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

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.

Methodology for estimating building integrated photovoltaics electricity production under shadowing conditions and case study

Building integrated photovoltaic (BIPV) systems are a relevant application of photovoltaics. In countries belonging to the International Energy Agency countries, 24% of total installed PV power corresponds to BIPV systems. Electricity losses caused by shadows over the PV generator have a significant impact on the performance of BIPV systems, being the major source of electricity losses. This paper presents a methodology to estimate electricity produced by BIPV systems which incorporates a model for shading losses. The proposed methodology has been validated on a one year study with real data from two similar PV systems placed on the south façade of a building belonging to the Technical University of Madrid. This study has covered all weather conditions: clear, partially overcast and fully overcast sky. Results of this study are shown at different time scales, resulting that the errors committed by the best performing model are below 1% and 3% in annual and daily electricity estimation. The use of models which account for the reduced performance at low irradiance levels also improves the estimation of generated electricity.

Corporate social responsibility in portfolio selection: A "goal games" against nature approach

Nowadays, there is an uprising social pressure on big companies to incorporate into their decision-making process elements of the so-called social responsibility. Among the many implications of this fact, one relevant one is the need to include this new element in classic portfolio selection models. This paper meets this challenge by formulating a model that combines goal programming with "goal games" against nature in a scenario where the social responsibility is defined through the introduction of a battery of sustainability indicators amalgamated into a synthetic index. In this way, we have obtained an efficient model that only implies solving a small number of linear programming problems. The proposed approach has been tested and illustrated by using a case study related to the selection of securities in international markets.

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.

Economic Impacts of Rules-based vs Risk-based Cybersecurity Regulations in Critical Infrastructure Providers (Bulk Electricity Providers)

Economics of Cybersecurity Part 2. SPSI-2015-01-0024.

Portfolio efficiency tests with conditioning information using empirical likelihood estimation

We evaluate the use of Generalized Empirical Likelihood (GEL) estimators in portfolios efficiency tests for asset pricing models in the presence of conditional information. Estimators from GEL family presents some optimal statistical properties, such as robustness to misspecification and better properties in finite samples. Unlike GMM, the bias for GEL estimators do not increase as more moment conditions are included, which is expected in conditional efficiency analysis. We found some evidences that estimators from GEL class really performs differently in small samples, where efficiency tests using GEL generate lower estimates compared to tests using the standard approach with GMM. With Monte Carlo experiments we see that GEL has better performance when distortions are present in data, especially under heavy tails and Gaussian shocks.

Risco de mercado na comercialização de energia elétrica: uma análise estruturada com foco no ambiente de contratação livre - ACL.

A motivação para o desenvolvimento desse trabalho surge em um momento em que se verifica uma participação cada vez mais significativa das fontes energéticas renováveis não convencionais no País. Não obstante, o cenário de evolução evidencia que o arcabouço regulatório e as regras de mercado não acompanharam as especificidades inerentes à exploração dessas fontes. Assim, para que se mantenha adequado ritmo de inserção na matriz energética, devem ser buscadas opções para que fontes alternativas sejam cada vez mais competitivas na atual configuração do mercado energético. A contribuição dessa pesquisa, portanto, centra-se na análise dos riscos de mercado incorridos por esses geradores de fontes intermitentes de energia ao comercializarem energia no ambiente de contratação livre. Nessa perspectiva, a Dissertação foi desenvolvida abordando tipos de geração de energia e suas características técnicas e econômicas, legislação do setor elétrico, regras de comercialização, balanço energético do sistema, formação de preços no mercado de curto prazo e precificação de contratos no ACL, diferença de preços entre submercados, requisitos de flexibilidade e sazonalidade nos contratos de venda a consumidores livres e seu impacto na precificação de contratos, identificação de comportamento energético complementar para mitigação de riscos de mercado entre fontes renováveis e rebatimento na formulação de mecanismo de hedge, análise de portfólio de contratos e estratégia ótima de contratação de energia para agentes geradores atuando no ACL. Como resposta ao desafio de equacionar o impasse surgido na comercialização de fontes de produção sazonal, propõe-se um modelo para definir estratégias de contratação para agentes geradores e comercializadores a partir da complementação energética entre diferentes tipos de fontes, de forma a maximizar os ganhos de comercialização para um risco estabelecido. Busca-se a composição ideal dessas fontes na carteira de um comercializador para minimizar o risco de exposição à volatilidade dos preços do mercado de curto prazo. Isso é possível em virtude das compensações energéticas feitas entre as diferentes fontes em um portfólio combinado, mitigando a receita em risco decorrente das variações que existem nos preços de curto prazo e na produção energética. De forma complementar, estruturou-se um modelo de negócio no qual uma empresa detentora de ativos de geração hidrelétrica compra os direitos de produção de uma eólica e/ou biomassa para incorporar ao seu portfólio e vender como contrato por quantidade. Determinou-se o volume de energia a ser comprado de cada fonte, o preço, a estratégia mais indicada de contratação e a mitigação de fatores de risco contemplados nos contratos de venda, buscando maximizar os ganhos de comercialização condicionada a critérios de risco pré-fixados.

