Multi-agent management system for electric vehicle charging

This paper proposes an implementation, based on a multi-agent system, of a management system for automated negotiation of electricity allocation for charging electric vehicles (EVs) and simulates its performance. The widespread existence of charging infrastructures capable of autonomous operation is recognised as a major driver towards the mass adoption of EVs by mobility consumers. Eventually, conflicting requirements from both power grid and EV owners require automated middleman aggregator agents to intermediate all operations, for example, bidding and negotiation, between these parts. Multi-agent systems are designed to provide distributed, modular, coordinated and collaborative management systems; therefore, they seem suitable to address the management of such complex charging infrastructures. Our solution consists in the implementation of virtual agents to be integrated into the management software of a charging infrastructure. We start by modelling the multi-agent architecture using a federated, hierarchical layers setup and as well as the agents' behaviours and interactions. Each of these layers comprises several components, for example, data bases, decision-making and auction mechanisms. The implementation of multi-agent platform and auctions rules, and of models for battery dynamics, is also addressed. Four scenarios were predefined to assess the management system performance under real usage conditions, considering different types of profiles for EVs owners', different infrastructure configurations and usage and different loads on the utility grid (where real data from the concession holder of the Portuguese electricity transmission grid is used). Simulations carried with the four scenarios validate the performance of the modelled system while complying with all the requirements. Although all of these have been performed for one charging station alone, a multi-agent design may in the future be used for the higher level problem of distributing energy among charging stations. Copyright (c) 2014 John Wiley & Sons, Ltd.

Gender asymmetries in family and work spheres: strategies and conflicts of qualified women working in portuguese state administration

In Portugal, feminine activity rate of working mother is high but remains structural asymmetries of responsibilities between women and men in familiar spheres. Based on quantitative and qualitative data results are presented that show that, in spite of a global feminization rate of 58,6%, women workers in State Administration remains with major responsibilities in familiar/private lives than men. Women in technical and leadership functions have the same patterns of familiar and domestic responsibilities but different patterns of work-time. Women in technical functions tend to have a strategy of work-family time balance, despite less career opportunities, while women in leadership functions adopt a supremacy of wok-time, just as men. Nevertheless, both women, in technical and leadership functions, feel a permanent conflict between career and family responsibilities, which is not felt by men. Gender roles define dominant models of work and family organisation which conduct to different professional strategies and career opportunities.

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Reactive power management strategies in future smart grids

The reactive power management in distribution network with large penetration of distributed energy resources is an important task in future power systems. The control of reactive power allows the inclusion of more distributed recourses and a more efficient operation of distributed network. Currently, the reactive power is only controlled in large power plants and in high and very high voltage substations. In this paper, several reactive power control strategies considering a smart grids paradigm are proposed. In this context, the management of distributed energy resources and of the distribution network by an aggregator, namely Virtual Power Player (VPP), is proposed and implemented in a MAS simulation tool. The proposed methods have been computationally implemented and tested using a 32-bus distribution network with intensive use of distributed resources, mainly the distributed generation based on renewable resources. Results concerning the evaluation of the reactive power management algorithms are also presented and compared.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Digital marketing strategies

Internship Report presented to Instituto de Contabilidade e Administração do Porto for the Master’s degree in Marketing Digital under the guidance of Dr. José Magalhães Author Note This internship was carried out under the Erasmus Program for college students and under the agreement between the sending institution, Instituto Superior de Contabilidade e Administração do Porto and the host company, eRise, located in Budapest, Hungary, under the guidance of Vilmos Schwarz.

