Space benefits: The secondary application of aerospace technology in other sectors of the economy

Benefit cases of aerospace technology utilization are presented for manufacturing, transportation, utilities, and health. General, organization, geographic, and field center indexes are included

Effective data compression model for on-line power system applications

The main objective of this paper is to develop an efficient data compression model for online power system applications such as load flow studies, state estimation, contingency analysis etc. and to calculate the round triptime taken for sending the compressed data in client/server architecture. Martin Burtscher algorithm is used for data compression since most of the power system data is expressed in per unit representation which is in floating point format. Many research works have been reported for representing and solving power system problems in distributed environments which include RMI, Component based, SOA and Grid computing. As the size of power systems is growing larger and larger due to increase in demand and as the interconnections between large power systems may vary from time to time due to addition of new generating units and due to geographic conditions, it becomes difficult to estimate the current operating states of the real time electric power system networks and data communication between the networks becomes difficult. The proposed method of power system data compression finds faster rate of data communications where the data is required for real–time analysis in a distributed environment

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Cost Efficient Distributed Load Frequency Control in Power Systems

The introduction of new technologies and increased penetration of renewable resources is altering the power distribution landscape which now includes a larger numbers of micro-generators. The centralized strategies currently employed for performing frequency control in a cost efficient way need to be revisited and decentralized to conform with the increase of distributed generation in the grid. In this paper, the use of Multi-Agent and Multi-Objective Reinforcement Learning techniques to train models to perform cost efficient frequency control through decentralized decision making is proposed. More specifically, we cast the frequency control problem as a Markov Decision Process and propose the use of reward composition and action composition multi-objective techniques and compare the results between the two. Reward composition is achieved by increasing the dimensionality of the reward function, while action composition is achieved through linear combination of actions produced by multiple single objective models. The proposed framework is validated through comparing the observed dynamics with the acceptable limits enforced in the industry and the cost optimal setups

Digitalization of Educational and Methodological Support for the Training of Aviation Dispatchers

The tasks of improving the educational process of training civil aviation dispatchers on the basis of the development and implementation of digital teaching aids are considered. Legislative and regulatory documents are accepted as an object of digitalization. The end result of the research is expressed in the provision of the educational process with a special electronic educational complex, which has the functions of providing the necessary information and conducting practical exercises to deepen, consolidate and control knowledge in the field of aviation documents

Hardware Prototype for a Multi Agent Grid Management System

There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect

Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities

The development of local energy communities observed in the last years requires the reorganization of energy consumption and production. In these newly considered energy systems, the commercial and technical decision processes should be decentralized in order to reduce their maintenance costs. This will be allowed by the progressive spreading of IoT systems capable of interacting with distributed energy resources, giving local sources the ability to be optimally coordinated in terms of network and energy management. In this context, this paper presents a decentralized controlling architecture that performs a wide spectrum of power system optimization procedures oriented to the local market management. The controller framework is based on a decentralized genetic algorithm. The manuscript describes the structure of the tool and its validation, considering an automated distributed resource scheduling for local energy markets. The simulation platform permits implementing the blockchain-based trading process and the automated distributed resource scheduling. The effectiveness of the tool proposed is discussed with a hardware-in-the-loop case study