Allocation Methodology Research Articles

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Purpose This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

An environmental based techno-economic assessment for battery energy storage system allocation in distribution system using new node voltage deviation sensitivity approach

Photovoltaic project with Battery energy storage system (BESS) provide a sustainable and low-cost fuel technology to world and would help to meet world countries decarbonization target. This paper presents a multi-objective based methodology for BESS allocation in distribution networks. In proposed work, objective function combines various benefits from energy arbitrage, energy losses reduction, reduction in transmission access fee and environmental emission and reduction in investment costs of BESS. The new node voltage sensitivity analysis strategy utilized in initial step to recognize and visualize the prominent nodes in system and additionally decreases the search space for genetic algorithm (GA). Battery charge/discharge function is formulated for optimal allocation and every day charge/discharge of BESS. The proposed method is tested on IEEE 33- bus network. The results of this method shows reduction in energy losses, environmental emission and improvement in system parameter are also observed.

Methodology and principles of assets allocation

Every year the number of those wishing to dive in investments is growing at exponential way. In despite of situation in 2020 many people could efficiently use this time, and some of them could even reserve the assets. But now the other challenge is arisen, how should the assets be invested in an effective way using all power of modern financial markets? The slight knowledge intended for experienced people are presented in the most books, articles and manuals. But how the novice investors should manage which do not see the difference between stock and bond, and the “diversification” word ignites panic-driven fear and discourages from development in this area. The material helping every of those wishing to start without fear and doubts the diving in investment from the very beginning is presented in this work. The main classes of assets with which the Wall Street’s financial experts work, principles for assets allocation and methods helping to choose the optimal model for profit-risk calculation are described in this article.

Factor-targeted Asset Allocation: a Reverse Optimization Approach

When assets' expected returns follow a factor structure subject to pricing errors, we show that the mean-variance portfolio can be used to obtain a set of implied factor risk premia. Contrary to the instability of the mean-variance asset portfolio, we show that such implied factor risk premia imply stable factor exposures. To translate factor exposures into asset weights, we propose an asset allocation methodology that targets such stable factor exposures. Our empirical results demonstrate that our factor-targeted portfolios exhibit higher Sharpe ratios than mean-variance and various risk-based allocation rules and are robust against pricing errors in expected returns.

A realistic cost allocation methodology based on power flow tracing in deregulated power systems

In earlier days, there was no competition among the generating companies. With the concept of deregulation in the power industry, consumer takes the power from the generation or transmission or distribution directly. In this paper, for deregulated environment, transmission cost is dependent on the transmission loss and shunt capacitor compensators. This cost must be allocated to generators based on their realistic contributions. These contributions are then calculated using power flow tracing mechanism. The economic scheduling of generations is identified by solving cubic cost equation and the generations are then balanced. The losses thus obtained are balanced using tracing-based loss allocation procedure. The total analysis is performed on IEEE-14 bus system with supporting numerical and graphical results.

A realistic cost allocation methodology based on power flow tracing in deregulated power systems

In earlier days, there was no competition among the generating companies. With the concept of deregulation in the power industry, consumer takes the power from the generation or transmission or distribution directly. In this paper, for deregulated environment, transmission cost is dependent on the transmission loss and shunt capacitor compensators. This cost must be allocated to generators based on their realistic contributions. These contributions are then calculated using power flow tracing mechanism. The economic scheduling of generations is identified by solving cubic cost equation and the generations are then balanced. The losses thus obtained are balanced using tracing-based loss allocation procedure. The total analysis is performed on IEEE-14 bus system with supporting numerical and graphical results.

Robust Power Allocation for Resource-Aware Multi-Target Tracking With Colocated MIMO Radars

Power allocation is an essential part of the design of colocated multiple-input and multiple-output (C-MIMO) radar systems for multi-target tracking (MTT). A widely adopted power allocation approach is to directly minimize the amount of allocated power while satisfying the desired performance for each tracked object so as to conserve the power resources. However, this approach may not lead to a satisfactory solution when the available power is not sufficient to achieve the desired performance for all the targets. To overcome the limitations of this approach, a robust power allocation (RPA) methodology is proposed in this paper based on the quality of service framework. At its core, the proposed RPA employs a task utility function that quantifies the tracking performance for different power allocations in a flexible manner. The Bayesian Cramér-Rao lower bound (BCRLB) is utilized to formulate the task utility function since it provides a lower bound on the accuracy of target state estimates. By using a set of weights to quantify the importance of different tracked objects, the objective function of RPA is modeled as the weighted sum of task utility functions. This formulation of the RPA problem is demonstrated to be a non-convex optimization problem in general. We show that the task utility function is unimodal. Based on the specific structure of the task utility function, we propose an iterative parallel search algorithm to find the solution. Numerical experiments involving scenarios with both sufficient and insufficient power resources demonstrate the robustness and efficiency of the proposed strategy.

