Search

In this paper, the main objective is the simulation of the electric supply for homes in remote areas located in Morocco (Oujda and Ouarzazate), Spain (Granada), and Algeria (Bechar). This simulation study is divided into two ideas, the first one is to optimize the hybrid system under a varied number of houses and the second part is to fix it in chosen values. This work relied on the particle swarm optimization (PSO) method to optimize and analyze the proposed stand-alone photovoltaic/wind/diesel/battery hybrid system. MATLAB software is used to run the simulations and to carry out the optimal solutions of the optimized system using a developed PSO algorithm. The PSO approach contains five principal operator axes, which are problem definition, PSO initial parameters, initialization, PSO main loop, and then run of the algorithm. Optimizing the system component sizes and reaching the minimum cost of energy (COE) were the function objectives of this optimization study. Moreover, calculation of the hydrogen production from the generated energy by PV and wind systems is performed. [ABSTRACT FROM AUTHOR]

In this paper, we retrieve data about the frequent users of electronic commerce during the period 2011–2016 from the Spanish National Institute of Statistics. These data, coming from surveys, have intrinsic uncertainty that we describe using appropriate random variables. Then, we propose a stochastic model to study the dynamics of frequent users of electronic commerce. The goal of this paper is to solve the inverse problem that consists of determining the model parameters as suitable parametric random variables, in such a way the model output be capable of capturing the data uncertainty, at the time instants where sample data are available, via adequate probability density functions. To achieve the aforementioned goal, we propose a computational procedure that involves building a nonlinear objective function, based on statistical moment measures, to be minimized using a variation of the particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we explore two strategies for reducing the cash flow uncertainty of wind energy producers ascribed to variable weather conditions. The first strategy is based on the idea of aggregating the output of geographically-dispersed generating units. The second strategy employs financial instruments that compensate producers for unanticipated declines in the power delivery. Using a blend of advanced weather modeling, time series analysis, simulation and optimization techniques, we empirically access the ability of the two risk management approaches to control volumetric risk in Spain. With the aid of factor analysis techniques, we proceed with the decomposition of the remaining risk levels and assess the exposure of each strategy's revenue to systematic risk factors. Motivated by the results of this analysis, we propose new financial contracts and mixed-style strategies that are better suited to the risk position of each market player. • We introduce various strategies for managing the wind volumetric risk in Spain. • We quantify their ability to reduce the revenue variance of wind power producers. • Spatial aggregation of resources is not a cure for wind energy stochasticity. • Commercial wind power futures cannot hedge away all risk dimensions. • New contract designs are needed to control systematic variations in producers' income. [ABSTRACT FROM AUTHOR]

This paper studies the economically significant problem of the optimization of customs fraud control, which is a critical issue for many countries. The European Union (EU) alone handles 4693 tons of goods every minute (2018 figures). Even though 70% of goods are imported at zero tariff, the EU raised EUR 25.4 billions in 2018, and customs-related income transferred by member states to the EU accounts for nearly 13% of its overall budget. In this field, (a) the conflicting objectives are qualitative and cannot be reduced to a common measure (security and terrorism, health, drug market access control, taxes, etc.); (b) each submitted item has dozens of characteristics; (c) there are constraints; and (d) risk analysis systems have to make decisions in real time. Although the World Customs Organization has promoted the use of artificial intelligence to increase the precision of controls, the problem is very complex due to the data characteristics and interpretability, which is a requirement established by customs officers. In this paper, we propose a new Bayesian-based hybrid approach combining machine learning and multi-objective linear programming (MOLP), called multi-objective Bayesian with dynamic optimization (MOBADO). We demonstrate that it is possible to more than double (with a 237% increase) the precision of current inspection systems, freeing up almost 50% of human resources, and outperform past results with respect to each of the above objectives. MOBADO is an optimization technique that could be combined with any artificial intelligence approach capable of optimizing the quality of multi-objective risk analysis in real time. [ABSTRACT FROM AUTHOR]

