Load Allocation Research Articles

Intelligent Driving Vehicle Trajectory Tracking Control Based on an Improved Fractional‐Order Super‐Twisting Sliding Mode Control Strategy

ABSTRACTAiming at resolving trajectory tracking control challenges during high‐speed lane changes in intelligent driving vehicles, an innovative fractional‐order sliding mode control approach is introduced in the present study. The control strategy comprises upper and lower‐level controls. First, the upper‐level control designs the vehicle trajectory tracking controller, integrating a non‐singular terminal sliding mode (NTSM) surface with a fractional‐order fast super‐twisted sliding mode control (FOF‐STSMC) algorithm. The NTSM surface properties ensure rapid convergence of the system tracking error to zero within a finite time, while the fractional‐order control extends the control system's regulation range and enhances algorithm flexibility. Additionally, the integration with the super‐twisting algorithm effectively mitigates oscillation issues in the control input, achieving a smooth input. Second, the lower‐level control aims to enhance vehicle driving stability. Utilizing the reference yaw rate, and sideslip angle and accounting for tire force saturation, a fractional‐order sliding mode control (FOSMC) algorithm is developed to compute the external yaw moment. Through dynamic load allocation, considering the vertical load for each tire, intelligent external yaw moment distribution significantly improves vehicle stability. Finally, the results of the Carsim–Simulink co‐simulation demonstrate that, compared to the STSMC strategy, the FOSMC strategy with front‐wheel‐only steering, and the linear quadratic regulator (LQR) control strategy, the proposed control strategy in this paper reduces the tracking error by 77%, 61%, and 58%, respectively, achieving more precise and stable trajectory tracking under high‐speed conditions.

An Optimization Strategy for Unit Commitment in High Wind Power Penetration Power Systems Considering Demand Response and Frequency Stability Constraints

To address the issue of accommodating large-scale wind power integration into the grid, a unit commitment model for power systems based on an improved binary particle swarm optimization algorithm is proposed, considering frequency constraints and demand response (DR). First, incentive-based DR and price-based DR are introduced to enhance the flexibility of the demand side. To ensure the system can provide frequency support, the unit commitment model incorporates constraints such as the rate of change of frequency, frequency nadir, steady-state frequency deviation, and fast frequency response. Next, for the unit commitment planning problem, the binary particle swarm optimization algorithm is employed to solve the mixed nonlinear programming model of unit commitment, thus obtaining the minimum operating cost. The results show that after considering DR, the load becomes smoother compared to the scenario without DR participation, the overall level of load power is lower, and the frequency meets the safety constraint requirements. The results indicate that a comparative analysis of unit commitment in power systems under different scenarios verifies that DR can promote rational allocation of electricity load by users, thereby improving the operational flexibility and economic efficiency of the power system. In addition, the frequency variation considering frequency safety constraints has also been significantly improved. The improved binary particle swarm optimization algorithm has promising application prospects in solving the accommodation problem brought by large-scale wind power integration.

Response-driven adaptive under frequency load shedding scheme based on energy model

Developing reasonable and effective adaptive under frequency load shedding (AUFLS) schemes is crucial for preventing system frequency drops. In order to overcome the mismatching of event-driven load shedding scheme in the traditional second line of defense, this paper proposes a response-driven AUFLS scheme based on energy model. In this scheme, the energy model is established by the kinetic energy theorem, which represents the equivalent conversion relationship between rotor kinetic energy deviation (KED) and the unbalanced power does work (UPDW) of each component. Furthermore, a kinetic energy margin indicator (KEMI) reflecting the system frequency is introduced, and the minimum load shedding amount (MLSA) corresponding to the critical KEMI is calculated. The comprehensive load shedding allocation weight is established by combining the UPDW of load and shedding costs. The combination of MLSA and comprehensive load allocation weights enables precise control with minimal cost. Modified IEEE 10-generator 39-bus test system and CSEE-SSFS 197-bus real system are used to verify the performance of the proposed AUFLS scheme.

Robustness of LEO satellite hypernetworks under different network topologies and attack strategies

The development of low earth orbit (LEO) satellites has become a crucial and essential component with the advancement of digitalization. However, LEO satellites also face various security challenges within their networks, and the failure of certain satellites may trigger cascading failures. To gain a deeper understanding of cascading failure process in LEO satellite networks and enhance its robustness, this letter proposes a novel satellite network model by combining complex network and hypernetwork theories. By altering the network structure and model parameters, the robustness performance of the LEO satellite network under three types of attacks and two different load distribution methods is thoroughly investigated. The results indicate that satellite networks with varying structures are distinct and exhibit different robustness characteristics under specific attack types. Furthermore, the robustness of satellite networks in multiple attack scenarios can be significantly enhanced by implementing improved load allocation strategies. These findings not only provide a theoretical foundation for the design and optimization of satellite networks but also ignite new perspectives and ideas for future research on their robustness.