Optimal allocation of limited test resources for the quantification of COVID-19 infections.

The systematic identification of infected individuals is critical for the containment of the COVID-19 pandemic. Currently, the spread of the disease is mostly quantified by the reported numbers of infections, hospitalisations, recoveries and deaths; these quantities inform epidemiology models that provide forecasts for the spread of the epidemic and guide policy making. The veracity of these forecasts depends on the discrepancy between the numbers of reported, and unreported yet infectious, individuals. We combine Bayesian experimental design with an epidemiology model and propose a methodology for the optimal allocation of limited testing resources in space and time, which maximises the information gain for such unreported infections. The proposed approach is applicable at the onset and spread of the epidemic and can forewarn of a possible recurrence of the disease after relaxation of interventions. We examine its application in Switzerland; the open source software is, however, readily adaptable to countries around the world. We find that following the proposed methodology can lead to vastly less uncertain predictions for the spread of the disease, thus improving estimates of the effective reproduction number and the future number of unreported infections. This information can provide timely and systematic guidance for the effective identification of infectious individuals and for decision-making regarding lockdown measures and the distribution of vaccines.

Dose dependent reduction in median effective concentration (EC50) of ropivacaine with adjuvant dexmedetomidine in labor epidural analgesia: An up-down sequential allocation study

Study objectiveAdjuvant dexmedetomidine can be used to reduce the required concentration of ropivacaine for labor epidural analgesia. However, the potency of dexmedetomidine has not been fully studied. The purpose of this study was to determine the median effective concentration (EC50) of ropivacaine with adjuvant dexmedetomidine. DesignProspective, double-blind, up-down sequential allocation study. SettingAcademic medical center specializing in the care of women and children. PatientsOne hundred and fifty healthy, term parturients requesting labor epidural analgesia were randomly assigned to 1 of 5 different concentrations of dexmedetomidine: 0 μg/ml, 0.3 μg/ml, 0.4 μg/ml, 0.5 μg/ml, or 0.6 μg/ml. InterventionsThe study solution for the first patient in each group included the randomly assigned concentration of dexmedetomidine in 0.1% ropivacaine. Subsequent patients in each randomization group received the assigned concentration of dexmedetomidine in a new concentration of ropivacaine as determined by the up-down allocation methodology. Effective analgesia was defined as pain on the visual analogue scale of<3 at30 min after administration of local anesthetic. The up-down sequential allocation method and probit regression were used to calculate the EC50 of epidural ropivacaine. MeasurementsThe primary outcome was pain 30 min after administration of local anesthetic via epidural catheter. Exploratory outcomes included side effects, neonatal outcomes, and obstetric outcomes. Main resultsThe EC50 values for ropivacaine in dexmedetomidine 0.4 μg/ml, 0.5 μg/ml, and 0.6 μg/ml (0.044% [95% CI 0.036% to 0.045%], 0.035% [95% CI 0.031% to 0.041%], and 0.039% [95% CI 0.034% to 0.045%], respectively) were lower compared to ropivacaine in dexmedetomidine 0 μg/ml and 0.3 μg/ml (0.086% [95% CI 0.081% to 0.092%], and, 0.069% [95% CI 0.056% to 0.076%], respectively). Differences between EC50 values for ropivacaine in dexmedetomidine 0.4 μg/ml, 0.5 μg/ml, and 0.6 μg/ml were not statistically significant. Results of our exploratory analyses did not reveal differences in side effects, neonatal outcomes, or obstetric outcomes. ConclusionsIn this study, the lowest concentration of dexmedetomidine in ropivacaine with the greatest clinical effect was 0.4 μg/ml, which is important because there may be no additional analgesic benefit of dexmedetomidine greater than 0.4 μg/ml.

