Search Results (136)

The integration of high-penetration renewable energy sources (RESs) into transmission networks introduces profound uncertainty that challenges traditional infrastructure planning approaches. Existing transmission expansion planning (TEP) models either rely on static scenario sets or over-conservative worst-case assumptions, failing to capture the operational stress triggered by rare but structurally impactful renewable behaviors. This paper proposes a novel bi-level optimization framework for transmission planning under adversarial uncertainty, coupling a distributionally robust upper-level investment model with a lower-level operational response embedded with physics and market constraints. The uncertainty space was not exogenously fixed, but instead dynamically generated through a physics-informed spatiotemporal generative adversarial network (PI-ST-GAN), which synthesizes high-risk renewable and load scenarios designed to maximally challenge the system’s resilience. The generator was co-trained using a composite stress index—combining expected energy not served, loss-of-load probability, and marginal congestion cost—ensuring that each scenario reflects both physical plausibility and operational extremity. The resulting bi-level model was reformulated using strong duality, and it was decomposed into a tractable mixed-integer structure with embedded adversarial learning loops. The proposed framework was validated on a modified IEEE 118-bus system with high wind and solar penetration. Results demonstrate that the GAN-enhanced planner consistently outperforms deterministic and stochastic baselines, reducing renewable curtailment by up to 48.7% and load shedding by 62.4% under worst-case realization. Moreover, the stress investment frontier exhibits clear convexity, enabling planners to identify cost-efficient resilience strategies. Spatial congestion maps and scenario risk-density plots further illustrate the ability of adversarial learning to reveal latent structural bottlenecks not captured by conventional methods. This work offers a new methodological paradigm, in which optimization and generative AI co-evolve to produce robust, data-aware, and stress-responsive transmission infrastructure designs. Full article

The Beijing–Tianjin–Hebei (BTH) region is one of China’s most water-scarce yet economically vital areas, facing increasing challenges due to climate change and intensive human activities. This study develops an integrated Water–Food–Energy–Ecology (WFEE) simulation and regulation model to assess the system’s stability under coordinated development scenarios and extreme climate stress. A 500-year precipitation series was reconstructed using historical drought and flood records combined with wavelet analysis and machine learning models (Random Forest and Support Vector Regression). Results show that during the reconstructed historical megadrought (1633–1647), with average precipitation anomalies reaching −20% to −27%, leading to a regional water shortage rate of 16.9%, food self-sufficiency as low as 44.7%, and a critical reduction in ecological river discharge. Under future recommended scenario with enhanced water conservation, reclaimed water reuse, and expanded inter-basin transfers, the region could maintain a water shortage rate of 2.6%, achieve 69.3% food self-sufficiency, and support ecological water demand. However, long-term water resource degradation could still reduce food self-sufficiency to 62.9% and ecological outflows by 20%. The findings provide insights into adaptive water management, highlight the vulnerability of highly coupled systems to prolonged droughts, and support regional policy decisions on resilience-oriented water infrastructure planning. Full article

Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation. Full article

Erosion represents a significant global threat to coastal zones, especially beaches, which are among the most valuable coastal landforms. This study evaluates the vulnerability to coastal erosion along the Brazilian Amazon coast, focusing on eight recreational beaches. The research is based on an assessment of geological, physical, ecological, and anthropogenic indicators. Some of these indicators were proposed in this study to enhance the evaluation of vulnerability in Amazonian beaches. The analysis reveals that most of the beaches studied are highly vulnerable to erosion due to a combination of natural factors and human activities. The barrier–beach ridge, composed of unconsolidated sediments, exhibits the highest vulnerability, while low cliffs present a moderate level of risk. The study highlights that semi-urban beaches with significant infrastructure development are particularly susceptible to erosion, a problem exacerbated by unplanned land use. Conversely, rural beaches, especially those located in protected areas, show lower vulnerability due to reduced human impact and better conservation of natural ecosystems. Furthermore, the study underscores the effects of extreme climatic events, such as prolonged rainfall and high-energy waves, which can intensify erosion risks. The findings suggest that anthropogenic changes, combined with extreme climate events, significantly influence the dynamics of coastal erosion. This research emphasizes the importance of targeted management strategies that address both natural and human-induced vulnerabilities, aiming to enhance coastal resilience and sustainability for Amazonian beaches. Full article

