Maximum Utilization Of Available Resources Research Articles

Modelling and Formal Verification Approach for Microgrid Energy Management Systems under Random Load

This paper presents the modelling and a formal verification approach of energy management control systems for microgrids that are subject to unknown demand and uncertainties in the availability of renewable energy sources. The main objective of this study is to develop a management system for a microgrid composed of renewable and non-renewable energy sources, specifying the different possible modes. A supervisory control method is proposed to autonomously control and monitor a microgrid and maintain more stable energy flows with maximum utilization of available resources. A timed automata network model of the microgrid has been developed by modeling the dynamics of components such as PV, BESS, diesel generator, load, and controller. Possible operating modes in the microgrid between sources and load are derived and classified into allowed and unallowed operating modes. Verification of the designed microgrid models was performed using simulation and formal methods. The advantageous performance of the proposed strategy is illustrated via a numerical example.

GTQC: Automated Genotyping Array Quality Control and Report.

Genotyping array is the most economical approach for conducting large-scale genome-wide genetic association studies. Thorough quality control is key to generating high integrity genotyping data and robust results. Quality control of genotyping array is generally a complicated process, as it requires intensive manual labor in implementing the established protocols and curating a comprehensive quality report. There is an urgent need to reduce manual intervention via an automated quality control process. Based on previously established protocols and strategies, we developed an R package GTQC (GenoTyping Quality Control) to automate a majority of the quality control steps for general array genotyping data. GTQC covers a comprehensive spectrum of genotype data quality metrics and produces a detailed HTML report comprising tables and figures. Here, we describe the concepts underpinning GTQC and demonstrate its effectiveness using a real genotyping dataset. R package GTQC streamlines a majority of the quality control steps and produces a detailed HTML report on a plethora of quality control metrics, thus enabling a swift and rigorous data quality inspection prior to downstream GWAS and related analyses. By significantly cutting down on the time on genotyping quality control procedures, GTQC ensures maximum utilization of available resources and minimizes waste and inefficient allocation of manual efforts. GTQC tool can be accessed at https://github.com/slzhao/GTQC.

Power sharing through interlinking converters in adaptive droop controlled multiple microgrid system

Multiple microgrids (MMGs) may be interconnected through voltage source converters (VSC) for controlled power sharing among themselves. VSCs in large interconnected micro- grids may use non-communication based droop control to share total power demand in the ratio of their droop coefficients. However, a constant droop ratio restricts the power delivery capability of the VSCs although surplus power may be available in their respective sources. In this paper, along with a unified power management strategy for intra and inter microgrid power sharing in a hybrid AC-DC MMG system, a novel adaptive droop control algorithm is presented that dynamically changes droop coefficients of the VSCs to ensure maximum utilization of available resources in each microgrid under different operating modes. Stability analysis of the proposed control method is performed and comparison of the system performance with fixed and adaptive droop ratio is illustrated by simulating the MMG in MATLAB SIMULINK using real solar irradiance field data. It is validated from simulation results that the adaptive droop control method increases utilization of renewable energy sources within microgrids and reduces energy import from utility grid.

Dynamic Scheduling in Cloud Computing using Particle Swarm Optimization

Objectives: Dynamic assignment of tasks to different VM in cloud datacenter and Load-Balancing by migration of tasks from an Overloaded VM to Candidate VM. Methods/Statistical Analysis: Particle Swarm Optimization (PSO) algorithm is used finding each iteration the P_BEST (Personal Best Solution) i.e. current solution is compared with the G_BEST (Global Best Solution) i.e. previous best solution and the G_BEST value is updated at each iteration for calculating the minimum execution time. The parameters being calculated are Global_BEST Solution Function, Personal_BEST Solution Function and Average Utilization for each processor. Findings: A Dynamic Approach for Task-Scheduling using the load-balancing technique is implemented in this paper. Two algorithms namely - Particle Swarm Optimization (PSO) and Improved PSO are used and a comparison is made between them based on number of performance parameters like Scheduling Length (Make Span), Total Execution Time and Total Transfer or Migration Time. A Utilization Graph is plotted to show this comparison which compares these algorithms based on their Cloudlet Length (Scheduling Length) and Total Execution Time. Improved PSO algorithm has lesser or minimum execution time as compared to the PSO algorithm because in the Improved PSO Algorithm two parameters are being considered namely- Cloudlet Length and MIPS (Million Instructions Per Second) which leads to maximum utilization of available resources by all VM. Application/Improvements: This approach is used for dynamically assigning tasks to VM and checking maximum utilization of available resources through load balancing and minimizing the overall execution time and migration time. Keywords: Candidate VM, Cloud Computing, Cloudlet, PSO, Task Migration, Transfer Time, Virtual Machine

Managing Pavement Maintenance and Rehabilitation Projects under Budget Uncertainties

A well-developed and maintained pavement management system (PMS) empowers a decision maker to select the best maintenance program, i.e., which maintenance treatment to use and where and when to apply it, so that a maximum utilization of available resources can be achieved. This paper addresses a decision making problem for managing pavement maintenance and rehabilitation projects under budget uncertainty (MPMRPBU). A stochastic linear programming model is formulated and solved for the MPMRPBU so that a set of candidate projects can be optimally selected from the highway network over a planning horizon. Numerical results are discussed based upon a pilot case study. Different optimization solutions based on deterministic optimization and stochastic programming approaches are discussed and compared. The effect of the budget constraint on the optimized solutions is investigated. The computational result indicates a high quality MPMRPBU solution using stochastic programming approach, suggesting that there is a potential that the algorithm can be used for real world applications.

Dynamic Resource Allocation in Hybrid Access Femtocell Network

Intercell interference is one of the most challenging issues in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduction at lower node density, but most of them are ineffective at higher node density. In this paper, a dynamic resource allocation management algorithm (DRAMA) for spectrum shared hybrid access OFDMA femtocell network is proposed. To reduce the macro-femto-tier interference and to improve the quality of service, the proposed algorithm features a dynamic resource allocation scheme by controlling them both centrally and locally. The proposed scheme focuses on Femtocell Access Point (FAP) owners' satisfaction and allows maximum utilization of available resources based on congestion in the network. A simulation environment is developed to study the quantitative performance of DRAMA in hybrid access-control femtocell network and compare it to closed and open access mechanisms. The performance analysis shows that higher number of random users gets connected to the FAP without compromising FAP owners' satisfaction allowing the macrocell to offload a large number of users in a dense heterogeneous network.