Smart grid allows the integration of distributed renewable energy resources into the conventional electricity distribution power grid such that the goals of reduction in power cost and in environment pollution can be met through an intelligent and efficient matching between power generators and power loads. Currently, this rapidly developing infrastructure is not as “smart” as it should be because of the lack of a flexible, scalable, and adaptive structure. As a solution, this work proposes smart grid as a service (SGaaS), which not only allows a smart grid to be composed out of basic services, but also allows power users to choose between different services based on their own requirements. The two important issues of service-level agreements and composition of services are also addressed in this work. Finally, we give the details of how SGaaS can be implemented using a FIPA-compliant JADE multiagent system.
Network-on-Chip (NoC) has been proposed as a promising communication architecture to replace the dedicated interconnections and shared buses for future embedded system platforms. In such a parallel platform, mapping application tasks to the NoC is a key issue because it affects throughput significantly due to the problem of communication congestion. Increased communication latency, low system performance, and low resource utilization are some side-effects of a bad mapping. Current mapping algorithms either do not consider link utilizations or consider only the current utilizations. Besides, to design an efficient NoC platform, mapping task to computation nodes and scheduling communication should be taken into consideration. In this work, we propose an efficient algorithm for dynamic task mapping with congestion speculation (DTMCS) that not only includes the conventional application mapping, but also further considers future traffic patterns based on the link utilization. The proposed algorithm can reduce overall congestion, instead of only improving the current packet blocking situation. Our experiment results have demonstrated that compared to the state-of-the-art congestion-aware Path Load algorithm, the proposed DTMCS algorithm can reduce up to 40.5% of average communication latency, while the maximal communication latency can be reduced by up to 67.7%.
The ill-famed von Neumann bottleneck has been the main performance hurdle since the invention of computers. Although several techniques such as separate data/instruction caches, branch prediction, and parallel computing have been proposed and improved efficiency, the throughput bottleneck between CPU and memory is still very much there. We propose a novel reconfigurable multi-core architecture (RMA) to address this issue via the dynamic allocation of heterogeneous computing resources and distributed memory. We show how this is feasible with the state-of-the-art technologies of dynamic partial reconfiguration of hardware resources and runtime operating system configuration. Experiments and analysis show how RMA alleviates the performance bottleneck. he is a Full Professor. His main research interests include: reconfigurable computing and system design, system-on-chip (SoC) design and verification, embedded software synthesis and verification, real-time system design and verification, hardware-software codesign and coverification, and component-based object oriented application frameworks for real-time embedded systems. This paper is a revised and expanded version of a paper entitled 'Reconfigurable multi-core architecture -a plausible solution to the von Neumann performance bottleneck' presented at 2013 IEEE 7th International Symposium on Embedded SoCs (MCSoC-13), Tokyo, Japan, 26-28 September 2013.
The traditional centralized power system is gradually being replaced by smart grids. However, an important design issue is how to perform accurate demand-response such that the power distribution management is effective. This includes two sub-problems, namely the accurate prediction of future electricity demand-response situations and the optimization of power distribution. In this work, we propose a novel Model Predictive Optimization (MPO) method for the advanced distribution management system in smart grids. Future electricity situations (surplus/deficit) are predicted using a customized Autoregressive Integrated Moving Average (ARIMA) model. Pairing between buyers and sellers of electricity are performed based on not only the current situation, but also considering future situations. As a result, trading pairs with overall near-optimal cost are found through concurrent and multiple instances of Particle Swarm Optimization (PSO), along with conflict resolution. Experimental results on 30 micro-grids show the error rate of the ARIMA prediction model to be less than 10%. The proposed MPO method saves totally 19.38% overall trading cost, if predictions are made for 4 future time slots.
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