Search Results (2,118)

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency. Full article

The intermittent and stochastic characteristics of wind power pose a higher demand on the complementarity of hydropower. Studying the optimal coordinated operation of hydro–wind power systems has become an extremely effective way to create safe and efficient systems. This paper aims to study the optimal coordinated operation of a hybrid power system based on a newly established Simulink model. The analysis of the optimal coordinated operation undergoes two simulation steps, including the optimization of the complementary mode and the optimization of capacity allocation. The method of multiple complementary indicators is adopted to enable the optimization analysis. The results from the complementary analysis show that the hydraulic tracing effect obviously mitigates operational risks and reduces power losses under adverse wind speeds. The results from the analysis of capacity allocation also show that the marginal permeation of installed wind capacity will not exceed 250 MW for a 100 MW hydropower plant under random wind speeds. These simulation results are obtained based on the consideration of some real application scenarios, which help power plants to make the optimal operation plan with a high efficiency of wind energy and high hydro flexibility. Full article

Motivated by South Africa’s need for the transition to a net-zero economy, this study investigates the integration of renewable energy sources (RESs) into oil refineries, considering the unique challenges and opportunities therein. The research focuses on optimising RES allocation using particle swarm optimisation (PSO), a data-driven approach that adapts to real-time operational conditions. Traditional energy management systems often struggle with the inherent variability of RESs, leading to suboptimal energy distribution and increased emissions. Therefore, this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil refineries. It considers factors like the levelised cost of energy, geographical location, and available technology. The methodology involves formulating the optimisation problem, developing a PSO model, and implementing it in a simulated oil refinery environment. The results demonstrate significant convergence of the PSO algorithm, leading to an optimal configuration for integrating RESs and achieving cost reductions and sustainability goals. The optimisation result of ZAR 4,457,527.00 achieved through iterations is much better than the result of ZAR 4,829,638.88 acquired using linear programming as the baseline model. The mean cost, indicating consistent performance, has remained at its original value of ZAR 4,457,527.00, highlighting the convergence. The key findings include the average distance measurement decreasing from 4.2 to 3.4, indicating particle convergence; the swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; the average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the optimum cost function achieved at ZAR 4,457,527 with zero standard deviation, highlighting stability and optimal solution identification. This research offers a valuable solution for oil refineries seeking to integrate RESs effectively, contributing to South Africa’s transition to a sustainable energy future. Full article

Wireless Local-Area Networks (WLANs), as a popular internet access solution, are widely used in numerous places, including enterprises, campuses, and public venues. As the number of devices increases, large-scale deployments will cause the problem of dense wireless networks, including a lot of energy consumption. Thus, the optimization of energy-efficient wireless AP devices has become a focal point of attention. To reduce energy consumption, we have proposed the active access-point (AP) configuration algorithm for WLANs using APs with a dual interface. This uses the greedy algorithm combined with the local search optimization method to find the minimum number of activated APs while satisfying the minimum throughput constraint. However, the previous algorithm basically satisfies only the average throughput among the multiple hosts associated with one AP, wherein some hosts may not reach the required one. In this paper, to overcome this limitation, we propose an enhanced active AP configuration algorithm by incorporating the throughput request satisfaction method that controls the actual throughput at the target value (target throughput) for every host by applying traffic shaping. The target throughput is calculated from the single and concurrent communicating throughput of each host based on channel occupancy time. The minimum throughput constraint will be iteratively adjusted to obtain the required target throughput and achieve the fair throughput allocation. For evaluations, we conducted simulations using the WIMNET simulator and experiments using the testbed system with a Raspberry Pi 4B for APs in four topology cases with five APs and ten hosts. The results show that the proposed method always achieved the required minimum throughput in simulations as well as in experiments, while minimizing the number of active APs. Thus, the validity and effectiveness of our proposal were confirmed. Full article

In isolated operation, DC microgrids require multiple distributed energy storage units (DESUs) to accommodate the variability of distributed generation (DG). The traditional control strategy has the problem of uneven allocation of load current when the line impedance is not matched. As the state-of-charge (SOC) balancing proceeds, the SOC difference gradually decreases, leading to a gradual decrease in the balancing rate. Thus, an improved SOC droop control strategy is introduced in this paper, which uses a combination of power and exponential functions to improve the virtual impedance responsiveness to SOC changes and introduces an adaptive acceleration factor to improve the slow SOC balancing problem. We construct a sparse communication network to achieve information exchange between DESU neighboring units. A global optimization controller employing the consistency algorithm is designed to mitigate the impact of line impedance mismatch on SOC balancing and current allocation. This approach uses a single controller to restore DC bus voltage, effectively reducing control connections and alleviating the communication burden on the system. Lastly, a simulation model of the DC microgrid is developed using MATLAB/Simulink R2021b. The results confirm that the proposed control strategy achieves rapid SOC balancing and the precise allocation of load currents in various complex operational scenarios. Full article

