Operational Demands Research Articles

Operational effects on aquatic carbon dioxide and methane emissions from the Belo Monte hydropower plant in the Xingu River, eastern Amazonia

Operational demands and the natural inflow of water actively drive biweekly fluctuations in water levels in hydropower reservoirs. These daily to weekly fluctuations could have major effects on methane (CH4) and carbon dioxide (CO2) emissions via release of bubbles from reservoir bottom sediments (ebullition) or organic matter inputs, respectively. The impact of transient fluctuations in water levels on GHG emissions is poorly understood and particularly so in tropical run-of-the-river reservoirs. These reservoirs, characterized by high temperatures and availability of labile organic matter, are usually associated with extensive CH4 generation within bottom sediments. The aim of this study is to determine how water level fluctuations resulting from the operation of the Belo Monte hydropower plant on the Xingu River, eastern Amazon River Basin, affect local CO2 and CH4 emissions. Between February and December 2022, we monitored weekly fluxes and water concentrations of CO2 and CH4 in a site on the margin of the Xingu reservoir. Throughout the study period, fluxes of CO2 and CH4 were 118 ± 137 and 3.62 ± 8.47 mmol m−2 d−1 (average ± 1SD) while concentrations were 59 ± 29.77 and 0.30 ± 0.12 μM, respectively. The fluxes and water concentrations of CO2 were clearly correlated with the upstream discharge, and the variation observed was more closely associated with a seasonal pattern than with biweekly fluctuations in water level. However, CH4 fluxes were significantly correlated with biweekly water level fluctuations. The variations observed in CH4 fluxes occurred especially during the high-water season (February–April), when biweekly water level fluctuations were frequent and had higher amplitude, which increased CH4 ebullition. Reducing water level fluctuations during the high-water season could decrease ebullitive pulses and, consequently, total flux of CH4 (TFCH4) in the reservoir margins. This study underscores the critical role of water level fluctuations in near-shore CH4 emissions within tropical reservoirs and highlights significant temporal variability. However, additional research is necessary to understand how these findings can be applied across different spatial scales.

Fuzzy-based thermal management control analysis of vehicle air conditioning system

Automotive air conditioning (ACC) system designers faced unique challenges in satisfying customer demands for efficient and comfortable operation across a broad temperature range. The AAC system's effective or fixed temperature setting makes this possible. Many climate control models, like the state flow switching controller in MATLAB control, must handle heating and cooling due to a single Simulink/environment software and frequently contain steady-state error (SSE). The present research proposes to design an automatic temperature control scheme and fuzzy logic control (FLC) for an automotive air-conditioned (AAC) system. In addition, it employs MATLAB Simulink/environment software to model the fuzzy control technique to analyze the AAC system's response. A simulation has been set up to evaluate the suggested system performance to match a selection of user-specified reference temperatures and compressor speeds. Compared with a PID-controlled AAC system, the proposed FLC-based system reduced the undershoot to only 2.30% from 33.30% respectively, and was robust, quicker, and better at controlling temperature.

Integrated Planning and Operation Dispatching of Source–Grid–Load–Storage in a New Power System: A Coupled Socio–Cyber–Physical Perspective

The coupling between modern electric power physical and cyber systems is deepening. An increasing number of users are gradually participating in power operation and control, engaging in bidirectional interactions with the grid. The evolving new power system is transforming into a highly intelligent socio–cyber–physical system, featuring increasingly intricate and expansive architectures. Demands for stable system operation are becoming more specific and rigorous. The new power system confronts significant challenges in areas like planning, dispatching, and operational maintenance. Hence, this paper aims to comprehensively explore potential synergies among various power system components from multiple viewpoints. It analyzes numerous core elements and key technologies to fully unlock the efficiency of this coupling. Our objective is to establish a solid theoretical foundation and practical strategies for the precise implementation of integrated planning and operation dispatching of source–grid–load–storage systems. Based on this, the paper first delves into the theoretical concepts of source, grid, load, and storage, comprehensively exploring new developments and emerging changes in each domain within the new power system context. Secondly, it summarizes pivotal technologies such as data acquisition, collaborative planning, and security measures, while presenting reasonable prospects for their future advancement. Finally, the paper extensively discusses the immense value and potential applications of the integrated planning and operation dispatching concept in source–grid–load–storage systems. This includes its assistance in regards to large-scale engineering projects such as extreme disaster management, facilitating green energy development in desertification regions, and promoting the construction of zero-carbon parks.

