Performance of UV-enhanced PMMA fresnel lenses and impact on LCOE

Tiny increases in the transmittance of optical materials within a CPV module can have an important impact on the economy of a plant. This is certainly true in systems comprising multi-junction solar cells, whose high performance, based on a balanced photocurrent generation among the series-connected junctions, is very sensitive to spectrum variations. Every efficiency point gained causes not only an increase in the kilowatts hour produced, but a higher benefit on it, since the difference between electricity tariff and Levelized Cost of Electricity (LCOE) rises. This work studies the impact on the LCOE of a plant based on modules comprising PMMA lenses of two different types, standard UV blocking grade which is normally used for outdoor applications at high DNI climate and a specialty stabilized UV-enhanced transmittance acrylic (see Figure 1). Energy production will be compared for these two systems throughout the year at different sites to analyze when (season, time of the day) and where the usage of the enhanced PMMA is justified.

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.

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.

Estudio de viabilidad para instalar una planta de cogeneración

En el presente proyecto se estudia la viabilidad, tanto técnica como económica, de la instalación de una planta de cogeneración, en una industria alimentaria en Aldaya (Valencia). El diseño de la central de cogeneración se basa en la demanda energética de la fábrica, atendiendo a los requisitos necesarios para acogerse al Régimen Especial de producción de energía eléctrica establecidos en el Real Decreto 661/2007, de 25 de mayo. En cuanto a la viabilidad económica, se ha elegido la alternativa de venta de electricidad a la red a tarifa, (a un precio fijo y regulado) con el fin de asegurar los ingresos en el estudio económico y reducir la sensibilidad del proyecto a las variables del mercado. Como resultado se obtienen la reducción de los costes de energía de la fábrica, mejora de la fiabilidad de suministro, reducción de emisiones globales de CO2, y una elevada rentabilidad del proyecto. ABSTRACT The aim of this project is to study the technical and economic feasibility of a cogeneration plant in a food industry in Aldaya (Valencia). The design of the cogeneration plant is based on the energetic demand of the factory, attending to the requirements specified in the Special Regime of production of electric power established in the R.D. 661/2007. The studied alternative in terms of economic viability is selling electricity to the net by tariff, which is a fixed and regulated price in order to assure the revenue in the economic study and to reduce the project sensitivity to the market variation. The results obtained are: reduction in energy costs of the fabric, improvement of reliability in the electrical supply, reduction of global CO2 emission, and high profitability of the project.

Present and Future of Photovoltaic Solar Electricity

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

Diffusion of photovoltaic in Germany: policy vs. consumers

In some countries photovoltaic (PV) technology has already achieved a stage of development at which it can compete with conventional electricity sources. Germany provides a good illustration of this where PV market has reached a mature stage. As a manifest of this, the German government has recently reduced subsidies for households and industry by decreasing the feed in tariff for PV. This development raises fundamental questions: could the PV industry survive? Will consumers be motivated to continue to adopt PV when feed-in tariff diminish? The point of departure for the relevant literature on diffusion of PV has been on the effect of subsidies but little attention has paid to consumer motives when the policy support is scaled down. This paper introduces an in-depth analysis on understanding the consumer motives for adopting photovoltaic applications. Anchored in an extensive exploratory case study on PV consumers and PV system providers, this study aims to provide an encompassing explanation of diffusion of PV by revealing the link between consumer motives and the impact of policy.

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

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