Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

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.

Can power laws help us understand gene and proteome information?

Proteins are biochemical entities consisting of one or more blocks typically folded in a 3D pattern. Each block (a polypeptide) is a single linear sequence of amino acids that are biochemically bonded together. The amino acid sequence in a protein is defined by the sequence of a gene or several genes encoded in the DNA-based genetic code. This genetic code typically uses twenty amino acids, but in certain organisms the genetic code can also include two other amino acids. After linking the amino acids during protein synthesis, each amino acid becomes a residue in a protein, which is then chemically modified, ultimately changing and defining the protein function. In this study, the authors analyze the amino acid sequence using alignment-free methods, aiming to identify structural patterns in sets of proteins and in the proteome, without any other previous assumptions. The paper starts by analyzing amino acid sequence data by means of histograms using fixed length amino acid words (tuples). After creating the initial relative frequency histograms, they are transformed and processed in order to generate quantitative results for information extraction and graphical visualization. Selected samples from two reference datasets are used, and results reveal that the proposed method is able to generate relevant outputs in accordance with current scientific knowledge in domains like protein sequence/proteome analysis.

Dispachability improvement of wind generation by the virtual power producers

Nowadays, there is a growing environmental concern about were the energy that we use comes from, bringing the att ention on renewable energies. However, the use and trade of renewable e nergies in the market seem to be complicated because of the lack of guara ntees of generation, mainly in the wind farms. The lack of guarantees is usually addressed by using a reserve generation. The aggregation of DG p lants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of wind generation technologies, making them valuable in electricity markets. This paper presents some resul ts obtained with a simulation tool (ViProd) developed to support VPPs in the analysis of their operation and management methods and of their strat egies effects.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.

Corporate brand identity management in a technological faculty – how brand relationships influence students and companies reputations

Identity is traditionally defined as an emission concept [1]. Yet, some research points out that there are external factors that can influence it [2]; [3]; [4]. This subject is even more relevant if one considers corporate brands. According to Aaker [5] the number, the power and the credibility of corporate associations are bigger in the case of corporate brands. Literature recognizes the influence of relationships between companies in identity management. Yet, given the increasingly important role of corporate brands, it is surprising that to date no attempt to evaluate that influence has been made in the management of corporate brand identity. Also Keller and Lehman [6] highlight relationships and costumer experience as two areas requiring more investigation. In line with this, the authors intend to develop an empirical research in order to evaluate the influence of relationships between brands in the identity of corporate brand from an internal perspective by interviewing internal stakeholders (brand managers and internal clients). This paper is organized by main contents: theoretical background, research methodology, data analysis and conclusions and finally cues to future investigation.

Demand response in future power systems management – a conceptual framework and simulation tool

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

The importance of qualitative methodology in management empirical research

Purpose: The aim of this paper is to highlight the importance of qualitative research within the scope of management scientific studies, referring to its philosophy, nature and instruments. It also confronts it with quantitative methodology, approaching its differences as well as its complementariness and synergies, with the purpose of explaining, from a more analytic point of view, the relevance of qualitative methodology in the course of an authentic and real research despite its complexity. Design/methodology/approach: Regardless of its broad application, one may attest the scarcity literature that focuses on qualitative research applied to the management scientific area, as opposed to the large amount that refers to quantitative research. Findings: The paper shows the influence that qualitative research has on management scientific research. Originality/value:. Qualitative research assumes an important role within qualitative research by allowing for the study and analysis of certain types of phenomena that occur inside organisations, and in respect of which quantitative studies cannot provide an answer.

A questão da língua na União Europeia – implicações sobre a liberdade de circulação de mercadorias

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do Grau de Mestre em Empreendedorismo e Internacionalização Orientada pela Mestre Maria Luísa Verdelho Alves

Extraction of ochratoxin A in bread samples by the QuEChERS methodology

A QuEChERS method for the extraction of ochratoxin A (OTA) from bread samples was evaluated. A factorial design (23) was used to find the optimal QuEChERS parameters (extraction time, extraction solvent volume and sample mass). Extracts were analysed by LC with fluorescence detection. The optimal extraction conditions were: 5 g of sample, 15 mL of acetonitrile and 3 min of agitation. The extraction procedure was validated by systematic recovery experiments at three levels. The recoveries obtained ranged from 94.8% (at 1.0 μg kg -1) to 96.6% (at 3.0 μg kg -1). The limit of quantification of the method was 0.05 μg kg -1. The optimised procedure was applied to 20 samples of different bread types (‘‘Carcaça’’, ‘‘Broa de Milho’’, and ‘‘Broa de Avintes’’) highly consumed in Portugal. None of the samples exceeded the established European legal limit of 3 μg kg -1.