MCRNet: Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images, which can impede the ever-growing demand for underwater robot operations. To address these issues, we propose a Multi-Color space Residual Network (MCRNet) for underwater image enhancement. Our method takes advantage of the unique features of color representation in the RGB, HSV, and Lab color spaces. By utilizing the distinct feature representations of images in different color spaces, we can highlight and fuse the most informative features of the three color spaces. Our approach employs a self-attention mechanism in the multi-color space feature fusion module. Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images, particularly in severely degraded scenes. Consequently, our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Advancing maritime technology: Evaluating situation awareness support systems for engine room monitoring at the future shore control centre

ABSTRACT Remote operation offers promising advantages as a viable transitional phase toward autonomous ships, but at the same time, it introduces new challenges in maintaining situation awareness. However, situation awareness is misunderstood as an essential construct for only navigational tasks, albeit situation awareness issues are recurrently reported during system monitoring, e.g. power plants. Hence, this within-subjects study developed the two situation awareness support systems for engine room monitoring based on information requirements and schema instantiation. Sixteen marine engineers participated in the four simulation experiments, and their objective and subjective situation awareness, fault detection rate, response speed and change blindness were evaluated. The findings describe that presenting a few critical perception elements for comprehension is promising to mitigate interface complexity and enable efficient pattern matching. However, caution is necessary for selecting the means of access to elements of low importance, as it can induce attentional tunnelling and additional cognitive load, which can devastate the situation awareness and fault-detecting performance when compounded by high cognitive load from primary monitoring tasks. This study recommends entailing the current level of situation awareness, cognitive load and stress into the support system design to determine the extent and timing of assistance for varying operational demands.

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

The rapid advancement of unmanned aerial vehicle (UAV) technologies has led to an increasing demand for UAV operations in low-altitude, high-density, and complex airspace such as mountains or urban areas. In order to handle complex scenarios and ensure flight safety for UAVs with different flight missions beyond visual line of sight in such environments, a fusion framework of onboard autonomous flight trajectory management and decision-making system using global strategical path planning and local tactical trajectory optimization combination is proposed in this paper. The global strategical path planning is implemented by an enhanced A* algorithm under the multi-constraint of UAV positioning uncertainty and obstacle density to improve the safety and cost-effectiveness. The local tactical trajectory optimization is realized using quadratic programming to ensure smoothness, kinematic feasibility, and obstacle avoidance of the planned trajectory in dynamic environments. Receding-horizon control is used to ensure the flight path and trajectory planning efficiently and seamlessly. To assess the performance of the system, a terrain database and a navigation system are employed for environment and navigation performance simulation. The experimental results confirm that the fusion approach can realize better safety and cost-effectiveness through path planning with kino-dynamic feasible trajectory optimization.

