Search Results (1,846)

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

To address the coupled challenges of renewable power volatility, high operating cost, and electrolyzer degradation in grid-connected wind–PV hydrogen production systems, this paper proposes a hierarchical day-ahead scheduling strategy under time-of-use (TOU) electricity prices. The upper layer performs price-responsive economic dispatch to coordinate renewable utilization, battery operation, grid transactions, and aggregate hydrogen-production power with the objective of minimizing lifecycle operating cost. The lower layer introduces a health-aware non-uniform rotation mechanism to allocate the aggregate power command among electrolyzer units, thereby reducing fluctuation exposure and balancing lifetime consumption across the array. Practical constraints, including multi-state electrolyzer operation, unit-commitment logic, battery state-of-charge dynamics, hydrogen storage limits, and system power balance, are explicitly considered. A case study of a wind–PV hydrogen production project in Northern China shows that the proposed strategy shifts electricity purchases to valley-price periods and promotes electricity export during peak-price periods. Compared with the benchmark strategy, hydrogen production during low wind–PV generation periods increases from 342,000 to 381,000 Nm³, the share of fluctuating operating time decreases from 62.5% to 12.5%, and the average daily start–stop frequency declines from 8.0 to 4.8. Consequently, the degradation penalty is reduced by about 40%, and lifecycle operating cost decreases by 27.3%. Full article

Drone logistics is emerging as a key trend in future delivery systems due to its efficiency. However, current benefit assessments are often one-dimensional, focusing on single-node modes and overlooking load variations and charging processes in continuous multi-node delivery. To address this gap, this paper develops an integrated assessment framework across three dimensions: environment, economy, and time. Based on lifecycle emissions and total cost of ownership, a structured time-performance indicator, time value, is introduced. By incorporating an energy consumption model that accounts for dynamic loads and a charging model that considers charging behavior, an improved genetic algorithm is designed to optimize large-scale urban drone dispatch. Furthermore, a comparative sensitivity analysis with electric trucks quantifies the effects of market demand, charging strategy and technological progress. Results show that, under the modeled scenarios and parameter assumptions, electric trucks remain preferable in the short term, while drones demonstrate stronger long-term potential. Enterprises should align drone and truck deployment with demand and manage charging dynamically, while governments should combine initial subsidies with long-term guidance and systemic support to enable large-scale drone logistics adoption. Full article

Fluorapatite is a typical phosphate rock resource. Fluorapatite tends to generate fine mud agglomeration, which induces dehydration challenges owing to its inherently fine particle size and negative surface charge. In this paper, phosphate tailings slurries from a phosphate mine in Hubei Province, China, were selected as the research object, and flocculation–dehydration experiments were conducted using anionic, cationic, and nonionic polyacrylamide (PAM) flocculants. The results show that the maximum settling velocity is 51 mm/s and the moisture content of filter cake is 41.54%, which were obtained when the unit consumption of cationic flocculant with molecular weight 12 million was 1000 g/t. The mechanism of sedimentation and dehydration was studied by infrared spectroscopy and a particle size analyzer. The results showed that polyacrylamide was effectively adsorbed on the mineral surface, and the size of flocs increased significantly. Finally, the mechanism of sedimentation and dehydration was proposed. It has important guiding significance for the efficient solid–liquid separation and water circulation of fluorapatite mineral processing wastewater. Full article

Against the background of the acceleration of the integration of the “double carbon” target and transportation energy, the green transformation and business model innovation of highway service areas, as a high-energy-consumption traffic node, are becoming more and more urgent. However, the existing research focuses on a single technology path, and lacks a systematic quantitative evaluation of the “PV–charging–new format” coordination mechanism and its operating efficiency. Therefore, this paper proposes a collaborative framework that integrates photovoltaic power generation, new energy charging piles, and diversified new formats, and introduces a random forest regression algorithm. Based on the actual operation data of the Guangxi expressway service area, the synergistic effect and regional heterogeneity of multiple factors are systematically evaluated. The results show that a photovoltaic system can reduce the unit electricity price by 25–35%, and the investment recovery period is about 7 years. When the penetration rate of charging piles increases to 35%, the annual income can reach CNY 3.285 million, and the return on investment increases to 2.3 times when the utilization rate exceeds 80%. The new business combination can increase the average daily income by 13.3–26.7%. At the same time, the coordinated implementation of the three elements can achieve an annual net income increase of 27–32%, which is better than the linear superposition of the benefits of a single measure. In addition, the analysis of regional heterogeneity shows that the photovoltaic benefit in the western mountainous area is outstanding, the charging benefit in the coastal area is significant, and the comprehensive benefit in the central hub area is the best. This study provides a quantitative basis to support decisions on the differentiated development path of expressway service areas in the background of traffic–energy integration. Full article

