emerged as a critical focus for emission reduction, particularly because office buildings account for over 30% of building energy consumption. However, a systematic and regionally adaptive low-carbon technology evaluation framework is lacking. To address this gap, this study develops a multidimensional decision-making system to quantify and rank low-carbon technologies for office buildings in Beijing. The method includes four core components: (1) establishing three archetypal models—low-rise (H ≤ 24 m), mid-rise (24 m < H ≤ 50 m), and high-rise (50 m < H ≤ 100 m) office buildings—based on 99 office buildings in Beijing; (2) classifying 19 key technologies into three clusters—Envelope Structure Optimization, Equipment Efficiency Enhancement, and Renewable Energy Utilization—using bibliometric analysis and policy norm screening; (3) developing a four-dimensional evaluation framework encompassing Carbon Reduction Degree (CRD), Economic Viability Degree (EVD), Technical Applicability Degree (TAD), and Carbon Intensity Degree (CID); and (4) conducting a comprehensive quantitative evaluation using the AHP-entropy-TOPSIS algorithm. The results indicate distinct priority patterns across the building types: low-rise buildings prioritize roof-mounted photovoltaic (PV) systems, LED lighting, and thermal-break aluminum frames with low-E double-glazed laminated glass. Mid- and high-rise buildings emphasize integrated PV-LED-T8 lighting solutions and optimized building envelope structures. Ranking analysis further highlights LED lighting, T8 high-efficiency fluorescent lamps, and rooftop PV systems as the top-recommended technologies for Beijing. Additionally, four policy recommendations are proposed to facilitate the large-scale implementation of the program. This study presents a holistic technical integration strategy that simultaneously enhances the technological performance, economic viability, and carbon reduction outcomes of architectural design and renovation. It also establishes a replicable decision-support framework for decarbonizing office and public buildings in cities, thereby supporting China’s “dual carbon” goals and contributing to global carbon mitigation efforts in the building sector. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

In order to reduce gas consumption and increase the renewable energy proportion, this paper proposes a poly-generation system that couples geothermal, solar, and liquid natural gas (LNG) cold energy to produce steam, gaseous natural gas, and low-temperature nitrogen. The high-temperature flue gas is used to heat LNG; low-temperature flue gas, mainly nitrogen, can be used for cold storage cooling, enabling the staged utilization of the energy. Solar shortwave is used for power generation, and longwave is used to heat the working medium, which realizes the full spectrum utilization of solar energy. The influence of different equipment and operating parameters on the performance of a steam generation system is studied, and the multi-objective model of the multi-generation system is established and optimized. The results show that for every 100 W/m² increase in solar radiation, the renewable energy ratio of the system increases by 1.5%. For every 10% increase in partial load rate of gas boiler, the proportion of renewable energy decreases by 1.27%. The system’s energy efficiency, cooling output, and the LNG vaporization flow rate are negatively correlated with the scale of solar energy utilization equipment. The decision variables determined by the TOPSIS (technique for order of preference by similarity to ideal solution) method have better economic performance. Its investment cost is 18.14 × 10 CNY, which is 7.83% lower than that of the LINMAP (linear programming technique for multidimensional analysis of preference). Meanwhile, the proportion of renewable energy is only 0.29% lower than that of LINMAP. Full article

With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power grid dispatching, is essential for maintaining the grid’s long-term stable operation. Traditional fault diagnosis methods encounter challenges such as limited samples and data quality issues under complex operating conditions. To overcome these problems, this study proposes a fault sample data enhancement method based on the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Firstly, a simulation model of the AC/DC hybrid system is constructed to obtain the original fault sample data. Then, through the adoption of the Wasserstein distance measure and the gradient penalty strategy, an improved WGAN-GP architecture suitable for feature learning of the AC/DC hybrid system is designed. Finally, by comparing the fault diagnosis performance of different data models, the proposed method achieves up to 100% accuracy on certain fault types and improves the average accuracy by 6.3% compared to SMOTE and vanilla GAN, particularly under limited-sample conditions. These results confirm that the proposed approach can effectively extract fault characteristics from complex fault data. Full article