Design and Simulation of Multi Unmanned Boat Cooperative Obstacle Avoidance System Based on 5G Edge Computing

Background:: Compared to single unmanned boat, multi unmanned boats have more flexible mobility and efficient task completion capabilities, which can effectively expand the types of tasks. However, the traditional independent path planning and obstacle avoidance methods of unmanned boats are difficult to meet the requirements of collaborative operation among multiple unmanned boats due to the lack of information exchange. Objective:: According to the actual demand of multi unmanned boats' cooperative operation, a method of multi unmanned boats cooperative obstacle avoidance based on 5G edge computing is proposed to realize the unified planning and scheduling of multi unmanned boats. Methods:: Firstly, 5G technology and Kubeedge edge computing tools are used to build a multi unmanned boat collaborative obstacle avoidance system based on cloud, edge and end collaboration, and the Kubeedge edge computing platform was optimized by optimizing communication strategies, building a highly available Kubeedge cluster, building a Harbor image center, and using Web management interfaces further to improve the reliability and stability of the system. Secondly, the YOLOR-Deepport multi-target recognition and tracking algorithm based on cloud, edge and end collaborative network is used to complete the recognition and tracking tasks of obstacle targets, and a set of EECBS path planning methods based on the Kubedge centralized control platform is designed to plan collision-free and efficient paths for each unmanned boat in realtime. Finally, the effectiveness of the system was verified through simulation experiments. Results:: The experimental results show that compared to the traditional autonomous planning obstacle avoidance method for unmanned boats, the collaborative planning obstacle avoidance method proposed in this paper can exhibit excellent performance in dense and narrow scenarios, with a more reasonable navigation path, a range reduction of 20% - 50%, and higher safety. Conclusion:: The results show that the cooperative obstacle avoidance system based on 5G edge computing designed in the paper is feasible, and it can effectively realize the cooperative path planning and obstacle avoidance of multi unmanned boats.

A Field Test Study on the Instability of a Pump-Turbine under Turbine Mode

Abstract In the context of increasing demand for flexible operation of hydropower plants, this study analyses the factors leading to instability in a prototype pump turbine under turbine mode, based on experimental data collected on-site. Pressure sensors and accelerometers were installed to compare pressure signals and vibration acceleration signals. The test results show that the characteristics of pressure fluctuation are dominated by three specific factors under different output power. Vibration reaches its maximum value at approximately 30% of the rated output and its minimum value at around 70% of the rated output, and the causes of twice increases in vibration is not the same. These characteristics often indicate potential unsteady flow, vortex-induced vibration, and flow separation, subsequently affecting the stability of the pump turbine. Understanding the conditions and severity of the occurrence of instability characteristics in the unit is of great significance for power plant personnel to adjust the unit’s operation strategy and improve operational efficiency.

Separation processes for the treatment of industrial flue gases – Effective methods for global industrial air pollution control

The treatment of flue gases has become a crucial area of interest with the increasing air emissions into the atmosphere from industries involved in combustion of fossil fuels in their operations. In essence, there is a critical need for effective methods of treatment more than ever. Treatment and separation are now a demand for the overall industrial operations to control the rate of flue gas emissions. The major culprit in this wise is power generating industry. The major associated air pollutants are carbon dioxide, sulfur oxides, trace metals, volatile organic compounds, particulate matters, and nitrogen oxides. However, the choice of technologies to be utilized requires more than just knowledge of the separation process, but also a good understanding of the properties of the pollutants. This review explored and evaluated the various separation processes and technologies for the treatment of industrial flue gases for the control of the associated air pollutants. It also analyzed the performance with references to cost and efficiency, the advantages and disadvantages, principles for selection, research direction, and/or potential opportunities in existing separation processes and technologies.

Multifunctional polyimide/boron nitride nanosheet/Ti3C2Tx MXene composite film with three-dimensional conductive network for integrated thermal conductive, electromagnetic interference shielding, and Joule heating performances

