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Though GPRS is no longer used in typical multimedia applications, they are being extensively utilized in remote monitoring and control application due to their high reliability and relatively low cost.However the delay characteristics of lower layers of GPRS system play major role in its overall performance.In literature it is proved through Simulations that the shape of the delay distribution of small IP packets significantly differs from that of the, generally studied, large IP packets. This difference is mainly attributed to the Backward Error Correction (BEC) protocol employed in Radio Link Control (RLC) layer. In this paper we present a statistical analysis of these delay characteristics using discrete time Markov chain based analysis. The results show that the shape of delay distribution depends not only on the packet size but also on channel quality. Index Terms-GPRS, SR-ARQ, Small packet, RLC DelayI.

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Net-load variability and uncertainty in high renewable penetration networks have imposed new challenges to the system operators; adequate ramping, restart, and wider frequency and voltage operation capabilities provided by responsive resources are required to preserve acceptable service quality. Flexibility indicators and resource requirement constraints are proposed for unit commitment studies to address fast net-load variations. However, there is a gap in demonstrating the application of proposed flexibility indicators to determine the required minimum cost flexibility reserves to maintain desired operational performance. To address this issue for system operation planning purposes, a two-stage corrective flexibility constrained unit commitment (FCUC) formulation supported by a data-driven scheme for uncertainty quantification of flexibility shortage driven under and over frequency events is proposed. The first stage unit commitment (UC) is solved with relaxed flexibility constraints to determine the nominal resource schedules. A statistical test is used to determine if the second stage UC is needed. The second stage UC is solved with tighter flexibility constraints. A procedure to quantify the additional flexibility resources needed in the second stage UC to achieve adequate frequency regulation performance with lower operational costs is presented. Test results under various operation scenarios and comparisons with previous flexibility deployment methods illustrate the effectiveness of the proposed method. Test results indicate that in certain situations, flexible resources should remain online and be prioritized against less flexible (although cheaper) resources. The costs incurred by the additional system flexibility required to maintain good frequency control performance can be evaluated.