Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

A probabilistic methodology for distributed generation location in isolated electrical service area

Distributed generation unlike centralized electrical generation aims to generate electrical energy on small scale as near as possible to load centers, interchanging electric power with the network. This work presents a probabilistic methodology conceived to assist the electric system planning engineers in the selection of the distributed generation location, taking into account the hourly load changes or the daily load cycle. The hourly load centers, for each of the different hourly load scenarios, are calculated deterministically. These location points, properly weighted according to their load magnitude, are used to calculate the best fit probability distribution. This distribution is used to determine the maximum likelihood perimeter of the area where each source distributed generation point should preferably be located by the planning engineers. This takes into account, for example, the availability and the cost of the land lots, which are factors of special relevance in urban areas, as well as several obstacles important for the final selection of the candidates of the distributed generation points. The proposed methodology has been applied to a real case, assuming three different bivariate probability distributions: the Gaussian distribution, a bivariate version of Freund’s exponential distribution and the Weibull probability distribution. The methodology algorithm has been programmed in MATLAB. Results are presented and discussed for the application of the methodology to a realistic case and demonstrate the ability of the proposed methodology for efficiently handling the determination of the best location of the distributed generation and their corresponding distribution networks.

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.

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.

Agar extraction from integrated multitrophic aquacultured Gracilaria vermiculophylla: Evaluation of a microwave-assisted process using response surface methodology

Microwave-assisted extraction (MAE) of agar from Gracilaria vermiculophylla, produced in an integrated multitrophic aquaculture (IMTA) system, from Ria de Aveiro (northwestern Portugal), was tested and optimized using response surface methodology. The influence of the MAE operational parameters (extraction time, temperature, solvent volume and stirring speed) on the physical and chemical properties of agar (yield, gel strength, gelling and melting temperatures, as well as, sulphate and 3,6-anhydro-Lgalactose contents) was evaluated in a 2^4 orthogonal composite design. The quality of the extracted agar compared favorably with the attained using traditional extraction (2 h at 85ºC) while reducing drastically extraction time, solvent consumption and waste disposal requirements. Agar MAE optimum results were: an yield of 14.4 ± 0.4%, a gel strength of 1331 ± 51 g/cm2, 40.7 ± 0.2 _C gelling temperature, 93.1 ± 0.5ºC melting temperature, 1.73 ± 0.13% sulfate content and 39.4 ± 0.3% 3,6-anhydro-L-galactose content. Furthermore, this study suggests the feasibility of the exploitation of G. vermiculophylla grew in IMTA systems for agar production.

TRAILER Project (Tagging, Recognition, Acknowledgment of Informal Learning Experiences) a methodology to make learners' informal learning activities visible to the institutions

The application of information technologies (specially the Internet, Web 2.0 and social tools) make informal learning more visible. This kind of learning is not linked to an institution or a period of time, but it is important enough to be taken into account. On the one hand, learners should be able to communicate to the institutions they are related to, what skills they possess, whether they were achieved in a formal or informal way. On the other hand the companies and educational institutions need to have a deeper knowledge about the competencies of their staff. The TRAILER project provides a methodology supported by a technological framework to facilitate communication about informal learning between businesses, employees and learners. The paper presents the project and some of the work carried out, an exploratory analysis about how informal learning is considered and the technological framework proposed. Whilst challenges remain in terms of establishing the meaningfulness of technological engagement for employees and businesses, the continuing transformation of the social, technological and educational environment is likely to lead to greater emphasis for the effective exploitation of informal learning.

Response surface methodology applied to SPE for the determination of ibuprofen in various types of water samples

Over the last few years, there has been a growing concern about the presence of pharmaceuticals in the environment. The main objective of this study was to develop and validate an SPE method using surface response methodology for the determination of ibuprofen in different types of water samples. The influence of sample pH and sample volume on the ibuprofen recovery was studied. The effect of each studied independent variable is pronounced on the dependent variable (ibuprofen recovery). Good selectivity, extraction efficiency, and precision were achieved using 600 mL of sample volume with the pH adjusted to 2.2. LC with fluorescence detection was employed. The optimized method was applied to 20 water samples from the North and South of Portugal.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Development of a SPE–UHPLC–MS/MS methodology for thedetermination of non-steroidal anti-inflammatory and analgesicpharmaceuticals in seawaterPaula

An analytical methodology for the simultaneous determination of seven pharmaceuticals and two metabolites belonging to the non-steroidal anti-inflammatory drugs (NSAIDs) and analgesics therapeutic groups was developed based on off-line solid-phase extraction and ultra-high performance liquid chromatography coupled to tandem mass spectrometry (SPE–UHPLC–MS/MS). Extraction conditions were optimized taking into account parameters like sorbent material, sample volume and sample pH. Method detection limits (MDLs) ranging from 0.02 to 8.18 ng/L were obtained. This methodology was successfully applied to the determination of the selected pharmaceuticals in seawater samples of Atlantic Ocean in the Northern Portuguese coast. All the pharmaceuticals have been detected in the seawater samples, with pharmaceuticals like ibuprofen, acetaminophen, ketoprofen and the metabolite hydroxyibuprofen being the most frequently detected at concentrations that can reach some hundreds of ng/L.

Ontology methodology building criteria for crowdsourcing innovation intermediaries

Crowdsourcing innovation intermediaries are organizations that mediate the communication and relationship between companies that aspire to solve some problem or to take advantage of any business opportunity with a crowd that is prone to give ideas based on their knowledge, experience and wisdom. A significant part of the activity of these intermediaries is carried out by using a web platform that takes advantage of web 2.0 tools to implement its capabilities. Thus, ontologies are presented as an appropriate strategy to represent the knowledge inherent to this activity and therefore the accomplishment of interoperability between machines and systems. In this paper we present an ontology roadmap for developing crowdsourcing innovation ontology of the intermediation process. We start making a literature review on ontology building, analyze and compare ontologies that propose the development from scratch with the ones that propose reusing other ontologies, and present the criteria for selecting the methodology. We also review enterprise and innovation ontologies known in literature. Finally, are taken some conclusions and presented the roadmap for building crowdsourcing innovation intermediary ontology.

Science fair project: O CLIL na educaçã pré-escolar

O ensino bilingue torna-se cada vez mais relevante no contexto Europeu. O Content and Language Integrated Learning (CLIL) apresenta-se como uma metodologia importante como podemos observar no European Profile for Language Teacher Education. Após realizar uma revisão de literatura cujo conteúdo versa sobretudo acerca os princípios orientados pelos preconizadores desta abordagem metodológica, nomeadamente Coyle (2010), Marsh (2010), Mehisto (2008), entre outros, e no sentido de tentarmos aferir a sua viabilidade, realizámos um Projeto de Investigação no qual implementámos esta abordagem pedagógica, ensinando a disciplina de ciências a grupo de alunos do Pré-escolar. Assim, neste estudo de caso, com contornos da metodologia de investigação-ação, tivemos como principal objetivo verificar a aplicabilidade do CLIL na Educação Préescolar. Com a implementação deste projeto, conseguimos perceber que a metodologia CLIL é uma abordagem metodológica eficaz, sendo que, nos permitiu concluir que mesmo na Educação Pré-escolar é possível ensinarmos uma disciplina através de uma segunda língua.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

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 .