Search Results (635)

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

Vespid wasps (Hymenoptera: Vespidae) represent ecologically and economically significant insect resources, possessing extremely high nutritional and medicinal value. In response to escalating market demand and declining wild populations, artificial indoor rearing of vespid wasps has emerged as a sustainable strategy. This approach not only helps conserve and utilize this valuable resource, supporting traditional medicine and local cuisine, but also contributes to invasive species control in affected regions. The large-scale rearing of wasps must integrate their biological characteristics with advanced rearing technologies to achieve rational rearing practices, while continuously optimizing management models. This article systematically reviews the biological characteristics of wasps, traditional rearing methods, and the current status of wasp rearing. It focuses on the methods and key technologies for the outdoor rearing of Vespa mandarinia and year-round indoor rearing. Additionally, it discusses the advantages of artificial indoor rearing, identifies critical technical challenges, and provides a summary and outlook on the future development trends, aiming to provide theoretical support for the large-scale and industrialized development of indoor wasp rearing. Full article

Demand-responsive transit (DRT) is an innovative public transportation model that dynamically adjusts routes based on passengers’ specific demands. While existing studies offer insights into routing, scheduling, and network design, they remain fragmented, with limited integration of user behavior, policy relevance, and sustainability. To address these gaps, this paper develops a scenario-based evaluation framework that synthesizes bibliometric evidence, operational conditions, modeling approaches, and evaluated outcomes. Using CiteSpace, we conducted keyword co-occurrence and clustering analysis. Thematic clusters such as “routing and scheduling,” “network design,” “stated preference,” “public transport,” and “demand-responsive transit” were mapped to a three-tier analytical structure. Scenarios integrate economic, environmental, and social dimensions, enabling comparative insights across urban, rural, and intercity scenarios. The scenario-based approach offers two key advantages: (1) it captures heterogeneity across operational environments, ensuring that evaluation frameworks are not overly generalized. Research shows that urban scenarios emphasize scheduling precision, rural pilots face cost-efficiency but enhance resilience, and intercity services depend on multimodal synchronization. (2) It facilitates synthesis by linking technical models with real-world outcomes, enhancing policy relevance. This study contributes to sustainable transport research by providing a coherent, empirically validated, and conceptually integrated framework for evaluating DRT systems. Full article

Reactive Powder Concrete (RPC) is widely recognized for its high strength and durability, yet its dependence on large amounts of Portland cement (PC) and silica fume (MS) raises environmental and economic concerns. This study explores the combined incorporation of milled electric arc furnace slag (MEAS) and calcium carbonate powder (CCP) as partial substitutes for cement and MS in RPC, employing a Central Composite Design (CCD) to optimize cement dosage, water-to-binder ratio, and polycarboxylate ether (PCE) content. Particle packing was guided by the Modified Andreasen–Andersen (MAA) model. The experimental program included 20 mixtures, evaluating rheological performance through slump flow and mechanical strength at 1, 7, 14, and 28 days. Incorporating MEAS (up to ≈20% of the binder) and CCP (≈15%) improved workability, with slump flow values reaching ≈285 mm compared to ≈230 mm for the baseline mixture. The optimal formulation achieved a 28-day compressive strength of ≈152 MPa, comparable to the reference RPC (≈138 MPa), while reducing cement consumption by ≈15% and MS by ≈50% relative to conventional dosages. Quadratic response surface models for slump flow and compressive strength at 1–28 days showed excellent goodness of fit (R² = 0.90–0.98, adjusted R² = 0.85–0.96; model F-tests p < 0.001), confirming the adequacy of the statistical optimization. Moreover, statistical analysis confirmed that cement dosage was the dominant factor for strength development (p < 0.05), while the interaction between cement content and water-to-binder ratio significantly influenced flowability. These results demonstrate the potential of MEAS and CCP to lower binder demand in RPC without compromising mechanical performance, advancing sustainable alternatives for ultra-high-performance concrete. Full article

Food waste is one of the planet’s most pressing challenges, directly linked to food security, resource depletion, greenhouse gas emissions, and, more broadly, environmental concerns demanding immediate attention. This issue occurs throughout the entire food value chain; however, households are the primary source of waste. This research examines the key factors influencing household food waste behavior and investigates how these factors can contribute to the development of sustainable practices that minimize environmental impacts. Six research hypotheses were examined, focusing on consumers’ knowledge of environmental and food waste issues, their awareness of their community, their emotional responses to their actions, and their social and economic status. A structured questionnaire was administered to a sample of 870 individuals in a region of Greece, and the data were analyzed using factor and path analyses. The results showed that education and accurate information about environmental issues, as well as strategies for reducing waste and its impact on the environment and the economy, were strongly correlated with consumers’ food waste behaviors. The proposed model demonstrated moderate explanatory power (R² = 0.396) and excellent fit indices (χ² = 10.58, p < 0.001, NFI = 0.99, IFI = 0.995, CFI = 0.98, RMSEA = 0.06), highlighting the significance of the main predictors identified. Full article

