Modelling real solar cell using PSCAD/MATLAB

This paper presents the development of a solar photovoltaic (PV) model based on PSCAD/EMTDC - Power System Computer Aided Design – including a mathematical model study. An additional algorithm has been implemented in MATLAB software in order to calculate several parameters required by the PSCAD developed model. All the simulation study has been performed in PSCAD/MATLAB software simulation tool. A real data base concerning irradiance, cell temperature and PV power generation was used in order to support the evaluation of the implemented PV model.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Neural networks for condition monitoring of wind turbines gearbox

Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.

Energy Harvesting for Autonomous Underwater Vehicles

In this study, energy production for autonomous underwater vehicles is investigated. This project is part of a bigger project called TURTLE. The autonomous vehicles perform oceanic researches at seabed for which they are intended to be kept operational underwater for several months. In order to ful l a long-term underwater condition, powerful batteries are combined with \micro- scale" energy production on the spot. This work tends to develop a system that generates power up to a maximum of 30 W. Latter energy harvesting structure consists basically of a turbine combined with a generator and low-power electronics to adjust the achieved voltage to a required battery charger voltage. Every component is examined separately hence an optimum can be de ned for all, and subsequently also an overall optimum. Di erent design parameters as e.g. number of blades, solidity ratio and cross-section area are compared for di erent turbines, in order to see what is the most feasible type. Further, a generator is chosen by studying how ux distributions might be adjusted to low velocities, and how cogging torque can be excluded by adapted designs. Low-power electronics are con gured in order to convert and stabilize heavily varying three-phase voltages to a constant, recti ed voltage which is usable for battery storage. Clearly, di erent component parameters as maximum power and torque are matched here to increase the overall power generation. Furthermore an overall maximum power is set up for achieving a maximum power ow at load side. Due to among others typical low velocities of about 0.1 to 0.5 m/s, and constructing limits of the prototype, the vast range of components is restricted to only a few that could be used. Hence, a helical turbine is combined in a direct drive mode to a coreless-stator axial- ux permanent-magnet generator, from which the output voltage is adjusted subsequently by a recti er, impedance matching unit, upconverter circuit and an overall control unit to regulate di erent component parameters. All these electronics are combined in a closed-loop design to involve positive feedback signals. Furthermore a theoretical con guration for the TURTLE vehicle is described in this work and a solution is proposed that might be implemented, for which several design tests are performable in a future study.

Autoconsumo: Um Elemento de Eficiência Energética

Nos últimos anos assistiu-se ao crescente aumento do custo da Energia Elétrica (EE), com grande impacto após o ano 2012 devido à alteração no escalão da taxa de IVA aplicável. Por outro lado tem-se ainda vindo a verificar o aumento do défice tarifário devido a um conjunto de medidas e decisões estratégicas que atualmente estão a ser pagas por todos os consumidores de energia. A introdução dos programas da microprodução seguida da miniprodução, por parte da Direção Geral de Energia e Geologia (DGEG), permitiu aos pequenos e grandes consumidores de EE, efetuar localmente produção de EE por intermedio de fontes renováveis. Contudo, segundo as “limitações” por parte destes programas, apenas era permitido aos novos pequenos produtores injetar toda a eletricidade produzida na rede elétrica, não proporcionando qualquer benefício ao nível do consumo de energia local. Ano após ano, tem-se verificado uma revisão negativa, por parte da DGEG, sobre as tarifas de remuneração da energia produzida por estes sistemas, o que abalou significativamente um setor que até aqui tinha vindo a crescer a passos largos. Tendo em conta esta nova realidade surge a necessidade de procurar alternativas mais viáveis. A alternativa proposta, não é nada mais do que uma “revisão eficiente” dos atuais sistemas em vigor, permitindo assim aos pequenos produtores, atenuar os consumos energéticos e injetar na rede os excedentes de energia. O Autoconsumo revoluciona assim os atuais mecanismos existentes, garantindo deste modo que os consumidores de EE possam reduzir a sua fatura de eletricidade através da geração local de energia.