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption. Full article

Animal personalities play a crucial role in invasion dynamics. During the invasion process, the behavioral strategies of native species vary among personalities, just as the invasive species exhibit variations in behavior strategies across personalities. However, the impact of personality interactions between native species and invasive species on behavior and growth are rarely illustrated. The red-eared slider turtle (Trachemys scripta elegans) is one of the worst invasive species in the world, threatening the ecology and fitness of many freshwater turtles globally. The Chinese pond turtle (Mauremys reevesii) is one of the freshwater turtles most threatened by T. scripta elegans in China. In this study, we used T. scripta elegans and M. reevesii to investigate how the personality combinations of native and invasive turtles would impact the foraging strategy and growth of both species during the invasion process. We found that M. reevesii exhibited bolder and more exploratory personalities than T. scripta elegans. The foraging strategy of M. reevesii was mainly affected by the personality of T. scripta elegans, while the foraging strategy of T. scripta elegans was influenced by both their own personality and personalities of M. reevesii. Additionally, we did not find that the personality combination would affect the growth of either T. scripta elegans or M. reevesii. Differences in foraging strategy may be due to the dominance of invasive species and variations in the superficial exploration and thorough exploitation foraging strategies related to personalities. The lack of difference in growth may be due to the energy allocation trade-offs between personalities or be masked by the slow growth rate of turtles. Overall, our results reveal the mechanisms of personality interaction effects on the short-term foraging strategies of both native and invasive species during the invasion process. They provide empirical evidence to understand the effects of personality on invasion dynamics, which is beneficial for enhancing comprehension understanding of the personality effects on ecological interactions and invasion biology. Full article

Blue honeysuckle (Lonicera caerulea L.) is a deciduous shrub with perennial rootstock found in China. The objectives of this study were to explore the drought tolerance of blue honeysuckle, determine the effect of drought stress on two photosystems, and examine the mechanism of acquired drought tolerance. In this study, blue honeysuckle under four levels of simulated field capacity (100%, 85%, 75%, and 65% RH) was grown in split-root pots for drought stress treatment, for measuring the changes in chlorophyll content, photosynthetic characteristics, and leaf chlorophyll fluorescence parameters. The chlorophyll content of each increased under mild stress and decreased under moderate and severe stress. The net photosynthetic rate, transpiration rate, intercellular carbon dioxide concentration, and stomatal conductance of blue honeysuckle decreased with the increase in water stress. However, the water utilization rate and stomatal limit system increased under mild and moderate stress and decreased under severe stress. The maximum fluorescence (Fm), maximum photochemical efficiency, and quantum efficiency of photosystem II decreased with the decrease in soil water content, and the initial fluorescence increased significantly (p < 0.01). With the decrease in soil water content, the energy allocation ratio parameters decreased under severe drought stress. The main activity of the unit reaction center parameters first increased and then decreased. ABS/CSm, TRo/CSm, ETo/CSm, and REo/CSm gradually declined. After a comprehensive analysis, the highest scores were obtained under adequate irrigation (CK). Overall, we concluded that the water irrigation system of blue honeysuckle should be considered adequate. Full article

Incorporating renewable energy sources into the grid poses challenges due to their volatility and uncertainty in optimizing dispatch strategies. In response, this article proposes a cloud–edge collaborative scheduling strategy for distributed multi-energy systems, operating across various time scales. The strategy integrates day-ahead dispatch, intra-day optimization, and real-time adjustments to minimize operational costs, reduce the wastage of renewable energy, and enhance overall system reliability. Furthermore, the cloud–edge collaborative framework helps mitigate scalability challenges. Crucially, the strategy considers the multi-timescale characteristics of two types of energy storage systems (ESSs) and three types of demand response (DR), aimed at optimizing resource allocation efficiently. Comparative simulation results evaluate the strategy, providing insights into the significant impacts of different ESS and DR types on system performance. By offering a comprehensive approach, this strategy aims to address operational complexities. It aims to contribute to the seamless integration of renewable energy into distributed systems, potentially enhancing sustainability and resilience in energy management. Full article