Temperature Tracking and Management With Number-Limited Thermal Sensors for Thermal-Aware NoC Systems

As the complexity of multicore system grows with respect to the technology development, the Network-on-Chip (NoC) provides flexible and scalable interconnection for the multicore systems. However, as the complexity of the network increases, the large workload diversity and the time-varying workload distribution result in large power density, which leads to severer thermal problems and makes the temperature distribution of the system become time-varying. To prevent the multicore systems from overheating, in a practical way, many thermal sensors are embedded in the system. However, due to the manufacturing cost constraints, it is not a viable option to involve a massive number of embedded thermal sensors. Therefore, searching for the appropriate locations in offline design phase to allocate the number-limited thermal sensors, which will be used to sense the time-varying system temperature behavior at runtime, is a design challenge. On the other hand, full-chip temperature distribution tracking based on the restricted temperature sensing information affects the efficiency of the involved temperature management. In this paper, we first present a novel thermal sensor allocation methodology by considering the time-varying temperature behavior on the chip according to different applications. Besides, a linear-regression-based reconstruction algorithm is proposed to estimate the full-chip temperature distribution according to the number-limited thermal sensing results. At last, a framework of temperature management with restricted temperature sensing information is introduced. Compared with the conventional methods, the proposed approach can reduce 28% to 92% average error of full-chip temperature estimation, which helps to improve the average system throughput by 60% to 70%.

Optimum Power Allocation Based on Channel Conditions in Optical Satellite Downlinks

The integration of optical satellite links in the next-generation networks and in the fifth generation cellular systems has been proposed in order to guarantee the handling of the extreme data traffic growth and the high-pitched demand for networks’ resources. The optical satellite communication downlink is studied and more specifically, a geostationary satellite with multiple transmitters and an optical ground station with multiple receiving terminals are considered. In this contribution a novel power allocation methodology is proposed for the downlink. The allocation methodology takes into account the scintillation effects due to atmospheric turbulence and maximizes the ergodic network capacity under total expected power and peak power constraints. The analytical optimizing schemes are based on convex optimization methods and have been inspired by waterfilling algorithm. We present emulated power allocation results using real experimental downlink data from ARTEMIS optical satellite campaign and then we investigate the performance of the proposed algorithm with extended numerical results and comparison with other allocation policies. In particular, the new power allocation strategy achieves the highest spectral efficiency, according to the power constraints, for various channel conditions and attenuation profiles and also surpasses two simple baseband allocation methods by intelligently taking advantage of the number of channels and the total expected power.

RETRACTED ARTICLE: An advanced artificial intelligence technique for resource allocation by investigating and scheduling parallel-distributed request/response handling

Cloud computing is an emerging technology undergoing various challenges that integrate parallel and distributed computing together. In the multi-tenant environment cloud applications can be utilized as a service. User request are enormous and therefore the attributes to be concerned about are scalability, reliability, and resource availability and server response. Utilization of software, platform and infrastructure increases in this environment paving way for resource consumption. This scenario arises various types of issues through collision, traffic jam, data loss, request dropout and delay in response. The past research provides solutions for aspects like scalability, resource allocation, scheduling, load balancing and optimized request and response handling, resource management through virtualization. The process of virtualization and migration of environment is difficult. The cost for allocating VM for a single user is less. The paper proposed a novel scheduling approach for handling unlimited incoming request with quality of service through energy and throughput. The allocated resource focus on maintaining incoming job request, request for dispatch to the server and an acknowledgement for the receipt of response. The paper provides resource allocation methodology through scheduling approaches called integrating of AI techniques namely Genetic Algorithms (GA) and Artificial Neural Networks (ANN). The property of the request is analyzed and priorityis applied for scheduling the request using resource allocation.

Dynamic Sectorization and parallel processing for device-to-device (D2D) resource allocation in 5G and B5G cellular network

Beyond Fifth Generation (B5G) network aims to provide a very high data rate with minimum latency to an ultra-dense user environment. To achieve this demand, the possible approaches are network-centric and device-centric approach. In a network-centric approach, a new frequency band is introduced, and the existing network infrastructure is modified. The device-centric approach does not require any modification in the existing network infrastructure, and the demand of B5G network is achieved through optimum resource allocation methodology. Device-to-Device (D2D) is an effective device-centric approach that supports the B5G network. In this paper, we propose a dynamic sectorization technique in which eNodeB (eNB) varies the number of sectors dynamically in the network and allocates the Resource Block (RB) to D2D users. Sectoring improves Signal-to-Interference-Noise-Ratio (SINR) and network performance. We derive an expression for the probability of successful transmission and threshold to make a decision on the number of sectors based on available RBs and D2D users in the network. Further, dynamic sectoring helps eNB to perform parallel processing for reducing the denial of the request. Simulation results validate the probability of successful transmission of D2D pairs with available RBs and the effectiveness of parallel processing to reduce the denial of request with improved SINR.

