Roberto Mínguez

Locational marginal price sensitivities

Within an optimal power flow market clearing framework, this paper provides expressions to compute the sensitivities of locational marginal prices with respect to power demands. Sensitivities with respect to other parameters can also be obtained. An example and a case study are used to illustrate the expressions derived.

Optimal Network Placement of SVC Devices

This paper addresses the optimal placement of static Var compensators (SVCs) in a transmission network in such a manner that its loading margin is maximized. A multi scenario framework that includes contingencies is considered. This problem is formulated as a nonlinear programming problem that includes binary decisions, i.e., variables to decide the actual placement of the SVCs. Given the mixed-integer nonconvex nature of this problem, a Benders decomposition technique within a restart framework is used. Detailed numerical simulations on realistic electric energy systems demonstrate the appropriate behavior of the proposed technique. Conclusions are duly drawn.

Sensitivity analysis in optimization and reliability problems

The paper starts giving the main results that allow a sensitivity analysis to be performed in a general optimization problem, including sensitivities of the objective function, the primal and the dual variables with respect to data. In particular, general results are given for non-linear programming, and closed formulas for linear programming problems are supplied. Next, the methods are applied to a collection of civil engineering reliability problems, which includes a bridge crane, a retaining wall and a composite breakwater. Finally, the sensitivity analysis formulas are extended to calculus of variations problems and a slope stability problem is used to illustrate the methods.

Power System State Estimation Considering Measurement Dependencies

State estimation measurements within a substation are routinely considered Gaussian and independent. In this paper, the questionable independence assumption is dropped and a statistical procedure is proposed to estimate the measurement variance-covariance matrix. The well-known weighted least squares technique for estimation is then modified to take into account measurement dependencies. Two case studies are analyzed and conclusions duly drawn.

Directional Calibration of Wave Reanalysis Databases Using Instrumental Data

AbstractWave reanalysis databases (WRDBs) offer important advantages for the statistical characterization of wave climate (continuous time series, good spatial coverage, constant time span, homogeneous forcing, and more than a 40-yr-long time series) and for this reason, they have become a powerful tool for the design of offshore and coastal structures. However, WRDBs are not quantitatively perfect and corrections using instrumental observations must be addressed before they are used; this process is called calibration. The calibration is especially relevant near the coast and in areas where the orography is complex, since in these places the inaccuracy of WRDB is evident because of the bad description of the wind fields (i.e., insufficient forcing resolution). The quantitative differences between numerical and instrumental data suggest that different corrections should be applied depending on the mean direction of the sea state. This paper proposes a calibration method based on a nonlinear regression pro...

An alternative approach for addressing the failure probability-safety factor method with sensitivity analysis

Abstract The paper introduces a method for solving the failure probability-safety factor problem for designing engineering works proposed by Castillo et al. that optimizes an objective function subject to the standard geometric and code constraints, and two more sets of constraints that simultaneously guarantee given safety factors and failure probability bounds associated with a given set of failure modes. The method uses the dual variables and is especially convenient to perform a sensitivity analysis, because sensitivities of the objective function and the reliability indices can be obtained with respect to all data values. To this end, the optimization problems are transformed into other equivalent ones, in which the data parameters are converted into artificial variables, and locked to their actual values. In this way, some variables of the associated dual problems become the desired sensitivities. In addition, using the proposed methodology, calibration of codes based on partial safety factors can be done. The method is illustrated by its application to the design of a simple rubble mound breakwater and a bridge crane.

Calculation of Measurement Correlations Using Point Estimate

Currents, voltages, and voltage-current phase angles are directly measured in substations and converted through current complex measurement systems into power injection and power-flow measurements. Since voltages, currents, and phase angles are directly measured, they are affected by errors that are statistically independent and generally Gaussian distributed. However, power injections and flows, which are fabricated out of currents, voltages, and phase angles, are affected by errors that are not generally independent. This paper describes a procedure to estimate the correlation matrix that characterizes the dependencies among all measurements within a substation. The proposed technique that relies on point estimate is accurate and computationally efficient. A realistic case study is used to compare the results obtained by using the proposed technique with those obtained using a cumbersome Monte Carlo algorithm.

Multiple Bad Data Identification Considering Measurement Dependencies

This paper analyzes the multiple bad data originated by a gross error in any voltage or current transformer of the measurement equipment. Considering the statistical correlations among measurements, an identification algorithm based on the largest normalized residual test is specifically designed to deal with multiple bad data. Two case studies are analyzed and conclusions duly drawn.

State Estimation Sensitivity Analysis

Within energy management systems, state estimation is a key function for building a network real-time model, which is a quasi-static mathematical representation of the current conditions in an interconnected network. The obtained model is dependent on the assumptions, and sensitivity analysis can be used to show how measurement schemes, transmission line modeling, and other parameters affect the quality of the state estimation solution. This paper provides expressions to compute all these sensitivities, and an example and a case study are used to illustrate them.

Design and sensitivity analysis using the probability-safety-factor method. An application to retaining walls

This paper presents a new method for designing engineering works that makes the classical approach, based on safety factors, and the modern, probability-based, approach compatible, and includes a sensitivity analysis. The method consists of a sequence of classical designs, based on given safety factors, that (a) minimize cost or optimize an alternative objective function, (b) calculate the different failure mode probabilities or their upper bounds, and (c) update the safety factors to satisfy both the safety factors and the failure probability requirements. The process is repeated until convergence. As a result, an automatic design of the engineering work, the safety factors and the corresponding probabilities of failure for all failure modes are obtained. A double safety check is used and the correspondence between safety factors and probabilities of failure for the different modes are easily understood. An advantage of this approach is that the optimization procedure and the reliability calculations are decoupled. In addition, a sensitivity analysis is performed using a method that consists of transforming the data parameters into artificial variables and using the dual associated problem. The method is illustrated by its application to a retaining wall design.