In this study, we construct heterogeneous agent general equilibrium models to investigate the relative importance of labor endowment in driving structural transformation. We aim to explore the following question: beyond the demand-side and supply-side structural transformation driving forces extensively studied in the existing literature, does labor, as a crucial endowment, play a pivotal role in facilitating structural transformation and the energy economy? In contrast to the prevalent partial equilibrium analyses, our study employs a general equilibrium framework to conduct a policy evaluation of college expansion, a significant policy that has altered the labor endowment structure in China. Our approach begins with developing a multi-sector model that integrates a nested CES production function and incorporates workers with different skill levels to assess the macroeconomic impact of college expansion on structural transformation. We calibrate the base model to reflect labor allocations across sectors and skill levels using the simulated method of moments (SMM), ensuring that the model-generated data align closely with actual labor allocation data. Utilizing this calibrated model, we perform counterfactual experiments to assess the impact and relative importance of the college expansion policy. Our counterfactual analysis demonstrates that the policy has resulted in an average decrease of 7.7% in labor allocation in the agricultural sector, alongside an average increase of 8.9% in the industry sector and 28.7% in the services sector. These results highlight the significant, yet often overlooked, contribution of labor in endowment-driven structural transformation. Furthermore, we extend the base model by constructing an industry-level heterogeneous agent general equilibrium model, enabling us to pinpoint which industries have developed as a result of the college expansion policy and recalibrate it at the industry level. This approach allows us to analyze the impact of changes in labor endowment on the energy economy. Counterfactual experiments conducted show that the college expansion policy has prompted a labor shift from industries with low energy efficiency and high pollution to high-end services. This macroeconomic pattern of structural transformation suggests that the college expansion policy has facilitated a transition toward a low-carbon economy by reducing dependency on high energy-consuming industries and promoting high-end services. Full article

The aim of this study was to examine the impact of daily calcium propionate (CaPr) supplementation (0 or 20 g/calf) on growth performance, dietary energetics, body fat reserves, serum metabolites, and hematological responses in high-risk beef calves fed diets with varying (50, 60, or 70%) concentrate (CON) levels. In addition, a cost/income analysis of CaPr supplementation was carried out. Forty-eight crossbred bull calves (152.8 ± 1.56 kg body weight and 5.5 months of age) were involved in a fully randomized experimental design employing a 2 × 3 factorial arrangement of treatments. Calves were allocated (n = 8 per treatment) to individual pens (3.14 × 5.25 m) and were subjected to one of the following treatments during 42 d: No CaPr supplementation in diets containing 50, 60, or 70% CON (NoCaPr + 50, NoCaPr + 60, NoCaPr + 70, respectively) or daily CaPr supplementation dosed at 20 g/calf in diets containing 50, 60, or 70% CON (20CaPr + 50, 20CaPr + 60, 20CaPr + 70, respectively). Non-supplemented calves exhibited decreased dry matter intake (DMI) with increasing CON levels in their diets, while CaPr-supplemented calves displayed the opposite effect (interaction, p = 0.04). In calves fed a lower-CON diet (50%), those supplemented with CaPr showed greater average daily gain (ADG, 20.2%, p = 0.05) and lower DMI (2.2%, p = 0.03), resulting in improved ADG/DMI ratio, dietary energy, and energy retention (24.6, 14.4, and 18%, p < 0.05). These effects diminished when calves received diets with 60 or 70% CON but led to a 14.2% increase in rump fat thickness (p = 0.04). Only in non-supplemented CaPr calves, increasing the level of CON from 50 to 70% in the diet increased ADG (21.2%), decreased DMI (2.2%), and improved the ADG/DMI ratio (22.7%), with no impact on dietary net energy utilization. Non-supplemented calves exhibited an increase in lymphocytes as CON levels rose in their diets, whereas CaPr-supplemented calves showed the opposite effect (interaction, p = 0.05). Supplementation of CaPr decreased total protein (TP, p = 0.03) and albumin (ALB, p < 0.01) serum concentrations, with lower concentrations observed in 20CaPr + 50. CaPr supplementation reduced (p = 0.01) total cholesterol (TCHO) levels. An interaction between CaPr and CON level (p = 0.02) was observed since TCHO levels remained consistently low at higher CON levels. Glucose was decreased with increasing levels of CON (p = 0.02) but not (p = 0.85) for CaPr-supplemented calves. NoCaPr + 50 and NoCaPr + 70 increased (p = 0.05) ALB concentration. Gamma glutamyltransferase levels increased (p = 0.05) with increasing CON levels irrespective of CaPr supplementation. Comparing the profit within the same CON level in the diet, CaPr treatments yielded higher income, with the largest difference in profit observed when CaPr was supplemented at 50% CON level (USD 29 more/calf). In conclusion, CaPr supplementation proves to be an effective strategy for enhancing growth performance and dietary energy among high-risk beef calves, resulting in greater economic returns. The groups that received CaPr demonstrated superior profitability, particularly in calves fed diets with lower CON levels. Under the conditions in which this experiment was carried out, the optimal response occurred when the low-CON diet (50%) was supplemented with CaPr. Full article