Background: Disasters generate abrupt surges in humanitarian demand, requiring response strategies that balance operational performance with vulnerability considerations. This study examines how temporary Points of Distribution (PODs) can be planned and activated to support timely and equitable resource distribution after a high-magnitude earthquake. Methods: A two-stage framework is proposed. First, a modular p-median model identifies POD locations and allocates modular capacity to minimize population-weighted distance under capacity constraints; travel-distance percentiles guide the selection of p. Second, a SMART-based multi-criteria model ranks facilities using operational metrics and vulnerability indicators, including seismic and economic conditions and the presence of at-risk groups. Results: Evaluation of p values from 3 to 30 shows substantial reductions in travel distances as PODs increase, with an elbow at $p=12$, where 50% of the residents are within 500 m, 75% within 675 m, and 95% within 1200 m. The SMART analysis forms three priority clusters: facilities 24 and 9 as highest priority; 23, 4, 12, and 22 as medium priority; and the remaining sites as lower priority. Sensitivity analysis shows that rankings are responsive to vulnerability weights, although clusters remain stable. Conclusions: The framework integrates optimization and multi-criteria decision analysis without increasing model complexity, enabling meaningful decision-maker involvement throughout the modeling process. Full article

Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration. Full article

The construction industry is responsible for nearly one-third of global greenhouse gas emissions and consumes over 50% of the planet’s natural resources. As population growth continues, the demand for these resources is expected to rise. Within this context, where business models are still largely based on the Linear Economy (LE), the Circular Economy (CE) emerges as a strategy for promoting economic development while reducing dependence on natural resource consumption. To enable the transition from LE to CE, digital tools such as Material Passports (MP) are essential. An MP compiles data and information describing the characteristics of materials to facilitate their recovery and reuse. This study aims to model the MP of a wood-frame panel commercially produced by Tecverde in Brazil. The panel was designed for a building project using 2024 version of Autodesk Revit software. The proposed MP contains 49 parameters grouped into nine categories, and the data were obtained from open databases provided by the company. The results highlight existing challenges related to sustainability parameters, as well as opportunities to incorporate circular value principles into the construction industry. Full article

The high proportion of renewable energy connected to the grid poses new challenges to the safe and economic operation of active distribution networks (ADNs). However, most of the existing research focuses on single-objective optimization or ignores the influence of the uncertainty of renewable energy output and the demand response mechanism, and lacks verification of the scalability of models in large-scale systems. For an active distribution network system with distributed wind power and photovoltaic access, this paper establishes a multi-objective day-ahead optimal dispatching model that takes into account economy, reliability, and safety. The research adopts a scenario-based method and chance-constrained programming (CCP) to handle the uncertainty of wind and solar output. It combines the quasi-Monte Carlo (QMC) method and Kantorovich distance to achieve scenario generation and reduction, and introduces price-based and incentivized demand response mechanisms to form four combined optimization models. The multi-objective optimization solution was carried out based on the multi-objective evolutionary algorithm based on decomposition (MOEA/D), verifying the effectiveness of the proposed method in terms of operation cost, load shedding expectation, and node voltage limit control. The case study is based on the improved IEEE 30-node and 200-node 49-generator systems. The results indicate that this method can effectively balance multiple objectives such as operation costs, load shedding expectations, and node voltage limit; can significantly enhance the renewable energy consumption capacity of active distribution networks; and can provide an effective solution for the optimal dispatching of active distribution networks with a high proportion of renewable energy. Full article

The sustainability of managing archaeological iron collections presents both environmental and economic challenges for heritage institutions. Energy-intensive climate control and rising operational costs necessitate evaluation of conservation treatments and preventive storage strategies. This study examines the environmental impacts of treatments commonly used for archaeological iron, including sodium hydroxide and sodium disulfite desalination, as well as emerging microbially derived “greener” approaches. Life cycle assessment (LCA) analyses quantify the global warming potential, toxicity, and energy requirements of these treatments. Preventive conservation strategies, including relative humidity (RH) control in storage and display, are assessed for energy efficiency and sustainability. Air exchange rates, dehumidifier performance, and silica gel replacement schedules were measured and modelled to estimate energy consumption and associated environmental impacts. Results highlight that chemical treatments contribute minimally to overall environmental burden, whereas operational energy demands for storage and display are significant. The findings provide evidence-based guidance for implementing more sustainable conservation practices for archaeological iron, balancing material preservation, resource efficiency, and environmental responsibility. Full article