To enhance the sustainability of temporary office buildings, energy-saving and emissions-reduction technologies, as well as the optimization of photovoltaic (PV) energy storage systems in such structures, are of great importance. In this study, a distributed energy storage system was developed for a temporary office building in Jincheng, China. Measurements showed climatic factors had the greatest effect on building energy consumption due to the building envelope’s low thermal performance and airtightness. The air conditioning system accounted for the highest proportion (87%) of building energy consumption. The PV system’s peak output occurred in the morning due to illumination conditions and module orientation. On this basis, a time-of-use (TOU)- and state-of-charge (SOC)-aware scheduling strategy was developed for the PV-ESS of the temporary office building to improve renewable-energy utilization and reduce user-end electricity cost. Unlike purely theoretical optimization studies, this work focuses on the practical application and validation of the scheduling framework in a real temporary office building using monitored data. The electricity cost decreased by 0.3 RMB/kWh, and the revenue from electricity sales during the scheduling period increased by 0.03 RMB/kWh after model optimization. The optimized scheduling strategy resulted in significantly fewer charge–discharge cycles of the storage battery, substantially decreasing the battery’s storage capacity and the system’s investment costs. Full article

The growing flexibility of load dispatching in modern smart grids has exposed critical limitations in conventional capacity pricing mechanisms, which calculate charges based solely on monthly maximum demand without distinguishing when peak demand occurs. This approach fails to reflect the temporal value of capacity and provides insufficient incentives for demand-side optimization. To address these challenges, this paper proposes a time-of-use (TOU) capacity pricing method that integrates user load characteristics to enable more equitable cost allocation and optimized electricity consumption patterns. The methodology employs K-means clustering analysis of user load profiles to partition pricing periods, accurately capturing differential capacity value across temporal intervals. We validate the clustering approach through the elbow method and silhouette analysis, confirming k = 3 as optimal and demonstrating K-means superiority over hierarchical and density-based alternatives. This data-driven approach ensures that period delineation reflects actual consumption patterns of commercial and industrial users. A capacity cost allocation model is established using the Shapley value method, incorporating maximum demand in each designated period while maintaining revenue neutrality for the grid operator. The 80% load simultaneity factor is empirically validated using 12 months of Shanghai industrial data (May 2023–April 2024). A Stackelberg game-based pricing model for TOU capacity tariffs is developed, incentivizing users to deploy energy storage systems and optimize charging strategies. We prove game convergence theoretically and demonstrate equilibrium achievement within 3–5 iterations across diverse initialization scenarios. Energy storage capacity is optimized by sector (3.5–6.5% of peak demand) rather than uniformly, and realistic battery self-discharge rates (0.006%/hour) are incorporated. Case study analysis using real operational data from 11 commercial and industrial sub-sectors in Shanghai demonstrates effectiveness. Extended to 12 months with seasonal analysis, results show the proposed strategy reduces the peak-to-valley difference ratio by 2.4% [95% CI: 1.9%, 2.9%], p < 0.001; increases the system load factor by 1.3% [95% CI: 0.9%, 1.7%], p < 0.001; and achieves reductions in users’ total capacity costs of 3.6% [95% CI: −4.2%, −3.0%], p < 0.001. Comparative analysis shows the proposed method significantly outperforms simple TOU (improvement +1.2 pp) and peak-responsibility pricing (improvement +0.6 pp). Monte Carlo robustness analysis (1000 scenarios) confirms performance stability under demand uncertainty. This research provides theoretical foundations and practical methodologies for capacity cost allocation, offering valuable insights for policymakers and utilities seeking to enhance demand-side response mechanisms and improve power resource allocation efficiency. Full article