Battery packs found in electric vehicles (EVs) require thermal management systems to maintain safe operating temperatures in order to improve device performance and alleviate irregular temperatures that can cause irreversible damage to the cells. Cylindrical lithium-ion batteries are widely used in the electric vehicle industry due to their high energy density and extended life cycle. This report investigates the thermal performance of three liquid cooling designs for a six-cell battery pack using computational fluid dynamics (CFD). The first two designs, vertical flow design (VFD) and horizontal flow design (HFD), are influenced by existing linear and wavy channel structures. They went through multiple geometry optimisations, where parameters such as inlet velocity, the number of channels, and channel diameter were tested before being combined into the third and final optimal design (OD). All designs successfully maintained the maximum temperature of the cells below 306.5 K at an inlet velocity of 0.5 ms⁻¹, meeting the predefined performance thresholds derived from the literature. The HFD design was the only one that failed to meet the temperature uniformity goal of 5 K. The optimal design achieved a maximum temperature of 301.311 K, which was 2.223 K lower than the VFD, and 4.707 K lower than the HFD. Furthermore, it produced a cell temperature difference of 1.144 K, outperforming the next-best design by 1.647 K, thus demonstrating superior temperature regulation. The OD design can manage temperatures by using lower inlet velocities and reducing power consumption. However, the increased cooling efficiency comes at the cost of an increase in weight for the system. This prompts the decision on whether to accommodate the added weight for improved safety or to allocate it to the addition of more batteries to enhance the vehicle’s power output. Full article

To further improve the braking energy recovery efficiency of battery electric vehicles and increase the range of the cars, this paper proposes a multi-mode switching braking energy recovery control strategy based on fuzzy control. The control strategy is divided into three modes: single-pedal energy recovery, coasting energy recovery, and conventional braking energy recovery. It takes the accelerator pedal and brake pedal opening as the switching conditions. It calculates the front and rear wheel braking ratio allocation coefficients and the motor braking ratio through fuzzy control to recover braking energy. The genetic algorithm (GA) is used to update the optimized affiliation function to optimize the motor braking allocation ratio through fuzzy control, and joint simulation is carried out based on the NEDC (New European Driving Cycle) and CLTC-P (China Light-duty Vehicle Test Cycle for Passenger vehicles) cycle conditions. The results show that the multi-mode braking energy recovery control strategy proposed in this paper improves the energy recovery rate and range contribution rate by 4% and 9.6%, respectively, and increases the range by 22.5 km under NEDC cycle conditions. It also improves the energy recovery rate and range contribution rate by 8.7% and 5.5%, respectively, and increases the range by 13 km under CLTC-P cycle conditions, which can effectively improve the energy recovery efficiency of the vehicle and increase the range of battery electric vehicles. Full article

Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). However, maximizing the long-term energy efficiency (EE) of a user-cooperative WPT-MEC system presents significant challenges due to uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. In this paper, we propose an online control algorithm to maximize the long-term EE of a WPT-MEC system by making decisions on time allocations and transmission powers of mobile devices (MDs) for a three-node network. We formulate a stochastic programming problem considering the stability of network queues and time-coupled battery levels. By leveraging Dinkelbach’s method, we transform the fractional optimal problem into a more manageable form and then use the Lyapunov optimization technique to decouple the problem into a deterministic optimization problem for each time slot. For the sub-problem in each time slot, we use the variable substitution technique and convex optimization theory to convert the non-convex problem into a convex problem, which can be solved efficiently. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline algorithms, achieving a 20% improvement in energy efficiency. Moreover, our algorithm achieves an $\left[O\right(1/V),O(V\left)\right]$ trade-off between EE and network queue stability. Full article

Environmental concerns and the Paris agreements have prompted intensive efforts towards greener and more sustainable transportation. Persistent expansion of electric vehicles (EV) in the transportation sector requires electric vehicle charging stations (EVCSs) to accommodate the increased demand. Offsetting demand and alleviating the resultant electrical grid stress necessitates establishing grid-integrated renewable energy sources (RESs) where these sustainable strategies are accompanied by variable-weather-related obstacles, such as voltage fluctuations, grid instability, and increased energy losses. Strategic positioning of EVCSs and RES as distributed generation (DG) units is crucial for addressing technical issues. While technical constraints have received considerable attention, there is still a gap in the literature with respect to incorporating the additional complex optimization problems and decision-making processes associated with economic viability, social acceptance, and environmental impact. A possible solution is the incorporation of an appropriate multi-criteria decision analysis (MCDA) approach for feasible trade-off solutions. Such methods offer promising possibilities that can ease decision-making and facilitate sustainable solutions. In this context, this paper presents a review of published approaches for optimizing the allocation of renewable energy DG units and EVCSs in active distribution networks (ADNs). Promising published optimization approaches for the strategic allocation of multiple DG units and EVCSs in ADNs have been analyzed and compared. Full article