With the demand for further miniaturization and higher frequency operation of electronic devices, polymer films with high thermal conductivity and electromagnetic interference (EMI) shielding effectiveness (SE) are urgently required. Herein, polyimide (PI)/boron nitride nanosheets (BNNS) aerogels with oriented porous structure are fabricated by unidirectional-freezing and freeze-drying methods. Subsequently, hierarchical PI/BNNS/Ti3C2Tx composite films with consecutively electrically and thermally conductive networks are successfully prepared via a unidirectional PI/BNNS aerogels-assisted immersion and hot-pressing strategy. Owing to the three-dimensional conductive dual networks of BNNS and Ti3C2Tx, the composite film exhibits excellent thermal conductivity with the maximum in-plane value of 4.73 W/(m·K), increased by 456 % compared to pure PI film. Moreover, the conductive network of Ti3C2Tx is conducive to the excellent EMI shielding performance, endowing the PI/BNNS/Ti3C2Tx composite film with an outstanding EMI SE value of 49.2 dB at 8.2 GHz with a low MXene content of 6 wt% and thickness of 300 μm. Furthermore, the composite film shows the superior Joule heating performance with fast thermal response and sufficient reliability. Therefore, the resulting composite film exhibits excellent thermal conductive, EMI shielding and Joule heating performance, which enables the potential application of multifunctional composites for thermal management and EMI shielding.

Dynamic fleet management: Integrating predictive and preventive maintenance with operation workload balance to minimise cost

The optimization of fleet maintenance management is of utmost importance to ensure the efficient and reliable operation of asset fleets. Traditional maintenance strategies are often reactive or rely on predetermined schedules, which can lead to inefficient resource allocation and increased operational costs. The advent of digital technologies has allowed the integration of predictive maintenance into fleet management. This paradigm shift towards a more data-driven approach enables fleet management to dynamically respond to issues identified through sensors and algorithms that detect anomalies and provide prognostic insights regarding the remaining useful life of components. However, a notable deficiency exists in the integration of predictive maintenance with calendar-based preventive maintenance and fleet operational allocation, thereby impeding value creation for businesses. This paper presents an optimisation model to address this issue by incorporating preventive and predictive maintenance while simultaneously striving to balance the workload to meet operational demand and mitigate potential penalties resulting from failure to meet these demands. The paper also examines the role of component criticality as well as the precision of the RUL (Remaining Useful Life) prognosis. Through the experiments conducted, it has been demonstrated that the allocation of the fleet is subject to change depending on the level of criticality of monitored components. These findings reveal the potential risks and penalties that could arise from an insufficient definition of failure impact severity when integrating predictive maintenance with existing preventive approaches and operational workload balance. Additionally, the experiments underscore the impact of RUL distributions precision on total operating costs, showing the confidence intervals through a sensitivity analysis.

Analysis of the Current Situation of Service Quality in Private Elderly Institutions and Reflections on Improvement

In recent years, with the exacerbation of societal aging, private eldercare institutions have assumed increasingly pivotal roles within our nation's eldercare service framework. Nevertheless, beneath the veneer of rapid development, the current state of service quality presents a less than satisfactory scenario. Within the realm of private eldercare institutions, numerous factors collectively influence the caliber of service. Foremost among these concerns is the issue of singular service offerings coupled with inconsistent quality. Many establishments lack rich and comprehensive service provisions in daily care, rehabilitative assistance, and psychological support, resulting in significant disparities in service standards. Moreover, inadequacies in staffing and professional capabilities serve as critical impediments to enhancing service quality. Numerous private eldercare institutions suffer from shortages in both the number and expertise of caregiving personnel, not only hampering the effective execution of daily services but also diminishing the satisfaction levels of service recipients to a certain extent. Furthermore, the issues of management systems and operational efficiency demand equal attention. Many institutions lack innovative and efficient mechanisms in management systems and operational models, leading to irrational resource allocations, inefficient service processes, thereby compromising overall service quality. Against the backdrop of these aforementioned challenges, the comprehensive enhancement of service quality in private eldercare institutions emerges as an urgent issue in need of resolution.