The increasing deployment of photovoltaic and energy storage systems (ESSs) in modern power grids has highlighted the critical challenge of component degradation, which significantly impacts system efficiency, operational costs, and long-term reliability. Conventional energy dispatch and optimization approaches fail to adequately mitigate the progressive efficiency loss in PV modules and battery storage, leading to suboptimal performance and reduced system longevity. To address these challenges, this paper proposes a quantum-enhanced degradation pathway optimization framework that dynamically adjusts operational strategies to extend the lifespan of PV storage systems while maintaining high efficiency. By leveraging quantum-assisted Monte Carlo simulations and hybrid quantum–classical optimization, the proposed model evaluates degradation pathways in real time and proactively optimizes energy dispatch to minimize efficiency losses due to aging effects. The framework integrates a quantum-inspired predictive maintenance algorithm, which utilizes probabilistic modeling to forecast degradation states and dynamically adjust charge–discharge cycles in storage systems. Unlike conventional optimization methods, which struggle with the complexity and stochastic nature of degradation mechanisms, the proposed approach capitalizes on quantum parallelism to assess multiple degradation scenarios simultaneously, significantly enhancing computational efficiency. A three-layer hierarchical optimization structure is introduced, ensuring real-time degradation risk assessment, periodic dispatch optimization, and long-term predictive adjustments based on PV and battery aging trends. The framework is tested on a 5 MW PV array coupled with a 2.5 MWh lithium-ion battery system, with real-world degradation models applied to reflect light-induced PV degradation (0.7% annual efficiency loss) and battery state-of-health deterioration (1.2% per 100 cycles). A hybrid quantum–classical computing environment, utilizing D-Wave’s Advantage quantum annealer alongside a classical reinforcement learning-based optimization engine, enables large-scale scenario evaluation and real-time operational adjustments. The simulation results demonstrate that the quantum-enhanced degradation optimization framework significantly reduces efficiency losses, extending the PV module’s lifespan by approximately 2.5 years and reducing battery-degradation-induced wear by 25% compared to conventional methods. The quantum-assisted predictive maintenance model ensures optimal dispatch strategies that balance energy demand with system longevity, preventing excessive degradation while maintaining grid reliability. The findings establish a novel paradigm in degradation-aware energy optimization, showcasing the potential of quantum computing in enhancing the sustainability and resilience of PV storage systems. This research paves the way for the broader integration of quantum-based decision-making in renewable energy infrastructure, enabling scalable, high-performance optimization for future energy systems. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article

The transition to renewable energy requires sustainable solar manufacturing through optimized Production–Usage–Recycling (PUR) cycles, where electromagnetic (EM) sensing offers non-destructive monitoring solutions. This review categorizes EM methods into low- (<100 MHz) and medium-frequency (100 MHz–10 GHz) techniques for material evaluation, defect detection, and performance optimization throughout the solar lifecycle. During production, eddy current testing and impedance spectroscopy improve quality control while reducing waste. In operational phases, RFID-based monitoring enables continuous performance tracking and early fault detection of photovoltaic panels. For recycling, electrodynamic separation efficiently recovers materials, supporting circular economies. The analysis demonstrates the unique advantages of EM techniques in non-contact evaluation, real-time monitoring, and material-specific characterization, addressing critical sustainability challenges in photovoltaic systems. By examining capabilities and limitations, we highlight EM monitoring’s transformative potential for sustainable manufacturing, from production quality assurance to end-of-life material recovery. The frequency-based framework provides manufacturers with physics-guided solutions that enhance efficiency while minimizing environmental impact. This comprehensive assessment establishes EM technologies as vital tools for advancing solar energy systems, offering practical monitoring approaches that align with global sustainability goals. The review identifies current challenges and future opportunities in implementing these techniques, emphasizing their role in facilitating the renewable energy transition through improved resource efficiency and lifecycle management. Full article

This article presents proposals to increase the efficiencies of energy conversion systems from renewable sources using a soft-switching technique in three-phase voltage source inverters. The first part of this article briefly presents basic systems for generating energy from renewable sources. Special attention is paid to both photovoltaic and wind power plants. The next section describes the voltage source inverter with the soft-switching system of transistors, which is resistant to disturbances in the control systems of inverters. Laboratory tests on cooperation between the voltage source inverter and the AC grid are carried out for two cases, when energy is transmitted from the DC circuit to the AC grid and vice versa. In the final part, the efficiencies of energy conversion systems operating under the voltage source inverter with the soft-switching technique are compared with those of an inverter using hard switching of transistors. A comparison is made for energy conversion systems with a rated power of 100 kW and 1 MW. Full article