Geothermal Electricity Generation in Colorado: Actual and Perceived Obstacles to Greater Implementation

The objective of this paper is to provide an analysis of the potential and obstacles to the development of geothermal energy resources in Colorado. Geothermal energy is the only renewable resource that can provide base-load electricity. While Colorado has significant geothermal energy potential, there are no such power plants. Layers of federal and state laws and regulations represent one barrier to further geothermal development. Transmission constraints represent another major barrier. High exploration and construction costs along with high-risk profiles for geothermal projects form another major barrier. Perceived barriers such as misunderstanding the impacts, risks, and benefits of geothermal energy hinder further development. Recommendations are provided to help overcome these obstacles.

Developing Autonomous Microgrids: A Theoretical Case Study

Microgrids are autonomously operated, geographically clustered electricity generation and distribution systems that supply power in closed system settings; they are highly compatible with renewable energy sources and distributed generation technologies. Mocrogrids are currently a serially underutilized and underappreciated commodity in the energy infrastructure portfolio worldwide. To demonstrate feasibility under poor conditions (little renewable energy potential and high demand) this capstone project develops a theoretical case study in which a renewable microgrid is employed to power rural communities of southern Montgomery County, Arkansas. Utilizing commercially manufactured 1.5-megawatt wind turbines and a 1-megawatt solar panel generation system, 4-megawatts of lithium ion battery storage, and demand response technology, a microgrid is designed that supplies 235 households with reliable electricity supply.

Assessment of Competences in the Physical Chemistry Area: Use of the Department Teaching Portfolio

Competences have become a standard learning outcome in present university education within the European Higher Education Area (EHEA). In this regard, updated tools for their assessment have turned out essential in this new teaching-learning paradigm. Among them, one of the most promising tools is the “learner´s portfolio”, which is based on the gathering and evaluation of a range of evidences from the student, which provides a wider and more realistic view of his/her competence acquisition. Its appropriate use as a formative (continuous) assessment instrument allows a deeper appraisal of student´s learning, provided it does not end up as another summative (final) evaluation tool. In this contribution we propose the use of the portfolio as a unifying assessment tool within a university department (Physical Chemistry), exemplifying how the portfolio could yield both personalized student reports and averaged area reports on competence acquisition. A proposed stepwise protocol is given to organize the individual competence reports and estimate the global competence level following a bottom-up approach (i.e. ranging from the class group, subject, grade, and academic course).

The effect of electricity tariffs over the operating costs of the Spanish wastewater treatments plants

Given the complex structure of electricity tariffs and their steady growth in Spanish, we've studied its effect over the operating costs of the wastewater treatment plants (WWTP), concluding that in the last three years the revisions of electricity rates have meant increases in electricity costs of 64.5% in the rate 3.1.A and 79.1% in the rate 6.1. This has caused the cost of electricity, which was the most important, has increased from a 44% of total operating costs in the year 2009, to more than a 56% in the year 2012.