Approach For Optimal Allocation Research Articles

A new approach for optimal allocation and sizing a distributive energy source or a parallel compensation device in radial distribution networks

A new approach for optimal allocation and sizing a distributive energy source or a parallel compensation device in radial distribution networks

Covariate-adjusted response-adaptive designs for semiparametric survivalmodels.

Covariate-adjusted responseadaptive (CARA) designs are effective in increasing the expected number of patients receiving superior treatment inan ongoing clinical trial, given a patient's covariate profile. There has recently been extensive research on CARA designs with parametric distributional assumptions on patient responses. However, the range of applications for such designs becomes limited in real clinical trials. Sverdlov etal. have pointed out that irrespective of a specific parametric form of the survival outcomes, their proposed CARA designs based on the exponential model provide valid statistical inference, provided the final analysis is performed using the appropriate accelerated failure time (AFT) model. In real survival trials, however, the planned primary analysis is rarely conducted using an AFT model. The proposed CARA designs are developed obviating any distributional assumptions about the survival responses, relying only on the proportional hazards assumption between the two treatment arms. To meet the multiple experimental objectives of a clinical trial, the proposed designs are developed based on an optimal allocation approach. The covariate-adjusted doubly adaptive biased coin design and the covariate-adjusted efficient-randomized adaptive design are used to randomize the patients to achieve the derived targets on expectation. These expected targets are functions of the Cox regression coefficients that are estimated sequentially with the arrival of every new patient into the trial. The merits of the proposed designs are assessed using extensive simulation studies of their operating characteristics and then have been implemented to re-design a real-life confirmatory clinical trial.

Optimizing the Allocation of Trials to Sub-regions in Crop Variety Testing with Multiple Years and Locations

AbstractField experiments in crop variety testing are conducted in multiple environments. When the targeted growing area can be stratified into sub-regions (zones), a design problem arises regarding the number of trials to be performed in each sub-region. We propose an analytical solution and a computational approach for optimal allocation of trials to sub-regions in multi-environment crop variety testing for multi-year experiments. For our purpose, we use a linear mixed model and we determine allocations that are optimal for the prediction of pairwise linear contrasts of genotype effects. For computations, we use the OptimalDesign package. The analytical results are illustrated by a real data example.

Physics-informed convolutional neural network for microgrid economic dispatch

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches.

A Stochastic Bi-Level Optimal Allocation Approach of Intelligent Buildings Considering Energy Storage Sharing Services

A Stochastic Bi-Level Optimal Allocation Approach of Intelligent Buildings Considering Energy Storage Sharing Services

Intelligent towing and pushing system for unmanned tugboats under wind and wave disturbances

ABSTRACT Intelligent unmanned tugboats have the potential to significantly improve the efficiency of barge manipulation. This paper investigates the kinetic modelling, optimal force distribution, and cooperative control within the unmanned tugboat towing and pushing system. A practical kinetic model for the towing and pushing system has been developed, precisely taking into account factors like physical cable forces and disturbances caused by wind and waves in turbulent sea conditions. Following this, an optimal force allocation approach is investigated to allocate the desired force and determine the towing and pushing angles. Furthermore, a cooperative model predicted control strategy is proposed for the dual unmanned tugboats' towing and pushing systems. This strategy aims to ensure the barge quickly reaches its designated location, maintaining the desired heading and velocity. Simulation results indicate that unmanned tugboats can collaboratively tow and push the unpowered barge to the specified location with the desired heading and speed.

An integrated hybrid approach for assembly tolerance transfer and allocation

PurposeThis study aims to develop an optimal tolerance allocation strategy involves integrating the unique transfer (UT) approach and the difficulty coefficient evaluation (DCE) routine in an interactive hybrid method. This method combines the strengths of both UT and DCE, ensuring simultaneous utilization for enhanced performance. The proposed tolerancing model manifests an integrated computer-aided design (CAD) tool.Design/methodology/approachBy combining UT and DCE based on failure mode, effects and criticality analysis (FMECA) tool and the Ishikawa diagram, the proposed collaborative hybrid tool ensures an efficient and optimal tolerance allocation approach. The integration of these methodologies not only addresses specific transfer challenges through UT but also conducts a thorough evaluation of difficulty coefficients via DCE routine using reliability analysis tools as FMECA tool and the Ishikawa diagram. This comprehensive framework contributes to a robust and informed decision-making process in tolerance allocation, ultimately optimizing the design and manufacturing processes.FindingsThe presented methodology is implemented with the aim of generating allocated tolerances that align with specific difficulty requirements, facilitating the creation of a mechanical assembly characterized by high quality and low cost. To substantiate and validate the conceptual framework and methods, an integrated tool has been developed, featuring a graphical user interface (GUI) designed in MATLAB. This interface serves as a platform to showcase various interactive and integrated tolerance allocation approaches that adhere to both functional and manufacturing prerequisites. The proposed integrated tool, designed with a GUI in MATLAB, offers the capability to execute various examples that effectively demonstrate the benefits of the developed tolerance transfer and allocation methodology.Originality/valueThe originality of the proposed approach is the twining between the UT and DCE simultaneous in an integrated and concurrent tolerance transfer and allocation model. Therefore, the proposed approach is named an integrated CAD/tolerance model based on the manufacturing difficulty tool. The obtained results underscore the tangible advantages stemming from the integration of this innovative tolerance transfer and allocation approach. These benefits include a notable reduction in total cost and a concurrent enhancement in product quality.