Tacho-less spur gear condition monitoring at variable speed operation using the adaptive application of variational mode extraction

Effective condition monitoring of machine components contributes to a safer working environment for operators and assists in averting critical machinery shutdowns. In real-world industrial scenarios, fault detection must remain simplified, user-friendly and robust despite speed variations. The operational demands of machinery frequently result in speed fluctuations, leading to spectral smearing, thereby compromising the efficiency of the analysis methodology. Furthermore, the intricacies of parameter initialisation often increase the complexity of the analysis methods. This study introduces a fault diagnosis algorithm that requires minimal initialisation and operates independently of tacho pulses. The algorithm proposed in the study incorporates variational mode extraction with the maxima tracking algorithm for instantaneous frequency estimation. Hankel matrix-based selective spectral fusion is proposed to mitigate the impact of frequency tracking errors caused by transient noise. The results of spectral side-band-based fault severity analysis, conducted on an in-house spur gearbox test-bed with seeded tooth chips, underscore the superior performance of the proposed algorithm when compared to contemporary non-stationary analysis methods.

Safeguarding maritime transport: disclosing the paradox of safety in domestic ferry operations

ABSTRACT The domestic ferry sector plays a significant role in transportation but faces complex challenges requiring a comprehensive examination within a sociotechnical systems framework. This study explores into the inherent paradoxes of the sector, analyzing the interplay between safety dynamics and operational exigencies. Through a systematic review across micro (firm/operations), meso (industry), and macro (national/global) levels, it uncovers a landscape where safety awareness competes with operational demands, creating a paradoxical coexistence. Micro-level challenges like poor enforcement and inadequate safety awareness lead to noncompliance and unsafe practices, as seen in incidents like the Eastern Star tragedy. Strategic planning is vital for long-term industry improvements, requiring policymakers to address regulatory complexities. Interconnected dynamics across meso and macro levels highlight the International Maritime Organization's role in shaping safety standards and policy evolution. Insights into transitioning to international safety standards highlight the need for attitudinal shifts aligned with proactive risk management. This research provides a holistic view of challenges (Themes A to C) and enablers (Themes D to F), traversing micro, meso, and macro levels. By advocating for cross-industry collaboration and emphasizing a more secure and sustainable future for the domestic ferry sector, contributes significantly to the enhancement of safety and sustainable operations.

Examining the Influence of Cognitive Load and Environmental Conditions on Autonomic Nervous System Response in Military Aircrew: A Hypoxia-Normoxia Study.

Executing flight operations demand that military personnel continuously perform tasks that utilize low- and high-order cognitive functions. The autonomic nervous system (ANS) is crucial for regulating the supply of oxygen (O2) to the brain, but it is unclear how sustained cognitive loads of different complexities may affect this regulation. Therefore, in the current study, ANS responses to low and high cognitive loads in hypoxic and normoxic conditions were evaluated. The present analysis used data from a previously conducted, two-factor experimental design. Healthy subjects (n = 24) aged 19 to 45 years and located near Fort Novosel, AL, participated in the parent study. Over two, 2-h trials, subjects were exposed to hypoxic (14.0% O2) and normoxic (21.0% O2) air while simultaneously performing one, 15-min and one, 10-min simulation incorporating low- and high-cognitive aviation-related tasks, respectively. The tests were alternated across five, 27-min epochs; however, only epochs 2 through 4 were used in the analyses. Heart rate (HR), HR variability (HRV), and arterial O2 saturation were continuously measured using the Warfighter MonitorTM (Tiger Tech Solutions, Inc., Miami, FL, USA), a previously validated armband device equipped with electrocardiographic and pulse oximetry capabilities. Analysis of variance (ANOVA) regression models were performed to compare ANS responses between the low- and high-cognitive-load assessments under hypoxic and normoxic conditions. Pairwise comparisons corrected for familywise error were performed using Tukey's test within and between high and low cognitive loads under each environmental condition. Across epochs 2 through 4, in both the hypoxic condition and the normoxic condition, the high-cognitive-load assessment (MATB-II) elicited heightened ANS activity, reflected by increased HR (+2.4 ± 6.9 bpm) and decreased HRV (-rMSSD: -0.4 ± 2.7 ms and SDNN: -13.6 ± 14.6 ms). Conversely, low cognitive load (ADVT) induced an improvement in ANS activity, with reduced HR (-2.6 ± 6.3 bpm) and increased HRV (rMSSD: +1.8 ± 6.0 ms and SDNN: vs. +0.7 ± 6.3 ms). Similar observations were found for the normoxic condition, albeit to a lower degree. These within-group ANS responses were significantly different between high and low cognitive loads (HR: +5.0 bpm, 95% CI: 2.1, 7.9, p < 0.0001; rMSSD: -2.2 ms, 95% CI: -4.2, -0.2, p = 0.03; SDNN: -14.3 ms, 95% CI: -18.4, -10.1, p < 0.0001) under the hypoxic condition. For normoxia, significant differences in ANS response were only observed for HR (+4.3 bpm, 95% CI: 1.2, 7.4, p = 0.002). Lastly, only high cognitive loads elicited significant differences between hypoxic and normoxic conditions but just for SDNN (-13.3 ms, 95% CI, -17.5, -8.9, p < 0.0001). Our study observations suggest that compared to low cognitive loads, performing high-cognitive-load tasks significantly alters ANS activity, especially under hypoxic conditions. Accounting for this response is critical, as military personnel during flight operations sustain exposure to high cognitive loads of unpredictable duration and frequency. Additionally, this is likely compounded by the increased ANS activity consequent to pre-flight activities and anticipation of combat-related outcomes.