Análise e optimização global de uma linha de extrusão

Os objectivos principais deste estudo são a caracterização de uma das linhas de extrusão existentes na Cabelte, nomeadamente a linha de extrusão de referência EP5, composta por duas extrusoras. Pretende-se fazer a determinação de indicadores energéticos e de processo e a optimização do consumo energético, no que diz respeito à energia consumida e às perdas térmicas relativas a esta linha. Para fazer a monitorização da linha de extrusão EP5 foi colocado no quadro geral dessa linha um equipamento central de medida de forma a ser possível a sua monitorização. No entanto, para a extrusora auxiliar as medições foram efectuadas com uma pinça amperimétrica e um fasímetro. Foram também efectuados ensaios onde foi avaliada a quantidade de material transformada, para isso foi utilizado um equipamento de pesagem, doseador gravimétrico aplicado nas extrusoras. As medições de temperatura para os cálculos das perdas térmicas da extrusora principal e para a caracterização dos materiais plásticos, foram efectuadas utilizando um termómetro digital. Foram efectuados ensaios de débito às extrusoras auxiliar e principal e foi estudada a variação do factor de potência em função da rotação do fuso. Na perspectiva do utilizador final a optimização para a utilização racional de energia está na redução de encargos da factura de energia eléctrica. Essa factura não depende só da quantidade mas também do modo temporal como se utiliza essa energia, principalmente a energia eléctrica, bastante dependente do período em que é consumida. Uma metodologia diferente no planeamento da produção, contemplando o fabrico dos cabos com maior custo específico nas horas de menor custo energético, implicaria uma redução dos custos específicos de 18,7% para o horário de verão e de 20,4% para o horário de inverno. Os materiais de revestimento utilizados (PE e PVC), influenciam directamente os custos energéticos, uma vez que o polietileno (PE) apresenta sempre valores de entalpia superiores (0,317 kWh/kg e 0,281 kWh/kg)) e necessita de temperaturas de trabalho mais elevadas do que o policloreto de vinilo (PVC) (0,141 kWh/kg e 0,124 kWh/kg). O consumo específico tendencialmente diminui à medida que aumenta a rotação do fuso, até se atingir o valor de rotação óptimo, a partir do qual esta tendência se inverte. O cosφ para as duas extrusoras em estudo, aumenta sempre com o aumento de rotação do fuso. Este estudo permitiu avaliar as condições óptimas no processo de revestimento dos cabos, de forma a minimizarmos os consumos energéticos. A redução de toda a espécie de desperdícios (sobre consumos, desperdício em purgas) é uma prioridade de gestão que alia também a eficácia à eficiência, e constitui uma ferramenta fundamental para assegurar o futuro da empresa. O valor médio lido para o factor de potência (0,38) da linha EP5, valor extremamente baixo e que vem associado à energia reactiva, além do factor económico que lhe está inerente, condiciona futuras ampliações. A forma de se corrigir o factor de potência é instalando uma bateria de condensadores de 500 kVAr. Considerando o novo sistema tarifário aplicado à energia reactiva, vamos ter um ganho de 36167,4 Euro/ano e o período de retorno de investimento é de 0,37 ano (4,5 meses). Esta medida implica também uma redução anual na quantidade de CO2 emitida de 6,5%. A quantificação das perdas térmicas é importante, pois só desta forma se podem definir modos de actuação de forma a aumentar a eficiência energética. Se não existir conhecimento profundo dos processos e metodologias correctas, não podem existir soluções eficientes, logo é importante medir antes de avançar com qualquer medida de gestão.

Optimization of Cu(II) biosorption onto ascophyllum nodosum by factorial design methodology

A Box–Behnken factorial design coupled with surface response methodology was used to evaluate the effects of temperature, pH and initial concentration in the Cu(II) sorption process onto the marine macroalgae Ascophyllum nodosum. The effect of the operating variables on metal uptake capacitywas studied in a batch system and a mathematical model showing the influence of each variable and their interactions was obtained. Study ranges were 10–40ºC for temperature, 3.0–5.0 for pH and 50–150mgL−1 for initial Cu(II) concentration. Within these ranges, the biosorption capacity is slightly dependent on temperature but markedly increases with pH and initial concentration of Cu(II). The uptake capacities predicted by the model are in good agreement with the experimental values. Maximum biosorption capacity of Cu(II) by A. nodosum is 70mgg−1 and corresponds to the following values of those variables: temperature = 40ºC, pH= 5.0 and initial Cu(II) concentration = 150mgL−1.