The shift toward decentralized energy systems and the rapid growth of renewable integration have brought renewed attention to the Multi-Area Economic Dispatch (MAED) problem. Unlike single-area dispatch, which focuses only on local balance, MAED must also coordinate inter-area exchanges, respect regional operating limits, and ensure overall reliability. This paper reviews both MAED and its dynamic extension, the Multi-Area Dynamic Economic Dispatch (MADED). The review examines core objectives—cost minimization, emission reduction, and renewable utilization—and surveys a wide range of solution methods. These include classical mathematical programming, metaheuristic and hybrid approaches, and more recent advances based on machine learning and reinforcement learning. Special emphasis is placed on uncertainty-oriented models that address demand variability, market dynamics, and renewable fluctuations. The discussion also highlights the role of Distributed Energy Resources (DERs), Energy Storage Systems (ESSs), and Demand Response (DR) in improving system flexibility and resilience. Despite notable progress, research gaps remain, including limited treatment of uncertainty, insufficient integration of DR, oversimplified modeling of electric vehicles, and the marginal role of reliability. To address these issues, a research agenda is proposed that aims to develop more adaptive, scalable, and sustainable dispatch models. The insights provided are intended to support both academic research and practical applications in the planning and operation of interconnected grids. Full article

Accurate and automated credit assessment systems are fundamental to the integrity of financial ecosystems, underpinning responsible lending, risk mitigation, and sustainable economic growth. In light of persistent economic uncertainties and an increasing frequency of credit defaults, financial entities face urgent demands for robust and scalable risk evaluation tools. While a diverse array of statistical and machine learning techniques have been proposed for credit scoring, prevailing methods remain labor-intensive and operationally cumbersome. This paper introduces VeriCred, a novel credit evaluation framework that synergistically combines automated machine learning with blockchain-based oversight to overcome these limitations. The proposed approach incorporates a data augmentation strategy to enrich limited and heterogeneous credit datasets, thereby improving model generalization. A distinctive blockchain layer is embedded to immutably trace data provenance and model decisions, ensuring full auditability. By orchestrating the end-to-end workflow—including feature extraction, hyperparameter optimization, and model selection—within a unified AutoML pipeline, the system drastically reduces manual dependency. Architecturally, the framework introduces C-NAS, a neural architecture search mechanism customized for credit risk prediction, alongside A-Triplet loss, an objective function tailored to refine feature discrimination. To address opacity concerns, an interpretability component elucidates feature contributions and model reasoning. Empirical evaluations demonstrate that VeriCred achieves superior predictive accuracy with significantly reduced computational overhead, offering financial institutions a transparent, efficient, and trustworthy credit scoring solution. Full article

Against the backdrop of the full market integration of renewable energy, determining a reasonable proportion between medium- and long-term (MLT) contracts and spot trading has become a core issue in power market reform. Current Chinese policy requires that the share of MLT contracts should not be less than 90%, which helps ensure system security but may suppress the price discovery function of the spot market and limit renewable energy integration. This paper constructs a three-layer model: the first layer describes spot market clearing through Direct Current Optimal Power Flow (DC-OPF), yielding system energy prices and nodal prices; the second layer models bilateral contract decisions between generators and users based on Nash bargaining, incorporating risk preferences via a mean–variance framework; and the third layer introduces two evaluation indicators—contract penetration rate and economic proportion—and applies outer-layer optimization to search for the optimal contract ratio. Parameters are calibrated using coal prices, wind speed, solar irradiance, and load data, with numerical solutions obtained through Monte Carlo simulation and convex optimization. Results show that increasing the share of spot trading enhances overall system efficiency, primarily because renewable energy has low marginal costs and high supply potential, thereby reducing average market prices and mitigating volatility. Simulations indicate that the optimal contract coverage rate may exceed the current policy lower bound, which would expand spot market space and promote renewable energy integration. Sensitivity analysis further reveals that fuel price fluctuations, renewable output, load structure, and risk preferences all affect the optimal proportion, though the overall conclusions remain robust. Policy implications suggest moderately relaxing the constraints on MLT contract proportions, improving contract design, and combining this with transmission expansion and demand response, in order to establish a more efficient and flexible market structure. Full article

To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.7–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article

To further explore the potential of CO₂ emission reduction and optimize the cost of microgrids, a two-stage robust optimization configuration method for multi-energy microgrids is proposed, considering uncertainty, tiered carbon trading, and demand response. The model incorporates power-to-gas (P2G) and carbon capture and storage (CCS) technologies to enhance renewable energy utilization and reduce carbon emissions. A tiered carbon trading mechanism is introduced to penalize high emissions, while incentive-based demand response is employed to adjust load profiles and improve economic performance. The optimization model is formulated as a two-stage robust problem: the outer stage minimizes annual investment and maintenance costs, while the inner stage identifies the worst-case scenario under uncertainties. The model is solved using the Column-and-Constraint Generation (C&CG) algorithm and implemented in MATLAB R2022b with the Gourbi solver. Simulation results demonstrate that the proposed approach reduces carbon emissions by up to 31.9% and total costs by 3.28% compared to conventional configurations, while increasing the penetration of renewable energy. This study provides practical reference for the low-carbon and economic planning of microgrids with P2G and CCS integration. Full article