• Cost-effective and sustainable energy systems retrofit for buildings. • 2nd life Li-Ion batteries in polygeneration systems for buildings. • To cut off the natural gas consumption at affordable cost. The building sector represents around one-third of the energy related to the EU CO 2 eq emissions, which makes it a crucial sector for achieving the EU's energy and environmental goals. Thus, the EU has established a legislative framework to foster, among others, the modernisation of the existing building stock through a better energy system integration. In this sense, bearing in mind the needs of energy system retrofit of the public buildings in Spain, this paper carried out a thorough analysis of different trade-off solutions obtained from the multiobjective optimization of a polygeneration system for the TR5 building of the Polytechnic University of Catalunya. The results highlight the selection of PV panels, cogeneration modules and 2 nd life Li-Ion batteries, among others, to achieve cost-effective and sustainable energy systems. By covering the available area, 2000 m 2 , the PV panels attend about 23% of the electricity required for the building. On the other hand, considering the current geopolitical tensions, it presents a potential configuration that allows to cut off the natural gas consumption reducing about 6% the current cost. The study was carried out by using a Mixed Integer Linear Programming model maximizing the Net Present Value of the project considering the environmental impact. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a multi-method ensemble for optimization in a turbine layout problem for wind farms is discussed. Specifically, the Coral Reefs Optimization algorithm with Substrate Layer (CRO-SL) is evaluated. The CRO-SL is an evolutionary-type meta-heuristic, able to combine different search procedures or methods within a single population, leading to a multi-method ensemble. The CRO-SL has been adapted to the wind turbines layout problem by including a discrete encoding, based on sparse matrices (though it can also work over continuous encodings) and a convex geometric procedure to speed-up the wake effect calculation for each turbine. The performance of the proposed CRO-SL algorithm in the turbine layout problem is evaluated by means of different experiments, considering simulated and real data from a wind farm in Spain. The performance of the CRO-SL is also evaluated in a different wind turbine layout problem from a NREL-IEA case competition. The contribution of each substrate of the CRO-SL algorithm (search procedure) is discussed in terms of the global search ability of the algorithm in the different problems considered. The global performance of the proposed approach has been finally compared to that of existing approaches in the literature, obtaining excellent results in all scenarios considered. [Display omitted] • A multi-method ensemble meta-heuristic for wind turbine layout is proposed. • A discrete encoding based on sparse matrices is tested. • Different search operators work together to obtain optimal solutions. • Experiments in simulated and real wind turbine layout problems are carried out. [ABSTRACT FROM AUTHOR]

• A MILP is set to ex-post optimise the Interruptible Load program in Spain. • Consideration of real technical and regulatory constraints. • Definition of a series of metrics to compare the optimal performance of programs. • Recommendation to design and operate Interruptible Load programs. • The optimal usage of the resource could have saved the system a total of 163 M€. The deployment of demand response in reserve markets has been widely discussed. Interruptible Load programs contract demand capacity from consumers in exchange for fixed and variable payments. System operators contract these resources to increase system resilience and use them for economic purposes. Although common, no ex-post evaluations of the program's performance and efficiency exists in the literature. To fill this gap, the paper presents a procedure to evaluate the optimal usage of these resources from a system operator perspective. A Mixed Integer Linear Problem is set to minimise the overall cost of the system using the participant demand resources in the tertiary reserve market, while ensuring that all technical and regulatory constraints are fulfilled in the evaluation. The proposed method describes a series of metrics to compare the optimal performance with the current scenario, and we draw a set of conclusions and policy recommendations from it. We apply the method to the Spanish Interruptible Load program. Our results show that during the five and a half years of the program, demand resources could have provided savings to the system of up to 23% of the cost of tertiary reserve. [ABSTRACT FROM AUTHOR]

Background and objectives: Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the current state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments. Methods: We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simulating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records. Results : After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained. Conclusions: The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seroprevalence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations —even using non-parametric distributions between the different compartments in the model— that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorporate more complex and dynamic structures. [ABSTRACT FROM AUTHOR]

Improving the quality of education is one of the main objectives for governments, given that it plays an essential role in providing well-being to the population. In this regard, the efficiency of teachers may be a key issue to improve the students' academic performance and learning achievement. Specifically, in this paper, we analyse the efficiency of teachers from a multi-criteria optimization perspective. The purpose is to detect the most efficient teachers to gain knowledge about the characteristics of their teaching context, which contribute to increase the education performance of the students. To this aim, data from TIMSS and PIRLS 2011 for fourth-grade reading, mathematics and science teachers in Spain have been used. Three synthetic indexes gathering these data are calculated: a weak index allowing full compensation among the variables, a strong index informing about the worst variable value (no compensation scheme) and a synthetic index representing a different degree of compensation of the two formers. These indexes are built using aspiration and reservation levels for each variable. This allows us to include desirable ranges of values to be achieved by the variables in the study of the teachers' efficiency. With this, it is possible to know if the teachers are performing better, within or worse than the desired limits, and to rank the teachers according to their efficiency. The analysis carried out has contributed to have a better understanding of the policies to be followed in order to promote efficiency in teaching. • Our study examines the efficiency of teachers as a key issue to improve students' academic performance. • Data from TIMSS and PIRLS 2011 for 4th-grade reading, mathematics and science teachers in Spain is used. • Three synthetic indexes are built using multi-criteria optimization techniques. • These indexes are based on the double reference point scheme and allow different compensation degrees among the variables. • Particularly significant conclusions are extracted from the efficiency ranking of the teachers. [ABSTRACT FROM AUTHOR]