Example-based learning in heuristic domains: can using relevant content knowledge support the effective allocation of intrinsic, extraneous, and germane cognitive load?

Worked examples support initial skill acquisition. They often show skill application on content knowledge from another, "exemplifying" domain (e.g., argumentation skills have to be applied to some contents). Although learners' focus should remain on the skill, learners need to understand the content knowledge to benefit from worked examples. Previous studies relied on exemplifying domains that are familiar and contain simple topics, to keep learners' focus on skill acquisition. We examined whether using a relevant exemplifying domain would allow learners to acquire both skills and content knowledge simultaneously, or whether relevant content distracts from the main learning goal of skill acquisition. In a training study with 142 psychology students, we used example-based learning materials with an exemplifying domain that was either relevant or irrelevant for participants' course outcomes. We assessed cognitive load, declarative knowledge about skills and course-related content knowledge, and argumentation quality. Incorporating relevant content knowledge in worked examples did not reduce learning outcomes compared to a condition using an irrelevant exemplifying domain. Contrary to previous research, the results suggest that worked examples with a relevant exemplifying domain could possibly be an efficient teaching method for fostering skills and content knowledge simultaneously.

Multi-Fidelity Modelling of the Effect of Combustor Traverse on High-Pressure Turbine Temperatures

As turbine entry temperatures of modern jet engines continue to increase, additional thermal stresses are introduced onto the high-pressure turbine rotors, which are already burdened by substantial levels of centrifugal and gas loads. Usually, for modern turbofan engines, the temperature distribution upstream of the high-pressure stator is characterized by a series of high-temperature regions, determined by the circumferential arrangement of the combustor burners. The position of these high-temperature regions, both radially and circumferentially in relation to the high-pressure stator arrangement, can have a strong impact on their subsequent migration through the high-pressure stage. Therefore, for a given amount of thermal power entering the turbine, a significant reduction in maximum rotor temperatures can be achieved by adjusting the inlet temperature distribution. This paper is aimed at mitigating the maximum surface temperatures on a high-pressure turbine rotor from a modern commercial turbofan engine by conducting a parametric analysis and optimization of the inlet temperature field. The parameters considered for this study are the circumferential position of the high-temperature spots, and the overall bias of the temperature distribution in the radial direction. High-fidelity unsteady (phase-lag) and conjugate heat transfer simulations are performed to evaluate the effects of inlet clocking and radial bias on rotor metal temperatures. The optimized inlet distribution achieved a 100 K reduction in peak high-pressure rotor temperatures and 7.5% lower peak temperatures on the high-pressure stator vanes. Furthermore, the optimized temperature distribution is also characterized by a significantly more uniform heat load allocation on the stator vanes, when compared to the baseline one.

Optimal dispatch of unbalanced distribution networks with phase-changing soft open points based on safe reinforcement learning

Distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks, which may harm the health of power equipment and increase the operational costs. There is emerging opportunity to dispatch soft open points to improve the operation performance of active distribution network. This paper proposes an optimal dispatch strategy to improve the network balancing performance, where a new type of phase-changing soft open point is installed. First, a new type of phase-changing soft open point with full-phase changing ability is introduced to balance the three-phase power flow. Then, the optimization model is formulated for phase-changing soft open points dispatching to minimize the total cost of distribution network. Furthermore, the model is formed as a constrained Markov decision process and efficiently solved by the augmented Lagrangian-based safe deep reinforcement learning algorithm featuring the soft actor-critic method. Finally, numerical simulations are conducted to validate the effectiveness, accuracy, and efficiency of the proposed method.

Design of a mix network with elliptic curve cryptography and XOR shuffling for secure communication with 5‐node

AbstractThis research paper proposes a novel 5‐node mix network, an anonymous communication network to boost privacy within the network. Within the proposed framework the crypto‐operations like encryption, decryption, and shuffling are held with the help of the Elliptic Curve Cryptography (ECC) and XOR algorithm. This unique combination helps to exchange the message within the communication network with enhanced security and privacy. To analyze the processing time and load factor at each intermediate node of mix network, an exhaustive experimental analysis is carried out for key sizes of 256, 384, and 512 bits respectively. These crypto‐operations like encryption, decryption, and XOR shuffling need varying time slots to perform at each intermediate node thereby having a varying load factor associated with them. The objective of this research is to analyze the processing time and load factor at each node within the 5‐node mix network thereby helping to analyze the strengths and weaknesses of each intermediate node. Through our analysis, these percentages are computed, shedding light on the allocation of computational load and resource utilization among the nodes.

