María Jesús Sánchez

Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market

Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed. The main idea is to develop an efficient tool for one-step-ahead forecasting in the future, combining several prediction methods for which forecasting performance has been checked and compared for a span of several years. Also as a novelty, the 24 hourly time series has been modelled separately, instead of the complete time series of the prices. This allows one to take advantage of the homogeneity of these 24 time series. The purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting. These results have been obtained by means of a computational experiment. A mixed model which combines the advantages of the two new models discussed is proposed. Some numerical results for the Spanish market are shown, but this new methodology can be applied to other electricity markets as well

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

The Identification of Multiple Outliers in ARIMA Models

Abstract There are three main problems in the existing procedures for detecting outliers in ARIMA models. The first one is the biased estimation of the initial parameter values that may strongly affect the power to detect outliers. The second problem is the confusion between level shifts and innovative outliers when the series has a level shift. The third problem is masking. We propose a procedure that keeps the powerful features of previous methods but improves the initial parameter estimate, avoids the confusion between innovative outliers and level shifts and includes joint tests for sequences of additive outliers in order to solve the masking problem. A Monte Carlo study and one example of the performance of the proposed procedure are presented.

Reliability analysis for systems with large hydro resources in a deregulated electric power market

This work describes a procedure that determines the optimal allocation for the yearly energy resulting from random water inflows to the different subperiods of a year so that the expected benefits are maximized. Its main idea is to distribute the energy stored in reservoirs in each period into two parts: one is directly sold in the energy market, while the other is made available to cover any unplanned outages of thermal units. The method proposed fulfills two objectives, to distribute the hydro energy optimally according to economic criteria and to assess the impact of new market rules on the reliability of an electric system. The procedure will be illustrated by an example based on the Spanish generating system.

Long-term generation scheduling in systems with large hydro resources in a deregulated electric power market

This work describes a procedure that determines the optimal allocation for the yearly energy resulting from random water inflows to the different subperiods of a year so that the expected benefits are maximized. Its main idea is to distribute the energy stored in reservoirs in each period into two parts: one is directly sold in the energy market, while the other is made available to cover any unplanned outages of thermal units. The method proposed fulfills two objectives, to distribute the hydro energy optimally according to economic criteria and at the same time to maximize the reliability of the system. The procedure is illustrated by an example based on the Spanish generating system.

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.

A MELCOR Application to Two Light Water Reactor Nuclear Power Plant Core Melt Scenarios with Assumed Cavity Flooding Action

Abstract The MELCOR 1.8.4 code Bottom Head package has been applied to simulate two reactor cavity flooding scenarios for when the corium material relocates to the lower-plenum region in postulated severe accidents. The applications were preceded by a review of two main physical models, which highly impacted the results. A model comparison to available bibliography models was done, which allowed some code modifications on selected default assumptions to be undertaken. First, the corium convective heat transfer to the wall when it becomes liquid was modified, and second, the default nucleate boiling regime curve in a submerged hemisphere was replaced by a new curve (and, to a much lesser extent, the critical heat flux curve was slightly varied). The applications were devoted to two prototypical light water reactor nuclear power plants, a 2700-MW(thermal) pressurized water reactor (PWR) and a 1381-MW(thermal) boiling water reactor (BWR). The main conclusions of the cavity flooding simulations were that the PWR lower-head survivability is extended although it is clearly not guaranteed, while in the BWR sequence the corium seems to be successfully arrested in the lower plenum. Three applications of the CFX 4.4 computational fluid dynamics code were carried out in the context of the BWR scenario to support the first modification of the aforementioned two scenarios for MELCOR. Finally, in the same BWR context, a statistic predictor of selected output parameters as a function of input parameters is presented, which provides reasonable results when compared to MELCOR full calculations in much shorter CPU processing times.

Operational Issues for the Hybrid Wind-Diesel Systems: Lessons Learnt from the San Cristobal Wind Project

Hybrid wind-diesel power systems have a great potential in providing energy supply to remote communities. Compared with the traditional diesel systems, hybrid power plants can offer many advantages such as additional capacity, being more environmentally friendly, and potential reduction of cost. The O&M of a hybrid power project requires comprehensive knowledge from both technical and managerial points of view. This study focuses on one of the largest existing hybrid wind-diesel power system, the San Cristobal Wind Project. Performance analysis and computer simulation are conducted to illustrate the most representative operational issues. We demonstrate that the wind uncertainty, control strategies, energy storage, and the wind turbine power curve have a significant impact on the performance of the system.

Analytical results for a Bayesian bivariate cokriging model

In this paper, we extend the results of Handcock and Steins univariate Bayesian kriging model to a bivariate response, integrating out analytically the two stationary variances.