Search Results (1,898)

To enhance the participation enthusiasm of distributed energy resources (DERs) and DER aggregators in their demand response, this paper develops a three-level distributed scheduling model for the distribution network operators (DNO), DER aggregators, and DERs based on the conditional value-at-risk (CVaR) theory. First, a demand response model is established for the DNO, DER aggregators, and DERs. Next, we employ the analytical target cascading (ATC) method to construct a three-level distributed scheduling model, where incentive and compensation prices are shared as consensus variables across the model levels to amplify the influence of DER aggregators on incentive prices and DERs on compensation prices. Then, the photovoltaic output model is restructured using the CVaR theory to effectively measure the risk associated with photovoltaic output uncertainty. Finally, an analysis is conducted using the IEEE 33-node distribution network to validate the effectiveness of the proposed model. Full article

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap. Full article

The rising demand for clean energy production due to climate change emphasizes the importance of optimizing water resources, particularly in countries with significant hydropower potential. Existing models for the Operational Planning of Hydropower Systems (HPSOP) typically focus on the natural flows of rivers, often overlooking the potential of water transfers between rivers and basins. To address this gap, this article employs an improved mathematical model of hydropower production, considering the adjustment of the water transfer in the operation schedule as an additional optimization variable. A customized meta-heuristic, named the Evolutionary Socio-Bio Inspired Technique (ESBIT), has been tailored to integrate water transfer mechanisms into the operational planning model. The proposed model was validated through a case study at the Henry Borden Complex in São Paulo, Brazil, using real power plant parameters and inflow data from the Brazilian system. The results obtained from the test case, both with and without water transfer, demonstrate that the proposed methodology effectively captures the operational characteristics of a system that allows water transfers between rivers or basins to optimize the available water resources and system costs. Full article

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information (timeouts, NACKs) within NDN, consumers are unable to access the network congestion status from this information in a timely manner. To address the issues above, this paper combines the Q-learning algorithm with the NDN architecture, proposing Q-NDN. In Q-NDN, consumers can dynamically adjust the congestion window (cwnd) through the real-time monitoring of network status, leveraging the Q-learning algorithm, achieving automatic congestion control for the NDN architecture. Additionally, this paper introduces content popularity-based traffic scheduling for multi-user scenarioswhich adjusts the transmission rates of content with different popularity levels to maintain a dynamic balance in the network. The experimental results show that Q-NDN can converge quickly, make full use of bandwidth resources, and keep the packet loss rate to 0 in the basic network topology. In competing network topologies, Q-NDN can rapidly address conflict issues, efficiently utilize bandwidth resources, and maintain a relatively low packet loss rate. Full article

Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how digital technology can facilitate greener port operations. This study integrates sustainable supply chain management and system dynamics theories based on an in-depth analysis of existing research results and expert interviews. The analysis focuses on three key dimensions: integrating digital technologies with infrastructure, optimizing digital management and operations, and improving environmental and safety management in a digitally driven setting. Using the fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy Dematel) methodology, we collaborated with domain experts in port logistics to identify and confirm 12 pivotal factors that support the green digital transformation of ports. The research shows that the most critical success factors for using digital technology to drive ports’ green transformation are green supply chain information platforms, intelligent vessel scheduling, traffic optimization, and digital carbon emission monitoring. This study significantly contributes to the literature on green port transformation, offering indispensable practical insights for port operators, government entities, and shipping firms in identifying and deploying these key success factors. The findings will help maritime supply chain stakeholders develop actionable digital strategies, improving port efficiency and ecological resilience. Full article

Addressing the resource constraints, such as manpower and equipment, faced by mine backfilling operations, this study proposed an optimization model for backfill scheduling based on the Resource-Constrained Project Scheduling Problem (RCPSP). The model considered backfilling’s multi-process, multi-task, and multi-resource characteristics, aiming to minimize total delay time. Constraints included operational limits, resource requirements, and availability. The goal was to determine optimal resource configurations for each stope’s backfilling steps. A heuristic genetic algorithm (GA) was employed for solution. To handle equipment unavailability, a new encoding/decoding algorithm ensured resource availability and continuous operations. Case verification using real mine data highlights the advantages of the model, showing a 20.6% decrease in completion time, an 8 percentage point improvement in resource utilization, and a 47.4% reduction in overall backfilling delay time compared to traditional methods. This work provides a reference for backfilling scheduling in similar mines and promotes intelligent mining practices. Full article

