Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Automatic Electricity Markets Data Extraction for Realistic Multi-agent Simulations

Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.

Purification and parcial characterization of a hemagglutinating factor (HAF): a possible adhesive factor of the diffuse adherent of Escherichia coli (DAEC)

The mannose-resistant hemagglutinating factor (HAF) was extracted and purified from a diffuse adherent Escherichia coli (DAEC) strain belonging to the classic enteropathogenic E. coli (EPEC) serotype (0128). The molecular weight of HAF was estimated to be 18 KDa by SDS-PAGE and 66 KDa by Sephadex G100, suggesting that the native form of HAF consists of 3-4 monomeric HAF. Gold immunolabeling with specific HAF antiserum revealed that the HAF is not a rigid structure like fimbriae on the bacterial surface. The immunofluorescence test using purified HAF on HeLa cells, in addition to the fact that the HAF is distributed among serotypes of EPEC, suggests that HAF is a possible adhesive factor of DAEC strains

Load Control Timescales Simulation in a Multi-Agent Smart Grid Platform

Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers’ consumption profile, helping to reduce peak demand. Aiming to support small players’ participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques – the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

Biomarkers in Solea Senegalensis Kaup, 1858 exposed to contaminated estuarine sediments: a multi-level approach

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Sciences

Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Eficiência energética no sector dos transportes rodoviários: metodologia para quantificação do excesso de energia consumida devido ao factor comportamental na condução de veículos automóveis ligeiros

Dissertação apresentada à Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Energia e Bioenergia

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff.

Realistic Multi-Agent Simulation of Competitive Electricity Markets

The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.

Presence of Circulating Levels of Interferon-g, Interleukin-10 and Tumor Necrosis Factor-a in Patients with Visceral Leishmaniasis

Experimental murine L. major infection is characterized by the expansion of distinct CD4+ T cell subsets. The Th1 response is related to production of IFN-g and resolution of infection, whereas Th-2 response with production of IL-4 and IL-10 and dissemination of infection. The objective of this study was to measure the circulating levels of IFN-g, IL-10 and TNF-a in patients with visceral leishmaniasis (VL) before, during and at the end of therapy and to examine the association between cytokine levels and activity of VL. Fifteen patients with VL were evaluated. The cytokine determinations were done by using the enzyme-linked immunoassay (ELISA) before, during and at the end of therapy. At baseline, we detected circulating levels of IFN-g in 13 of 15 patients (median = 60 pg/ml); IL-10 in 14 of 15 patients (median = 141.4 pg/ml); and TNF-a in 13 of 14 patients (median = 38.9 pg/ml). As patients improved, following antimonial therapy, circulating levels of IL-10 showed an exponential decay (y = 82.34 e–0,10367x, r = –0.659; p < 0.001). IFN-g was no longer detected after 7/14 days of therapy. On the other hand, circulating levels of TNF-a had a less pronounced decay with time on therapy, remaining detectable in most patients during the first seven days of therapy (y = 36.99-0.933x, r = –0.31; p = 0.05). Part of the expression of a successful response to therapy may, therefore, include reduction in secretion of inflammatory as well as suppressive cytokines. Since IL-10 and IFN-g are both detected prior to therapy, the recognized cellular immune depression seen in these patients may be due to biological predominance of IL-10 (type 2 cytokine), rather than lack of IFN-g (type 1 cytokine) production.

Stability of the Transthyretin Molecule as a Key Factor in

Transthyretin (TTR) protects against A-Beta toxicity by binding the peptide thus inhibiting its aggregation. Previous work showed different TTR mutations interact differently with A-Beta, with increasing affinities correlating with decreasing amyloidogenecity of the TTR mutant; this did not impact on the levels of inhibition of A-Beta aggregation, as assessed by transmission electron microscopy. Our work aimed at probing differences in binding to A-Beta by WT, T119M and L55P TTR using quantitative assays, and at identifying factors affecting this interaction. We addressed the impact of such factors in TTR ability to degrade A-Beta. Using a dot blot approach with the anti-oligomeric antibody A11, we showed that A-Beta formed oligomers transiently, indicating aggregation and fibril formation, whereas in the presence of WT and T119M TTR the oligomers persisted longer, indicative that these variants avoided further aggregation into fibrils. In contrast, L55PTTR was not able to inhibit oligomerization or to prevent evolution to aggregates and fibrils. Furthermore, apoptosis assessment showed WT and T119M TTR were able to protect against A-Beta toxicity. Because the amyloidogenic potential of TTR is inversely correlated with its stability, the use of drugs able to stabilize TTR tetrameric fold could result in increased TTR/ABeta binding. Here we showed that iododiflunisal, 3-dinitrophenol, resveratrol, [2-(3,5-dichlorophenyl)amino] (DCPA) and [4- (3,5-difluorophenyl)] (DFPB) were able to increase TTR binding to A-Beta; however only DCPA and DFPB improved TTR proteolytic activity. Thyroxine, a TTR ligand, did not influence TTR/A-Beta interaction and A-Beta degradation by TTR, whereas RBP, another TTR ligand, not only obstructed the interaction but also inhibited TTR proteolytic activity. Our results showed differences between WT and T119M TTR, and L55PTTR mutant regarding their interaction with A-Beta and prompt the stability of TTR as a key factor in this interaction, which may be relevant in AD pathogenesis and for the design of therapeutic TTR-based therapies.

Agente de coligação com definição gráfica de processos habilitados por executor de regras: execução e composição dinâmica de skills num sistema de manufactura multiagente

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores