Evaluation of dynamic pass-through of carbon prices into electricity prices – a cointegrated VECM analysis

This paper addresses the impact of the CO2 opportunity cost on the wholesale electricity price in the context of the Iberian electricity market (MIBEL), namely on the Portuguese system, for the period corresponding to the Phase II of the European Union Emission Trading Scheme (EU ETS). In the econometric analysis a vector error correction model (VECM) is specified to estimate both long–run equilibrium relations and short–run interactions between the electricity price and the fuel (natural gas and coal) and carbon prices. The model is estimated using daily spot market prices and the four commodities prices are jointly modelled as endogenous variables. Moreover, a set of exogenous variables is incorporated in order to account for the electricity demand conditions (temperature) and the electricity generation mix (quantity of electricity traded according the technology used). The outcomes for the Portuguese electricity system suggest that the dynamic pass–through of carbon prices into electricity prices is strongly significant and a long–run elasticity was estimated (equilibrium relation) that is aligned with studies that have been conducted for other markets.

European Union emissions trading scheme impact on the Spanish electricity price during phase II and phase III implementation

The European Union Emissions Trading Scheme (EU ETS) is a cornerstone of the European Union's policy to combat climate change and its key tool for reducing industrial greenhouse gas emissions cost-effectively. The purpose of the present work is to evaluate the influence of CO2 opportunity cost on the Spanish wholesale electricity price. Our sample includes all Phase II of the EU ETS and the first year of Phase III implementation, from January 2008 to December 2013. A vector error correction model (VECM) is applied to estimate not only long-run equilibrium relations, but also short-run interactions between the electricity price and the fuel (natural gas and coal) and carbon prices. The four commodities prices are modeled as joint endogenous variables with air temperature and renewable energy as exogenous variables. We found a long-run relationship (cointegration) between electricity price, carbon price, and fuel prices. By estimating the dynamic pass-through of carbon price into electricity price for different periods of our sample, it is possible to observe the weakening of the link between carbon and electricity prices as a result from the collapse on CO2 prices, therefore compromising the efficacy of the system to reach proposed environmental goals. This conclusion is in line with the need to shape new policies within the framework of the EU ETS that prevent excessive low prices for carbon over extended periods of time.

Bit error models

The use of bit error models in communication simulation has been widely studied. In this technical report we present three models: the Independent Channel Model; the Gilbert-Elliot Model and the Burst-Error Periodic Model.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Hybrid adaptive control of a dragonfly model

Dragonflies show unique and superior flight performances than most of other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper, it is presented an adaptive scheme controlling a nonlinear model inspired in a dragonfly-like robot. It is proposed a hybrid adaptive (HA) law for adjusting the parameters analyzing the tracking error. At the current stage of the project it is considered essential the development of computational simulation models based in the dynamics to test whether strategies or algorithms of control, parts of the system (such as different wing configurations, tail) as well as the complete system. The performance analysis proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence.

A sensitivity study of the WRF model in wind simulation for an area of high wind energy

The performance of the Weather Research and Forecast (WRF) model in wind simulation was evaluated under different numerical and physical options for an area of Portugal, located in complex terrain and characterized by its significant wind energy resource. The grid nudging and integration time of the simulations were the tested numerical options. Since the goal is to simulate the near-surface wind, the physical parameterization schemes regarding the boundary layer were the ones under evaluation. Also, the influences of the local terrain complexity and simulation domain resolution on the model results were also studied. Data from three wind measuring stations located within the chosen area were compared with the model results, in terms of Root Mean Square Error, Standard Deviation Error and Bias. Wind speed histograms, occurrences and energy wind roses were also used for model evaluation. Globally, the model accurately reproduced the local wind regime, despite a significant underestimation of the wind speed. The wind direction is reasonably simulated by the model especially in wind regimes where there is a clear dominant sector, but in the presence of low wind speeds the characterization of the wind direction (observed and simulated) is very subjective and led to higher deviations between simulations and observations. Within the tested options, results show that the use of grid nudging in simulations that should not exceed an integration time of 2 days is the best numerical configuration, and the parameterization set composed by the physical schemes MM5–Yonsei University–Noah are the most suitable for this site. Results were poorer in sites with higher terrain complexity, mainly due to limitations of the terrain data supplied to the model. The increase of the simulation domain resolution alone is not enough to significantly improve the model performance. Results suggest that error minimization in the wind simulation can be achieved by testing and choosing a suitable numerical and physical configuration for the region of interest together with the use of high resolution terrain data, if available.

Reformulação de um Sistema de Gestão de Consumos de Energia

Atualmente a energia é considerada um vetor estratégico nas diversas organizações. Assim sendo, a gestão e a utilização racional da energia são consideradas instrumentos fundamentais para a redução dos consumos associados aos processos de produção do sector industrial. As ações de gestão energética não deverão ficar pela fase do projeto das instalações e dos meios de produção, mas sim acompanhar a atividade da Empresa. A gestão da energia deve ser sustentada com base na realização regular de diagnósticos energéticos às instalações consumidoras e concretizada através de planos de atuação e de investimento que apresentem como principal objetivo a promoção da eficiência energética, conduzindo assim à redução dos respetivos consumos e, consequentemente, à redução da fatura energética. Neste contexto, a utilização de ferramentas de apoio à gestão de energia promovem um consumo energético mais racional, ou seja, promovem a eficiência energética e é neste sentido que se insere este trabalho. O presente trabalho foi desenvolvido na Empresa RAR Açúcar e apresentou como principais objetivos: a reformulação do Sistema de Gestão de Consumos de Energia da Empresa, a criação de um modelo quantitativo que permitisse ao Gestor de Energia prever os consumos anuais de água, fuelóleo e eletricidade da Refinaria e a elaboração de um plano de consumos para o ano de 2014 a partir do modelo criado. A reformulação do respetivo Sistema de Gestão de Consumos resultou de um conjunto de etapas. Numa primeira fase foi necessário efetuar uma caraterização e uma análise do atual Sistema de Gestão de Consumos da Empresa, sistema composto por um conjunto de sete ficheiros de cálculo do programa Microsoft Excel©. Terminada a análise, selecionada a informação pertinente e propostas todas as melhorias a introduzir nos ficheiros, procedeu-se à reformulação do respetivo SGE, reduzindo-se o conjunto de ficheiros de cálculo para apenas dois ficheiros, um onde serão efetuados e visualizados todos os registos e outro onde serão realizados os cálculos necessários para o controlo energético da Empresa. O novo Sistema de Gestão de Consumos de Energia será implementado no início do ano de 2015. Relativamente às alterações propostas para as folhas de registos manuais, estas já foram implementadas pela Empresa. Esta aplicação prática mostrou-se bastante eficiente uma vez que permitiu grandes melhorias processuais nomeadamente, menores tempos de preenchimento das mesmas e um encurtamento das rotas efetuadas diariamente pelos operadores. Através do levantamento efetuado aos diversos contadores foi possível identificar todas as áreas onde será necessário a sua instalação e a substituição de todos os contadores avariados, permitindo deste modo uma contabilização mais precisa de todos os consumos da Empresa. Com esta reestruturação o Sistema de Gestão de Consumos tornou-se mais dinâmico, mais claro e, principalmente, mais eficiente. Para a criação do modelo de previsão de consumos da Empresa foi necessário efetuar-se um levantamento dos consumos históricos de água, eletricidade, fuelóleo e produção de açúcar de dois anos. Após este levantamento determinaram-se os consumos específicos de água, fuelóleo e eletricidade diários (para cada semana dos dois anos) e procedeu-se à caracterização destes consumos por tipo de dia. Efetuada a caracterização definiu-se para cada tipo de dia um consumo específico médio com base nos dois anos. O modelo de previsão de consumos foi criado com base nos consumos específicos médios dos dois anos correspondentes a cada tipo de dia. Procedeu-se por fim à verificação do modelo, comparando-se os consumos obtidos através do modelo (consumos previstos) com os consumos reais de cada ano. Para o ano de 2012 o modelo apresenta um desvio de 6% na previsão da água, 12% na previsão da eletricidade e de 6% na previsão do fuelóleo. Em relação ao ano de 2013, o modelo apresenta um erro de 1% para a previsão dos consumos de água, 8% para o fuelóleo e de 1% para a eletricidade. Este modelo permitirá efetuar contratos de aquisição de energia elétrica com maior rigor o que conduzirá a vantagens na sua negociação e consequentemente numa redução dos custos resultantes da aquisição da mesma. Permitirá também uma adequação dos fluxos de tesouraria à necessidade reais da Empresa, resultante de um modelo de previsão mais rigoroso e que se traduz numa mais-valia financeira para a mesma. Foi também proposto a elaboração de um plano de consumos para o ano de 2014 a partir do modelo criado em função da produção prevista para esse mesmo ano. O modelo apresenta um desvio de 24% na previsão da água, 0% na previsão da eletricidade e de 28% na previsão do fuelóleo.