Search Results (65)

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Objective: The rapid growth of electric vehicle (EV) adoption has led to an unprecedented increase in lithium-ion battery (LIB) demand and end-of-life waste, underscoring the urgent need for effective recycling strategies. This review evaluates current progress in EV battery recycling and explores future prospects. Design: Review based on PRISMA 2020. Data sources: Scientific publications indexed in major databases such as Scopus, Web of Science, and ScienceDirect were searched for relevant studies published between 2020 and 15 April 2025. Inclusion criteria: Studies were included if they were published in English between 2020 and 15 April 2025, and focused on the recycling of electric vehicle batteries. Eligible studies specifically addressed (i) recycling methods, technologies, and material recovery processes for EV batteries; (ii) the impact of recycled battery systems on power generation processes and grid stability; and (iii) assessments of materials used in battery manufacturing, including efficiency and recyclability. Review articles and meta-analyses were excluded to ensure the inclusion of only original research data. Data extraction: Data were independently screened and extracted by two researchers and analyzed for recovery rates, environmental impact, and system-level energy contributions. One researcher independently screened all articles and extracted relevant data. A second researcher validated the accuracy of extracted data. The data were then organized and analyzed based on reported quantitative and qualitative indicators related to recycling methods, material recovery rates, environmental impact, and system-level energy benefits. Results: A total of 23 studies were included. Significant progress has been made in hydrometallurgical and direct recycling processes, with recovery rates of critical metals (Li, Co, Ni) improving. Second-life battery applications also show promise for grid stabilization and renewable energy storage. Furthermore, recycled batteries show potential in stabilizing power grids through second-life applications in BESS. Conclusion: EV battery recycling is a vital strategy for addressing raw material scarcity, minimizing environmental harm, and supporting energy resilience. However, challenges persist in policy harmonization, technology scaling, and economic viability. Future progress will depend on integrated efforts across sectors and regions to build a circular battery economy. Full article

Urban planners are progressively recognizing the significant effects of the built environment and land use on PM_2.5 levels. However, in analyzing the drivers of PM_2.5 levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM_2.5 levels, potentially impeding accurate predictions during periods of high pollution. This study focuses on the area within the Sixth Ring Road of Beijing, China. It utilizes gridded monthly and annual mean PM_2.5 data from 2019 as the dependent variable. The research selects 33 independent variables from the perspectives of the built environment and land use. The Extreme Gradient Boosting (XGBoost) method is employed to reveal the driving impacts of the built environment and land use on PM_2.5 levels. To enhance the model accuracy and address the randomness in the division of training and testing sets, we conducted twenty comparisons for each month. We employed Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the models’ results and analyze the interactions between the explanatory variables. The results indicate that models incorporating both the built environment and land use outperformed those that considered only a single aspect. Notably, in the test set for April, the R² value reached up to 0.78. Specifically, the fitting accuracy for high pollution months in February, April, and November is higher than the annual mean, while July shows the opposite trend. The coefficient of variation for the importance rankings of the seven key explanatory variables exceeds 30% for both monthly and annual means. Among these variables, building density exhibited the highest coefficient of variation, at 123%. Building density and parking lots density demonstrate strong explanatory power for most months and exhibit significant interactions with other variables. Land use factors such as wetlands fraction, croplands fraction, park and greenspace fraction, and forests fraction have significant driving effects during the summer and autumn seasons months. The research on time scales aims to more effectively reduce PM_2.5 levels, which is essential for developing refined urban planning strategies that foster healthier urban environments. Full article

Water stress is a significant issue in many countries, including Portugal, which has seen a 20% reduction in water availability over the last 20 years, with a further 10–25% reduction expected by the end of the century. To address potable water consumption, this study aims to identify the optimal rainwater harvesting (RWH) system for a commercial building under various non-potable water use scenarios. This research involved qualitative and quantitative methods, utilizing the Rippl method for storage reservoir sizing and ETA 0701 version 11 guidelines. Various scenarios of non-potable water use were considered, including their budgets and economic feasibility. The best scenario was determined through cash flow analysis, considering the initial investment (RWH construction), income (water bill savings), and expenses (energy costs from hydraulic pumps), and evaluating the net present value (NPV), payback period (PB), and internal rate of return (IRR). The energy savings obtained were calculated by sizing a hybrid system with an RWH system and a photovoltaic (PV) system to supply the energy needs of each of the proposed scenarios and the water pump, making the system independent of the electricity grid. The results show that the best scenario resulted in energy savings of 92.11% for a 7-month period of regularization. These results also demonstrate the possibility for reducing potable water consumption in non-essential situations supported by renewable energy systems, thus helping to mitigate water stress while simultaneously reducing dependence on the grid. Full article