Under the background of “dual-carbon”, expanding renewable energy grid integration exacerbates grid net load volatility, and system climbing requirements escalate. In this paper, the problem of uncertain ramping demand prediction caused by net load prediction error in new power systems is investigated. First, the total system ramping demand calculation model is constructed, and the effects of deterministic and uncertain ramping demand on the total system ramping demand are analyzed. Secondly, a prediction model based on a CNN-LSTM hybrid neural network is proposed for the uncertain ramp-up demand, which extracts the spatial correlation features of the multi-source influencing factors through the convolutional layer, captures the dynamic evolution law in the time series by using the LSTM layer, and realizes the high-precision point prediction and reliable interval prediction by combining the quantile regression method. Finally, the actual operation data and forecast data of a provincial power grid are used for example verification, and the results show that the proposed model outperformed traditional models (SVM, RF, BPNN) and single deep learning models (CNN, LSTM) in point prediction performance, achieving higher prediction accuracy and validating the effectiveness of the spatio-temporal feature extraction module. In terms of interval prediction quality, compared with the histogram and QRF benchmark models, the proposed model achieves a significant reduction in the average width of the prediction interval, average upward ramp-up demand, and average downward ramp-down demand while maintaining 100% interval coverage. This demand realizes a better balance between prediction economic efficiency and safety, providing more reliable technical support for the precise assessment of uncertain ramp-up demand in new power systems. Full article

This paper explores cleaner and techno-economically viable solutions to provide electricity, heat, and cooling using green hydrogen (H₂) and green ammonia (NH₃) across the entire decarbonized value chain. We propose integrating a 100% hydrogen-fueled internal combustion engine (e.g., Jenbacher JMS 420) as a stationary backup solution and comparing its performance with other backup technologies. While electrochemical storage systems, or battery energy storage systems (BESSs), offer fast and reliable short-term energy buffering, they lack flexibility in relocation and typically involve higher costs for extended backup durations. Through five case studies, we highlight that renewable-based energy supply requires additional capacity to bridge longer periods of undersupply. Our results indicate that, for cost reasons, battery–electric solutions alone are not economically feasible for long-term backup. Instead, a more effective system combines both battery and hydrogen storage, where batteries address daily fluctuations and hydrogen engines handle seasonal surpluses. Despite lower overall efficiency, gas engines offer favorable investment and operating costs in backup applications with low annual operating hours. Furthermore, the inherent fuel flexibility of combustion engines eventually will allow green ammonia-based backup systems, particularly as advancements in small-scale thermal cracking become commercially available. Future studies will address CO₂ credit recognition, carbon taxes, and regulatory constraints in developing more effective dispatch and master-planning solutions. Full article

Shared energy storage (SES) represents a transformative approach to advancing sustainable energy systems through improved resource utilization and renewable energy integration. In order to enhance the economic benefits of energy storage and prosumers, as well as to increase the consumption rate of renewable energy, this paper proposes a bilevel optimization model for multi-prosumer power complementarity based on SES. The upper level is the long-term energy storage capacity configuration optimization, aiming to minimize the investment and operational costs of energy storage. The lower level is the intra-day operation optimization for prosumers, which reduces electricity costs through peer-to-peer (P2P) transactions among prosumers and the coordinated dispatch of SES. Meanwhile, an improved Nash bargaining method is introduced to reasonably allocate the P2P transaction benefits among prosumers based on their contributions to the transaction process. The case study shows that the proposed model can reduce the SES configuration capacity by 46.3% and decrease the annual electricity costs of prosumers by 0.98% to 27.30% compared with traditional SES, and the renewable energy consumption rate has reached 100%. Through peak–valley electricity price arbitrage, the annual revenue of the SES operator increases by 71.1%, achieving a win–win situation for prosumers and SES. This article, by optimizing the storage configuration and trading mechanism to make energy storage more accessible to users, enhances the local consumption of renewable energy, reduces both users′ energy costs and the investment costs of energy storage, and thereby promotes a more sustainable, resilient, and equitable energy future. Full article

Coastal regions of Ecuador, particularly Esmeraldas and Manabí, face significant challenges related to energy access, waste management, and sustainable agricultural development. This study evaluates the renewable energy potential of cocoa waste biomass generated by smallholder farms in these provinces. A total of 20 cocoa farms, either certified or in the process of certification under the Rainforest Alliance standard, were surveyed to quantify the volume of agricultural and agro-industrial residues. Residual biomass generation ranged from 50 to 6500 tons per year, depending on farm size, planting density, and management practices. Spatial analysis revealed that Esmeraldas holds the highest concentration of cocoa waste biomass, with some farms reaching a gross energy potential of up to 89.07 TJ/year. Using thermochemical conversion scenarios, effective energy potential was estimated, and 75% of the farms exceeded the viability threshold of 100 MWh/year. The results confirm the feasibility of cocoa biomass as a renewable energy source, mainly when managed collectively at the community level. Incorporating this waste into decentralized energy systems supports circular economy models, enhances energy self-sufficiency, and aligns with sustainable supply chain goals promoted by certification schemes. This study contributes to national efforts in energy diversification and provides a replicable model for integrating renewable energy into rural agricultural systems. Full article