New energy vehicle promotion (NEVP) is of great significance for the green and low carbon development of urban transportation. Based on the panel data of new energy vehicle sales, carbon emissions, and air quality in Chengdu, China, from 2014 to 2024, this paper employs multiple linear regression, distributed lag and multiple mediation pathway models to empirically examine the environmental benefits of NEVP. A heterogeneity analysis is also conducted by integrating the distribution of charging stations across urban circles. The results show that: (1) In the multiple mediation pathway model, the total effect of NEVP includes direct effect and indirect effect. Based on the total effect, the total carbon emission from the effect of NEVP is reduced by about 3.95% of the total carbon emissions, and 40% of carbon emission within the transportation sector in Chengdu. NEVP in Chengdu has a significant direct emission reduction effect, accounting for about 39.80% of the total effect, with the annual average carbon emissions being reduced by about 432,800 tons, accounting for about 1.57% of the total carbon emissions in Chengdu. In terms of indirect effects, NEVP significantly reduces carbon emissions through three pathways: industrial structure upgrading (1.02%), green consumption transformation (1.12%), and technological innovation (0.25%). However, the benefits of NEVP on improving urban air quality are limited. (2) The lag effect analysis shows that the environmental benefits of NEVP exhibit distinct characteristics of time lag and long-term persistence. (3) The environmental benefits show significant sub-circle heterogeneity. As carbon emissions decrease, the air quality of the central urban zone (the first circle) and the suburbs (the second circle) improves significantly, while the impact on the outer suburbs (the third circle) is not significant. There is an imbalance in the layout of charging piles in Chengdu. This research offers empirical evidence and policy insights for the green and low carbon development of urban transportation. Full article

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. To create artificial Sun-synchronous orbits, the force is applied to keep the longitude of the ascending node with the same angular velocity of the apparent motion of the Sun around the Earth, which is the definition of a Sun-synchronous orbit. These orbits are very important for remote sensing satellites, because in these orbits the satellite passes by a given point at the same time, helping in analyzing the data collected. The use of electrodynamic tethers can extend the regions of Sun-synchronous orbits, both in terms of inclination and semi-major axis. To perform the de-orbiting of the satellite, the same tether can apply a force in the opposite direction of the motion of the satellite, so reducing its energy and decreasing the semi-major axis until the satellite crashes into the atmosphere of the Earth. This is very important to avoid increasing the presence of space debris in space, a very serious problem nowadays. For the station-keeping maneuvers, we just need to use the appropriate control laws, from time to time, to correct any errors in the Keplerian elements. A significant advantage of employing an electrodynamic tether over traditional thrusters is that it does not require consumption of fuel. The study assumes that a current can flow in both directions through the tether, so interacting with the magnetic field of the Earth to create the Lorentz force. The possibility of using electrodynamic tethers with autonomous charge generation, to avoid dependence on plasma densities and other external factors, is considered. The results presented here help in space and planetary science, since they give more options for remote sensing satellites, which are a key element in planetary science. Full article

The rapid diffusion of electric vehicles (EVs) is expected to reshape electricity demand patterns, particularly in urban areas where charging infrastructure and mobility transitions are expanding rapidly. While the existing literature has mainly focused on the optimal location of charging infrastructure and on the direct technical implications of EV charging for electricity systems, relatively limited attention has been devoted to the broader relationship between the spatial distribution of public charging infrastructure and residential electricity demand. This study investigates the relationship between public charging infrastructure density and residential electricity consumption across Italian municipalities. Using a dataset covering 40 provincial capitals and applying spatial econometric techniques, the analysis explores both local associations and potential spatial spillover patterns across neighboring municipalities. In particular, Ordinary Least Squares (OLS), Spatial Autoregressive (SAR), and Spatial Durbin Models (SDM) are estimated in order to account for spatial interdependencies in the data. The results reveal a positive and statistically significant association between the density of public charging infrastructure and residential electricity consumption at the municipal level. The preferred Spatial Durbin specification also indicates the presence of spatial spillover patterns, suggesting that charging infrastructure density in neighboring municipalities is positively associated with residential electricity consumption locally. These patterns may reflect regional diffusion dynamics related to electric vehicle adoption, infrastructure visibility, and geographically interconnected urban development processes. Given the cross-sectional nature of the dataset, the results should be interpreted as associative rather than causal relationships. Nevertheless, the findings provide useful insights into how the spatial expansion of charging infrastructure is linked to evolving electricity demand patterns in urban contexts. Overall, the results highlight the importance of considering spatial interdependencies when planning charging infrastructure deployment and electricity network adaptation in the context of the transition toward sustainable electric mobility. Full article

