Search Results (10,628)

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

In response to the challenges faced by high-energy-consuming enterprises in utilizing renewable energy and implementing low-carbon operations, this paper proposes a multi-objective optimization strategy based on source–storage–load collaborative scheduling. The strategy establishes a refined model of aluminum electrolysis load, thoroughly considering the coupling relationship between temperature, production output, and power consumption. Additionally, it develops a dynamic coupling model between multi-functional crane loads and aluminum electrolysis production to reveal the influence mechanism of auxiliary equipment on the main production process. Based on this foundation, this paper constructs a multi-objective optimization model that targets the minimization of operating costs, the minimization of carbon emissions, and the maximization of the renewable energy consumption rate. An improved heuristic intelligent optimization algorithm is employed to solve the model. The simulation results demonstrate that, under a renewable energy penetration of 67.8%, the proposed multi-objective optimization strategy achieves a maximum reduction in carbon emissions of 1677.35 t and an increase in renewable energy consumption rate of 12.11%, compared to the conventional single-objective economic optimization approach, while ensuring the stability of aluminum electrolysis production. Furthermore, when the renewable energy penetration is increased to 76.2%, the maximum reduction in carbon emissions reaches 8260.97 t, and the renewable energy consumption rate is improved by 18.86%. Full article

Given the climate change observed in the past few decades, sustainable development and the use of renewable energy sources are urgent. In this scenario, hydrogen production through electrolyzers is a promising renewable source and energy vector because of its ultralow greenhouse emissions and high energy content. Hydrogen can be used in a variety of applications, from transportation to electricity generation, contributing to the diversification of the energy matrix. In this context, this paper presents an autonomous isolated DC microgrid system for generating and storing electrical energy to be exclusively used for feeding an electrolyzer hydrogen production plant, which has been retrofitted for green hydrogen production. Experimental verification was performed at Itaipu Parquetec, which consists of an alkaline electrolysis unit directly integrated with a battery energy storage system and renewable sources (e.g., photovoltaic and wind) by using an isolated DC microgrid concept based on DC/DC and AC/DC converters. Experimental results revealed that the new electrolyzer DC microgrid increases the system’s overall efficiency in comparison to the legacy thyristor-based power supply system by 26%, and it autonomously controls the energy supply to the electrolyzer under optimized conditions with an extremely low output current ripple. Another advantage of the proposed DC microgrid is its ability to properly manage the startup and shutdown process of the electrolyzer plant under power generation outages. This paper is the result of activities carried out under the R&D project of ANEEL program No. PD-10381-0221/2021, entitled “Multiport DC-DC Converter and IoT System for Intelligent Energy Management”, which was conducted in partnership with CTG-Brazil. Full article

The increasing integration of inverter-based renewable energy sources is displacing conventional synchronous generation, resulting in a progressive reduction in system inertia and heightened challenges to frequency stability. This study presents a detailed empirical analysis of the UK electricity grid over a representative 24 h period, utilizing high-resolution datasets that capture grid frequency, energy demand, generation mix, and wholesale market prices. An inertia proxy is developed based on the share of synchronous generation, enabling quantitative assessment of its relationship with the Rate of Change of Frequency (RoCoF). Through the application of change point detection and unsupervised clustering algorithms, the analysis identifies critical renewable penetration thresholds beyond which frequency stability significantly deteriorates. These findings underscore the increasing importance of synthetic inertia in maintaining grid resilience under high renewable scenarios. The results offer actionable insights for system operators aiming to enhance frequency control strategies and contribute to the formulation of policy and technical standards regarding synthetic inertia provision in future low-inertia power systems. Full article

Crystalline silicon photovoltaic power generation is a renewable energy source vigorously developed worldwide, while high-quality carbon/carbon thermal insulation tubes serve as key core components for fabricating high-performance large-diameter photovoltaic monocrystalline silicon rods. However, the isothermal chemical vapor infiltration densification process of large-diameter carbon/carbon thermal insulation tubes is complex and difficult to predict, and how to improve the radial and axial density uniformity of the insulation tubes remains an urgent issue to be addressed. To tackle this problem, this paper constructs a transient three-dimensional multi-field coupled model for the isothermal chemical vapor infiltration densification process. An optimization strategy involving the introduction of graphite pads is proposed, and the crucial factors affecting densification, uniformity, and densification rate are investigated. Moreover, a detailed geometric model, appropriate meshing method, and effective multi-field coupled simulation scheme are developed. This establishes a highly efficient simulation framework for multi-field coupling. Additionally, the role of the graphite pad is thoroughly explored, revealing that the thickness of the graphite pad is a crucial factor influencing the densification results. Numerical results demonstrate that when the graphite pad thickness is 80 mm, the average density of the insulation tube increases by 47% (from 0.975 × 10³ kg/m³ to 1.436 × 10³ kg/m³), with a densification rate of 2.55 × 10⁻³ kg/m/s, achieving optimal performance. This work provides valuable insights for evaluating the performance of carbon/carbon thermal insulation tubes of various sizes and offers a practical process reference value for the new product development. Full article

Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and residential consumers through customer clusterisation, tailoring the forecasting models to capture the unique consumption patterns of each group. Feature selection incorporated temporal, socio-economic, and weather-related data obtained from the Copernicus Earth Observation (EO) program. A variety of AI and machine learning algorithms for short-term load forecasting (STLF) and very-short-term load forecasting (VSTLF) are explored and compared, determining the most effective approaches. With all that, the main contribution of this work are the new forecasting approaches proposed, which have demonstrated superior performance compared to simpler models, both for STLF and VSTLF, highlighting the importance of customized forecasting strategies for different consumer groups and demonstrating the impact of incorporating detailed weather data on forecasting accuracy. These advancements contribute to more reliable power demand predictions, with our novel forecasting approaches reducing the Mean Absolute Percentage Error (MAPE) by up to 1–3% for industrial and 1–10% for commercial consumers compared to baseline models, thereby supporting grid stability. Full article

In the context of the urgent global transition toward sustainable materials, this study presents a comparative environmental life cycle assessment (LCA) of poly(3-hydroxybutyrate) (PHB), a biodegradable, bio-based polymer, against conventional petrochemical plastics (polystyrene—PS; polypropylene—PP) and another popular biopolymer, namely polylactic acid (PLA). The LCA was conducted using primary production data from a laboratory-scale PHB manufacturing process, integrating real-time energy consumption measurements across all production stages. Environmental indicators such as carbon footprint and energy demand were analyzed under cradle-to-gate and end-of-life scenarios. The results indicate that PHB, while offering biodegradability and renewable sourcing, currently exhibits a significantly higher carbon footprint than PP, PS, and PLA, primarily due to its energy-intensive downstream processing. However, the environmental impact of PHB can be markedly reduced—by over 67%—through partial integration of renewable energy. PLA demonstrated the lowest production-phase emissions, while PP showed the most favorable end-of-life outcomes under municipal waste management assumptions. The study highlights the critical influence of energy sourcing, production scale, and waste treatment infrastructure on the sustainability performance of biopolymers. These findings provide practical insights for industry and policymakers aiming to reduce the environmental burden of plastics and support a shift toward circular material systems. Full article

The environmental performance of natural aggregates for concrete and road construction, extracted from a dolomite quarry, was investigated. Environmental hotspots were identified, and potential optimization measures to further reduce the environmental footprint were proposed. The natural aggregates extracted from the dolomite quarry have relatively low GWP and a low environmental footprint in general. The GWP of 1 tonne of natural aggregates used in concrete production is 1.13 kg CO₂ equiv., while for 1 tonne of aggregates used in road construction, it is 0.97 kg CO₂ equiv. The dolomite rock in the quarry in question is tectonically fractured, such that very intensive extraction is not required, taking into account the blasting of the rock and further processing. The use of non-road mobile machinery is already optimized. Additional reductions in environmental impact could be achieved by powering the screening process exclusively with electricity from renewable sources, such as a photovoltaic system. In this context, integrating on-site battery storage systems might present a promising solution for addressing the seasonal mismatch between solar energy generation and processing demands. Full article

The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through these devices. Understanding and monitoring these harmonics is crucial to ensure the smooth and seamless operation of these networks, as well as to protect and manage the renewable energy sources-based power system. In this paper, we propose an advanced method of dynamic modal decomposition, called Higher-Order Dynamic Mode Decomposition (HODMD), one of the recently proposed data-driven methods used to estimate the frequency/amplitude and phase with high resolution, to identify the harmonic spectrum in power systems dominated by renewable energy generation. In the proposed method, several time-shifted copies of the measured signals are integrated to create the initial data matrices. A hard thresholding technique based on singular value decomposition is applied to eliminate ambiguities in the measured signal. The proposed method is validated and compared to Synchrosqueezing Transform based on Short-Time Fourier Transform (SST-STFT) and the Concentration of Frequency and Time via Short-Time Fourier Transform (ConceFT-STFT) using synthetic signals and real measurements, demonstrating its practical effectiveness in identifying harmonics in emerging power networks. Finally, the effectiveness of the proposed methodology is analyzed on the energy storage-based laboratory-scale microgrid setup using an Opal-RT-based real-time simulator. Full article