The reduction of harmful emissions is shaping trends across many industries, including architecture and building. With rising ecological awareness and the threat of climate change, architects, construction engineers, and developers are focusing on innovative solutions to minimize the construction sector’s environmental impact. This paper presents a technical and management approach system using renewable energy sources, based on an existing single-family house with known energy consumption. The aim is to achieve energy independence by relying solely on on-site electricity generation and storage, while remaining connected to water and sewage infrastructure. Utilizing renewable energy sources enhances self-sufficiency and investment profitability. The study evaluates the house’s energy consumption to optimally select electricity supply solutions, including a small wind farm and photovoltaic installation integrated with appropriate electricity storage. This is crucial due to the air heat pump used for heating and domestic hot water, which requires electricity. An hourly simulation of the system’s operation over a year verified the adequacy of the selected devices. Additionally, two different locations were analyzed to assess how varying climate and wind conditions influence the design and performance of off-grid energy systems. The analysis showed that solar and wind systems can meet annual energy demand, but limited storage capacity prevents full autonomy. Replacing the heat pump with a biomass boiler reduces electricity use by about 25% and battery needs by 40%, though seasonal energy surpluses remain a challenge. This concept aligns with the goal of achieving climate neutrality by 2050. Full article

IoT applications for building energy management, enhanced by artificial intelligence (AI), have the potential to transform how energy is consumed, monitored, and optimized, especially in distributed energy systems. By using IoT sensors and smart meters, buildings can collect real-time data on energy usage patterns, occupancy, temperature, and lighting conditions.AI algorithms then analyze this data to identify inefficiencies, predict energy demand, and suggest or automate adjustments to optimize energy use. Integrating renewable energy sources, such as solar panels and wind turbines, into distributed systems uses IoT-based monitoring to ensure maximum efficiency in energy generation and use. These systems also enable dynamic energy pricing and load balancing, allowing buildings to participate in smart grids by storing or selling excess energy.AI-based predictive maintenance ensures that renewable energy systems, such as inverters and batteries, operate efficiently, minimizing downtime. The case studies show how IoT and AI are driving sustainable development by reducing energy consumption and carbon footprints in residential, commercial, and industrial buildings. Blockchain and IoT can further secure transactions and data in distributed systems, increasing trust, sustainability, and scalability. The combination of IoT, AI, and renewable energy sources is in line with global energy trends, promoting decentralized and greener energy systems. The case study highlights that adopting IoT and AI for energy management offers not only environmental benefits but also economic benefits, such as cost savings and energy independence. The best achieved accuracy was 0.8179 (RMSE 0.01). The overall effectiveness rating was 9/10; thus, AI-based IoT solutions are a feasible, cost-effective, and sustainable approach to office energy management. Full article

The wind field plays an important role in the maintenance of large sport venues. Most wind field simulation research involves no quantitative analysis of the impacts of the wind environment on athletes’ safety and performance. Taking the National Sliding Centre (NSC) of the Beijing 2022 Winter Olympics as the research object, this paper conducts a wind field simulation study based on CFD, and innovatively explores the quantitative impact of building-scale wind environment characteristics on micro-athletes’ wind resistance and performance for the first time. First, an NSC model and a human body model of athletes are constructed and simplified. Grid independence verification is carried out, and the grid is divided and optimized. Second, wind environment simulation under different climatic conditions is completed, based on CFD technology. The defined wind speed dispersion indexes are calculated. The characteristics of the wind field outside the venue is quantitatively analyzed. Third, we define and calculate the main influencing parameters on athletes’ competition performance. The impacts of the wind field on micro-athletes’ performance are quantified. With a gradual increase in wind level (3.125, 3.5, 4.5, 5.5, 6.5, 7.5, 8.229), the optimized sliding route can reduce the air resistance by 0.2607 N, 0.3415 N, 0.4600 N, 0.6469 N, 0.9283 N, 1.1741 N, and 1.4535 N, which can improve the athletes’ competition results by 0.02 s, 0.03 s, 0.04 s, 0.06 s, 0.09 s, 0.11 s, and 0.14 s, respectively. This paper provides methodological support for exploring the mechanism of athlete performance from the perspective of a building-scale environment. Full article

