Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. 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. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming

In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General Algebraic Modeling Systems (GAMS). A Virtual Power Producer (VPP) can optimal operate the generation units, assured the good functioning of equipment, including the maintenance, operation cost and the generation measurement and control. A central control at system allows a VPP to manage the optimal generation and their load control. The application of methodology to a real case study in Budapest Tech, demonstrates the effectiveness of this method to solve the optimal isolated dispatch of the DC micro-grid renewable energy park. The problem has been converged in 0.09 s and 30 iterations.

Optimal methodology for distribution systems reconfiguration based on OPF and solved by decomposition technique

This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, balancing load among feeders and subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. The Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the Master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS. The effectiveness of the proposal is demonstrated through two examples extracted from the literature.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Solução doméstica para televisão digital

Mestrado em Engenharia Electrotécnica e de Computadores

Semantic social network analysis

Mestrado em Engenharia Informática

On the use of IEEE 802.15.4/Zigbee for time-sensitive wireless sensor network applications

Mestrado em Engenharia Electrotécnica e de Computadores

Towards an Academic Social Network for Bologna Process

The Bologna Process aimed to build a European Higher Education Area promoting student's mobility. The adoption of Bologna Declaration directives requires a self management distributed approach to deal with student's mobility, allowing frequent updates in institutions rules or legislation. This paper suggests a computational system architecture, which follows a social network design. A set of structured annotations is proposed in order to organize the user's information. For instance, when the user is a student its annotations are organized into an academic record. The academic record data is used to discover interests, namely mobility interests, among students that belongs the academic network. These ideas have been applied into a demonstrator that includes a mobility simulator to compare and show the student's academic evolution.

Academic social network in EHES

This paper presents the foundations of an Academic Social Network (ASN) focusing the Bologna Declaration and the Bologna Process (BP) mobility issues using ontological support. An ASN will permit students to share commons academic interests, preferences and mobility paths in the European Higher Education Space (EHES). The description of the conceptual support is ontology based allowing knowledge sharing and reuse. An approach is presented by merging Academic Ontology to Support the Bologna Mobility Process with Friend of a Friend ontology. The resulting ontology supports the student mobility profile in the ASN. The strategies to make available, in the network, knowledge about mobility issues, are presented including knowledge discovery and simulation approaches to cover student's mobility scenarios for BP.

Outermost european regions of the atlantic area network

This paper is part of the results from the project "Implementation Strategies and Development of an Open and Distance Education System for the University of the Azores" funded by the European Social Fund. http://hdl.handle.net/10400.3/2327

Acesso a laboratórios remotos via ambientes imersivos acesso remoto a um multímetro

Recentemente, tem-se assistido à utilização de ambientes imersivos 3D em vários domínios tais como: actividades empresariais, educativas, lúdicas, entre outras devido à expansão do Second Life. A finalidade deste conceito é oferecer aos utilizadores um acesso alternativo a valências existentes no mundo real, a partir de um computador ligado à Internet. Uma aplicação prática pode ser a sua utilização em laboratórios remotos, com a finalidade de controlar remotamente instrumentos de medição, a partir de um ambiente imersivo. Para isso, o mesmo deve permitir a construção de um laboratório virtual e respectivos instrumentos, também virtuais. Este tipo de solução é viável, devido a existirem dispositivos com interfaces de acesso remoto, e ambientes 3D desenvolvidos em linguagens de programação que possuem bibliotecas de código para protocolos de redes de computadores. A finalidade deste trabalho é desenvolver uma metodologia de acesso remoto, a instrumentos de medição em laboratórios de electricidade e electrónica, usando ambientes imersivos 3D. Como caso de estudo, o instrumento utilizado é um multímetro, controlado remotamente a partir de uma reprodução num mundo virtual, construído no ambiente 3D Open Wonderland. Nessa reprodução virtual, numa primeira fase, só serão disponibilizadas para medição, um conjunto limitado das variáveis eléctricas passíveis de medir através do multímetro seleccionado.

Neural-network approach to modeling liquid crystals in complex confinement

Finding the structure of a confined liquid crystal is a difficult task since both the density and order parameter profiles are nonuniform. Starting from a microscopic model and density-functional theory, one has to either (i) solve a nonlinear, integral Euler-Lagrange equation, or (ii) perform a direct multidimensional free energy minimization. The traditional implementations of both approaches are computationally expensive and plagued with convergence problems. Here, as an alternative, we introduce an unsupervised variant of the multilayer perceptron (MLP) artificial neural network for minimizing the free energy of a fluid of hard nonspherical particles confined between planar substrates of variable penetrability. We then test our algorithm by comparing its results for the structure (density-orientation profiles) and equilibrium free energy with those obtained by standard iterative solution of the Euler-Lagrange equations and with Monte Carlo simulation results. Very good agreement is found and the MLP method proves competitively fast, flexible, and refinable. Furthermore, it can be readily generalized to the richer experimental patterned-substrate geometries that are now experimentally realizable but very problematic to conventional theoretical treatments.

Cultural organizations and communication in portuguese decentralization policy

This presentation intends to show to what extent the Portuguese municipalities’ commitment, from the first decade of this century, in cultural facilities of municipal management and which has provided 12 of the 18 district capitals of mainland Portugal with equipment, resulted in a regular, diverse and innovative schedule. Investing in urban regeneration, local government has tried to convert cities’ demographic changes (strengthening of the most educated and professionally qualified groups) in effective cultural demands that consolidate the three axes of development competitiveness-innovation-creativity. What the empirical study to the programming and communication proposals of those equipment shows is that it is not enough to provide cities with facilities; to escape to a utilitarian conception of culture, there is a whole work to be done so that such equipment be experienced and felt as new public sphere. Equipment in which proposals go through a fluid bind, constructed through space and discourse with local community, devotes a diversified and innovative bet full filling development axis. This paper presents in a systematic way what contributes to this binding on the analyzed equipment.