Demand flexibility and its impact on a PEM fuel cell-based integrated energy supply system with humidity control

The development of efficient energy systems and renewable alternative energy sources has attracted substantial attention owing to the rise in environmental deterioration and energy usage. A combined cooling, heating, and power (CCHP) system can fully use different types of energy with low emissions and offers a viable solution to address these issues. Demand flexibility is a key issue in collaborating with different types of supplies and demands for optimal system operation. In this study, a proton-exchange membrane fuel cell driven CCHP system with humidity control is proposed to fully investigate the impact of demand flexibility on system operation. A novel flexible load regulation strategy was designed for the proposed system. The results show that flexible load regulation can reduce the thermal storage capacity by 25 %. Supplement heating and cooling do not need operate in winter and summer. With flexible regulation, the heating load can be reduced by 15.69 % in winter, and the cooling and dehumidification loads can be reduced by 30.71 % and 6.09 % in summer. The reductions in hydrogen consumption can reach 1.98 % and 7.51 % during winter and summer. The proposed method can simultaneously increase the exergy efficiency and reduce carbon emission, as well as the economy cost.

Flexible head-following motion planning for scalable and bendable continuum robots

Continuum robots, which are characterized by high length-to-diameter ratios and flexible structures, show great potential for various applications in confined and irregular environments. Due to the combination of motion modes, the existence of multiple solutions, and the presence of complex obstacle constraints, motion planning for these robots is highly challenging. To tackle the challenges of online and flexible operation for continuum robots, we propose a flexible head-following motion planning method that is suitable for scalable and bendable continuum robots. Firstly, we establish a piecewise constant curvature (PCC) kinematic model for scalable and bendable continuum robots. The article proposes an adaptive auxiliary points model and a method for updating key nodes in head-following motion to enhance the precise tracking capability for paths with different curvatures. Additionally, the article integrates the strategy for adjusting the posture of local joints of the robot into the head-following motion planning method, which is beneficial for achieving safe obstacle avoidance in local areas. The article concludes by presenting the results of multiple sets of motion simulation experiments and prototype experiments. The study demonstrates that the algorithm presented in this paper effectively navigates and adjusts posture to avoid obstacles, meeting the real-time demands of online operations. The average time for a single-step solution is 4.41×10−5 s, and the average tracking accuracy for circular paths is 7.8928 mm.

