Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm

n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project number S2013ICE-2933_02

Proposed Methodology to Evaluate CO2 Capture Using Construction and DemolitionWaste

Since the Industrial Revolution, levels of CO2 in the atmosphere have been constantly growing, producing an increase in the average global temperature. One of the options for Carbon Capture and Storage is mineral carbonation. The results of this process of fixing are the safest in the long term, but the main obstacle for mineral carbonation is the ability to do it economically in terms of both money and energy cost. The present study outlines a methodological sequence to evaluate the possibility for the carbonation of ceramic construction waste (brick, concrete, tiles) under surface conditions for a short period of time. The proposed methodology includes a pre-selection of samples using the characterization of chemical and mineralogical conditions and in situ carbonation. The second part of the methodology is the carbonation tests in samples selected at 10 and 1 bar of pressure. The relative humidity during the reaction was 20 wt %, and the reaction time ranged from 24 h to 30 days. To show the e ectiveness of the proposed methodology, Ca-rich bricks were used, which are rich in silicates of calcium or magnesium. The results of this study showed that calcite formation is associated with the partial destruction of Ca silicates, and that carbonation was proportional to reaction time. The calculated capture e ciency was proportional to the reaction time, whereas carbonation did not seem to significantly depend on particle size in the studied conditions. The studies obtained at a low pressure for the total sample were very similar to those obtained for finer fractions at 10 bars. Presented results highlight the utility of the proposed methodology

State Transition Algorithm

In terms of the concepts of state and state transition, a new heuristic random search algorithm named state transition algorithm is proposed. For continuous function optimization problems, four special transformation operators called rotation, translation, expansion and axesion are designed. Adjusting measures of the transformations are mainly studied to keep the balance of exploration and exploitation. Convergence analysis is also discussed about the algorithm based on random search theory. In the meanwhile, to strengthen the search ability in high dimensional space, communication strategy is introduced into the basic algorithm and intermittent exchange is presented to prevent premature convergence. Finally, experiments are carried out for the algorithms. With 10 common benchmark unconstrained continuous functions used to test the performance, the results show that state transition algorithms are promising algorithms due to their good global search capability and convergence property when compared with some popular algorithms.Comment: 18 pages, 28 figure

Coupled visco-mechanical and diffusion void growth modelling during composite curing

Most critical processing step during long fiber reinforced epoxy matrix composite laminate manufacturing is the polymerization stage. If not optimized, it gives birth to defects in the bulk material, such as voids. These defects are considered as possible sources of damage in the composite parts. The aim of this work is to model the evolution of void growth in thermoset composite laminates after ply collation (autoclave processes) or resin impregnation (RTM, LCM process). A coupled mechanical and diffusion model is presented to better predict the final void size at the end of polymerization. Amongst the parameter investigated, onset of pressure application and diffusive species concentration where found to have a major effect on void size evolution during curing process

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era