This article is devoted to planning water-energy regimes for hydropower plants, taking into account economic and ecologic criteria. A new methodology based on a probabilistic model of water inflow has been proposed. The probabilistic method requires the calculation of low-water and average-water year typical hydrographs based on the probability curve. This allows the determination of the guaranteed hydropower plant generation schedule with a month time-step. According to the method considered, the mathematical model of the reservoir filling and normal power station operation has been designed. The software for the automated water-energy mode calculation is presented in this paper. The economic feasibility of maximum replacement of thermal power plants in the energy system with more environmentally friendly hydropower plant is substantiated. The methodology of water resources cost calculation and economic efficiency assessment under various hydropower plant regime scenarios have been proposed in the paper. Using the data and characteristics of HPPs and TPPs, an assessment of energy efficiency will be obtained in accordance with the developed methodology to determine the price of water for HPPs and all participants in the water management complex. The results of the implementation of the developed approach indicate that the price of electricity sales in a competitive electricity market can be brought into line with the price of electricity sales generated by thermal power plants, which increases the economic feasibility of the maximum replacement of thermal power plant capacity in the system with more economical and environmentally friendly hydropower plant. The developed method allows for an increase in the efficiency of water resources use and the efficiency of hydropower plant participation in the energy balance, which makes it possible to displace part of the power generated by thermal power plants. Full article

To fully leverage the potential flexibility resources of a source-network-load-storage (SNLS) system and achieve the green transformation of multi-source systems, this paper proposes an economic and low-carbon operation strategy for an SNLS system, considering the joint operation of ladder-type green certificate trading (GCT)–carbon emission trading (CET), and integrated demand response (IDR). Firstly, focusing on the load side of electricity–heat–cooling–gas multi-source coupling, this paper comprehensively considers three types of flexible loads: transferable, replaceable, and reducible. An IDR model is established to tap into the load-side scheduling potential. Secondly, improvements are made to the market mechanisms: as a result of the division into tiered intervals and introduction of reward–penalty coefficients, the traditional GCT mechanism was improved to a more constraining and flexible ladder-type GCT mechanism. Moreover, the carbon offset mechanism behind green certificates serves as a bridge, leading to a GCT-CET joint operation mechanism. Finally, an economic low-carbon operation model is formulated with the objective of minimizing the comprehensive cost consisting of GCT cost, CET cost, energy procurement cost, IDR cost, and system operation cost. Simulation results indicate that by effectively integrating market mechanisms and IDR, the system can enhance its capacity for renewable energy penetration, reduce carbon emissions, and achieve green and sustainable development. Full article

Water resource management in agriculture faces complex challenges due to increasing scarcity, exacerbated by climate change, and the intensification of conflicts among various user groups. This study addresses the issue of predicting and managing these conflicts in the Longaví River Basin, Chile, by considering the intricate interactions between hydrological, social, and economic factors. A socio-hydrological agent-based model (SHABM) was developed, integrating hydrological, economic, and behavioral data. The methodology combined fieldwork with computational modeling, characterizing three types of agents (selfish, neutral, and cooperative) and simulating scenarios with varying levels of water availability and oversight across three water user organizations (WUOs). The key findings revealed that (1) selfish agents are more likely to disregard irrigation schedules under conditions of scarcity and low supervision; (2) high supervision (90%) significantly reduces conflicts; (3) water scarcity exacerbates non-cooperative behaviors; (4) high-risk conflict areas can be identified; and (5) behavioral patterns stabilize after the third year of simulation. This work demonstrates the potential of SHABM as a decision-making tool in water management, enabling the proactive identification of conflict-prone areas and the evaluation of management strategies. Full article

Large-scale infrastructure projects involve numerous complex processes, and even small construction management (CM) deficiencies can lead to significant resource waste. Digital twins (DTs) offer a potential solution to the management side of the problem. The current DT models focus on real-time physical space mapping, which causes the fragmentation of process data in servers and limits lifecycle algorithm implementation. In this paper, we propose a DT framework that integrates process twins to achieve process discovery through process mining and that serves as a supplement to DTs. The proposed framework was validated in a highway project. Based on BIM, GIS, and UAV physical entity twins, construction logs were collected, and process discovery was performed on them using process mining techniques, achieving process mapping and conformance checking for the process twins. The main conclusions are as follows: (1) the process twins accurately reflect the actual construction process, addressing the lack of process information in CM DTs; (2) process variants can be used to analyze abnormal changes in construction methods and identify potential construction risks in advance; (3) sudden changes in construction nodes during activities can affect resource allocation across multiple subsequent stages; (4) process twins can be used to visualize construction schedule risks, such as lead and lag times. The significance of this paper lies in the construction of process twins to complement the existing DT framework, providing a solution to the lost process relationships in DTs, enabling better process reproduction, and facilitating prediction and optimization. In future work, we will concentrate on conducting more in-depth research on process twins, drawing from a wider range of data sources and advancing intelligent process prediction techniques. Full article

This article investigates the application and physical mechanism exploration of distributed collaborative optimization algorithms in building multi-energy complementary energy systems, in response to the difficulties in coordinating various subsystems and insufficient dynamic control strategies. On the basis of modeling each subsystem, the Dual Decomposition algorithm is used to decompose the global optimization problem of the system into several independent sub problems, achieving independent optimization of each subsystem. Through an adaptive dynamic scheduling strategy, real-time data and predictive information are continuously updated and controlled, effectively allocating system resources. The experimental results show that compared to the original system before optimization, the improved algorithm in this paper reduces the total energy consumption of the system by 6.9% and 2.8% on typical summer and winter days, respectively. The conclusion shows that the algorithm proposed in this paper can effectively solve the problem of system coordination difficulties, improve system resource allocation and overall operation level, and provide a new perspective for the optimization design and operation control of energy systems. Full article

Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users. Full article

Pressure from population growth and climate change stress the limited water resources in the Mediterranean region and threaten food security and social stability. Enhancing food production requires the transformation of irrigation systems and enhancement of local capacity for sustainable water and soil management in irrigated agriculture. The aim of this work is the conversion of traditional irrigation practices, by introducing the practice of optimal irrigation scheduling based on local ET estimation and soil moisture monitoring, and the use of continuous feeding by fertigation to enhance both water and nutrient use efficiency. For this, two trials were established between August and November 2023 in two different pedoclimatic zones (Serein and Sultan Yacoub) of the inner Bekaa Plain of Lebanon, characterized by semi-arid and dry subhumid conditions and different soil types. Greenhouse cucumber was tested to compare the prevailing traditional farmers’ practices with the advanced, technology-based, methods of water management. Results showed a significantly higher amount of water applied by the farmers to the protected cucumber, with a potential for average saving of 105 mm of water applied in each season by improved practices. Water input in the traditional practices revealed potential stress to plants. With more than 20% increase in cucumber yield by the transformed practices, a general trend was observed in the fertilization approach and amounts, resulting in lower nutrient recovery in the farmer’s plots. The science-based practices of water and nutrient management showed higher application and agronomic water use efficiency of full fertigation, exceeding 60%, associated with double and triple higher nitrogen use efficiency, compared to those results obtained by the traditional water and fertilizer application methods. The monitored factors can contribute to severe economic and environmental consequences from nutrient buildup or leaching in the soil–groundwater system in the Mediterranean region. Full article

Water level forecasting has significant impacts on transportation, agriculture, and flood control measures. Accurate water level values can enhance the safety and efficiency of water conservancy hub operation scheduling, reduce flood risks, and are essential for ensuring sustainable regional development. Addressing the nonlinearity and non-stationarity characteristics of gate-front water level sequences, this paper introduces a gate-front water level forecasting method based on a GRU–TCN–Transformer coupled model and permutation entropy (PE) algorithm. Firstly, an analysis method combining Singular Spectrum Analysis (SSA) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is used to separate the original water level data into different frequency modal components. The PE algorithm subsequently divides each modal component into sequences of high and low frequencies. The GRU model is applied to predict the high-frequency sequence part, while the TCN–Transformer combination model is used for the low-frequency sequence part. The forecasting from both models are combined to obtain the final water level forecasting value. Multiple evaluation metrics are used to assess the forecasting performance. The findings indicate that the combined GRU–TCN–Transformer model achieves a Mean Absolute Error (MAE) of 0.0154, a Root Mean Square Error (RMSE) of 0.0205, and a Coefficient of Determination (R²) of 0.8076. These metrics indicate that the model outperforms machine learning Support Vector Machine (SVM) models, GRU models, Transformer models, and TCN–Transformer combination models in forecasting performance. The forecasting results have high credibility. This model provides a new reference for improving the accuracy of gate-front water level forecasting and offers significant insights for water resource management and flood prevention, demonstrating promising application prospects. Full article

Evapotranspiration (ET_c), crop water requirement (D_cr), irrigation (IR) and irrigation leaching amount (IR_lc) play a critical role in optimizing irrigation scheduling and are also important for hydrological cycle processes and ecological environment in arid regions. This research examined the spatiotemporal variability of the ET_c, D_cr and IR of cotton using data from 16 meteorological stations in the Tarim basin (TRB) of arid Northwest China during 1961–2017. The results showed that the mean annual ET_c of cotton exhibited a significant decreasing trend, with a change rate of 12.965 mm·10 a⁻¹ and 18.357 mm·10 a⁻¹ during 1961–2017 and 1961–1990, respectively. Subsequently, it experienced a substantial increase with a change rate of 16.833 mm·10 a⁻¹ after 1990. The D_cr of cotton followed a decreasing trend at a rate of 15.531 mm·10 a⁻¹ and 21.99 mm·10 a⁻¹ during 1961–2017 and 1961–1990, respectively. The D_cr of cotton provided an increasing trend at a rate of 20.164 mm·10 a⁻¹ during 1991–2017. The IR of cotton followed a decreasing trend at a rate of 19.66 mm·10 a⁻¹ in 1961–2017 and 24.531 mm·10 a⁻¹ in 1961–1990, but an increasing trend at a rate of 14.437 mm·10 a⁻¹ in 1991–2017. The IR_lc of cotton decreased by 2.566 mm·10 a⁻¹ and 3.663 mm·10 a⁻¹ during 1961–2017 and 1961–1990, respectively. After 1990, it experienced a substantial increase by 3.331 mm·10 a⁻¹. Wind speed exerted the greatest influence on the variability in D_cr and IR between 1961 and 1990, while shine hour played a more prominent role in explaining the variability in D_cr and precipitation may have played a more significant role in explaining the variability in IR. This study is helpful for the scientific planning for agriculture, water resource allocation and water-saving irrigation in arid regions. Full article