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.

Improving photovoltaics grid integration through short time forecasting and self-consumption

The uncertainty associated to the forecast of photovoltaic generation is a major drawback for the widespread introduction of this technology into electricity grids. This uncertainty is a challenge in the design and operation of electrical systems that include photovoltaic generation. Demand-Side Management (DSM) techniques are widely used to modify energy consumption. If local photovoltaic generation is available, DSM techniques can use generation forecast to schedule the local consumption. On the other hand, local storage systems can be used to separate electricity availability from instantaneous generation; therefore, the effects of forecast error in the electrical system are reduced. The effects of uncertainty associated to the forecast of photovoltaic generation in a residential electrical system equipped with DSM techniques and a local storage system are analyzed in this paper. The study has been performed in a solar house that is able to displace a residential user?s load pattern, manage local storage and estimate forecasts of electricity generation. A series of real experiments and simulations have carried out on the house. The results of this experiments show that the use of Demand Side Management (DSM) and local storage reduces to 2% the uncertainty on the energy exchanged with the grid. In the case that the photovoltaic system would operate as a pure electricity generator feeding all generated electricity into grid, the uncertainty would raise to around 40%.

A dynamic factor model for mid-term forecasting of wind power generation

The main objective of this paper is the development and application of multivariate time series models for forecasting aggregated wind power production in a country or region. Nowadays, in Spain, Denmark or Germany there is an increasing penetration of this kind of renewable energy, somehow to reduce energy dependence on the exterior, but always linked with the increaseand uncertainty affecting the prices of fossil fuels. The disposal of accurate predictions of wind power generation is a crucial task both for the System Operator as well as for all the agents of the Market. However, the vast majority of works rarely onsider forecasting horizons longer than 48 hours, although they are of interest for the system planning and operation. In this paper we use Dynamic Factor Analysis, adapting and modifying it conveniently, to reach our aim: the computation of accurate forecasts for the aggregated wind power production in a country for a forecasting horizon as long as possible, particularly up to 60 days (2 months). We illustrate this methodology and the results obtained for real data in the leading country in wind power production: Denmark

Unit Commitment and Electricity Prices Forecasting for Market Strategy Preparation in Interconnected Systems

In this paper we present a solution for building a better strategy to take part in external electricity markets. For an optimal strategy development, both the internal system costs as well as the future values of the series of electricity prices in external markets need to be known. But in practice, the real problems that must be faced are that both future electricity prices and costs are unknown. Thus, the first ones must be modeled and forecasted and the costs must be calculated. Our methodology for building an optimal strategy consists of three steps: The first step is modeling and forecasting market prices in external systems. The second step is the cost calculation on internal system taking into account the expected prices in the first step. The third step is based on the results of the previous steps, and consists of preparing the bids for external markets. The main goal is to reduce consumers' costs unlike many others that are oriented to increase GenCo's profits.

Estimating and forecasting generalized fractional Long memory stochastic volatility models

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.

When Wells Run Dry: Water and Tourism Along the Western Coast of Nicaragua

This dissertation uses a political ecology approach to examine the relationship between tourism development and groundwater in southwest Nicaragua. Tourism in Nicaragua is a booming industry bolstered by ‘unspoiled’ natural beauty, low crime rates, and government incentives. This growth has led to increased infrastructure, revenue, and employment opportunities for many local communities along the Pacific coast. Not surprisingly, it has also brought concomitant issues of deeper poverty, widening gaps between rich and poor, and competition over natural resources. Adequate provisions of freshwater are necessary to sustain the production and reproduction of tourism; however, it remains uncertain if groundwater supplies can keep pace with demand. The objective of this research is to assess water supply availability amidst tourism development in the Playa Gigante area. It addresses the questions: 1) are local groundwater supplies sufficient to sustain the demand for freshwater imposed by increased tourism development? and 2) is there a power relationship between tourism development and control over local freshwater that would prove inequitable to local populations? Integrating the findings of groundwater monitoring, geological mapping, and ethnographic and survey research from a representative stretch of Pacific coastline, this dissertation shows that diminishing recharge and increased groundwater consumption is creating conflict between stakeholders with various levels of knowledge, power, and access. Although national laws are structured to protect the environment and ensure equitable access to groundwater, the current scramble to secure water has powerful implications on social relations and power structures associated with tourism development. This dissertation concludes that marginalization due to environmental degradation is attributable to the nexus of a political promotion of tourism, poorly enforced state water policies, insufficient water research, and climate change. Greater technical attention to hydrological dynamics and collaboration amongst stakeholders are necessary for equitable access to groundwater, environmental sustainability, and profitability of tourism.

Short-term Load Forecasting Using Neural Network For Future Smart Grid Application

Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

Competences in Masters in Tourism Management and Planning

Presentación sobre las competencias del Máster en Dirección y Planificación del Turismo realizada en el marco de un proyecto TEMPUS.

The role of authenticity in tourism planning: empirical findings from southeast Spain

Tourism is the main economic activity in many towns in the province of Alicante in southeast Spain and has turned this area into a paradigmatic example of mass tourism on the Mediterranean coast. Since the 1960s, the province's coastal towns have opted for a development model centred on what is known as 'residential tourism' or 'second-home tourism', with few exceptions, such as Benidorm. We wish to put forward the argument that the main social agents in the tourism sector have not perceived the 'search for authenticity' as a factor that may attract tourists to this area. To this end, we will start by reviewing critically the theoretical discourse about the role played by authenticity in the motivation of tourists. Then we will discuss some of the results obtained from empirical, qualitative research that included 37 in-depth interviews. As a guide for our empirical research, we use a model based on the stakeholder theory. The epistemological difficulties faced by researchers do not justify certain critical arguments that try to highlight the impossibility of operationalising essential concepts and approaches such as that of authenticity. Therefore, it is necessary that empirical research continues to delve into the sociological keys that determine the 'search for authenticity' in the tourists' experience.

Tourism dependence and host community perceptions: notes on the social exchange theory

This article examines the opinions of the local population on the south coast of the Spanish province of Alicante regarding the development of tourism in recent years, analysing their perception of the benefits of tourism using the social exchange theory. This study is presented in two stages. The qualitative stage, which is based on in-depth interviews and focus groups, acts as a guide for the second stage, which consists of a survey conducted with the resident Spanish population. It was found that people linked to the tourist sector through their work view tourism as the driving force behind the economic and social development of their towns, although they are more critical than others of the model that has been established. They defend the development process that has taken place, but feel that overcrowding brings their towns to a standstill and needs to be resolved.

Stochastic choice analysis of tourism destinations

The analysis of tourist destination choice, defined by intra-country administrative units and by product types “coastal/inland and village/city”, permits the characterisation of tourist flow behaviour, which is fundamental for public planning and business management. In this study, we analyse the determinant factors of tourist destination choice, proposing various research hypotheses relative to the impact of destination attributes and the personal characteristics of tourists. The methodology applied estimates Nested and Random Coefficients Multinomial Logit Models, which allow control over possible correlations among different destinations. The empirical application is realised in Spain on a sample of 3,781 individuals and allows us to conclude that prices, distance to the destination and personal motivations are determinants in destination choice.

Testing reference dependence, loss aversion and diminishing sensitivity in Spanish tourism

Based on Tversky and Kahneman’s Prospect Theory, we test the existence of reference dependence, loss aversion and diminishing sensitivity in Spanish tourism. To do this, we incorporate the reference-dependent model into a Multinomial Logit Model with Random Parameters -which controls for heterogeneity- and apply it to a sample of vacation choices made by Spaniards. We find that the difference between reference price and actual price is considered to make decisions, confirming that reference dependence exists; that people react more strongly to price increases than to price decreases relative to their reference price, which represents evidence in favor of the loss aversion phenomenon; and that there is diminishing sensitivity for losses only, showing convexity for these negative values.

Beyond megaprojects?. Water alternatives for mass tourism in coastal Mediterranean Spain

Water availability in adequate quantities and qualities is a fundamental requirement for tourism. In the Mediterranean, one of the world’s leading tourist destinations, water availability is subject to modest and erratic precipitation figures which may decline with climate change. The tourist industry therefore may have to assure future supplies by either recurring to new technologies such as desalination or increasing efficiency in water use. A third and yet little explored alternative would be to seek for complementary of uses with irrigation, the traditional user in many coastal Mediterranean areas and holder of substantial amounts of water. In this paper we present the example of the Consorcio de Aguas de la Marina Baja to show how Benidorm, in Mediterranean Spain and one of the most important tourist centers of the Mediterranean, obtains part of its water through agreements with farmers by which these trade their water with Benidorm and other towns’ treated wastewater of enough quality to be used for irrigation, and obtain several compensations in return. The advantages and disadvantages of the water trade between farmers and tourist interests in the Benidorm area are discussed and we argue that solutions to the pending water crisis of many coastal Mediterranean tourist areas may not need to rely uniquely on expensive technologies to generate new resources but may attempt other alternatives.