Previsão e definição de perfis na gestão dos recursos de energia no âmbito de edifícios auto-sustentáveis

Nos últimos anos o consumo de energia elétrica produzida a partir de fontes renováveis tem aumentado significativamente. Este aumento deve-se ao impacto ambiental que recursos como o petróleo, gás, urânio, carvão, entre outros, têm no meio ambiente e que são notáveis no diaa- dia com as alterações climáticas e o aquecimento global. Por sua vez, estes recursos têm um ciclo de vida limitado e a dada altura tornar-se-ão escassos. A preocupação de uma melhoria contínua na redução dos impactos ambientais levou à criação de Normas para uma gestão mais eficiente e sustentável do consumo de energia nos edifícios. Parte da eletricidade vendida pelas empresas de comercialização é produzida através de fontes renováveis, e com a recente publicação do Decreto de Lei nº 153/2014 de 20 outubro de 2014 que regulamenta o autoconsumo, permitindo que também os consumidores possam produzir a sua própria energia nas suas residências para reduzir os custos com a compra de eletricidade. Neste contexto surgiram os edifícios inteligentes. Por edifícios inteligentes entende-se que são edifícios construídos com materiais que os tornam mais eficientes, possuem iluminação e equipamentos elétricos mais eficientes, e têm sistemas de produção de energia que permitem alimentar o próprio edifício, para um consumo mais sustentado. Os sistemas implementados nos edifícios inteligentes visam a monitorização e gestão da energia consumida e produzida para evitar desperdícios de consumo. O trabalho desenvolvido visa o estudo e a implementação de Redes Neuronais Artificiais (RNA) para prever os consumos de energia elétrica dos edifícios N e I do ISEP/GECAD, bem como a previsão da produção dos seus painéis fotovoltáicos. O estudo feito aos dados de consumo permitiu identificar perfis típicos de consumo ao longo de uma semana e de que forma são influenciados pelo contexto, nomeadamente, com os dias da semana versus fim-de-semana, e com as estações do ano, sendo analisados perfis de consumo de inverno e verão. A produção de energia através de painéis fotovoltaicos foi também analisada para perceber se a produção atual é suficiente para satisfazer as necessidades de consumo dos edifícios. Também foi analisada a possibilidade da produção satisfazer parcialmente as necessidades de consumos específicos, por exemplo, da iluminação dos edifícios, dos seus sistemas de ar condicionado ou dos equipamentos usados.

Valorização da Borra de Café: Otimização da Produção de Biodiesel por Catálise Enzimática

