Spatio-temporal prediction os electric power systems including emergent renewable energy sources

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2014.A atividade de planejamento de sistemas de potência inclui, como um de seus maiores desafios, a predicação do comportamento da carga. Com a finalidade de otimizar oinvestimento ante os dados de consumo, as empresas do setor elétrico lançam mão de várias técnicas de previsão da evolução da demanda que devem atender. No presente trabalho, o tema da predição espacial e temporal da carga é enfrentado, estudando e incorporando, simultaneamente, a tendência hoje já observada de inclusão de fonte sem microgeração distribuída. Três fontes renováveis e emergentes de geração foram consideradas como geradoras de energia pelos consumidores: enguias elétricas, painéis fotovoltaicos para aproveitamento da luz solar e de interiores, e antenas para reciclagemda energia existente nas ondas eletromagnéticas de radiodifusão. Quatro métodos preditivos foram empregados para prever o comportamento da carga: modelo Auto-Regressivo (AR), Auto-Regressivo com Variável eXógena (ARX), Auto-Regressivo deMédia Móvel com Variável eXógena (ARMAX) e Redes Neurais Artificiais (ANN). Os dados de consumo foram as máximas demandas semanais registradas em 8 Subestações da cidade de Leipzig (Saxônia, Alemanha), durante os anos de 2001, 2002, 2003 e 2004.O dado exôgeno considerado foi a temperatura, em valores diretos e logarítmicos. Das 209 semanas existentes entre 2001 e 2004, as 200 primeiras destinaram-se ao ajuste dos coeficientes nos modelos AR e ao treinamento da rede neural; as 9 semanas restantesforam destinadas à comparação de resultados. A aplicação das técnicas deu-se, assim,em dois estágios: no primeiro, os dados reais da rede de Leipzig foram considerados, eno segundo estágio trabalhou-se com novos valores de demandas máximas, originadaspela inserção de valores hipotéticos de energia recebida das três fontes citadas. Emambos os estágios, o modelo ARMAX foi o de melhor precisão na previsão de dados.O sistema de redes neurais demonstrou ser um sistema sub-ótimo de previsão. ______________________________________________________________________________ ABSTRACTPower systems planning activities include load behavior prediction as one of its mostchallenging tasks. In order to optmize investments related to consumption data, utilitiesfrom the Electrical Sector resort to several forecasting techniques so that theycan predict the power demand which these utilities must support. Along the presentwork, issues related to the spatial and temporal predictions are faced, considering,simultaneously, the observed trend of microgeneration spread. Three emergent renewablesources were proposed to be taken on by consumers: electric eels, photovoltaicsolar panels for outdoor generation and indoor light energy harvesting, and antennasfor radio frequency energy recycling. Four predictive methods were employed in orderto forecast load evolution: Auto-Regressive (AR), Auto-Regressive with eXogeneousinputs (ARX), Auto-Regressive Moving Average with eXogeneous inputs (ARMAX)models and Artificial Neural Networks (ANN). Consumption data were the maximumweekly power demands registered over 8 Power Substations from the city of Leipzig(Saxony, Germany), during the years 2001, 2002, 2003 and 2004. The exogeneousvariable adopted was temperature, in realistic and in logarithmic values. During the209 weeks which are comprised between 2001 and 2004, the _rst 200 weeks served tocoe_cients adjustments, with regards to AR models, and the trainning of the neuralnetwork, in the case of ANN. The last 9 weeks were destinated for results comparison.Techniques were undertaken in two stages: _rstly, only realistic data from LeipzigSubstations were considered, and in the second stage, new values for maximum powerdemands were obtained by means of simulations upon the three emergent sources. Inboth stages, ARMAX model returned the _ttest results, whereas ANN characterizeditself as a sub-optimal prediction system

Data security and trading framework for smart grids in neighborhood area networks

Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user’s electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers’ identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design

Energy harvesting photovoltaic system to charge a cell phone in indoor environments

Abstract-Research advances in materials science improved gradually photovoltaic systems efficiency. However, such systems are limited to work in the presence of sun light, and they also depend on the geographic localization and on the period of the year, usually limited to 6 to 8 hours a day. In order to take maximum advantage of solar panels, it is crucial to use them also in cloudy weather or even at night. Therefore, in this paper, we propose to recycle light energy from artificial light sources to enable the use photovoltaic systems along 24 hours a day. We validate our proposal by means of measurements performed using artificial light in indoor environments. As a practical result, we show that 7 hours recharging in an indoor environments implies in 94.08 % of the overall cell phone battery capacity. Furthermore, we also propose a circuit for charging of a battery of a cell phone. I

M-estimator based Chinese Remainder Theorem with few remainders using a Kroenecker product based mapping vector

The Chinese Remainder Theorem (CRT) explains how to estimate an integer-valued number from the knowledge of the remainders obtained by dividing such unknown integer by co-prime integers. As an algebraic theorem, CRT is the basis for several techniques concerning data processing. For instance, considering a single-tone signal whose frequency value is above the sampling rate, the respective peak in the DFT informs the impinging frequency value modulo the sampling rate. CRT is nevertheless sensitive to errors in the remainders, and many efforts have been developed in order to improve its robustness. In this paper, we propose a technique to estimate real-valued numbers by means of CRT, employing for this goal a Kroenecker based M-Estimation (ME), specially suitable for CRT systems with low number of remainders. Since ME schemes are in general computationally expensive, we propose a mapping vector obtained via Kroenecker products which considerably reduces the computational complexity. Furthermore, our proposed technique enhances the probability of estimating an unknown number accurately even when the errors in the remainders surpass 1/4 of the greatest common divisor of all moduli. We also provide a version of the mapping vectors based on tensorial n-mode products, delivering in the end the same information of the original method. Our approach outperforms the state-of-the-art CRT methods not only in terms of percentage of successful estimations but also in terms of smaller average error