Abstract

An electric power supply is the backbone of development in advanced as well as in developing economies. An integral part of ensuring a secure power supply system is a power communication system. Due to the high and sustained performance requirements of power communication systems, electric companies prefer to construct their own communication networks privately rather than relying solely on a public communication system. The focus of this paper is on the optimal topological design of a power communication network. Based on advanced optimization models in public communication networks, and taking into account the specific Quality of Service, as demanded by various applications, such as protection, SCADA, voice, etc., an optimization model (PC/ISO) has been developed. The PC/ISO requires tedious numerical processing. Hence, a Genetic Algorithm (GA) is proposed to solve the optimization problem. In order to demonstrate the application of the proposed model for a power system communication network design and in order to evaluate GA solver results, a case study on designing the optimal communication network topology of one of the Iranian Regional Electric Companies has been conducted. The results suggest that the PC/ISO model and its GA solver are entirely viable and offer a simple, accurate, and cost effective solution.

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Traditional traffic measurements meter throughput on time scales in the order of 5 minutes, e.g., using the Multi Router Traffic Grapher (MRTG) tool. The time scale on which users and machines perceive Quality of Service (QoS) is, obviously, orders of magnitudes smaller. One of many possible reasons for degradation of the perceived quality, is congestion on links along the path network packets traverse. In order to prevent quality degradation due to congestion, network links have to be dimensioned in such a way that they appropriately cater for traffic bursts on time scales similarly small to the time scale that determines perceived QoS. It is well-known that variability of link load on small time scales (e.g., 10 milliseconds) is larger than on large time scales (e.g., 5 minutes). Few quantitative figures are known, however, about the magnitude of the differences between fine and coarse-grained measurements. The novel aspect of this paper is that it quantifies the differences in measured link load on small and large time scales. The paper describes two case studies. One of the surprising results is that, even for a network with 2000 users, the difference between short-term and long-term average load can be more than 100%. This leads to the conclusion that, in order to prevent congestion, it may not be sufficient to use the 5 minute MRTG maximum and add a small safety margin. Document type: Part of book or chapter of book

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