Coordinated Optimal Allocation of Distributed Generations in Smart Distribution Grids Considering Active Management and Contingencies

This study presents a multi-objective bi-level optimization model for distributed generation (DG) allocation in smart distribution grids integrating energy storage devices. As part of smart distribution grids, four active management schemes, coordinated on-load tap-changer voltage control, DG power factor control, DG curtailment and demand side management, are embedded in the proposed model. Uncertainties related to DGs, loads and contingencies and the capability of energy storage devices for peak shaving and renewable energy compensation are also inherent. The allocation model simulates the network transfer process to postpone the DG investment. The trade-off between the defined annual total cost and N-1 security margin index is achieved in the optimal allocation methodology considering operation thresholds and security improvements. The DG allocation solutions are solved by a hybrid algorithm. The correlated input parameters of the optimization problem, such as wind speed, illumination intensity and load, are generated using quasi Monte Carlo simulation and singular value decomposition and then simplified by fuzzy C-means clustering to improve the computation efficiency of optimal power flow. A modified 104-bus distribution case is used to demonstrate the effectiveness and flexibility of the proposed model.

Waste load allocation under uncertainty using game theory approach and simulation-optimization process

AbstractThis article aims to present a new methodology for waste load allocation (WLA) in a riverine system considering the uncertainty and achieve the lowest amount of inequity index, cost, and fuzzy risk of standard violation. To find a surface of undominated solutions, a new modified PAWN method, initially designed for sensitivity analysis, was developed and coupled with a simulation-optimization process using multi-objective particle swarm optimization (MOPSO) algorithm, to consider the uncertainty of all affecting variables and parameters by using their probability distribution. The proposed methodology applied to Sefidrood River in the northern part of Iran. Graph model for conflict resolution (GMCR) as a subset of game theory was implemented to attain a compromise on WLA among the stakeholders of a river system's quality in Iran: Department of Environment, Municipal Waste Water, and Private Sector. Some undominated solutions were used in GMCR model and modeling the conflict among decision makers reveals that their preferences and the status quo do not lead to a solely stable equilibrium; thus the intervention of a ruler as arbitrator leads them to reach a compromise on a scenario that has a median FRVS and cost. Sensitivity analysis was done using the PAWN method to assess the sensitivity of three intended objectives to all variables and parameters.

Resource Allocation and Sharing in URLLC for IoT Applications Using Shareability Graphs

The current development trend of wireless communications aims at coping with the very stringent reliability and latency requirements posed by several emerging Internet-of-Things (IoT) application scenarios. Since the problem of realizing ultrareliable low-latency communications (URLLCs) is becoming more and more important, it has attracted the attention of researchers, and new efficient resource allocation algorithms are necessary. In this article, we consider a challenging scenario where the available spectrum might be fragmented across nonadjacent portions of the band, and channels are differently affected by interference coming from surrounding networks. Furthermore, channel state information (CSI) is assumed to be unavailable, thus requiring an allocation of resources-based only on topology information and channel statistics. To address this challenge in a dense smart factory scenario, where devices periodically transmit their data to a common receiver, we present a novel resource allocation methodology based on a graph-theoretical approach originally designed to allocate mobility resources in on-demand, shared transportation. The proposed methodology is compared with two benchmark allocation strategies, showing its ability of increasing spectral efficiency of as much as 50% with respect to the best performing benchmark. Contrary to what happens in many resource allocation settings, this increase in spectrum efficiency does not come at the expense of fairness, which is also increased as compared to benchmark algorithms.

Exchange rate predictability and dynamic Bayesian learning

SummaryWe consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross‐section.

An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

Redesigning residual cost allocation in electricity tariffs: A proposal to balance efficiency, equity and cost recovery

In most power systems, residual costs (including, but not limited to, residual network costs and renewable support costs), are allocated through volumetric charges. This design, whose inefficiency has been latent in the last decades, is being challenged by the deployment of distributed energy resources, through which high-consumption customers may dramatically reduce the share of residual costs they pay, leaving a deficit to be paid by other customers. In principle, the most efficient alternative appears to be to allocate residual costs through fixed charges, but if these charges are flat, tariff equity is endangered. This article analyses the problem according to ratemaking theory, reviews the different allocation methodologies considered to date, and proposes a solution, based on uneven fixed charges, that allows to achieve efficiency, equity and cost recovery in modern electricity tariffs.

Modeling and transportation planning for US noncombatant evacuation operations in South Korea

Purpose The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions. Design/methodology/approach It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO. Findings This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment. Originality/value The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.