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Coral
Composite Generation-Transmission Reliability Analysis
The reliability of a generation-transmission system assesses the adequacy of the system to supply load considering generation and transmission equipment outages, taking into account limits and operating constraints of the network in steady state operation.
Coral uses a probabilistic simulation of the states of generation and transmission equipments while considering load variations and the random effects of primary sources.
The performance of generation and transmission equipment states is assessed by means of an optimal power flow (OPF) which enforces network constraints while obeying operational limits of generators and circuits, minimizing total load shedding if outages of equipments result in insufficient generation or transmission capacity needed for load supply. The OPF algorithm uses a linearized power flow representation of the network, being capable of dealing with the effects of overloads, insufficient capacity and/or islanding and network separations; all violations are translated in terms of total load shedding.
Outages of generators and circuits are represented by their unavailability rates; load variations along time are represented by load blocks and their durations. Uncertainties in primary sources which affect generating capacity such as inflows to hydro plants are represented by scenarios of available capacity produced by SDDP, a hydrothermal scheduling model developed by PSR.
Reliability indices such as LOLP, EENS and the expected cost of reliability are estimated by Monte Carlo simulation for each stage of the horizon period, and disaggregated in terms of the contribution due to generation, transmission and composite outages. Bus and area indices are also estimated, as well as bus expected power marginal costs and expected sensitivities of the cost of reliability with respect to marginal generation and transmission reinforcements.
Variance reduction techniques specially devised to take part of the characteristics of combined outages of generators and circuits are used to reduce the sample size – and thus, the computational effort - the Monte Carlo simulation required to estimate reliability indices. The convergence of the indices is statistically monitored during the simulation by means of the variances of the corresponding estimators, allowing the control of the accuracy of estimators and of the computational effort of the simulation.
The model reads SDDP system configuration data files; system data can be also input and checked using a friendly user interface. System configuration changes and maintenance schedules are automatically updated along the simulation. Outputs of chosen reliability indices and sensitivities are plotted for selected load blocks and sub-periods of the study horizon; they can also be averaged along the load curve and annualized.
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