Mechanical evaluation of human cadaveric lumbar soft tissues suggests possible physiological stress shielding within musculoskeletal soft tissues by the thoracolumbar fascia.

This study investigates the potential for stress shielding within musculoskeletal soft tissues through analysis of stress distributions between lumbar fascial and muscle tissues via mechanical testing. Using a custom apparatus, 51 posterior thoracolumbar fascia (TLF) samples and 18 erector spinae (ES) cadaveric samples underwent tensile testing involving three loading-unloading cycles, followed by loading to 6% strain, to mechanically characterize samples. Parallel tensile testing using 20 pairs of two TLF samples, and seven pairs of TLF and ES samples was then conducted for stress distribution analysis between tissues. P<0.05 was deemed significant. The TLF and ES exhibited an average elastic modulus of 150.9MPa and 0.6MPa, respectively. At 6% strain, parallel testing of the TLF pairs yielded an average tensile stress of 8.4MPa and 1.7MPa (p<0.001) exhibited by the stiffer and less stiff TLF samples, respectively. Similarly, TLF-ES parallel testing resulted in average tensile stresses of 7.1MPa and 0.07MPa of the TLF and ES (p<0.002). Results suggest elevated loading towards stiffer TLF samples relative to less stiff TLF and ES samples. In soft tissues affected by LBP, skewed stress distributions may result in the TLF withstanding the majority of stress, yielding cyclical stress shielding that may contribute to and/or promote LBP. This novel study demonstrates a potential load allocation bias towards the TLF, laying the foundation for stress shielding within lumbar musculoskeletal soft tissues affected by degenerative musculoskeletal conditions.

A novel load allocation strategy based on the adaptive chiller model with data augmentation

Model-based load allocation strategy is an impactful solution to enhance energy efficiency of multiple-chiller system. Its performance is heavily dependent on the accuracy of chiller model. Data-driven model is a pretty-good solution. However, in real multiple-chiller system, the range of operation condition in historical data is commonly narrow, so it is challenging to develop an accurate data-driven model of chiller throughout full range of operation condition. In this paper, data augmentation algorithm is presented to generate the data outside of historical data, which is based on conditional generative adversarial network (CGAN) and elastic weight consolidation algorithm (EWC). Combined historical data and generated data, augmented training dataset is set up and updated by online operation data. Trained by online updated augmented training dataset periodically, adaptive chiller model is set up. Based on adaptive chiller model, a novel load allocation strategy presented for multiple-chiller system. The proposed strategy is verified by field test in multiple-chiller system. The results show that adaptive chiller model, with the aid of data augmentation algorithm, is more accurate. The proposed strategy can achieve 5.03 % energy saving compared with fixed set-point strategy, and the EER of proposed strategy is 6.27 % higher than that of fixed set-point strategy.

Optimization of IPOP-connected dissimilar power-rated converters: An equal incremental cost-based approach

As electric vehicles gain prominence, optimizing the efficiency of charging stations has become a critical issue. This paper explores the application of dual active bridge (DAB) converters arranged in an input-parallel-output-parallel (IPOP) configuration, offering a flexible solution for power conversion across varied power-rated loads. While previous research mainly focused on optimizing single DABs and establishing the system using identical converters, this study leverages dissimilar modules, enhancing flexibility and operational efficiency. We introduce a novel IPOP system incorporating diverse modules and corresponding switch regulators. Additionally, an efficiency optimization strategy featuring dynamic load allocation is proposed. Herein, each module or each group of modules is responsible for its corresponding power range. Based on curve-fitted power profiles, an equal incremental cost-based method is employed to formulate the path of power allocation. Moreover, a simulated annealing algorithm determines the optimal power output strategy to accommodate dynamic power flow requirements. Experimental comparisons with the conventional equal-power sharing scheme underscore the proposed method’s effectiveness and superiority.