O decréscimo das reservas de petróleo e as consequências ambientais resultantes do recurso a combustíveis fósseis nos motores a diesel têm levado à procura de combustíveis alternativos. Esta pesquisa alicerçada nas fontes de energia renovável tornou-se essencial, face à crescente procura de energia e ao limitado fornecimento de combustíveis fósseis . Resíduos de óleo de cozinha, gordura animal, entre outros resíduos de origem biológica, tais como a borra de café, são exemplos de matérias-primas para a produção de biodiesel. A sua valorização tem interesse quer pela perspetiva ambiental, quer pela económica, pois aumenta não só a flexibilidade e diversificação das matérias-primas, mas também contribui para uma estabilidade de custos e alteração nas políticas agrícolas e de uso do solo. É neste contexto que se enquadra o biodiesel e a borra de café, pretendendo-se aqui efetuar o estudo da produção, à escala laboratorial, de biodiesel a partir da borra de café, por transesterificação enzimática, visando a procura das melhores condições reacionais. Iniciando-se com a caracterização da borra de café, foram avaliados antes e após a extração do óleo da borra de café, diversos parâmetros, de entre os quais se destacam: o teor de humidade (16,97% e 6,79%), teor de cinzas (1,91 e 1,57%), teor de azoto (1,71 e 2,30%), teor de proteínas (10,7 e 14,4%), teor de carbono (70,2 e 71,7%), teor de celulose bruta (14,77 e 18,48%), teor de lenhina (31,03% e 30,97%) e poder calorifico superior (19,5 MJ/kg e 19,9 MJ/kg). Sumariamente, constatou-se que os valores da maioria dos parâmetros não difere substancialmente dos valores encontrados na literatura, tendo sido evidenciado o potencial da utilização desta biomassa, como fonte calorifica para queima e geração de energia. Sendo a caracterização do óleo extraído da borra de café um dos objetivos antecedentes à produção do biodiesel, pretendeu-se avaliar os diferentes parâmetros mais significativos. No que diz respeito à caracterização do óleo extraído, distingue-se a sua viscosidade cinemática (38,04 mm2/s), densidade 0,9032 g/cm3, poder calorífico de 37,9 kcal/kg, índice de iodo igual a 63,0 gI2/ 100 g óleo, o teor de água do óleo foi de 0,15 %, o índice de acidez igual a 44,8 mg KOH/g óleo, ponto de inflamação superior a 120 ºC e teor em ácidos gordos de 82,8%. Inicialmente foram efetuados ensaios preliminares, a fim de selecionar a lipase (Lipase RMIM, TL 100L e CALB L) e álcool (metanol ou etanol puros) mais adequados à produção de biodiesel, pelo que o rendimento de 83,5% foi obtido através da transesterificação mediada pela lipase RMIM, utilizando como álcool o etanol. Sendo outro dos objetivos a otimização do processo de transesterificação enzimática, através de um desenho composto central a três variáveis (razão molar etanol: óleo, concentração de enzima e temperatura), recorrendo ao software JMP 8.0, determinou-se como melhores condições, uma razão molar etanol: óleo 5:1, adição de 4,5% (m/m) de enzima e uma temperatura de 45 ºC, que conduziram a um rendimento experimental equivalente a 96,7 % e teor de ésteres 87,6%. Nestas condições, o rendimento teórico foi de 99,98%. Procurou-se ainda estudar o efeito da adição de água ao etanol, isto é, o efeito da variação da concentração do etanol pela adição de água, para teores de etanol de 92%, 85% e 75%. Verificou-se que até 92% decorreu um aumento da transesterificação (97,2%) para um teor de ésteres de (92,2%), pelo que para teores superiores de água adicionada (75% e 85%) ocorreu um decréscimo no teor final em ésteres (77,2% e 89,9%) e no rendimento da reação (84,3% e 91,9%). Isto indica a ocorrência da reação de hidrólise em maior extensão, que leva ao desvio do equilíbrio no sentido contrário à reação de formação dos produtos, isto é, dos ésteres. Finalmente, relativamente aos custos associados ao processo de produção de biodiesel, foram estimados para o conjunto de 27 ensaios realizados neste trabalho, e que corresponderam a 767,4 g de biodiesel produzido, sendo o custo dos reagentes superior ao custo energético, de 156,16 € e 126,02 €, respetivamente. Naturalmente que não esperamos que, a nível industrial os custos sejam desta ordem de grandeza, tanto mais que há economia de escala e que as enzimas utilizadas no processo deveriam ser reutilizadas diversas vezes.

Virtual power producers simulation - negotiating renewable distributed generation in competitive electricity markets

Sustainable development concerns made renewable energy sources to be increasingly used for electricity distributed generation. However, this is mainly due to incentives or mandatory targets determined by energy policies as in European Union. Assuring a sustainable future requires distributed generation to be able to participate in competitive electricity markets. To get more negotiation power in the market and to get advantages of scale economy, distributed generators can be aggregated giving place to a new concept: the Virtual Power Producer (VPP). VPPs are multi-technology and multisite heterogeneous entities that should adopt organization and management methodologies so that they can make distributed generation a really profitable activity, able to participate in the market. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, in the context of MASCEM, a multi-agent based eletricity market simulator.