Neuro-Adaptive Cooperative Control for Dual-Arm Robots With Position and Velocity Constraints: An Optimal Torque Allocation Approach

Neuro-Adaptive Cooperative Control for Dual-Arm Robots With Position and Velocity Constraints: An Optimal Torque Allocation Approach

Full/half-duplex unmanned aerial vehicles assisted wireless systems: Performance analysis and optimization

In this paper, we explore the utilization of both full-duplex (FD) and half-duplex (HD) transmission modes on unmanned aerial vehicles (UAVs) to enhance wireless system performance. We consider two practical scenarios: one without a direct transmitter-user link and the other with such a link. For both cases, we derive mathematical expressions of outage probabilities (OPs) and throughputs of FD-UAV and HD-UAV systems, employing a realistic channel model aligned with the fifth and beyond generations (5G-B5G) standards. In cases where imperfect self-interference cancellation (SIC) occurs in FD-UAV systems, we introduce an optimal power allocation approach to enhance system performance. Numerical results underscore the benefits of cooperative communications, particularly when combining the direct link with the UAV link at the user, leading to an overall enhancement in system performance. Furthermore, we conduct a comprehensive analysis of various system parameters, including predefined rates, residual self-interference (RSI) levels, high carrier frequencies of Wi-Fi networks, and high altitudes of UAVs. The impact of RSI is particularly notable, and the proposed optimal power allocation approach significantly improves system performance in such cases. Specifically, the introduced scheme helps avoid error floors in regions of high transmit power, enabling throughputs to reach desired targets in FD-UAV systems. Crucially, the optimal power value is considerably lower than the traditional value often used without optimal power allocation, extending the operational duration of FD-UAV. Finally, to validate the derived mathematical expressions and affirm the effectiveness of the proposed approaches, we conduct Monte-Carlo simulations.

Simulation of a Novel Integrated Multi-Stack Fuel Cell System Based on a Double-Layer Multi-Objective Optimal Allocation Approach

A multi-stack fuel cell system (MFCS) is a promising solution for high-power PEM fuel cell applications. This paper proposes an optimized stack allocation approach for power allocation, considering economy and dynamics to establish integrated subsystems with added functional components. The results show that an MFCS with target powers of 20 kW, 70 kW, and 120 kW satisfies lifetime and efficiency factors. The common rail buffer at the air supply subsystem inlet stabilizes pressure, buffers, and diverts. By adjusting the volume of the common rail buffer, it is possible to reduce the maximum instantaneous power and consumption of the air compressor. The integrated hydrogen supply subsystem improves hydrogen utilization and reduces parasitic power consumption. However, the integrated thermal subsystem does not have the advantages of integrated gas supply subsystems, and its thermal management performance is worse than that of a distributed thermal subsystem. This MFCS provides a solution for high-power non-average distribution PEM fuel cell systems.

Optimal allocation of multiple energy storage in the integrated energy system of a coastal nearly zero energy community considering energy storage priorities

Energy storage technologies play a vital role in the low-carbon transition of the building energy sector. However, integrating multiple energy storage (MES) into integrated energy system (IES) in high-demand coastal communities remains a challenging task. This study proposes a novel regional IES that incorporates batteries, compressed air energy storage, and thermal energy storage for the simulated coastal community in Hong Kong; then developed the multi-objective optimization considering matching, economic, and environmental performance on MES capacity allocation with specially consideration of three energy management strategies under different energy storage priorities. The results show that all three storage priorities perform favorable outcomes across all objectives; compared to a reference scenario without storage, they achieve a relative net present value (NPVr) over 40 million HKD, a 0.29 improvement in matching performance, and over 2600 tCO2 in carbon emission reduction. Moreover, the renewable energy consumption rate exceeds 90 %, and the annual electricity import demand reduction rate exceeds 50 %. Sensitivity analysis highlights that larger energy storage capacities do not necessarily yield better outcomes, emphasizing the importance of synergistic interactions among multiple storage technologies. The presented energy system with MES and the optimal allocation approach herein may provide valuable insights for future energy planning in nearly-zero energy communities.

