ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Energy resources management for more sustainable distribution systems: an intelligent approach

Energy Resources Management can play a very relevant role in future power systems in SmartGrid context, with high penetration of distributed generation and storage systems. This paper deals with the importance of resources management in incident situation. The system to consider a high penetration of distributed generation, demand response, storage units and network reconfiguration. A case study evidences the advantages of using a flexible SCADA to control the energy resources in incident situation.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Navegação de um robô autónomo semi-industrial usando diferentes técnicas sensoriais

Mestrado em Engenharia Electrotécnica e de Computadores

Caracterização de antioxidantes com potencial interesse industrial

Mestrado em Engenharia Química

Evaluating the efficiency of a vegetal coagulant in the treatment of industrial effluents

The present work aims at evaluating the efficiency of an organic polymer from vegetal source used as coagulant for treating different types of industrial effluents. This coagulant (Flox-QT) is obtained from the Black Acacia (Acacia mearnsii). The effluents studied were produced in petrochemical, leather, cork stoppers, metalworking, olive oil, glue, paint (printing), textile and paper industries. The parameters analyzed in the effluents before and after treatment were selected according to the type of wastewater and included pH, conductivity, apparent colour, turbidity, total suspended solids (TSS), chemical oxygen demand (COD) and some metals. The coagulant proved to be efficient for almost all effluents tested. The best results were obtained for the paper industry wastewater, with 91% removal of chemical oxygen demand and 95% of total suspended solids removal. The estimated cost of this treatment would be only 0.24 Euro per cubic meter of treated effluent, only regarding the price of the coagulant and the required dosage. The use of this coagulant is also adequate for the valorisation of the sludge obtained, which in this case could be recycled for paper production.

Análise de risco em sequenciamento de actividades com restrições de recursos

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização em Sistemas e Planeamento Industrial.

Análise e optimização dos consumos de utilidades (vapor e energia eléctrica) na Petibol – Embalagens de Plásticos, S.A.

Mestrado em Engenharia Química. Ramo optimização energética na indústria química.

Produção de hidrogénio por fotoeletrólise da água – aproveitamento do subproduto oxigénio

A noção de Economia relativa ao Hidrogénio no vocabulário dos líderes políticos e empresariais tem vindo a mudar sobretudo pela preocupação da poluição global, segurança energética e mudanças climáticas, para além do crescente domínio técnico dos cientistas e engenheiros. O interesse neste composto, que é o elemento mais simples e abundante no universo, está a crescer, devido aos avanços tecnológicos das células de combustível – as potenciais sucessoras das baterias dos aparelhos portáteis eletrónicos, centrais elétricas e motores de combustão interna. Existem métodos já bem desenvolvidos para produzir o hidrogénio. Contudo, destacase a eletrólise da água, não só por ser um método simples mas porque pode utilizar recursos energéticos renováveis, tais como, o vento ou os painéis fotovoltaicos, e aumentar a sua eficiência. Os desafios para melhorar a utilização deste método consistem em reduzir o consumo, a manutenção e os custos energéticos e aumentar a confiança, a durabilidade e a segurança. Mais ainda, consistem em rentabilizar o subproduto oxigénio pois é um gás industrial e medicinal muito importante. Neste trabalho, estudou-se a viabilidade económica da instalação de uma unidade de produção de hidrogénio e oxigénio puros por eletrólise da água, utilizando como fonte energética a energia solar, na empresa Gasoxmed – Gases Medicinais S.A., pretendendo num futuro próximo, comercializar o hidrogénio como fonte de energia, e por outro lado, aproveitar o subproduto oxigénio para utilização industrial. Projetou-se assim uma unidade utilizando um eletrolisador da marca Proton, modelo C30, com capacidade de produção gasosa de 3 kg/h (30 m3/h) de hidrogénio e 20 kg/h (15 m3/h) de oxigénio. Os gases produzidos são comprimidos num compressor da marca RIX a 200 bares para posterior armazenamento em cilindros pressurizados. Dimensionou-se ainda um sistema de miniprodução fotovoltaico com potência 250 kW para alimentar eletricamente a instalação. A realização do projeto na nova área de produção necessitará de 1.713.963€, os quais serão adquiridos por empréstimo bancário. Definiram-se todos os custos fixos associados ao projeto que perfazem um total de 62.554€/mês para os primeiros 5 anos (duração do crédito bancário) findo o qual diminuirão para 21.204€/mês. Da comercialização do hidrogénio, do oxigénio industrial e da eletricidade produzida no sistema de miniprodução de 250 kW, prevê-se um lucro mensal de 117.925€, perfazendo assim um total líquido mensal positivo de 55.371€ durante os primeiros 5 anos e a partir daí de 96.721€/mês, resultando uma amortização do investimento inicial no final do 3º ano.