New Strategies on Molecular Biology Applied to Microbial Systematics

Systematics is the study of diversity of the organisms and their relationships comprising classification, nomenclature and identification. The term classification or taxonomy means the arrangement of the organisms in groups (rate) and the nomenclature is the attribution of correct international scientific names to organisms and identification is the inclusion of unknown strains in groups derived from classification. Therefore, classification for a stable nomenclature and a perfect identification are required previously. The beginning of the new bacterial systematics era can be remembered by the introduction and application of new taxonomic concepts and techniques, from the 50’s and 60’s. Important progress were achieved using numerical taxonomy and molecular taxonomy. Molecular taxonomy, brought into effect after the emergence of the Molecular Biology resources, provided knowledge that comprises systematics of bacteria, in which occurs great evolutionary interest, or where is observed the necessity of eliminating any environmental interference. When you study the composition and disposition of nucleotides in certain portions of the genetic material, you study searching their genome, much less susceptible to environmental alterations than proteins, codified based on it. In the molecular taxonomy, you can research both DNA and RNA, and the main techniques that have been used in the systematics comprise the build of restriction maps, DNA-DNA hybridization, DNA-RNA hybridization, sequencing of DNA sequencing of sub-units 16S and 23S of rRNA, RAPD, RFLP, PFGE etc. Techniques such as base sequencing, though they are extremely sensible and greatly precise, are relatively onerous and impracticable to the great majority of the bacterial taxonomy laboratories. Several specialized techniques have been applied to taxonomic studies of microorganisms. In the last years, these have included preliminary electrophoretic analysis of soluble proteins and isoenzymes, and subsequently determination of deoxyribonucleic acid base composition and assessment of base sequence homology by means of DNA-RNA hybrid experiments beside others. These various techniques, as expected, have generally indicated a lack of taxonomic information in microbial systematics. There are numberless techniques and methodologies that make bacteria identification and classification study possible, part of them described here, allowing establish different degrees of subspecific and interspecific similarity through phenetic-genetic polymorphism analysis. However, was pointed out the necessity of using more than one technique for better establish similarity degrees within microorganisms. Obtaining data resulting from application of a sole technique isolatedly may not provide significant information from Bacterial Systematics viewpoint

Expression of antibodies and retroviralVectors from defined chromosomal sites: strategies towards reliable production systems

Dissertation presented to obtain a Ph.D. degree in Sciences of Engineering and Technology, Cell Technology, at the Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

L’IMPORTANCE DES OBJETS DANS LA “MAISON DE PASSAGE”: ENTRE NOSTALGIE ET STRATEGIES D’AFFIRMATION IDENTITAIRE

Relatório de Estágio do Mestrado em Migrações, Inter-etnicidades e Transnacionalismo

Probabilistic maintenance and optimization strategies for deteriorating civil infrastructures

In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role. In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating civil infrastructures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis,through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

Distributed Linear Precoding and User Selection in Coordinated Multicell Systems

In this manuscript we tackle the problem of semidistributed user selection with distributed linear precoding for sum rate maximization in multiuser multicell systems. A set of adjacent base stations (BS) form a cluster in order to perform coordinated transmission to cell-edge users, and coordination is carried out through a central processing unit (CU). However, the message exchange between BSs and the CU is limited to scheduling control signaling and no user data or channel state information (CSI) exchange is allowed. In the considered multicell coordinated approach, each BS has its own set of cell-edge users and transmits only to one intended user while interference to non-intended users at other BSs is suppressed by signal steering (precoding). We use two distributed linear precoding schemes, Distributed Zero Forcing (DZF) and Distributed Virtual Signalto-Interference-plus-Noise Ratio (DVSINR). Considering multiple users per cell and the backhaul limitations, the BSs rely on local CSI to solve the user selection problem. First we investigate how the signal-to-noise-ratio (SNR) regime and the number of antennas at the BSs impact the effective channel gain (the magnitude of the channels after precoding) and its relationship with multiuser diversity. Considering that user selection must be based on the type of implemented precoding, we develop metrics of compatibility (estimations of the effective channel gains) that can be computed from local CSI at each BS and reported to the CU for scheduling decisions. Based on such metrics, we design user selection algorithms that can find a set of users that potentially maximizes the sum rate. Numerical results show the effectiveness of the proposed metrics and algorithms for different configurations of users and antennas at the base stations.