Distributed Generation and Demand Response Dispatch for a Virtual Power Player Energy and Reserve Provision

Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets' environment, with deep concerns at the efficiency level. In this context, grid operators, market operators, utilities and consumers must adopt strategies and methods to take full advantage of demand response and distributed generation. This requires that all the involved players consider all the market opportunities, as the case of energy and reserve components of electricity markets. The present paper proposes a methodology which considers the joint dispatch of demand response and distributed generation in the context of a distribution network operated by a virtual power player. The resources' participation can be performed in both energy and reserve contexts. This methodology contemplates the probability of actually using the reserve and the distribution network constraints. Its application is illustrated in this paper using a 32-bus distribution network with 66 DG units and 218 consumers classified into 6 types of consumers.

Coalition of distributed generation units to Virtual Power Players - a game theory approach

Smart Grids (SGs) have emerged as the new paradigm for power system operation and management, being designed to include large amounts of distributed energy resources. This new paradigm requires new Energy Resource Management (ERM) methodologies considering different operation strategies and the existence of new management players such as several types of aggregators. This paper proposes a methodology to facilitate the coalition between distributed generation units originating Virtual Power Players (VPP) considering a game theory approach. The proposed approach consists in the analysis of the classifications that were attributed by each VPP to the distributed generation units, as well as in the analysis of the previous established contracts by each player. The proposed classification model is based in fourteen parameters including technical, economical and behavioural ones. Depending of the VPP strategies, size and goals, each parameter has different importance. VPP can also manage other type of energy resources, like storage units, electric vehicles, demand response programs or even parts of the MV and LV distribution network. A case study with twelve VPPs with different characteristics and one hundred and fifty real distributed generation units is included in the paper.

Energy and reserve provision dispatch considering distributed generation and demand response

In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot market´s efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Distributed generation producers' reserve management

Sustainable development concerns are being addressed with increasing attention, in general, and in the scope of power industry, in particular. The use of distributed generation (DG), mainly based on renewable sources, has been seen as an interesting approach to this problem. However, the increasing of DG in power systems raises some complex technical and economic issues. This paper presents ViProd, a simulation tool that allows modeling and simulating DG operation and participation in electricity markets. This paper mainly focuses on the operation of Virtual Power Producers (VPP) which are producers’ aggregations, being these producers mainly of DG type. The paper presents several reserve management strategies implemented in the scope of ViProd and the results of a case study, based on real data.

Renewable-based generation integration in electricity markets with virtual power producers

The development of renewable energy sources and Distributed Generation (DG) of electricity is of main importance in the way towards a sustainable development. However, the management, in large scale, of these technologies is complicated because of the intermittency of primary resources (wind, sunshine, etc.) and small scale of some plants. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. VPPs can ensure a secure, environmentally friendly generation and optimal management of heat, electricity and cold as well as optimal operation and maintenance of electrical equipment, including the sale of electricity in the energy market. For attaining these goals, there are important issues to deal with, such as reserve management strategies, strategies for bids formulation, the producers’ remuneration, and the producers’ characterization for coalition formation. This chapter presents the most important concepts related with renewable-based generation integration in electricity markets, using VPP paradigm. The presented case studies make use of two main computer applications:ViProd and MASCEM. ViProd simulates VPP operation, including the management of plants in operation. MASCEM is a multi-agent based electricity market simulator that supports the inclusion of VPPs in the players set.

Demsi - a demand response simulator in the context of intensive use of distributed generation

Demand response can play a very relevant role in future power systems in which distributed generation can help to assure service continuity in some fault situations. This paper deals with the demand response concept and discusses its use in the context of competitive electricity markets and intensive use of distributed generation. The paper presents DemSi, a demand response simulator that allows studying demand response actions and schemes using a realistic network simulation based on PSCAD. Demand response opportunities are used in an optimized way considering flexible contracts between consumers and suppliers. A case study evidences the advantages of using flexible contracts and optimizing the available generation when there is a lack of supply.