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.

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.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

Acompanhamento do reforço de potência Salamonde II

Tendo Portugal objetivos cada vez mais ambiciosos na utilização de energias renováveis, aliados às condicionantes socioeconómicas e ambientais existentes que têm dificultado a realização de novos investimentos em novos aproveitamentos, a EDP Gestão de Produção de Energia realizou estudos onde verificou que a realização de reforços de potência seria uma forma bastante atrativa de ultrapassar essas dificuldades e ao mesmo tempo responder às solicitações energéticas atuais. É neste âmbito que se insere o Reforço de Potência de Salamonde, Salamonde II. Este trabalho foi elaborado na sequência de um estágio realizado para elaboração do trabalho final de Mestrado em Engenharia Civil do ISEL, na área de especialização de Hidráulica, e tem como objetivo relacionar o conhecimento académico com os aspetos práticos do exercício profissional na área de construção dos órgãos e equipamentos de uma barragem. O conteúdo deste trabalho é composto pelos seguintes temas: i) um breve enquadramento da obra e definição dos objetivos do projeto, bem como uma descrição das características e processos construtivos utilizados nos diferentes órgãos do circuito hidráulico; ii) demonstração analítica das opções construtivas tomadas na execução do desvio provisório do Rio Mau, concluindo-se que este estava bem dimensionado; iii) estudo das precipitações máximas diárias, recorrendo à Lei de Gumbel, durante o período de estiagem, com o intuito de se estimar o caudal máximo que irá escoar no Rio Mau durante a execução do seu desvio definitivo, verificando que este ocorrerá no mês de setembro; iv) acompanhamento dos trabalhos de execução da ensecadeira de jusante recorrendo, em parte, a uma nova metodologia de betonagem, o BPCA (Betão com Prévia Colocação de Agregado) e também uma análise comparativa dos custos unitários dos diferentes processos construtivos utilizados, onde se evidenciou que esta técnica inovadora tem potencial, mas ainda carece de automatização de processos.

Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Distributed energy resources management with cyber-physical SCADA in the context of future smart grids

In the energy management of a small power system, the scheduling of the generation units is a crucial problem for which adequate methodologies can maximize the performance of the energy supply. This paper proposes an innovative methodology for distributed energy resources management. The optimal operation of distributed generation, demand response and storage resources is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The paper deals with a vision for the grids of the future, focusing on conceptual and operational aspects of electrical grids characterized by an intensive penetration of DG, in the scope of competitive environments and using artificial intelligence methodologies to attain the envisaged goals. These concepts are implemented in a computational framework which includes both grid and market simulation.

Smart offices and intelligent decision rooms

Nowadays computing technology research is focused on the development of Smart Environments. Following that line of thought several Smart Rooms projects were developed and their appliances are very diversified. The appliances include projects in the context of workplace or everyday living, entertainment, play and education. These appliances envisage to acquire and apply knowledge about the environment state in order to reason about it so as to define a desired state for its inhabitants and perform adaptation adaptation to these desires and therefore improving their involvement and satisfaction with that environment.

Hardware and software in smart decision rooms

Since the last decade research in Group Decision Making area have been focus in the building of meeting rooms that could support the decision making task and improve the quality of those decisions. However the emergence of Ambient Intelligence concept contributes with a new perspective, a different way of viewing traditional decision rooms. In this paper we will present an overview of Smart Decision Rooms providing Intelligence to the meeting environment, and we will also present LAID, an Ambient Intelligence Environment oriented to support Group Decision Making and some of the software tools that we already have installed in this environment.

LAID - a smart decision room with ambient intelligence for group decision making and argumentation support considering emotional aspects

Decision Making is one of the most important activities of the human being. Nowadays decisions imply to consider many different points of view, so decisions are commonly taken by formal or informal groups of persons. Groups exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. Group Decision Making is a social activity in which the discussion and results consider a combination of rational and emotional aspects. In this paper we will present a Smart Decision Room, LAID (Laboratory of Ambient Intelligence for Decision Making). In LAID environment it is provided the support to meeting room participants in the argumentation and decision making processes, combining rational and emotional aspects.

Synthesis, structure and electrochemical behaviour of Na, Mg-II, Mn-II, Cd-II and Ni-II complexes of 3-(2-carboxyphenylhydrazone)pentane-2,4-dione

Mononuclear manganese(II) [Mn(kappa O-HL)(2)(CH3OH)(4)] (4), nickel(II) [Ni(kappa O-2, kappa N-L)(H2O)(3)] (5), cadmium(II) [Cd(kappa O-2-HL)(2)(CH3OH)(3)] (7), tetranuclear zinc(II) [Zn-4(mu-OH)(2)(1 kappa O:2 kappa O-HL)(4)(kappa O-HL)(2)(H2O)(4)] (6) and polynuclear aqua sodium(I) [Na(H2O)(2)(mu-H2O)(2)](n)(HL)(n) (2) and magnesium(II) [Mg(OH)(H2O)(mu-H2O)(2)](n)(-HL)(n) (3) complexes were synthesized using 3-(2-carboxyphenyl-hydrazone)pentane-2,4-dione (H2L, 1) as a ligand precursor. The complexes were characterized by single crystal X-ray diffraction, elemental analysis, IR, H-1 and C-13 NMR (for 2, 3, 6 and 7) spectroscopies. Mono- or dianionic deprotonated derivatives of H2L display different coordination modes and lead to topologies and nuclearities of the complexes depending on metal ions and conditions used for the syntheses. Extensive intermolecular H-bonds form supramolecular arrangements in 1D chains (4 and 6), 1D chains of the organic anion and 2D networks of the metal-aqua aggregates (2 and 3), 2D networks (7) or even 3D frameworks (5). Electrochemical studies, by cyclic voltammetry and controlled potential electrolysis, show ligand centred redox processes as corroborated by theoretical DFT calculations in terms of LUMO and HOMO compositions. (C) 2012 Elsevier Ltd. All rights reserved.