Eduardo Caro

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.

Yearly Maintenance Scheduling of Transmission Lines Within a Market Environment

Within a yearly horizon, a transmission system operator needs to schedule the maintenance outages of the set of transmission lines due for maintenance. Facing this task, two conflicting objectives arise: on one hand, the transmission system adequacy should be preserved as much as possible, and, on the other hand, market operation should be altered in the least possible manner. To address this scheduling problem, a bilevel model is proposed whose upper-level problem schedules line maintenance outages pursuing maximum transmission capacity margin. This upper-level problem is constrained by a set of lower-level problems that represent the clearing of the market for all the time periods considered within the yearly planning horizon. This bilevel model is conveniently converted into a nonlinear mathematical program with equilibrium constraints (MPEC) that can be recast as a mixed-integer linear programming problem solvable with currently available branch-and-cut techniques.

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.

Decentralized State Estimation and Bad Measurement Identification: An Efficient Lagrangian Relaxation Approach

This paper proposes a decentralized state-estimation approach that relies on an elaborated instance of the Lagrangian relaxation decomposition technique. The proposed algorithm does not require a central coordinator but just to moderate interchanges of information among neighboring regions, and exploits the structure of the problem to achieve a fast and accurate convergence. Additionally, a decentralized bad measurement identification procedure is developed, which is efficient and robust in terms of identifying bad measurements within regions and in border tie-lines. The accuracy and efficiency of the proposed procedures are assessed by a large number of simulations, which allows drawing statistically sound conclusions.

Breaker Status Identification

This paper presents a procedure to identify the on/off statuses of breakers at substations throughout a power network. It is intended as a structural data preprocessor prior to running a state estimator. The procedure relies on a dc model of power lines and busbars, and requires the solution of a well-behaved mixed-integer quadratic programming problem. The proposed procedure is illustrated using a simple example and a realistic case study.

Impact of Transformer Correlations in State Estimation Using the Unscented Transformation

This paper addresses the power system state estimation problem considering the correlations among instrument transformer signals. The correlations among processed measurements are estimated using the Unscented Transformation given the correlation and basic statistical information of internal measurements. In addition, the paper analyzes the impact of these correlations on the estimated states using the IEEE 118-bus test system under various operating conditions and measurement configurations.

Analytical Study of the Series Resonance in Power Systems With the Steinmetz Circuit

In traction systems, it is usual to connect reactances in delta configuration with single-phase loads to reduce voltage unbalances. This set is called a Steinmetz circuit. Parallel and series resonances can occur between the Steinmetz capacitor and system inductors, increasing harmonic voltage distortion. It is important to analyze the series resonance ldquoobservedrdquo from the supply system to avoid harmonic problems due to background voltage distortion. The paper studies this resonance analytically and presents simple expressions to locate it. Experimental measurements are also provided to validate the obtained analytical results.

Student academic performance stochastic simulator based on the Monte Carlo method

Abstract In this paper, a computer-based tool is developed to analyze student performance along a given curriculum. The proposed software makes use of historical data to compute passing/failing probabilities and simulates future student academic performance based on stochastic programming methods (Monte Carlo) according to the specific university regulations. This allows to compute the academic performance rates for the specific subjects of the curriculum for each semester, as well as the overall rates (the set of subjects in the semester), which are the efficiency rate and the success rate. Additionally, we compute the rates for the Bachelors degree, which are the graduation rate measured as the percentage of students who finish as scheduled or taking an extra year and the efficiency rate (measured as the percentage of credits of the curriculum with respect to the credits really taken). In Spain, these metrics have been defined by the National Quality Evaluation and Accreditation Agency (ANECA). Moreover, the sensitivity of the performance metrics to some of the parameters of the simulator is analyzed using statistical tools (Design of Experiments). The simulator has been adapted to the curriculum characteristics of the Bachelor in Engineering Technologies at the Technical University of Madrid (UPM).

Electricity Price Forecasting Accounting for Renewable Energies: Optimal Combined Forecasts

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al (2007 and 2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24 h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models (according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combination of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to December 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.