Bruno Coelho

Concentrated solar power for renewable electricity and hydrogen production from water—a review

Todays world suffers from an increasing dependence on fossil fuels, either for electricity production, transportation or reagent for the chemical industry. A technological revolution in hydrogen and electricity production is important to support the future needs and lead the world towards a better future. For that, technological and economical barriers have to be broken. Concentrated solar power (CSP) has been proving to be a valid means to start this revolution and produce electricity and hydrogen from completely renewable sources—water and the sun. Although solid steps should be taken to solve the current limitations and increase the technical and economical viability of these projects, there are conditions to begin this revolution using factual bridges from the current fossil technologies to renewable technologies.

A multi-objective green UAV routing problem

Introduce a new time-dependent UAV heterogeneous fleet routing problem.Consider several objective functions and respect drones operational requirements.Design a MILP model in order to find sets of non-dominated solutions.Consider a model able to tackle multi-layer scenarios with package exchanging points.Integrate UAV into the new concepts of mini/microgrid systems inside smart cities. This paper introduces an Unmanned Aerial Vehicle (UAV) heterogeneous fleet routing problem, dealing with vehicles limited autonomy by considering multiple charging stations and respecting operational requirements. A green routing problem is designed for overcoming difficulties that exist as a result of limited vehicle driving range. Due to the large amount of drones emerging in the society, UAVs use and efficiency should be optimized. In particular, these kinds of vehicles have been recently used for delivering and collecting products. Here, we design a new real-time routing problem, in which different types of drones can collect and deliver packages. These aerial vehicles are able to collect more than one deliverable at the same time if it fits their maximum capacity. Inspired by a multi-criteria view of real systems, seven different objective functions are considered and sought to be minimized using a Mixed-Integer Linear Programming (MILP) model solved by a matheuristic algorithm. The latter filters the non-dominated solutions from the pool of solutions found in the branch-and-bound optimization tree, using a black-box dynamic search algorithm. A case of study, considering a bi-layer scenario, is presented in order to validate the proposal, which showed to be able to provide good quality solutions for supporting decision making.

A hybrid variable neighborhood search algorithm for targeted offers in direct marketing

Abstract This paper focuses on the targeted offers problem in direct marketing campaigns. The main objective is to maximize the feedback of customers purchases, offering products for the set of customers with the highest probability of positively accepting the offer and, at the same time, minimizing the operational costs of the campaign. Given the combinatorial nature of the problem and the large volume of data, involving instances with up to one million customers, approaches solely based on mathematical programming methods, said exact, appear limited and infeasible. In this paper, the use of a hybrid heuristic algorithm, based on the Greedy Randomized Adaptive Search Procedures and General Variable Neighborhood Search, is proposed. Computational experiments performed on a set of test problems from the literature show that the proposed algorithm was able to produce competitive solutions.

Red bulgeless galaxies in SDSS DR7. Are there any AGN hosts

With the main goal of finding bulgeless galaxies harbouring super massive black holes and showing, at most, just residual star formation activity, we have selected a sample of massive bulgeless red sequence galaxies from the SDSS-DR7, based on the NYU-VAGC catalogue. Multivavelength data were retrieved using EURO-VO tools, and the objects are characterised in terms of degree of star formation and the presence of an AGN. We have found seven objects that are quenched massive galaxies, that have no prominent bulge and that show signs of extra activity in their nuclei, five of them being central in their halo. These objects are rather robust candidates for rare systems that, though devoid of a significant bulge, harbor a supermassive black hole with an activity level likely capable of having halted the star formation through feedback.

A General Variable Neighborhood Search heuristic for short term load forecasting in Smart Grids environment

The importance of short-term load forecasting has been increasing lately. Electric grids are changing from a centralized single supply model towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and very dynamic scenario. On the other hand, with deregulation and competition, energy price forecasting has become a big business. Bus-load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and the nonstationarity of loads are becoming worse due to the dynamics of energy prices. Hence, the load forecasting problem has become more difficulty and more autonomous load predictors are needed in this new conjecture. In this paper a novel method for load forecasting which combines the heuristics procedures Multi-Start (MS) and General Variable Neighborhood Search (GVNS) is described. The pseudo code of MSGVNS is presented and explained in details. MSGVNS was implemented in C++ via OptFrame framework. Our main goal is to evaluate the performance of this algorithm in a grid environment. Real data from an electric utility have been used in order to test the proposed methodology. The obtained results are fully described and analyzed.

Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Cross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances. HighlightsUse of profit variability measure in connection to the client response.Consider the Sharpe ratio index for calculating risk-adjusted profit in targeted offers.Introduction of a bi-objective direct marketing promotional campaign.Adaptation, description and use of a general Pareto local search.

AGN Feedback and Quenching of Star Formation: A Multiwavelength Approach with the EURO-VO

We selected bulgeless red sequence galaxies in the SDSS (DR7) [Abazajian et al., ApJS 182, 543 (2009)] using data from NYU-VAGC [Blanton et al., AJ 129, 2562 (2005)]. Using EURO-VO tools we obtained multiwavelength data, we built spectral energy distributions, and undertook a thorough analysis to ascertain: the frequency of AGN among these galaxies, the degree of star formation and intrinsic extinction. We aim at verifying whether there are bulgeless quenched galaxies hosting SMBHs in order to test the AGN feedback paradigm.