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.

Forecasting Electricity Prices and their volatilities using Unobserved Components.

The liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.

Grid-connected roof solar plants in educational institutions for testing and electricity generation: a case in Spain.

The use of photovoltaic experimental plants in engineering educational buildings contributes to an increase in acceptance of this technology by future engineers. There are some photovoltaic (PV) systems in educational buildings in Spain, but they are usually limited to buildings in relation to electrical technologies or research areas. They are not common in other educational or official buildings. This paper presents the project of a grid-connected solar plant with two main objectives. First, different PV module technologies will be compared. Second, an emphasis on agronomical areas in educational settings will be reviewed in an attempt to facilitate student engagement in the use of the power plant. The system is grid-connected in order to pay-back the investment in the plant. In fact the electricity generated by the plant will be used by the installations of the building, as it is the closest consumer. This work intends to approximate photovoltaic technology to university degrees not directly related with it and at the same time research in comparison of systems with different technologies. This is a good example of an solar plant for both optimum production and educational purposes.

The first renewable forward market mechanisms in the Iberian Electricity Market

A description of the first renewable forward market mechanisms in the Iberian Electricity Market is provided. A contract for difference mechanism is available in Spain since March 2011between the last resort suppliers and the special regime (renewables and cogeneration) settling the price differences between the equilibrium price of the forward regulated auctions for the last resort supply and the spot price of the corresponding delivery period. Regulated auctions of baseload futures of the Portuguese zone in which the Portuguese last resort supplier sells the special regime production exist since December 2011. The experience gained from renewables auctions in Latin America could be used in the Iberian Electricity market, complementing these first market mechanisms. Introduction of renewable auctions at least for the most mature technologies (i.e. wind) in Spain and Portugal providing a fair price for the renewable generation will be of utmost importance in the short term to diminish the tariff deficit caused by the massive deployment of the feed-in-tariff scheme. Liquidity in the forward markets will also increase as a result of the entry of renewable generation companies intending to maximize their profits due to gradual suppression of feed in tariff schemes.

Present and Future of Photovoltaic Solar Electricity

Ponencia Invitada presentada en el IEEE Region 8 Student Branch and GOLD Congress

Meter data management for smart monitoring power networks

The electrical power distribution and commercialization scenario is evolving worldwide, and electricity companies, faced with the challenge of new information requirements, are demanding IT solutions to deal with the smart monitoring of power networks. Two main challenges arise from data management and smart monitoring of power networks: real-time data acquisition and big data processing over short time periods. We present a solution in the form of a system architecture that conveys real time issues and has the capacity for big data management.

Electricity production usíng different photovoltaic technologies working at the same conditíons , a real case

The manufacture of photovoltaic (PV) modules has greatly increased in the past few years. The classical PV module is based on crystalline silicon (e-Si) , nevertheless the so called thinfilm technology is gaining importance each year. In this research paper we present a experimental grid-connected solar plant situated in one of the buildings of the Technical University of Madrid, with two main objectives.

Analysis of the Iberian electricity forward market hedging efficiency

An assessment of the hedging performance in the Iberian Forward Electricity Market is performed. Aggregated data from the Portuguese and Spanish clearing houses for energy derivatives are considered. The hedging performance is measured through the ratio of the final open interest of a month derivatives contract divided by its accumulated cleared volume. The base load futures in the Iberian energy derivatives exchange show the lowest ratios due to good liquidity. The peak futures show bigger ratios as their reduced liquidity is produced by auctions fixed by Portuguese regulation. The base load swaps settled in the clearing house located in Spain show initially large values due to low registered volumes, as this clearing house is mainly used for short maturity (daily and weekly swaps). This hedging ratio can be a powerful oversight tool for energy regulators when accessing to all the derivatives transactions as envisaged by European regulation.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

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

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 24h. 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 combi nation 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 Decem ber 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.

Present and future of photovoltaic solar electricity

Introduction Crystalline silicon technology, from quartz to system Economical and environmental issues Alternatives to cristalline silicon technology Conclusions

Extracción de energía del agua mediante generadores piezoeléctricos

El desarrollo del Proyecto consiste, por una parte, en el estudio sobre la respuesta de los materiales piezoeléctricos como generadores de energía en un entorno acuoso que está sometido a variaciones de potencial y, por otra, en el estudio técnico y económico de un equipo basado en la utilización esta fuente de energía y su comercialización en el mercado energético. Esta energía es la que se obtiene de la fuerza de arrastre del agua al desplazarse a causa del movimiento ondulatorio de las olas y que el elemento al flexionar es capaz de transformar. El proyecto contempla a partir de modelos, el funcionamiento eléctrico y mecánico de un generador piezoeléctrico, el sistema de electrónica necesaria para su inserción optimizada en la red eléctrica. A partir de esto se desarrolla una instalación que puede adaptarse a los requerimientos del estudio previo. Empleando un modelo de relación técnica y económica se establece una conexión entre estas dos secciones para hacerse una idea de la rentabilidad económica de un equipo de este estilo a día de hoy. El propósito final, es el de estudiar un nuevo tipo de energía, y comenzar una línea que puede llevar a un lugar muy interesante del sector energético. ABSTRACT This Project presents an analysis on the performance of piezoelectric materials as energy producer in an aquatic environment subject to potential variations. Additionally, the Project contains a technical and an economic analysis on the equipment based on the use of this energy source, as well as its commercialization. Energy is obtained by the water drag force when it is moved by the wave’s movement and by its transformation by the piezoelectric material. The Project studies the electric and mechanic functioning of a piezoelectric generator and the necessary electronic system for its optimized insertion on the electricity distribution network. Based on this, it is developed a system that can be adapted to the previous study requirements. The use of a technical and economic relation model allows the establishment of a connection among them in order to estimate the economic profitability of such equipment nowadays. The final objective of this Project is analyzing a new source of energy, which could start a new investigation line that may lead the energetic sector to a very interesting future.

Optimal Combined Forecasts for Electricity Prices: Influence of Clean Energies

In this work, an electricity price forecasting model is developed. The performance of the proposed approach is improved by considering renewable energies (wind power and hydro generation) as explanatory variables. Additionally, the resulting forecasts are obtained as an optimal combination of a set of several univariate and multivariate time series models. The large computational experiment carried out using out-of-sample forecasts for every hour and day allows withdrawing statistically sound conclusions

Compatison of S-SI, A-SI and CDTE technologies wording at the same conditions, after the first year of electricity production

Grid connected solar plants are a good opportunity for their use for research as a secondary objective. In countries were feed-in tariffs are still active, it is possible to include in the design of the solar plant elements for its use for research. In the case of the solar plant presented here both objectives are covered. The solar plant of this work is formed by PV modules of three different technologies: Multicrystalline, amorphous and CdTe. In one part of the solar plant, the three technologies are working at the same conditions, not only ambient conditions but also similar voltage and current input to the inverters. Both the commercial and the experimental parts of the solar plant have their own independent inverters with their meters but are finally connected to the same meter to inject. In this work we analyse the results for the first year of operation of the experimental solar plant. Productions of three different technologies in exactly the same conditions are compared and presented. According to the results, all the three technologies have conversion efficiencies dropping when the temperature increases. Amorphous module experiences the lesser reduction, whereas the multicrystalline module suffers the most.