Cianobactérias e toxicidade: impacte na saúde pública em Portugal e no Brasil

Um aumento da concentração de nutrientes na água poderá desencadear fluorescências de cianobactérias (densidades >200 cel/mL). Sob determinadas condições as cianobactérias produzem toxinas responsáveis pelo envenenamento de animais e humanos. O objetivo deste estudo é relacionar a ocorrência de fluorescências toxicas em Portugal e no Brasil. Para tal, em 2005 e 2006 foi estudado o fitoplâncton em três reservatórios em Portugal (região sul) e dois no Brasil (Minas Gerais e Pará). Comparativamente foi verificado maior diversidade nos reservatórios portugueses, com dominância de cianobactérias em período de primavera/verão/outono, pertencentes a géneros produtores de hépato e neurotoxinas (Microcystis sp, Aphanizomenon sp, Oscillatoria sp e Planktothrix sp.). No Brasil observou-se dominância de cianobactérias ao longo de todo o ano, com presença de Microcystis aeruginosa, produtora de hepatotoxina. Conclui-se que os reservatórios estudados apresentam géneros produtores de toxinas, com risco para a saúde pública, sendo fundamental implementar medidas que contribuam para mitigar esta situação. - ABSTRACT - An increasing of nutrients in water can conduct to the development of cyanobacteria blooms (density>2000 cels/mL). Under specific conditions cyanobacteria produce toxins responsible for acute poisoning of animals and humans. The aim of this study is to describe toxic blooms in Portugal and Brazil. Therefore, phytoplankton from three Portuguese reservoirs (South region) and two from Brazil (Minas Gerais and Pará) were studied in 2005 and 2006. Portuguese reservoirs showed more diversity with dominance of hepatic and neurotoxin genera producers (Microcystis sp, Aphanizomenon sp, Oscillatoria sp e Planktothrix sp.) along spring/summer/autumn seasons. In Brazil dominance of cyanobacteria was observed all along the year with the presence of Microcystis aeruginosa hepatotoxic producer. The studied reservoirs present toxins producers’ genera, with risk for public health, being fundamental the implementation of mitigation measures to reverse this situation.

Toxicidade a cianobactérias: impacte potencial na saúde pública em populações de Portugal e Brasil

