José Luis Martínez Ramos

Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques

This paper presents a simple technique to forecast next-day electricity market prices based on the weighted nearest neighbors methodology. First, it is explained how the relevant parameters defining the adopted model are obtained. Such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction. Then, results corresponding to the Spanish electricity market during 2002 are presented and discussed. Finally, the performance of the proposed method is compared with that of recently published techniques.

Fast computation of voltage stability security margins using nonlinear programming techniques

This paper presents a simple, fast, and efficient method for determining the maximum loading point (MLP) and the voltage stability security margin of electric power systems. The proposed method is based on nonlinear programming techniques. The MLP is accurately obtained after a few load change steps. The computational procedure involves two kinds of load changes. Initially, load increases toward the MLP are defined for minimizing an objective function based on sensitivities. In case an overestimated load increase drives the system outside the feasible (stable) operating region, another very simple optimization-based procedure aimed to minimize the power mismatches determines the load adjustment (curtailment) to pull the system back onto the feasibility boundary. Simulation results for small test to large realistic systems are shown to validate the proposed method.

Time-Series Prediction: Application to the Short-Term Electric Energy Demand

This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and minimum forecasting errors.

Short-term hydro-thermal coordination based on interior point nonlinear programming and genetic algorithms

This paper presents a combined primal-dual logarithmic-barrier interior point and genetic algorithm for short-term hydro-thermal coordination. The genetic algorithm is used to compute the optimal on/off status of thermal units, while the interior point module deals with the optimal solution of the hydraulically-coupled short-term economic dispatch of thermal and hydro units. Inter-temporal constraints both due to cascaded reservoirs and maximum up and down ramps of thermal units are included in the latter module. Results from realistic cases based on the Spanish power system are reported.

Electricity Market Price Forecasting: Neural Networks versus Weighted-Distance k Nearest Neighbours

In todays deregulated markets, forecasting energy prices is becoming more and more important. In the short term, expected price profiles help market participants to determine their bidding strategies. Consequently, accuracy in forecasting hourly prices is crucial for generation companies (GENCOs) to reduce the risk of over/underestimating the revenue obtained by selling energy. This paper presents and compares two techniques to deal with energy price forecasting time series: an Artificial Neural Network (ANN) and a combined k Nearest Neighbours (kNN) and Genetic algorithm (GA). First, a customized recurrent Multi-layer Perceptron is developed and applied to the 24-hour energy price forecasting problem, and the expected errors are quantified. Second, a k nearest neighbours algorithm is proposed using a Weighted-Euclidean distance. The weights are estimated by using a genetic algorithm. The performance of both methods on electricity market energy price forecasting is compared.

A Comparison of Two Techniques for Next-Day Electricity Price Forecasting

In the framework of competitive markets, the markets participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported.

Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.

Slack bus selection to minimize the system power imbalance in load-flow studies

This paper reconsiders the notion of slack bus in load-flow studies. Instead of determining a priori which bus plays the role of slack bus, it is selected on the fly during the load-flow iterative process in such a way that the system power imbalance is minimized. The problem of selecting the best slack bus, or buses, is first formulated as a nonlinear optimization problem. Then, the results obtained are justified on the basis of the involved equality constraint being a quasilinear function, leading to an LP problem with trivial solution. It turns out that the optimal solution can be easily found from the results of a conventional load flow at a moderate cost. The proposed heuristic procedure is tested on the IEEE test systems.

Lack of current implantable cardioverter defibrillator guidelines application for primary prevention of sudden cardiac death in Latin American patients with heart failure: a cross-sectional study.

AIMS This cross-sectional study evaluated the application of accepted international implantable cardioverter defibrillator (ICD) guidelines for primary prevention of sudden cardiac death in patients with heart failure. METHODS AND RESULTS The PLASMA (Probabilidad de Sufrir Muerte Arritmica) study was designed to characterize management of cardiac patients in Latin America. Twelve centres included 1958 consecutively admitted patients in cardiology units in 2008 and 2009. Discharged patients were evaluated for primary prevention, ICD indication and prescription by general cardiologists. Of 1711 discharged patients, 1525 (89%) had data available for evaluating indication status. Class I indications for ICD therapy were met for 153 (10%) patients based on collected data. Only 20 (13%, 95% confidence interval: 7.7-18.4%) patients with indication were prescribed an ICD. Patients prescribed an ICD were younger than patients who were not prescribed an ICD (62 vs. 68 years, P < 0.01). The reasons given by cardiologists for not prescribing an ICD for 133 patients with an indication were: indication criteria not met (75%), life expectancy <1 year (9.7%), rejection by the patient (5.2%), no medical coverage paying for the device (3.7%), psychiatric patient (2.2%), and other reasons (4.2%). CONCLUSIONS In Latin America, international guidelines for primary prevention ICD implantation are not well followed. The main reason is that cardiologists believe that patients do not meet indication criteria, even though study data confirm that criteria are met. This poses a significant challenge and underlines the importance of continuous and improved medical education.

Load following control issues in the Spanish power system

Load following is carried out in a centralized way in the current Spanish power system, with several AGC modules of the main generation companies responding to the requirements of a master regulator. This service is provided under competition as an ancillary service, including a dedicated daily market. This paper shows that the current structure of the Spanish AGC system can be potentially improved from the load following performance point of view by using new algorithms of load following control. Load following control is proposed to reduce frequency deviations during normal system operation, using, at the same time, the lowest amount of regulation energy as possible, and, consequently, enhancing system reliability and economy.