Small-scale systems are vulnerable to changes. The development of solution strategies for changing conditions should, therefore, be considered from the outset in planning. For planners and installers of mini-grids for...
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Small-scale systems are vulnerable to changes. The development of solution strategies for changing conditions should, therefore, be considered from the outset in planning. For planners and installers of mini-grids for rural villages without grid connection, first of all, the question of the expected electricity demand arises. So far, this is determined primarily by interviewing the population. However, studies have shown that there are still major differences between the power demand estimated and the real load after electrification. Reinforcing here is the inadequate assessment of load development through immigration and increased prosperity. On the basis of a currently developing mini-grid in the Eastern Cape of South Africa, we could show that a load increase of similar to 60% within 10 years is to be expected. The following possibilities were outlined as possible ways of counteracting the increase in load: (i) communicating with the population, i.e. on energy efficiency to raise awareness and understanding, (ii) early planning of capacity building and identification of key performance parameters to trigger the expansion based on local socio-cultural conditions and grid supporting qualities. An adequate database for the initial electrification and power development should also be established and available on open-source basis for researchers, developers, communities, and installers.
systems for automated technical diagnostics of promising complex distributed information systems, often with critical properties of application, as well as actively added agent properties of autonomy, mobility, intell...
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ISBN:
(纸本)9781728140698
systems for automated technical diagnostics of promising complex distributed information systems, often with critical properties of application, as well as actively added agent properties of autonomy, mobility, intelligence, and cooperativity, should ensure their complete real-time testing. Moreover, the online testing should be carried out both for the static hardware-software and information environment of the system location, and for the dynamic aggregate of information-check flows corresponding to the main functions and scenarios of its work. This paper discusses a multi-level method of behavioral online testing, based on a multi-level model for check of behavior of hierarchical Petri nets. The check model allows us to present both the active processes of the basic functioning of the static environment for placing distributed information systems and dynamic information and control flows in it, as well as passive processes of behavioral online testing of the environment and flows. The proposed online testing method differs in the background hierarchical organization of the temporal development of processes and check flows with evolutionary optimization based on multi-agent organization and space-time decomposition and distribution in the hosting environment. The behavioral online testing method allows defining the main verification procedures and its preparation, reducing the time and resources costs due to the evolutionary search and multi-agent decomposition of the control analysis, which is important for real-time verification.
Triangle counting is a foundational graph-analysis kernel in network science. It has also been one of the challenge problems for the "Static Graph Challenge". In this work, we propose a novel, hybrid, parall...
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ISBN:
(纸本)9781728150208
Triangle counting is a foundational graph-analysis kernel in network science. It has also been one of the challenge problems for the "Static Graph Challenge". In this work, we propose a novel, hybrid, parallel triangle counting algorithm based on its linear algebra formulation. Our framework uses MPI and Cilk to exploit the benefits of distributed-memory and shared-memory parallelism, respectively. The problem is partitioned among MPI processes using a two-dimensional (2D) Cartesian block partitioning. One-dimensional (1D) row-wise partitioning is used within the Cartesian blocks for shared-memory parallelism using the Cilk programming model. Besides exhibiting very good strong scaling behavior in almost all tested graphs, our algorithm achieves the fastest time on the 1.4B edge real-world twitter graph, which is 3.217 seconds, on 1,092 cores. In comparison to past distributed-memory parallel winners of the graph challenge, we demonstrate a speed up of 2.7x on this twitter graph. This is also the fastest time reported for parallel triangle counting on the twitter graph when the graph is not replicated.
Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufacturing of highly customized products, ...
ISBN:
(数字)9781728169583
ISBN:
(纸本)9781728169590
Industry 4.0 is changing fundamentally the way data is collected, stored and analyzed in industrial processes. While this change enables novel application such as flexible manufacturing of highly customized products, the real-time control of these processes, however, has not yet realized its full potential. We believe that modern virtualization techniques, specifically application containers, present a unique opportunity to decouple control functionality from associated hardware. Through it, we can fully realize the potential for highly distributed and transferable industrial processes even with real-time constraints arising from time-critical sub-processes. In this paper, we present a specifically developed orchestration tool to manage the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or (edge) cloud computing platforms. Using off-the-shelf technology, the proposed tool can manage the execution of containerized applications on shared resources without compromising hard real-time execution determinism. Through first experimental results, we confirm the viability and analyzed the behavior of resource shared systems with strict real-time requirements. We then describe experiments set out to deliver expected results and gather performance, application scope and limits of the presented approach.
Modern power systems would compose of networked microgrids (MGs) operating independently. Respectively, MGs’ control units (MCUs) are responsible for optimising the operation of independent agents associated with loc...