Novel load allocation analysis for energy management and carbon emissions reduction of chiller system in hotel buildings

The operation of chiller systems for cooling energy generation in hotel buildings consumes the highest proportion of electricity. This study aims to introduce load allocation analysis to assess the potential for energy savings by a chiller system. The cooling load profile of a hotel in a subtropical climate is simulated using EnergyPlus. Eleven design options for the system include 4 to 8 chillers of the same or different capacities. Restoring chiller sequencing with high load operation can reduce the system's energy use intensity (EUI) by 12.7 %. An optimal design of four large and two small chillers with proper sequencing can reduce the EUI by up to 26.82 % or carbon emissions by 551,516 kgCO2-e. Increasing capacity steps above ten brings no significant energy savings and imposes complicated chiller staging. The area of the load allocation region can be evaluated simply from the chiller system design and used to analyze the system's energy performance. The EUI can be reduced by 0.3261 % for a 1 % drop in the area of load allocation region. The novelty of this study lies in using the load allocation region to assess energy savings of a chiller system without referring to sophisticated operating data at short timesteps. The significance of this study is to highlight the limitations of energy savings from the optimum chiller system design and address the need for incorporating more low energy technologies to enhance decarbonization.

Modelling and Optimization of Residential Electricity Load under Stochastic Demand

The paper considers a modelling framework for a set of households in residential areas; using electricity as a form of energy for domestic consumption. Considering the demand and availability of units for electricity consumption, optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this problem as a stochastic decision-making process model where electricity demand is characterized by Markovian demand. The loading and operational framework is governed by the demand and supply phenomena; where shortage costs are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly intervals over a period of four months. The major problem focussed on determining an optimal electricity loading decision in order to minimize consumption costs as demand changes from one state to another. Considering a multi-period planning horizon, an optimal decision was determined for loading or not loading additional electricity units using Markov decision process approach. The model was tested; whose results demonstrated the existence of an optimal state-dependent decision and consumption costs considering the case study used in this study. The proposed model can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for the distribution systems of electricity to residential areas was realised; with subsequent enhanced reliability of service to essential customers of the energy market.

Application of multi-criteria group decision-making for water quality management.

As waste discharge into numerous river systems escalates, the pollution of water bodies typically rises. Given the limited capacity of rivers to withstand pollution and their constrained self-cleaning capabilities, treated pollutants from waste discharge must be released into the river. Despite numerous models and algorithms proposed for managing river water quality to meet standards, literature, to our awareness, lacks the utilization of a comprehensive multi-criteria group decision-making approach for water quality management, particularly in river systems. Therefore, this research introduces a new, comprehensive multi-criteria group decision-making for the management of water quality in the Haraz River basin, located in Iran. To do so, the water quality of the basin, a one-dimensional water quality model, QUAL2Kw, was employed to simulate and calibrate the water quality along the river. The simulation results revealed that the downstream water quality violates the water quality standards. To mitigate this issue, various scenarios for waste load allocation (WLA) were evaluated, including no wastewater treatment, primary wastewater treatment, advanced secondary wastewater treatment utilizing the activated sludge (AS) method, and advanced wastewater treatment via the membrane bioreactor (MBR) method. Utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Fuzzy TOPSIS group decision-making model, it was determined that the optimal solution was the implementation of secondary wastewater treatment utilizing the activated sludge method for the 11 PS of pollution, while still adhering to Iranian water quality standard. In addition, the findings of the present study indicate that the implementation of primary wastewater treatment, advanced secondary wastewater treatment utilizing AS, and advanced wastewater treatment through MBR within the study area led to a significant enhancement in water quality. This enhancement ranged from 35 to 105% across various scenarios when compared to conditions where no actions were taken to the treatment of water.

Optimizing smart grid performance: A stochastic approach to renewable energy integration

Smart grids offer numerous possibilities for developing and executing sustainable and efficient electricity supply chains that exhibit increased resilience to disturbances. The exploration of the Electric Supply Chain Network (ESCND) design problem using smart grids has been limited. This article aims to tackle this challenge through the application of a robust multi-objective optimization approach, encompassing three economic goals (maximizing profit), environmental goals (minimizing greenhouse gas emissions), and resilience goals (maximizing network resilience). Additionally, the optimization process takes into account the dual objectives of maximizing efficiency and minimizing energy loss by incorporating relevant cost considerations. The proposed methodology incorporates distinctive features of smart grids, such as demandside management programs, microgrid structures, bidirectional distribution lines, and consumer–supplier nodes. It addresses various interconnected decisions, including location placement, capacity expansion, load allocation, and pricing. To solve the formulated model, a potent combination of multi-objective optimization methods based on cutting-plane algorithms and AUGMECON2 is introduced. Subsequently, the proposed model and solution approach are applied to a practical case study, and the obtained results are comprehensively examined. The findings suggest that, despite potential contradictions between economic and environmental objectives, the implementation of smart grids can concurrently improve environmental performance and network flexibility.

A novel integration of regret-based methodology and bankruptcy theory for waste load allocation.