Fuzzy logic-based approach for optimal allocation of distributed generation in a restructured power system

Fuzzy logic emerges as a powerful tool for optimizing power flow solutions, particularly in the context of deregulated power systems. By employing fuzzy logic controls, the ideal placement of distribution generators (DGs) can be determined, ensuring the reliability indices are identified through optimal power flow solutions and fuzzy logic controllers to maintain system feasibility. In a deregulated power system, strategic placement of distribution generator units plays a crucial role in minimizing power loss and enhancing overall system performance by mitigating fluctuations. To identify areas of weakness, especially within transmission companies, accessing optimal power flow algorithms becomes essential in a deregulated power system. Both transmission and distribution networks should be appropriately adjusted to alleviate congestion within the respective companies. The aggregator must assess system performance, utilizing data obtained from distribution and transmission companies within the deregulated power system.

Optimal allocation of multiple capacitors in a hybrid AC/DC microgrid for power quality improvement

Along with the various features for implementing the Hybrid AC/DC Microgrid (HMG), this article proposes an approach for optimal allocation of multiple capacitors which are investigated in a proposed modeling based on the IEEE 14-bus distribution system. The power quality of the HMG has been investigated during the urgent intermittent of Distributed Energy Resources (DERs) and Reactive Power Compensation (RPC) methods. Moreover, the investigation has been achieved in the presence of unbalanced loads and nonlinear loads with maximum and minimum demand scenarios. To cope with the power quality concerns in the studied cases, the fixed capacitor bank as an RPC system in Medium Voltage (MV) level load buses has been utilized. Although the performance indices of the power quality improved in MV-level buses, the Low Voltage (LV) level load buses still endure extensive operation performance deteriorations caused by unbalanced loads. Therefore, in this article, a compensation scheme applied in LV-level load buses and MV-level buses has been proposed consistent with the power flow computations. The Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm is implemented to optimize both the size and location of capacitor banks over different voltage levels with high accuracy. The comprehensive assessment and discussion of the simulation results demonstrate the superiority of utilizing the proposed compensation scheme in both MV-level and LV-level load buses. Hence, the power quality is not only enhanced but also the installation cost is reduced. The complete model of the studied system has been validated using MATLAB/ Simulink.Article HighlightsThe reactive power compensation method is used to improve the power quality in a hybrid AC-DC microgrid.The applied RPC method verifies using the MOGWO algorithm to optimize both the location and size of multiple capacitor banks.Power quality challenges and future research trends are debated.

An approach for optimal tolerance allocation of five-axis machine tools by simultaneously considering volumetric error and processing simplicity index

Optimal tolerance allocation (OTA) is one of the most effective ways to improve the machining precision of machine tools. However, the existing OTA methods have the problem that tolerance-cost models are particularly vulnerable to subjective factors, which lead to tolerance allocation scheme with poor applicability and reliability. For this reason, a novel OTA method that simultaneously considers volumetric error and processing simplicity index (PSI) is proposed in this paper. Based on the multi-body system theory and homogeneous transform matrix, the volumetric error prediction model based on tolerance of machine tools’ key parts is firstly established by taking the geometric error as a bridge between tolerance and volumetric error of machine tools. Then, the weight coefficient of each tolerance parameter is calculated based on sensitivity analysis method. Moreover, PSI, which denotes the degree of processing simplicity to reach the required tolerance value, is innovatively proposed to replace manufacturing cost. And the tolerance-PSI model is established to replace tolerance-cost model. Then, an OTA model is established by minimizing volumetric error and maximizing PSI. The NSGA-II is employed to obtain OTA scheme. Finally, simulations and experiments are conducted to validate the feasibility and effectiveness of the presented method.