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Modern power systems would compose of networked microgrids (MGs) operating independently. Respectively, MGs’ control units (MCUs) are responsible for optimising the operation of independent agents associated with local resources to maximise the social welfare of the system. In this regard, agents schedule their respective resources with the aim of maximising their profits; while MCU as a mediator entity strives to facilitate the flexibility of service exchange among the agents. In MGs, renewable energy sources (RESs) as potential sources of energy confront with uncertainty originated from their dependence on meteorological resources. Consequently, MGs would confront energy imbalance in realtime as a result of probable inaccuracies corresponding with forecasting the power production of RESs in the day-ahead market. To tackle this issue, a transactive-based framework is developed in this study that enables MCUs to incentivise the cooperation of agents scheduling flexible resources in minimising energy imbalances in realtime. Correspondingly, MCU offers bonuses as transactive signals to local agents to contribute in minimising the energy imbalance; which consequently results in improving the MG flexibility. Finally, the developed framework is implemented on an MG composed of independent agents scheduling flexible resources to investigate its effectiveness in minimising the energy imbalance in MGs.
Nested parallelism is a well-known parallelization strategy to exploit irregular parallelism in HPC applications. This strategy also fits in critical real-time embedded systems, composed of a set of concurrent functio...
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ISBN:
(纸本)9783030285968;9783030285951
Nested parallelism is a well-known parallelization strategy to exploit irregular parallelism in HPC applications. This strategy also fits in critical real-time embedded systems, composed of a set of concurrent functionalities. In this case, nested parallelism can be used to further exploit the parallelism of each functionality. However, current run-time implementations of nested parallelism can produce inefficiencies and load imbalance. Moreover, in critical real-time embedded systems, it may lead to incorrect executions due to, for instance, a work non-conserving scheduler. In both cases, the reason is that the teams of OpenMP threads are a black-box for the scheduler, i.e., the scheduler that assigns OpenMP threads and tasks to the set of available computing resources is agnostic to the internal execution of each team. This paper proposes a new run-time scheduler that considers dynamic information of the OpenMP threads and tasks running within several concurrent teams, i.e., concurrent parallel regions. This information may include the existence of OpenMP threads waiting in a barrier and the priority of tasks ready to execute. By making the concurrent parallel regions to cooperate, the shared computing resources can be better controlled and a work conserving and priority driven scheduler can be guaranteed.
distributed rooftop PV generator systems have been increasing significantly in distribution networks. However, due to the intermittent nature of the PV power generation, it is challenging to handle the fast voltage va...
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distributed rooftop PV generator systems have been increasing significantly in distribution networks. However, due to the intermittent nature of the PV power generation, it is challenging to handle the fast voltage variation of the distribution system through dispatching traditional low response speed devices such as load tap changers and capacitor banks. In this paper, a distributed hierarchical control strategy is proposed to deal with the voltage fluctuation issues through real-time regulating the injection or consumption reactive power of the fast response PV inverters. The proposed control strategy includes a primary droop control level and an agent-based distributed secondary control level. The droop-based primary level control can quickly regulate the voltage of PV inverter with locally measured information to overcome the fast voltage variation. The agent-based secondary level control can guarantee the average bus voltage restoration and proportionally reactive power sharing among the PV inverters through distributed communication between neighboring agents. With the proposed control strategy, both the average bus voltage restoration and proportionally reactive power sharing can be satisfied, which improves the stability of the distribution system. Furthermore, the proposed strategy is a fully distributed method that can distribute the computational and communication tasks among the local controllers through working in parallel, which is more flexible, scalable, and insusceptible to single-point failure.
Since the emergence of the fourth industrial revolution, data analysis is being conducted in various fields. distributed data processing has already become essential for the fast processing of large amounts of data. H...
Since the emergence of the fourth industrial revolution, data analysis is being conducted in various fields. distributed data processing has already become essential for the fast processing of large amounts of data. However, in the defense sector, simulation used cannot fully utilize the unstructured data which are prevailing at real environments. At the Korea Combat Training Center (KCTC), training unit conducts battles for two consecutive days and nights, resulting in about 300GB of voice, video, and engagement data. If the information stored as unstructured data can be analyzed and presented as visual evidence, it can affect the commands decision in response to the problems faced in the field during training. To this end, we propose a distributed data processing platform. In the military, Adaptive distributedparallel Simulation environment for Interoperable and reusable Models (AddSIM) has been developed to link data utilization with combat training programs. To overcome the issue of data utilization, several simulations are duplicated in a single program, and relevant results are obtained. However, in the case of AddSIM, the conditions for utilizing various weapon systems must be established simultaneously. If a corps is small, the model is limited. In this study, we propose a distributed data processing platform that can be applied to battalion level simulation models to provide visualized data for command decisions during training. The proposed distributed data processing platform is composed of two layers - the App layer and the System layer. In the App layer, a remote function is delivered to all workers and the worker receiving the task executes it. The driver submits the remote function to the local scheduler, following which the function gets forwarded to the global scheduler. The workers receive the task from the scheduler. At this time, the objects and functions stored in the global control store corresponding to the system layer can be shared in the w
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