Energy conversion and infrastructure facilities consist of large amounts of metal and have lifetimes of several decades. When recycling metals, the methods of allocation play a decisive role in evaluating how primary and secondary materials, as well as the products that are produced with them, are to be evaluated ecologically. So-called credits for recycling are the subject of a particularly controversial discussion. This article shows that the current practice of giving credits for long-lasting products leads to a significant distortion of the actual emissions. Using the examples of steel, aluminum, and copper, prospective LCA data is used to show how the carbon footprint actually behaves. When credits are applied, the time dependency of emissions must be taken into account; otherwise, burden shifting into the future occurs, which can hardly be considered sustainable. The increase compared to the conventional time-independent practice lies, depending on the metal, at 70 to 300%. It is recommended that the cutoff approach be used conservatively when allocating recycling cascades in order to optimize environmental impact and avoid greenwashing. Full article

The challenges of global warming and other environmental concerns have prompted governments worldwide to transition from fossil-fuel vehicles to low-emission electric vehicles (EVs). The energy crisis, coupled with environmental issues like air pollution and climate change, has been a driving force behind the development of EVs. In recent years, EVs have emerged as one of the most innovative and vital advancements in clean transportation. According to recent reports, EVs are gradually replacing traditional automobiles, offering benefits such as pollution reduction and the conservation of natural resources. This research focuses on analyzing and reviewing the impact of EV integration on electrical networks, with particular attention to photovoltaic (PV) energy as a sustainable charging solution. It examines both current and anticipated challenges, especially those related to power quality, harmonics, and voltage imbalance. A special emphasis is placed on Tunisia, a country with high solar energy potential and increasing interest in EV deployment. By exploring the technical and infrastructural readiness of Tunisia for PV-based EV charging systems, this paper aims to inform regional strategies and contribute to the broader goal of sustainable energy integration in developing countries as part of future work. Full article

According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for mechanisms to match demand and supply better and increase power system flexibility has led to enhanced attention on demand-side management (DSM) practices to boost technology, infrastructure, and market efficiencies. Refrigeration requirements will continue to rise with development and climate change. In this work, particle swarm optimization (PSO) is used to evaluate energy saving and load factor improvement possibilities for refrigeration devices at a site in Kenya, using a combination of DSM load shifting and strategic conservation, and based on appliance temperature evolution measurements. Refrigeration energy savings of up to 18% are obtained, and the load factor is reduced. Modeling is done for a hybrid system with grid, solar PV, and battery, showing a marginal increase in solar energy supply to the load relative to the no DSM case, while the grid portion of the load supply reduces by almost 25% for DSM relative to No DSM. Full article

The Magallanes region, in southern Chile, is positioned as a strategic hub for the production of green hydrogen (GH2), green ammonia, and synthetic fuels, thanks to its exceptional wind potential and commitment to sustainability. This article analyzes the opportunities and challenges of these energy vectors in the context of global decarbonization, highlighting the key role of the Magallanes region in the energy transition. Green hydrogen production, through wind-powered electrolysis, takes advantage of the region’s constant, high-speed winds, enabling competitive, low-emission generation. In turn, green ammonia, derived from GH2, emerges as a sustainable alternative for the agricultural industry and maritime transport, while synthetic fuels (e-fuels) offer a solution for sectors that are difficult to electrify, such as aviation. The sustainability approach addresses not only emissions reduction but also the responsible use of water resources, the protection of biodiversity, and integration with local communities. The article presents the following structure: (i) introduction, (ii) wind resource potential, (iii) water resource potential, (iv) different forms of hydrogen and its derivatives production (green hydrogen, green ammonia, and synthetic fuels), (v) pilot-scale demonstration plant for Haru Oni GH2 production, (vi) future industrial-scale GH2 production projects, (vii) discussion, and (viii) conclusions. In addition, the article discusses public policies, economic incentives, and international collaborations that promote these projects, positioning Magallanes as a clean energy export hub. Finally, the article concludes that the region can lead the production of green fuels, contributing to global energy security and the fulfillment of the Sustainable Development Goals (SDGs). However, advances in infrastructure, regulation, and social acceptance are required to guarantee a balanced development between technological innovation and environmental conservation. Full article