The high energy consumption of lead anodes in zinc production is caused by the slow oxygen evolution reaction (OER). We made a La-Mn co-doped β-PbO₂ anode using electrodeposition to solve this issue. The XRD and XPS results show that adding La shrinks the lattice and changes the electron structure. This helps Mn⁴⁺ change into active Mn³⁺ and creates more active oxygen on the surface, making the reaction easier. EIS tests show that the charge transfer resistance (R_ct) decreased by 4.2 times, decreasing from 147.6 Ω to 34.72 Ω at 1.0 V. The Bode phase peak also moved to a lower frequency (from 122 Hz to 0.215 Hz), proving that the electrochemically active surface area (ECSA) increased significantly. At the industrial current of 50 mA cm⁻², the anode shows a low overpotential of 840 mV and a Tafel slope of 265 mV dec⁻¹. This improved performance saves 187.10 kWh of energy per ton of zinc. Therefore, the LaMn-β-PbO₂ anode is a promising and energy-saving option for industrial zinc production. Full article

Amidst the high global reliance on petroleum, this study addresses energy inefficiency in beam-pumping units used for oil extraction. We developed a tubular solid–liquid triboelectric nanogenerator (TENG) based on fluorinated polydimethylsiloxane (PDMS) films. Self-assembled surface modification with fluorosilane molecular chains enhanced charge transfer, achieving a 2.7-fold increase with 13F-PDMS. The enclosed tubular design utilizes periodic liquid-electrode contact to generate a volumetric effect. Experiments investigated the influence of liquid composition and device configuration on performance. Using a 1.69 mol/L FeCl₃ solution with four series-connected units, the TENG reached 29 V and 263 nA, powering 150 LEDs. This demonstrates its potential for harvesting reciprocating mechanical energy from pumping units to reduce operational energy consumption. Full article

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. Full article

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

Traditional cloud pricing models are allocation-centric, where users are charged based on reserved resources rather than workload energy consumption. However, modern AI workloads exhibit substantial and heterogeneous power behavior, limiting the effectiveness of such allocation-centric pricing. This paper presents a comprehensive experimental study of nine widely used workloads across 50 controlled configurations, including standalone and concurrent executions under varying resource partitions. Our results show that total system power is largely unaffected by how resources are divided among co-located workloads, except in cases of explicit resource under-provisioning or severe resource contention. Across 45 workload–core groups, 41 exhibit a coefficient of variation below 3% across different co-located workloads, demonstrating structural stability of workload-level power profiles under heterogeneous execution environments. In contrast, deployment choice (e.g., CPU versus GPU execution) can shift the same model into distinct power regimes. Based on measured power decomposition and scaling behavior, we derive an empirical categorization framework distinguishing GPU-dominant and CPU-dominant workloads, further characterized by utilization and memory dimensions. From an energy perspective, CPU utilization (for CPU-dominant workloads) and SM utilization (for GPU-dominant workloads) emerge as the primary determinants of power magnitude, while memory-related parameters contribute marginally to overall power. These findings provide empirical evidence that allocation-based pricing is a weak proxy for actual energy cost and motivate energy-aligned cloud management strategies grounded in workload power profiles. As our findings are derived from a controlled single-node experiment, evaluations under more realistic data center environments will be required for further generalization. Full article