Agent based modeling of energy networks

Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

Fibre Channel Switch Modeling at Fibre Channel-2 Level for Large Fabric Storage Area Network Simulations using OMNeT++

Abstract—Typically, in the current enterprise data centers dedicated fabrics or networks are implemented to meet their LAN, Inter-Processor communication and storage traffic requirements. The storage traffic requirements of a group of servers are met through multiple storage area networks based on fibre channel, which has become the standard connection type. Typically, this fibre channel storage area networks are small (maximum of 32 switches/directors in a single fabric) and do not experience any scaling, stability and other performance issues.The advent of I/O consolidation in enterprise data centers for multiple traffic types to converge on to a single fabric or network (typically Ethernet platform) to reduce hardware, energy and management costs has also the potential to allow implementation of large storage area networks based on the fibre channel standards. Large storage area networks are being planned with more than two hundred switches/directors in a single fabric or network in addition to servers and storages connected to the fabric on Ethernet platforms. Even though these large storage area networks are envisioned to operate on Ethernet platform, they still have to satisfy the stringent operating and performance requirement set forth by the fibre channel standards. The two important issues of concern with large storage area networks are scaling and stability. The scaling and stability issues are dependent on the interactions and performance capabilities of various fabric servers located on each switch/director in the fabric in order to provide fabric services. In order to determine the extent of scaling and stability issues of a large fabric first the detailed models of the switch/director addressing the operations of the individual fabric servers are required. Next, the interactions of the switches/directors using the detailed models are to be simulated to study the scaling and stability issues.In this paper, the detailed modeling of the fibre channel switch and the fabric servers using the OMNeT++ discrete event simulator is presented first. Detailed models are developed addressing the behavior of the switch at the level-2 of the fibre channel protocol since this layer addresses the requirements and operations of various mandatory fabric services like fabric build, directory, login, nameserver, management, etc. Next, using the OMNET++ discrete event simulator large fabrics are simulated. The results from the simulation are compared against the test bed traffic and the accuracy is demonstrated. Also, results and analysis of multiple simulations with increasing fabric size are presented

Modeling and simulation enabled UAV electrical power system design

With the diversity of mission capability and the associated requirement for more advanced technologies, designing modern unmanned aerial vehicle (UAV) systems is an especially challenging task. In particular, the increasing reliance on the electrical power system for delivering key aircraft functions, both electrical and mechanical, requires that a systems-approach be employed in their development. A key factor in this process is the use of modeling and simulation to inform upon critical design choices made. However, effective systems-level simulation of complex UAV power systems presents many challenges, which must be addressed to maximize the value of such methods. This paper presents the initial stages of a power system design process for a medium altitude long endurance (MALE) UAV focusing particularly on the development of three full candidate architecture models and associated technologies. The unique challenges faced in developing such a suite of models and their ultimate role in the design process is explored, with case studies presented to reinforce key points. The role of the developed models in supporting the design process is then discussed

Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks

Multilayered artificial neural networks (ANN) have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant interest in the development of efficient hardware implementations. In this work, we focus on designing energy efficient on-chip storage for the synaptic weights. In order to minimize the power consumption of typical digital CMOS implementations of such large-scale networks, the digital neurons could be operated reliably at scaled voltages by reducing the clock frequency. On the contrary, the on-chip synaptic storage designed using a conventional 6T SRAM is susceptible to bitcell failures at reduced voltages. However, the intrinsic error resiliency of NNs to small synaptic weight perturbations enables us to scale the operating voltage of the 6TSRAM. Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0.5%) in accuracy (22nm predictive technology). Scaling beyond that causes substantial performance degradation owing to increased probability of failures in the MSBs of the synaptic weights. We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due to decoupled read and write paths. In an effort to further minimize the area penalty, we present a synaptic-sensitivity driven hybrid memory architecture consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation framework shows that the proposed synaptic-sensitivity driven architecture provides a 30.91% reduction in the memory access power with a 10.41% area overhead, for less than 1% loss in the classification accuracy.Comment: Accepted in Design, Automation and Test in Europe 2016 conference (DATE-2016

Modeling and Analysis of Content Caching in Wireless Small Cell Networks

Network densification with small cell base stations is a promising solution to satisfy future data traffic demands. However, increasing small cell base station density alone does not ensure better users quality-of-experience and incurs high operational expenditures. Therefore, content caching on different network elements has been proposed as a mean of offloading he backhaul by caching strategic contents at the network edge, thereby reducing latency. In this paper, we investigate cache-enabled small cells in which we model and characterize the outage probability, defined as the probability of not satisfying users requests over a given coverage area. We analytically derive a closed form expression of the outage probability as a function of signal-to-interference ratio, cache size, small cell base station density and threshold distance. By assuming the distribution of base stations as a Poisson point process, we derive the probability of finding a specific content within a threshold distance and the optimal small cell base station density that achieves a given target cache hit probability. Furthermore, simulation results are performed to validate the analytical model.Comment: accepted for publication, IEEE ISWCS 201