Non-Intrusive Load Monitoring (NILM) includes a set of methods orientated to disaggregating the power consumption of a household per appliance. It is commonly based on a single metering point, typically a smart meter at the entry of the electrical grid of the building, where signals of interest, such as voltage or current, can be measured and analyzed in order to disaggregate and identify which appliance is turned on/off at any time. Although this information is key for further applications linked to energy efficiency and management, it may also be applied to social and health contexts. Since the activation of the appliances in a household is related to certain daily activities carried out by the corresponding tenants, NILM techniques are also interesting in the design of remote monitoring systems that can enhance the development of novel feasible healthcare models. Therefore, these techniques may foster the independent living of elderly and/or cognitively impaired people in their own homes, while relatives and caregivers may have access to additional information about a person’s routines. In this context, this work describes an intelligent solution based on deep neural networks, which is able to identify the daily activities carried out in a household, starting from the disaggregated consumption per appliance provided by a commercial smart meter. With the daily activities identified, the usage patterns of the appliances and the corresponding behaviour can be monitored in the long term after a training period. In this way, every new day may be assessed statistically, thus providing a score about how similar this day is to the routines learned during the training interval. The proposal has been experimentally validated by means of two commercially available smart monitors installed in real houses where tenants followed their daily routines, as well as by using the well-known database UK-DALE. Full article

Large photovoltaic stations, wind farms and high-voltage direct current (HVDC) transmission systems are being integrated into the grid, which is causing the stability of frequency and voltage of new power systems to decline, thereby imposing high requirements on the evaluation of power grid strength in regional grids. Taking into account the indicators of stability margin of frequency and voltage, this paper builds a key technical indicator system for the system of multi-type synchronous control equipment connected to the grid, including the equivalent inertia enhancement factor, steady-state frequency deviation reduction factor, voltage stiffness and steady-state voltage deviation. Considering that the objective weighting and subjective weighting can, respectively, be achieved by the independent information entropy weighing method (IIEWM), the analytic hierarchy process method (AHPM) and the integrating principal component analysis method (PCAM), an improved layered integration weight allocation method based on IIEWM-AHPM-PCAM is proposed. Meanwhile, a multi-objective comprehensive evaluation model for power grid strength is established, and a power grid strength evaluation method is proposed to accurately evaluate the support strength of frequency and voltage of grid-connected systems including multi-type synchronous control equipment. Finally, a modified model of IEEE-39 node systems is constructed using Matlab to verify the reliability of the proposed method. The results showed that, compared to IIEWM and AHPW, a better ability to reflect the degree of data independence and volatility is possessed by the proposed method. Full article

Housing informality (HI), and particularly unauthorized modifications, are a widely spread phenomenon in Morocco’s rapidly growing coastal suburb of Harhoura, Rabat. While previous research has already focused on the socio-economic aspects of informal adaptations in affordable and middle-class housing contexts in Morocco, it leaves a gap regarding how HI is expressed in affluent settings independently and in relation to the other contexts. This research aims to visually capture how residents adapt their housing through unauthorized modifications. The research objectives are to analyze informalities that are unique to affordable, middle-class, and affluent housing and to examine if there are any shared HI patterns that transcend socio-economic contexts. This paper utilizes a mixed-methods approach by superposing fieldwork data, including the recollection of existing buildings and authorized archival data, with the help of a referential grid based on three case studies in Harhoura, Rabat, affordable, middle-class, and affluent settings, which enables effective individual and communal spatial-morphological analyses. The findings reveal distinctive and shared patterns from one side and propagation dynamics from the other, including important concepts, such as mirroring (the replication of similar informalities) and contrast (the implementation of informalities in contrast with the existing ones), between the different socio-economic contexts, which suggest higher transcending shared needs between them. By showcasing that people of diverse socio-economic means adapt their homes in strikingly similar ways, this study discredits the assumption that poverty is the primary driver of renovation approaches. This broadened lens enriches our understanding of how urban housing evolves and points to the urgency of inclusive strategies addressing key housing priorities for all. Full article

