Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

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.

Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Modelação e identificação de motor DC

O modelo matemático de um sistema real permite o conhecimento do seu comportamento dinâmico e é geralmente utilizado em problemas de engenharia. Por vezes os parâmetros utilizados pelo modelo são desconhecidos ou imprecisos. O envelhecimento e o desgaste do material são fatores a ter em conta pois podem causar alterações no comportamento do sistema real, podendo ser necessário efetuar uma nova estimação dos seus parâmetros. Para resolver este problema é utilizado o software desenvolvido pela empresa MathWorks, nomeadamente, o Matlab e o Simulink, em conjunto com a plataforma Arduíno cujo Hardware é open-source. A partir de dados obtidos do sistema real será aplicado um Ajuste de curvas (Curve Fitting) pelo Método dos Mínimos Quadrados de forma a aproximar o modelo simulado ao modelo do sistema real. O sistema desenvolvido permite a obtenção de novos valores dos parâmetros, de uma forma simples e eficaz, com vista a uma melhor aproximação do sistema real em estudo. A solução encontrada é validada com recurso a diferentes sinais de entrada aplicados ao sistema e os seus resultados comparados com os resultados do novo modelo obtido. O desempenho da solução encontrada é avaliado através do método das somas quadráticas dos erros entre resultados obtidos através de simulação e resultados obtidos experimentalmente do sistema real.

Smart Plastic Antibody Material (SPAM) tailored on disposable screen printed electrodes for protein recognition: application to Myoglobin detection

This work introduces two major changes to the conventional protocol for designing plastic antibodies: (i) the imprinted sites were created with charged monomers while the surrounding environment was tailored using neutral material; and (ii) the protein was removed from its imprinted site by means of a protease, aiming at preserving the polymeric network of the plastic antibody. To our knowledge, these approaches were never presented before and the resulting material was named here as smart plastic antibody material (SPAM). As proof of concept, SPAM was tailored on top of disposable gold-screen printed electrodes (Au-SPE), following a bottom-up approach, for targeting myoglobin (Myo) in a point-of-care context. The existence of imprinted sites was checked by comparing a SPAM modified surface to a negative control, consisting of similar material where the template was omitted from the procedure and called non-imprinted materials (NIMs). All stages of the creation of the SPAM and NIM on the Au layer were followed by both electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). AFM imaging was also performed to characterize the topography of the surface. There are two major reasons supporting the fact that plastic antibodies were effectively designed by the above approach: (i) they were visualized for the first time by AFM, being present only in the SPAM network; and (ii) only the SPAM material was able to rebind to the target protein and produce a linear electrical response against EIS and square wave voltammetry (SWV) assays, with NIMs showing a similar-to-random behavior. The SPAM/Au-SPE devices displayed linear responses to Myo in EIS and SWV assays down to 3.5 μg/mL and 0.58 μg/mL, respectively, with detection limits of 1.5 and 0.28 μg/mL. SPAM materials also showed negligible interference from troponin T (TnT), bovine serum albumin (BSA) and urea under SWV assays, showing promising results for point-of-care applications when applied to spiked biological fluids.

Effects of Aerobic Exercise on Anxiety Disorders: A Systematic Review

Anxiety disorders are the most common psychiatric disorders observed currently. It is a normal adaptive response to stress that allows coping with adverse situations. Nevertheless, when anxiety becomes excessive or disproportional in relation to the situation that evokes it or when there is not any special object directed at it, such as an irrational dread of routine stimuli, it becomes a disabling disorder and is considered to be pathological. The traditional treatment used is medication and cognitive behavioral psychotherapy, however, last years the practice of physical exercise, specifically aerobic exercise, has been investigated as a new non-pharmacological therapy for anxiety disorders. Thus, the aim of this article was to provide information on research results and key chains related to the therapeutic effects of aerobic exercise compared with other types of interventions to treat anxiety, which may become a useful clinical application in a near future. Researches have shown the effectiveness of alternative treatments, such as physical exercise, minimizing high financial costs and minimizing side effects. The sample analyzed, 66.8% was composed of women and 80% with severity of symptoms anxiety as moderate to severe. The data analyzed in this review allows us to claim that alternative therapies like exercise are effective in controlling and reducing symptoms, as 91% of anxiety disorders surveys have shown effective results in treating. However, there is still disagreement regarding the effect of exercise compared to the use of antidepressant symptoms and cognitive function in anxiety, this suggests that there is no consensus on the correct intensity of aerobic exercise as to achieve the best dose-response, with intensities high to moderate or moderate to mild.