Under the “dual-carbon” strategy, accurately quantifying carbon emissions in water conservancy projects is crucial to promoting low-carbon construction. However, existing life cycle assessment (LCA) methods for carbon emissions during the mechanical construction stage often fail to reflect actual processes and are limited by high costs and lengthy data collection, potentially leading to inaccurate estimates. To address these challenges, this paper proposes a carbon emission evaluation method for the mechanical construction stage, based on carbon footprint theory and discrete event simulation (DES). This method quantifies equipment operation time and energy consumption during the drilling and blasting processes, enabling a detailed and dynamic emission analysis. Using the Fumin Pumped Storage Power Station Tunnel Project as a case study, a comparative analysis is conducted to examine the carbon emission characteristics of drilling and blasting operations under different surrounding rock conditions based on DES. The validity of the proposed model is confirmed by comparing its results with monitoring data and LCA results. The results show a clear upward trend in carbon emission intensity as surrounding rock conditions deteriorate, with emission intensity rising from 8405.82 kgCO₂e/m for Class II to 16,189.30 kgCO₂e/m for Class V in the headrace tunnel. The total carbon emissions of the water conveyance tunnels reach 40,019.64 tCO₂e, with an average intensity of 13,565.98 kgCO₂e/m. This study presents a refined and validated framework for assessing the carbon emissions of pumped storage tunnels. It addresses key limitations of traditional LCA methods in the mechanical construction stage and provides a practical tool to support the green transition of hydraulic infrastructure. Full article

The sustainable management of natural reserves is increasingly prioritized within the global tourism sector, especially in fragile ecosystems like the Santurbán Moorland in Colombia. As a high-altitude Andean ecosystem providing essential water resources, the Santurbán Moorland faces mounting pressures from tourism growth and mining activity. This study assesses the adoption of sustainable tourism practices among tourism service providers (TSPs) in the region and identifies key gaps to inform policy and academic interventions. A cross-sectional, mixed-methods approach was applied, integrating surveys based on the European Tourism Indicators System (ETIS) and the Global Sustainable Tourism Council (GSTC) criteria, as well as structured interviews, field observations, and document analysis. Confirmatory factor analysis identified “sustainable management” as the most robust dimension (Cronbach’s alpha = 0.953); however, no TSPs reported using renewable energy, and less than 5% of employees had received formal training in tourism. The main challenges include the lack of environmental certification, insufficient infrastructure, and limited communication of sustainability practices. Based on these findings, the study proposes targeted public policies, financial incentives, and specialized academic training to strengthen sustainable practices. The results offer insights into the challenges faced by emerging ecotourism destinations and provide strategic guidelines to support a balance between environmental conservation and local socioeconomic development. Full article

Inland waterway transport faces increasing recognition as a sustainable alternative to conventional transport modes, particularly due to its lower environmental impact and higher efficiency. However, its implementation remains limited in many regions, including Romania, despite substantial potential benefits. This study addresses this gap by assessing the Bucharest–Danube Canal as a strategic infrastructure project capable of supporting Romania’s transition to sustainable transport, aligned with the European Green Deal and the United Nations 2030 Agenda. Employing a structured methodological approach, this research includes a comprehensive literature review and detailed analysis of successful European inland waterway transport projects, systematically correlating findings with specific Sustainable Development Goals. The results illustrate clear relationships between the selected case studies and the targeted goals, highlighting approaches for integrating sustainability into waterway infrastructure. Specifically, the study identifies effective guidelines applicable to Romania and emphasizes the necessity of a comprehensive, multi-dimensional planning approach that exploits the canal’s multifunctional capabilities beyond transportation, encompassing agriculture, tourism, renewable energy, and biodiversity conservation. In conclusion, despite historical and current challenges, the Bucharest–Danube Canal represents a strategic opportunity for Romania, promising significant contributions toward achieving national and regional sustainability objectives. Full article

As electric vehicle (EV) adoption continues to surge globally, the question of what to do with retired EV batteries looms large. While these batteries may no longer meet the rigorous demands of automotive use, they often retain a significant portion of their capacity and functionality. This has led to growing interest in exploring second-life applications for retired EV batteries, ranging from stationary energy storage to grid stabilization and beyond. However, numerous challenges must be addressed to unlock the full potential of this emerging sector. This paper delves into the key challenges and prospects associated with the second life of retired EV batteries. It examines technical hurdles, such as battery degradation, safety concerns, and the development of efficient repurposing methods, along with regulatory and economic barriers, including standards for battery reuse, recycling infrastructure, and market dynamics. Additionally, it highlights the potential environmental benefits, including reduced carbon emissions and resource conservation. In conclusion, the second life of retired EV batteries presents both challenges and opportunities. Addressing technical, regulatory, and economic barriers will be essential for realizing the full potential of this growing sector. However, with continued innovation and collaboration across industries, the future looks bright for leveraging retired EV batteries to create a more sustainable energy ecosystem. Full article