Renewable energy sources and energy independence are becoming increasingly important worldwide, and reducing emissions and optimizing energy use are high on the EU’s agenda. In this context, electric and hybrid vehicles could not only be a means of transport but also an active part of the grid. This paper analyzes one year of empirical data of a hybrid vehicle using a linear programing method that allows the optimization of energy return under different settings. The aim of the study is to determine the contribution that vehicles can make to the stability of the grid and the functioning of energy communities. It also compares the distribution of energy sources used in the EU and presents the current range of V2G-capable vehicle models. The results show that hybrid vehicles can also be effective energy storage devices, especially at fleet level. V2G technology could influence the development of battery production and contribute to the expansion of secondary markets by enabling the recycling of degraded batteries for buildings or renewable energy systems. The article also summarizes the development opportunities and challenges for V2G technology, in particular its role in energy grids and sustainable transport. Full article

This paper examines Connected Smart Green Buildings (CSGBs) in Burnaby, BC, Canada, with a focus on townhouses with one to four bedrooms. The proposed model integrates sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. These elements improve energy efficiency and promote sustainability. Operating in island mode, CSGBs can function independently of the grid, providing resilience during power outages and reducing reliance on external energy sources. Real data on electricity, gas, and water consumption are used to optimize load management under isolated conditions. Electric Vehicles (EVs) are also considered in the system. They serve as energy storage devices and, through Vehicle-to-Grid (V2G) technology, can supply power when needed. A hybrid Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. The metrics considered include accuracy, efficiency, emissions, and cost. The performance was compared with several well-known models including Linear Regression (LR), CNN, LSTM, Random Forest (RF), Gradient Boosting (GB), and hybrid LSTM–CNN, and the results show that the proposed model provides the best results. For a four-bedroom Connected Smart Green Townhouse (CSGT), the Mean Absolute Percentage Error (MAPE) is 4.43%, the Root Mean Square Error (RMSE) is 3.49 kWh, the Mean Absolute Error (MAE) is 3.06 kWh, and $R^2$ is 0.81. These results indicate that the proposed model provides robust load optimization, particularly in island mode, and highlight the potential of CSGBs for sustainable urban living. Full article

The rising demand for water and energy is driving the overuse of natural resources and contributing to environmental degradation. To address these challenges, the focus has shifted to low- and zero-emission technologies that utilize alternative sources of water and energy. Although such systems are commonly applied in residential, commercial, and industrial buildings, facilities along transportation routes generally depend on grid connections. This study aimed to enhance operational independence and reduce environmental impacts by modernizing the Rest Area Stobierna (RAS) along Poland’s S19 expressway, part of the Via Carpatia road. A comprehensive technical, economic, and environmental analysis was conducted using HOMER Pro software (3.18.3 PRO Edition) and a simulation model based on YAS operating principles. The proposed Hybrid Renewable Energy System (HRES) incorporates photovoltaic panels, battery storage, and a rainwater harvesting system (RWHS). Two configurations of the HRES were evaluated, a prosumer-based setup and a hybrid-island mode. Optimization results showed that the hybrid-island configuration was most effective, achieving a 61.6% share of renewable energy in the annual balance, a 7.1-year return on investment, a EUR 0.77 million reduction in Net Present Cost (NPC), and a 75,002 kg decrease in CO₂ emissions over the system’s 25-year lifecycle. This study highlights the potential of integrating renewable energy and water systems to improve sustainability, reduce operational costs, and enhance service quality in road infrastructure facilities, offering a replicable model for similar contexts. Full article

In order to reduce CO₂ emissions in the brick manufacturing process, the effectiveness of the energy-intensive firing process needs to be improved. This can be achieved by enhancing the heat transfer in order to reduce firing times. As a result, current development of tunnel kilns is oriented toward fast firing as a long-term goal. However, a struggling building sector and complicated challenges, such as different requirements for product quality, have impeded developments in this direction. This creates potential for the further development of oven designs, such as improved airflow through the kiln. In this article, numerical flow simulations are used to investigate two different reconstruction measures and compare them to the initial setup. In the first measure, the kiln height is reduced, while in the second measure, the kiln cars are adjusted to alternate the height of the bricks so that every other pair of bricks is elevated, creating a staggered arrangement. Both measures are investigated to determine the effect on the heating rate compared to the initial configuration. A transient grid independence study is performed, ensuring numerical convergence and the setup is validated by experimental results from measurements on the initial kiln configuration. The simulations show that lowering the kiln height improves the heat transfer rate by $40\%$, while the staggered arrangement of the bricks triples it. This leads to an average brick temperature after two hours which is around 130 °C higher compared to the initial kiln configuration. Therefore, the firing time can be significantly reduced. However, the average pressure loss coefficient rises by $70\%$ to $90\%$, respectively, in the staggered configuration. Full article