In response to the climate crisis, Colombia has committed to reducing greenhouse gas (GHG) emissions by 2030 through an energy transition strategy that promotes Non-Conventional Renewable Energy Sources (NCRES) and, increasingly, natural gas. Although natural gas is regarded as a transitional fuel with lower carbon intensity than other fossil fuels, existing reserves could be depleted by 2030 if no new discoveries are made. To assess this risk, a System Dynamics model was developed to project supply and demand under alternative transition pathways. The model integrates: (1) GDP, urban population growth, and adoption of clean energy, (2) the behavior of six major consumption sectors, and (3) the role of gas-fired thermal generation relative to NCRES output and hydroelectric availability, influenced by the El Niño river-flow variability. The novelty and contribution of this study lie in the integration of supply and demand within a unified System Dynamics framework, allowing for a holistic understanding of the Colombian natural gas market. The model explicitly incorporates feedback mechanisms such as urbanization, vehicle replacement, and hydropower variability, which are often overlooked in traditional analyses. Through the evaluation of twelve policy scenarios that combine hydrogen, wind, solar, and new gas reserves, the study provides a comprehensive view of potential energy transition pathways. A comparative analysis with official UPME projections highlights both consistencies and divergences in long-term forecasts. Furthermore, the quantification of demand coverage from 2026 to 2033 reveals that while current reserves can satisfy demand until 2026, the expansion of hydrogen, wind, and solar sources could extend full coverage until 2033; however, ensuring long-term sustainability ultimately depends on the discovery and development of new reserves, such as the Sirius-2 well. Full article

This article examines the growing potential of low-emission hydrogen as an innovative solution supporting the decarbonization of the agricultural sector. It discusses its potential applications on farms, including as an energy source for powering agricultural machinery, producing fertilizers, and storing energy from renewable sources. Within the European context, it considers actions arising from the European Green Deal and the “Fit for 55” strategy, which promote the development of hydrogen infrastructure and support research into low-emission technologies. The article also discusses global initiatives and trends in the development of the hydrogen economy, pointing to international cooperation, investment, and the need for technology standardization. It highlights the challenges related to cost, infrastructure, and scalability, as well as the opportunities hydrogen offers for a sustainable and energy-efficient agriculture of the future. Full article

Corporate ESG (Environmental, Social, and Governance) reporting is increasingly envisioned as evidence of accountability in the energy transition, yet persistent gaps remain between commitments and practices. This study applied the Global Reporting Initiative (GRI) framework—specifically indicators 302 (Energy) and 305 (Emissions)—to evaluate the credibility, scope, and strategic depth of disclosures by 20 Wisconsin (WI) firms in the energy, manufacturing, food, and service sectors. Guided by accountability and legitimacy theory, a comparative content analysis was conducted, complemented by Spearman correlation to examine associations between firm size and disclosure quality. Results show that while firms consistently report basic metrics such as total energy consumption and Scope 1 emissions, disclosures on Scope 3 emissions, renewable sourcing, and energy-efficiency achievements remain partial and selectively framed. Third-party assurance is inconsistently applied, and methodological transparency—such as external audit and coding protocols—is limited, weakening credibility. A statistically significant negative correlation was observed between annual revenue and disclosure quality, indicating that greater financial capacity does not necessarily translate into greater transparency. These findings highlight methodological and governance shortcomings, including reliance on generic ESG frameworks rather than climate-focused standards such as Task Force on Climate-related Financial Disclosures (TCFD). Integrated reporting approaches are recommended to improve comparability, credibility, and alignment with Wisconsin’s Clean Energy Transition Plan. Full article

Promoting economic prosperity, social justice, and ecological sustainability requires the rapid decarbonization of our global energy system in favor of renewable sources of energy. Recent news analysis estimates that 15% of counties across the US have banned wind turbines, solar fields, and other green energy developments. We answer two overarching research questions: (1) How do regulations of wind facilities vary at the county level? And (2) what factors appear to explain the variation in local wind regulations? We created a GIS database of energy regulations for all 105 counties in Kansas, a top state for wind potential and a recent hotbed of local actions. We coupled descriptive statistics, mapping, and regression modeling to describe the variation in local policy approaches and identify factors driving the variation. We find counties using at least five different policy approaches to enable or block wind regulations. Factors driving variation include a combination of infrastructure capacity, demographic characteristics that shape local planning capacity, and the apparent reliance on large farming operations for local economic output but not partisan voting patterns or underlying wind capacity. Our findings provide vital insights for policymakers at the federal, state, and local levels, as well as providing a foundation for future scholarship on planning for a just energy future. Full article

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids. Full article

Accurate demand forecasting contributes to improved energy efficiency and the development of short-term strategies. Predictive management of energy storage using redox flow batteries is presented as a robust solution for optimizing the operation of microgrids from the demand side. This study proposes an intelligent architecture that integrates demand forecasting models based on artificial neural networks and active management strategies based on the instantaneous production of renewable sources within the microgrid. The solution is supported by a real-time monitoring platform capable of analyzing data streams using continuous evaluation algorithms, enabling dynamic operational adjustments and active methods for predicting the storage system’s state of charge. The model’s effectiveness is validated using performance indicators such as RMSE, MAPE, and MSE, applied to experimental data obtained in a specialized microgrid laboratory. The results also demonstrate substantial improvements in energy planning and system operational efficiency, positioning this proposal as a viable strategy for distributed and sustainable environments in modern electricity systems. Full article