Sustainable development of pavement base stabilization using future road base material (FROBM)

Sustainable road construction has emerged as a critical issue in assessing natural resources and urban development. With the rapid expansion of pavement networks to meet growing traffic demands, road base construction relies heavily on the consumption of natural aggregates and Ordinary Portland Cement (OPC) through the cement-treated base (CTB) technique. This practice contributes to the global warming and greenhouse gas (GHG) emissions associated with road construction. This study investigated a novel road base material, Future Road Base Material (FROBM), which emphasizes both eco-friendliness and economic advantages. Fine crushed rock (FCR) served as the parent aggregate and was modified with a chemical binder comprising 4% of the FCR weight. The proposed sustainable cementitious material, functioning as a non-OPC binder, incorporated fly ash and hydrated limestone as pozzolanic materials, along with sodium hydroxide (NaOH) as an alkali additive. An asphalt emulsion (AE) was employed as a special additive to enhance crucial pavement engineering properties. Through life cycle analysis (LCA), the construction activities and material acquisition of each stabilized base technique were evaluated in terms of project costs and carbon footprints. The non-OPC binder offered sufficient compressive strength for a stabilized base course. Furthermore, the addition of AE improved the elastic modulus and modulus of rupture of the non-OPC binder-stabilized mixture. Utilizing a non-OPC binder and AE in the base layer provides advantages in terms of construction cost and environmental impact. Although the costs increased slightly, the GHG emissions were reduced by approximately 30% compared to conventional base materials, with equivalent functionality and service life for road construction. The proposed stabilizing agent reduced the operational processes and demand for natural aggregates compared with the unbound FCR base course. Road-base stabilization using a non-OPC binder and AE represents an innovative approach to mitigate waste in coal mining and power plant industries by transitioning to a cleaner product. The concept of the FROBM serves as a valuable guideline for the development of future sustainable pavement materials.

Low-carbon economic operation strategy for multi-agent integrated energy system considering uncertainty of renewable energy power generation

The uncertainty of renewable energy output threatens the operation safety of multi-agent integrated energy system (MAIES), which makes it difficult to balance the low-carbon economic operation demands of various stakeholders. However, the existing research solely focuses on the operational strategy of multi-agent game involving integrated energy suppliers and users in deterministic scenarios, overlooking the complementary supporting role and game interaction of shared energy storage and wind farm as independent entities of interest under the instability of renewable energy power generation. Hence, this paper first establishes the optimal operation models for integrated energy system operator (IESO), user aggregator (UA), shared energy storage operator (SESO), and wind farm operator (WFO) considering the stepped carbon trading. Second, in the face of the actual situation of uncertainty of photovoltaic and wind power output, fuzzy chance-constrained programming is adopted for processing. Then, a bi-layer game equilibrium model with IESO as a leader and UA, SESO, and WFO as followers is proposed, and the existence and uniqueness of Stackelberg equilibrium solution are proved. Finally, simulation calculation is carried out based on the YALMIP toolbox in the Matlab R2023a software, and the improved particle swarm optimization algorithm and CPLEX solver are used to solve the model. The results demonstrate that the participation of SESO and WFO as independent stakeholders in the game interaction can improve the economic and environmental benefits of MAIES. The iterative optimization of demand response subsidy prices can effectively motivate users to participate in demand response, improve the ability of MAIES to cope with the uncertain risks of renewable energy generation and load, and reduce the power grid dispatch pressure.

Artificial potential field-based method for multi-spacecraft loose formation control

This study presents a control strategy based on artificial potential fields to maintain loose formation flying of satellites without a fixed formation, ensuring collision avoidance and stability under various conditions. The approach employs artificial potential fields for obstacle evasion and maintaining relative distances between satellites. Comprehensive simulations validate this method, with Monte Carlo techniques confirming control stability despite variations in initial conditions. The strategy effectively manages the integration of external satellites, maintaining optimal formation even as operational demands increase.