Developing a suitable index for Waste Load Allocation (WLA) is essential for both industrial polluters and environmental organizations. Identifying the index that best describes the quality conditions of the river is the main concern of this study. To achieve this purpose, a novel framework incorporating a regret-based index and a bankruptcy-based approach to address the impacts of low water quality and pollutant locations within the WLA are introduced. The framework includes a simulation-optimization model to minimize river quality regret for environmental organizations and total treatment cost for industrial polluters, employing Nash bargaining theory for conflict resolution. Additionally, a new bankruptcy approach, the Namin's rule, is proposed for redistributing the River Quality Regret Index among industrial polluters. Applying this methodology to data from the KhoramAbad River, a sensitivity analysis reveals that while there is no significant difference between the methodology and fuzzy risk when polluters are close, the methodology provides more accurate results as the distance between polluters increases. When the distance between two pollutants was 20km, the sum of WLA was evaluated to be 300kg per day higher than that in the compared method, potentially enhancing environmental justice.

Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

Foliar-applied zinc promotes cadmium allocation from leaf surfaces to grains in rice

The accumulation of Cd by rice poses significant health risks. Foliar fertilization with Zn can reduce grain Cd contents in rice grown in Cd-contaminated soils. However, atmospheric deposition on leaves is another vector of Cd contamination, and it remains unclear how Zn application affects the allocation of such Cd. We conducted an experiment where the flag leaves of rice plants were treated with solutions with various Zn concentrations and a constant Cd concentration. The 111Cd stable isotope was used to trace the flux of foliar-applied Cd. Higher levels of foliar-applied Zn enhanced Cd efflux and grain allocation. This is attributed to limited sequestration of foliar-applied Cd in the leaf cell symplasm and increased Cd desorption from leaf cell walls when a high Zn2+ concentration occurs in the apoplast. Nonionic Zn oxide nanoparticles mitigated these effects. Additionally, the expressions of OsLCT1 and OsZIP7 in flag leaves and OsHMA2 and OsZIP7 in the uppermost nodes were upregulated under high-Zn2+ treatment, which may facilitate Cd phloem loading and grain allocation. Caution is advised in using foliar Zn in areas with high atmospheric Cd due to potential grain-contamination risks.

The Proposed IT-TALB in Infrastructure as a Service Cloud

Objectives: The purpose of the proposed IT-TALB load balancing algorithm is to dynamically allocate the user's workload to the appropriate virtual machine in an Infrastructure as a Service (IaaS) cloud environment. Methods: This research work includes several key procedures. The user's workloads are distributed to the data center controller (DCC), which in turn uses the ECO-SBP service broker policy to select the efficient data center (DC) for processing the loads. The DCC forwards the load to the selected DC, and the IT-TALB load balancer picks the best Virtual Machine (VM) using CloudAnalyst simulation tool for load allocations according to metrics such as its size, current number of loads, and load size. IT-TALB partitions the available and busy VMs separately and stores them in the TreeMap structure. This algorithm also incorporates the scalability of the given VM when the load size is not compatible with the existing VMs by extending the resources of underutilized VMs. Findings: The research finding demonstrates that the proposed IT-TALB algorithm improves IaaS cloud performance compared to the existing algorithms. It achieves optimum load balancing, reduces the searching time of the VM, avoids the load waiting time, improves throughput, minimizes the response time, and enhances the resource utilization ratio. IT-TALB yields a throughput and resource utilization ratio of 98 to 99 percent. Novelty: The novelty of this research is that the IT-TALB algorithm incorporates the scalability of the underutilized VM and also introduces new metrics such as throughput and resource utilization ratio in the CloudAnalyst simulation tool for assessing the performance of the proposed algorithm. This study provides information for analyzing the proposed IT-TALB strategies with the existing two algorithms such as TLB and TALB in order to show its performance. Keywords: Cloud Computing, Infrastructure as a Service, Load Balancing, Throttled Load Balancing, Virtual Machine

Bi-Level Inverse Robust Optimization Dispatch of Wind Power and Pumped Storage Hydropower Complementary Systems

This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for wind power output based on the ideal perturbation constraints of the objective function. The OIRI represents the maximum distance by which decision variables in the non-dominated frontier can be perturbed. Compared to traditional methods for quantifying the worst-case sensitivity region using polygons and ellipses, the OIRI can more accurately quantify parameter uncertainty. We integrate the grid multi-objective bacterial colony chemotaxis algorithm and the bisection method to solve the proposed model. The former is adopted to solve the inner level problem, while the latter is used to calculate the OIRI. The proposed approach establishes the relationship between the maximum forecast deviation and the minimum generation cost associated with each non-dominated solution in the optimal load allocation. To demonstrate its economic viability and effectiveness, we simulate the proposed approach using real power system operation data and conduct a comparative analysis.