LMP triggered real time demand response events

Recent changes in power systems mainly due to the substantial increase of distributed generation and to the operation in competitive environments has created new challenges to operation and planning. In this context, Virtual Power Players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Demand response market implementation has been done in recent years. Several implementation models have been considered. An important characteristic of a demand response program is the trigger criterion. A program for which the event trigger depends on the Locational Marginal Price (LMP) used by the New England Independent System operator (ISO-NE) inspired the present paper. This paper proposes a methodology to support VPP demand response programs management. The proposed method has been computationally implemented and its application is illustrated using a 32 bus network with intensive use of distributed generation. Results concerning the evaluation of the impact of using demand response events are also presented.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

Energy and ancillary services joint market simulation

In order to develop a flexible simulator, a variety of models for Ancillary Services (AS) negotiation has been implemented in MASCEM – a multi-agent system competitive electricity markets simulator. In some of these models, the energy and the AS are addressed simultaneously while in other models they are addressed separately. This paper presents an energy and ancillary services joint market simulation. This paper proposes a deterministic approach for solving the energy and ancillary services joint market. A case study based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve, and Non-Spinning Reserve services is used to demonstrate that the use of the developed methodology is suitable for solving this kind of optimization problem. The presented case study is based on CAISO real AS market data considers fifteen bids.

Energy resources scheduling in competitive environment

The increasing use of distributed generation units based on renewable energy sources, the consideration of demand-side management as a distributed resource, and the operation in the scope of competitive electricity markets have caused important changes in the way that power systems are operated. The new distributed resources require an entity (player) capable to make them able to participate in electricity markets. This entity has been known as Virtual Power Player (VPP). VPPs need to consider all the business opportunities available to their resources, considering all the relevant players, the market and/or other VPPs to accomplish their goals. This paper presents a methodology that considers all these opportunities to minimize the operation costs of a VPP. The method is applied to a distribution network managed by four independent VPPs with intensive use of distributed resources.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of 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 32 bus distribution network.

VPP´s multi-level negotiation in smart grids and competitive electricity markets

The increase of distributed generation (DG) has brought about new challenges in electrical networks electricity markets and in DG units operation and management. Several approaches are being developed to manage the emerging potential of DG, such as Virtual Power Players (VPPs), which aggregate DG plants; and Smart Grids, an approach that views generation and associated loads as a subsystem. This paper presents a multi-level negotiation mechanism for Smart Grids optimal operation and negotiation in the electricity markets, considering the advantages of VPPs’ management. The proposed methodology is implemented and tested in MASCEM – a multiagent electricity market simulator, developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

O desenvolvimento da leitura em função de diferentes métodos

Dissertação apresentada à Escola Superior de Educação de Lisboa no âmbito do Mestrado em Ensino Especial

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.