Retracted: A Multirate Cognitive-Based Approach for Optimal Dynamic Allocation of English Online Teaching Resources and IoT Applications

Retracted: A Multirate Cognitive-Based Approach for Optimal Dynamic Allocation of English Online Teaching Resources and IoT Applications

A multi-agent decision approach for optimal energy allocation in microgrid system

A multi-agent decision approach for optimal energy allocation in microgrid system

Optimal allocation and sizing of ESSs for power system oscillation damping under high wind power penetration

This paper proposes a novel power oscillation damping approach for transient stability enhancement of a power system with high penetration of wind power, where the energy storage system (ESS) is utilized to assist the damping of power oscillation with power system stabilizer and power oscillation damper. First of all, the ESS-based power system stabilizer and power oscillation damper structures are proposed to enhance the flexibility of power input and output of the wind turbines and the synchronous generators. Subsequently, the effect of installing ESSs for the enhancement the SGs and WTs’ damping torque is analyzed. In the meantime, we proposed an optimal allocation approach to decide the installation nodes of the ESSs by minimizing the Euclidean distance of the system’s uncontrollable oscillation modes to its controllability Gramian, along with the optimal sizing approach of ESSs via a novel unit load energy function averaging approach. Simulations are conducted to validate the effectiveness of the proposed scheme, and the superior performance of the proposed approach is also proven via comparative studies.

A Novel Hybrid Optimization Approach for Optimal Allocation of Distributed Generation and Distribution Static Compensator with Network Reconfiguration in Consideration of Electric Vehicle Charging Station

Electric vehicle charging station (EVCS) infrastructure development is a crucial part of the sustainable growth of the electric vehicle (EV) industry. However, the installation of EVCS may negatively affect the performance of the distribution system. This article presents a novel hybrid optimization approach to solve the optimal allocation problem of EVCS with network reconfiguration in the presence of distributed generation (DG) and distribution static compensator (DSTATCOM), with the objectives of reducing the voltage deviation, real power loss, and investment costs of DGs and DSTATCOMs while improving the voltage stability of the distribution network. A hybrid of African vulture optimization and pattern search (HAVOPS) algorithm is proposed to solve this problem. The impacts of EVCSs are examined for the 33-bus and 136-bus systems with five different cases of DG and DSTATCOM placement with reconfiguration of the network. The efficacy and performance of the HAVOPS algorithm are validated by comparison to the results of the African vulture optimization algorithm, marine predators’ algorithm, grey wolf optimizer, artificial gorilla troops optimizer, and genetic algorithm. According to the findings, the HAVOPS algorithm is superior to the other methods in terms of the merit of the solutions it produces.

Multi-Objective Optimization-Based Approach for Optimal Allocation of Distributed Generation Considering Techno-Economic and Environmental Indices

Distribution networks have entered a new era with the broad adoption of the distributed generation (DG) allocation as a practical solution for addressing power losses, voltage variation, and voltage stability. The primary goal is to enhance techno-economic and environmental characteristics while meeting the limitations of the system. In order to allocate DGs in active distribution networks (ADNs) efficiently, this study demonstrates two optimization methods inspired by nature: ant lion optimization (ALO) and multiverse optimization (MVO). Various multi-criteria decision-making (MCDM) methods are used to find the best possible solution among the different alternatives. On the IEEE 33- and 69-bus active distribution networks, the proposed ALO was shown to be effective and produces the highest loss reduction in the IEEE 33- and 69-bus systems at 94.43% and 97.16%, respectively, and the maximum voltage stability index (VSI) was 0.9805 p.u and 0.9937 p.u, respectively; moreover, the minimum voltage deviation (VD) and annual energy loss cost for the given test systems was 0.00019 p.u and 3353.3 PKR, which shows that the suggested method can produce higher quality results as compared to other methods presented in the literature. Therefore, the proposed ALO is a very efficient, effective, and appealing solution to the optimal allocation of the distributed generation (OADG) problem.

Hierarchical Planar Platoon Control Toward Tracking Safety for Distributed-Driven Vehicles

This paper deals with the planar platoon control problem of distributed-driven electric vehicles considering modeling deviations and driving disturbances. The follower vehicle is arranged to track the leader vehicle on curved roads with high tracking accuracy and driving security. A hierarchical integrated control framework is proposed based on disturbance-rejection robust model predictive control (DRRMPC) strategy and optimal control allocation (OCA) approach. The upper layer is a feedforward control layer, where a linear time-varying (LTV) model predictive controller (MPC) produces constrained predictive control towards a nominal model. The time-interval-based reference states are obtained from a “shadow following” trajectory generator through vehicle-to-vehicle (V2V) communication. In the middle layer, the feedback control consisting of a robust controller (RC) and the disturbance compensation out of an extended state observer (ESO), take effect to reconfigure the LTV vehicle model into the nominal model with an off-line calculated constraint tube. The bottom layer performs optimal control allocation among decoupled motor torques and wheel angles towards minimal tire force utilization. Hardware-in-the-Loop (HIL) experiment validates the effectiveness and practicability of the proposed platoon control strategy.