The project of the flexible direct transmission of renewable energy has become an inevitable development trend for the large-scale grid connection of renewable energy. Its two-terminal weakly fed AC system is often composed of 100% power electronic equipment, which leads to an essential transformation in fault characteristics and protection requirements. At present, in research, the traditional directional elements are limited by the negative-sequence control strategy, resulting in the decline of their sensitivity and reliability. Therefore, this paper proposes a model for identifying directional elements using composite electrical quantities that is not affected by the control strategy of the two-terminal weakly fed AC system and can reliably identify the fault direction. Firstly, the adaptability of traditional directional elements under the negative-sequence current suppression strategy on both sides of the system when faults occur in the AC line was analyzed. Secondly, based on the idea of model recognition, the model relationship of fault voltage and current in the case of ground faults and non-ground faults occurring at different locations was analyzed. Finally, a fitted voltage was constructed and the Kendall correlation coefficient was introduced to achieve fault direction discrimination. Simulation results demonstrate that the proposed pilot protection scheme can operate reliably under conditions of 300 Ω transition resistance and 25 dB noise interference. Full article

This study aims to assess the carbon footprint for the organization of frozen processed seafood manufacturing plants and propose sustainable strategies for reducing greenhouse gas emissions. Organizational activity data from 2024 were utilized to evaluate the carbon footprint and develop targeted mitigation measures. The findings indicate that Scope 1 emissions amounted to 12,685 tons of CO₂eq, Scope 2 emissions amounted to 15,403 tons of CO₂eq, and Scope 3 emissions amounted to 31,564 tons of CO₂eq. The total greenhouse gas emissions across all three scopes were 59,652 tons of CO₂eq, with additional greenhouse gas emissions recorded at 34,027 tons of CO₂eq. Mitigation measures were considered for activities contributing to at least 10% of emissions in each scope. In Scope 1, the use of R507 refrigerant in the production cooling system accounted for 9907 tons of CO₂eq, representing 78.10% of emissions. In Scope 2, electricity consumption contributed 15,403 tons of CO₂eq, constituting 100% of emissions. In Scope 3, the procurement of surimi (processed fish meat) was responsible for 20,844 tons of CO₂eq, accounting for 66.04% of emissions. Based on these findings, key mitigation strategies were proposed. For Scope 1, reducing emissions involves preventive maintenance of cooling systems to prevent leaks, replacing corroded pipelines, installing shut-off valves, and switching to alternative refrigerants with no greenhouse gas emissions. For Scope 2, energy-saving initiatives include promoting electricity conservation within the organization, maintaining equipment for optimal efficiency, installing energy-saving devices such as variable speed drives (VSD), upgrading to high-efficiency motors, and utilizing renewable energy sources like solar power. For Scope 3, emissions can be minimized by sourcing raw materials from suppliers with certified carbon footprint labels, prioritizing purchases from producers committed to carbon reduction, and selecting suppliers closer to manufacturing sites to reduce transportation-related emissions. Implementing these strategies will contribute to sustainable greenhouse gas emission reductions. Full article

The bench-scale demonstration of the UKy-IDEA process for direct air capture (DAC) technology combines solvent-aided CO₂ capture with electrochemical regeneration (ER) through a pH swing process, enabling efficient CO₂ capture and simultaneous solvent regeneration, producing high-purity hydrogen as a valuable co-product. The system shows stable performance with over 90% CO₂ capture efficiency and approximately 80% CO₂ recovery, handling ambient air at 280 L/min. During testing, the unit captured 1 kg of CO₂ over 100 h, with a concentrated CO₂ output purity of around 70%. Operating efficiently at low voltage (<3 V), the system supports flexible and remote operation without AC/DC converters when using intermittent renewable energy. Techno-economic analysis (TEA) and Life Cycle Assessment (LCA) highlight its minimized required footprint and cost-effectiveness. Marketable hydrogen offsets capture costs, and compatibility with renewable DC power enhances appeal. Hydrogen production displacing CO₂ produced via electrolysis achieves 0.94 kg CO₂ abated per kg CO₂ captured. The project would be economic, with USD 26 per ton of CO₂ from the federal 45Q tax credit for carbon utilization, and USD 5 to USD 12 per kg for H₂. Full article