Como recurso natural fundamental à vida, a água e os ecossistemas aquáticos devem ser alvo de avaliação contínua, no que se reporta à sua qualidade física, química e biológica. Segundo a Organização Mundial de Saúde cerca de 1,1 biliões de pessoas estão impossibilitadas em aceder a qualquer tipo de água potável e, as populações residentes nas proximidades de rios, lagoas, e reservatórios utilizam estas águas para as suas necessidades de consumo, aumentando o risco de transmissão de doenças. Enquanto constituintes da comunidade fitoplanctónica, as cianobactérias são microrganismos procariotas, fotossintéticos, que obtêm os nutrientes diretamente da coluna de água e, um aumento da concentração de nutrientes (principalmente azoto e fósforo), associado a condições ambientais favoráveis, pode desencadear um crescimento rápido originando fluorescências. Sob determinadas condições as cianobactérias podem produzir toxinas existindo registos que evidenciam que fluorescências toxicas são responsáveis pelo envenenamento agudo e morte de animais e humanos pelo que, a água utilizada para consumo humano deverá ser regularmente monitorizada para este elemento biológico. O objetivo deste estudo é relacionar a ocorrência de fluorescências de cianobactérias (> 2000 cel/ml) e toxicidade associada, com o impacte potencial na Saúde Pública avaliado através do consumo direto ou indireto da água. Em Portugal foram selecionados oito reservatórios situados na região Sul, pertencentes às bacias hidrográficas do Sado e Guadiana e estudados entre 2000 e 2008. No Brasil foram selecionados os reservatórios de Três Marias (Estado de Minas Gerais) e de Tucuruí (Estado do Pará) e estudados em 2005 e 2006 respetivamente. Os reservatórios foram caracterizados em termos físicos e químicos, tendo-se igualmente procedido à caracterização da comunidade fitoplanctónica através da identificação e quantificação dos principais grupos presentes em diferentes épocas do ano. Em termos fitoplanctónicos os reservatórios portugueses apresentaram maior diversidade,verificando-se contudo dominância das cianobactérias na comunidade. Associados a fluorescências, foram registados nestes reservatórios géneros produtores de hepato e neurotoxinas como Aphanizomenon sp, Microcystis aeruginosa e Oscillatoria sp. No Brasil, em situação de fluorescências, os géneros produtores de neuro e hepatotoxinas foram Microcystis (> 350.000 cels/ml) e Cylindrospermopsis. A presença destes géneros, poderá constituir um risco potencial para a saúde pública, pelo que é importante a implementação de medidas de mitigação em todos os reservatórios objeto de estudo, devendo essa atuação passar pelo controle do estado trófico no sentido de evitar o desenvolvimento de fluorescências. Assim sugere-se a implementação de um tratamento adequado para a produção de água de consumo e a organização de ações de sensibilização e aviso e informação às populações que utilizam os reservatórios em Portugal e no Brasil para diversos usos. - ABSTRACT - As a life fundamental natural resource, water and aquatic ecosystems must be continuously evaluated in their physical, chemical and biological quality. According World Health Organization, 1.1 billion people has no chance to access any kind of potable water. Populations living near rivers, lagoons or reservoirs use those waters to content their needs, increasing risks disease transmission. As members of phytoplankton community, cyanobacteria are prokaryotic, photosynthetic microorganisms and get its nutrients directly from water column. The increase of this nutrients (especially nitrogen and phosphorus) associated with favorable environment conditions, can support a sudden grow and instigate blooms. Under specific conditions cyanobacteria can produce toxins and several records have shown that toxic blooms are responsible by acute poisoning and death in animals and humans so, water for human consumption must be regularly surveyed for this biologic element. The aim of this study is to correlate Cyanobacteria blooms (>2.000cels/ml) and connected toxicity with public health impact, evaluated through water consumption. In Portugal, eight reservoirs located in the South region were selected and study between 2000 and 2008. In Brazil, Três Marias reservoir (Minas Gerais Provence) and Tucuruí (Pará Provence) were selected and study in 2005 and 2006. Reservoirs were characterized in physical and chemical aspects, as well as phytoplankton community, through identification and counting of main present groups along study period. In bloom circumstances, liver toxins and neurotoxins producers like Aphanizomenon sp, Microcystis aeruginosa and Oscillatoria sp. were founded in Portuguese reservoirs. In Brazil, cyanobacteria genera involved in toxic bloom were Microcystis (> 350.000 cels/ml) and Cylindrospermopsis. This genera presence represents a potential risk for public health, and show the requirement to implement mitigation measures in all study reservoirs. These measures can be represented by water eutrophication control to avoid blooms, by appropriate treatments of water to human consumption, and public warnings or information to dose people in Portugal and Brazil that use these reservoirs to several activities.

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.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

Computational intelligence applications for future power systems

Power system planning, control and operation require an adequate use of existing resources as to increase system efficiency. The use of optimal solutions in power systems allows huge savings stressing the need of adequate optimization and control methods. These must be able to solve the envisaged optimization problems in time scales compatible with operational requirements. Power systems are complex, uncertain and changing environments that make the use of traditional optimization methodologies impracticable in most real situations. Computational intelligence methods present good characteristics to address this kind of problems and have already proved to be efficient for very diverse power system optimization problems. Evolutionary computation, fuzzy systems, swarm intelligence, artificial immune systems, neural networks, and hybrid approaches are presently seen as the most adequate methodologies to address several planning, control and operation problems in power systems. Future power systems, with intensive use of distributed generation and electricity market liberalization increase power systems complexity and bring huge challenges to the forefront of the power industry. Decentralized intelligence and decision making requires more effective optimization and control techniques techniques so that the involved players can make the most adequate use of existing resources in the new context. The application of computational intelligence methods to deal with several problems of future power systems is presented in this chapter. Four different applications are presented to illustrate the promises of computational intelligence, and illustrate their potentials.

Renewable-based generation integration in electricity markets with virtual power producers

The development of renewable energy sources and Distributed Generation (DG) of electricity is of main importance in the way towards a sustainable development. However, the management, in large scale, of these technologies is complicated because of the intermittency of primary resources (wind, sunshine, etc.) and small scale of some plants. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. VPPs can ensure a secure, environmentally friendly generation and optimal management of heat, electricity and cold as well as optimal operation and maintenance of electrical equipment, including the sale of electricity in the energy market. For attaining these goals, there are important issues to deal with, such as reserve management strategies, strategies for bids formulation, the producers’ remuneration, and the producers’ characterization for coalition formation. This chapter presents the most important concepts related with renewable-based generation integration in electricity markets, using VPP paradigm. The presented case studies make use of two main computer applications:ViProd and MASCEM. ViProd simulates VPP operation, including the management of plants in operation. MASCEM is a multi-agent based electricity market simulator that supports the inclusion of VPPs in the players set.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the 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 multiagent based, 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. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

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.