The high-filling engineering of airports is common in mountainous cities, and as critical infrastructure for urban development, airports are prone to slope instability under extreme climate and mechanical coupling effects. Therefore, it is essential to investigate the geometric form of failure surfaces under limit stability conditions for airport slopes. The rational determination of the form of the rupture surface of a soil nailing support structure is a key factor in the structural safety of a project. In this study, we analyzed the rupture surface form and reinforcement mechanism of four common soil nailing support structures in engineering. First, we established theoretical model I and verified the consistency of the upper-limit theorem of plastic mechanics and energy conservation in this model. Next, a theoretical analytical model of the rupture surface form was established taking into consideration the existence of tension depth in a certain depth range at the top. The mathematical expressions of the rupture surface form with respect to h/H and L/H were derived by combining plasticity mechanics and energy conservation. Finally, the rupture surface forms of the structure were analyzed for different slope angles of soil nail-supported structures and different friction angles within the soil. The findings were compared with the rupture surface forms in the existing codes and literature. The results showed that L/H decreased continuously with the slope angle $\beta$ of the soil nailing support structure and decreased gradually with an increasing friction angle $\phi$ within the soil. Furthermore, h/H decreased with the slope angle of the soil nailing support structure, but it showed a trend with the increase in soil internal friction angle and the slope angle of the soil nailing support structure. The analysis revealed that only in some specific cases were $\beta$ and $\phi$ closely aligned with the values acquired using standard methods in specifications and the literature. The theoretical analysis provided important reference values for the design and improvement of soil nailing length in soil nailing support structures under certain conditions, thereby ensuring their enhanced stability and strength. Full article

This paper introduces a groundbreaking framework for optimizing microgrid operations using the Quantum Approximate Optimization Algorithm (QAOA). The increasing integration of decentralized energy systems, characterized by their reliance on renewable energy sources, presents unique challenges, including the stochastic nature of energy supply-and-demand management. Our study leverages quantum computing to enhance the operational efficiency and resilience of microgrids, transcending the limitations of traditional computational methods. The proposed QAOA-based model formulates the microgrid scheduling problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, suitable for quantum computation. This approach not only accommodates complex operational constraints—such as energy conservation, peak load management, and cost efficiency—but also dynamically adapts to the variability inherent in renewable energy sources. By encoding these constraints into a quantum-friendly Hamiltonian, QAOA facilitates a parallel exploration of multiple potential solutions, enhancing the probability of reaching an optimal solution within a feasible time frame. We validate our model through a comprehensive simulation using real-world data from a microgrid equipped with photovoltaic systems, wind turbines, and energy storage units. The results demonstrate that QAOA outperforms conventional optimization techniques in terms of cost reduction, energy efficiency, and system reliability. Furthermore, our study explores the scalability of quantum algorithms in energy systems, providing insights into their potential to handle larger, more complex grid architectures as quantum technology advances. This research not only underscores the viability of quantum algorithms in real-world applications but also sets a precedent for future studies on the integration of quantum computing into energy management systems, paving the way for more sustainable, efficient, and resilient energy infrastructures. Full article

The agricultural sector faces challenges such as water scarcity, energy inefficiency, and declining productivity, particularly in arid regions. Traditional irrigation methods contribute to resource depletion and environmental impacts. Solar-powered smart irrigation systems integrate precision irrigation with renewable energy, improving water use and productivity. In Pakistan, where agriculture contributes 19% of gross domestic product and employs 40% of the workforce, these challenges are severe, especially in water-scarce areas like the Cholistan Desert. This study examines the impact of solar-powered smart irrigation on agricultural productivity, water conservation, and energy efficiency in the Cholistan Desert. Using a quantitative cross-sectional design, data were collected from 384 farmers via structured questionnaires. Statistical analyses, including multiple linear regression, paired sample t-tests, and Structural Equation Modeling (SEM), were conducted. Findings show significant improvements in crop yield (from 3.0 to 4.8 tons/hectare) and reductions in water and energy consumption. Regression analysis highlighted strong positive effects on yield and efficiency, while SEM confirmed reduced environmental impact and operational costs. The study concludes that solar-powered irrigation enhances productivity, conserves resources, and promotes sustainability. Policymakers should provide financial incentives, invest in renewable infrastructure, and implement training programs to support adoption. Collaborative efforts are essential for sustainable agriculture in arid regions. Full article