On the optimal demand-side management in microgrids through polygonal composition

This article presents a novel methodology for energy management in microgrids focused on the demand side. It is inspired by the Tangram puzzle. The energy demand and production profiles are represented by polygons and managed through computational geometry. Therefore, an optimization problem is defined to place n energy demand profiles (pieces) to cover the total energy production profile (target shape). The optimization problem is addressed with a genetic algorithm. It tries to calculate the optimal positions of the polygons of the demands covering the maximum energy production. Since the referred production comes from renewable energy sources in the microgrid, this method allows reducing both the consumption of fossil fuels and energy bills

An Effective Solution for Drug Discovery Based on the Tangram Meta-Heuristic and Compound Filtering

Ligand-Based Virtual Screening accelerates and cheapens the design of new drugs. However, it needs efficient optimizers because of the size of compound databases. This work proposes a new method called Tangram CW. The proposal also encloses a knowledge-based filter of compounds. Tangram CW achieves comparable results to the state-of-the-art tools OptiPharm and 2LGO- Pharmusing about a tenth of their computational budget without filtering. Activating it discards more than two thirds of the database while keeping the desired compounds. Thus, it is possible to consider molecular flexibility despite increasing the options. The implemented software package is public.Grant PID2021-123278OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”Projects PDC2022-133370-I00 and TED2021-132020B-I00 funded by MCIN/AEI/ 10.13039/5011 00011033 and by European Union Next GenerationEU/PRTRMinistry of Economic Transformation, Industry, Knowledge and Universities from the Andalusian government (PAIDI 2021: POSTDOC_21_00124)“Margarita Salas” grant (RR_A_2021_21), financed by the European Union (NextGenerationEU

Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity

The basal ganglia (BG) is a brain structure that has long been proposed to play an essential role in action selection, and theoretical models of spiking neurons have tried to explain how the BG solves this problem. A recently proposed functional and biologically inspired network model of the striatum(an important nucleus of the BG) is based on spike-timing-dependent eligibility (STDE) and captured important experimental features of this nucleus. The model can recognize complex input patterns and consistently choose rewarded actions to respond to such sensory inputs. However, model tuning is challenging due to two main reasons. The first is the expert knowledge required, resulting in tedious and potentially biased trial-and-error procedures. The second is the computational cost of assessing model configurations (approximately 1.78 h per evaluation). This study addresses the model tuning problem through numerical optimization. Considering the cost of assessing solutions, the selected methods stand out due to their low requirements for solution evaluations and compatibility with high-performance computing. They are the SurrogateOpt solver of Matlab and the RBFOpt library, both based on radial basis function approximations, and DIRECT-GL, an enhanced version of the widespread black-box optimizer DIRECT. Besides, a parallel random search serves as a baseline reference of the outcome of opting for sophisticatedmethods. SurrogateOpt turns out to be the best option for tuning this kind of model. It outperforms, on average, the quality of the configuration found by an expert and works significantly faster and autonomously. RBFOpt and the randomsearch share the second position, but their average results are belowthe option found by hand. Finally, DIRECT-GL follows this line becoming the worst-performing method.R+D+i projects - MCIN/AEI RTI2018-095993-B-I00 PID2021-123278OB-I00European CommissionJunta de Andalucia P18-RT-1193University of Almeria UAL18-TIC-A020-BAndalusian governmentSpanish Grant INTSENSO MICINN-FEDER-PID2019-109991GB-I00Regional grants Junta Andalucia-FEDER CEREBIO P18-FR-2378European Commission 945539Spanish Government FPU17/0443

A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization

The heliostat field of solar central receiver systems (SCRS) is formed by hundreds, even thousands, of working heliostats. Their adequate configuration and control define a currently active research line. For instance, automatic aiming methodologies of existing heliostat fields are being widely studied. In general, control techniques require a model of the system to be controlled in order to obtain an estimation of its states. However, this kind of information may not be available or may be hard to obtain for every plant to be studied. In this work, an innovative methodology for data-based analytical heliostat field characterization is proposed and described. It formalizes the way in which the behavior of a whole field can be derived from the study of its more descriptive parts. By successfully applying this procedure, the instantaneous behavior of a field could be expressed by a reduced set of expressions that can be seen as a field descriptor. It is not intended to replace real experimentation but to enhance researchers’ autonomy to build their own reliable and portable synthetic datasets at preliminary stages of their work. The methodology proposed in this paper is successfully applied to a virtual field. Only 30 heliostats out of 541 were studied to characterize the whole field. For the validation set, the average difference in power between the flux maps directly fitted from the measured information and the estimated ones is only of 0.67% (just 0.10946 kW/m2 of root-mean-square error, on average, between them). According to these results, a consistent field descriptor can be built by applying the proposed methodology, which is hence ready for use

Determining mehlich‐3 and DTPA extractable soil zinc optimum economic threshold for maize

Maize (Zea mays L.) is one of the most susceptible crops to zinc (Zn) deficiency. However, in much of the world, soil Zn tests are poorly calibrated, and thus determining a critical soil test, level for Zn is challenging. The objectives of this study were to: (i) produce a field calibration of the Zn Mehlich-3 (M3-Zn) method for predicting maize grain yield response to Zn fertilizer application, (ii) compare the capacity of DTPA extractable Zn (DTPA-Zn) with M3-Zn for predicting the response of maize yield to Zn fertilizer, (iii) determine whether inclusion of soil pH, organic matter (SOM) and extractable phosphorus in a M3-Zn model improves its predictive capacity and (iv) evaluate an economic approach for determining soil Zn thresholds. We conducted 55 field experiments covering a wide range of edaphic and climatic conditions. Maize responded to Zn fertilizer in 29% of the trials. The capacity of M3-Zn and DTPA-Zn to predict relative yield of maize was similar. Inclusion of other soil variables (pH, extractable phosphorus and SOM) did not or only slightly improve the prediction of M3-Zn. Based on the relationship between M3-Zn and DTPA-Zn (R2 = 0.89), one test can be predicted from the other without affecting the calibration. The M3-Zn 'economic threshold' ranged from 0.98 to 2.79 mg kg−1, while for DTPA-Zn it varied from 0.41 to 1.61 mg kg−1. The broad range of economic thresholds shows that differences in maize productivity and grain price between regions and seasons make establishing a single Zn threshold for all economic-productive situations inappropriate.Fil: Martínez Cuesta, Nicolás. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Wyngaard, Nicolás. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mar del Plata. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible. - Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Balcarce. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible.; ArgentinaFil: Sainz Rozas, Hernan Rene. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Reussi Calvo, Nahuel Ignacio. Laboratorio de Analisis de Suelos Fertilab; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Eyherabide, Mercedes. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mar del Plata. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible. - Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Balcarce. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible.; ArgentinaFil: Colazo, Juan Cruz. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Barraco, Mariano Manuel. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Guertal, Elizabeth A.. Auburn University.; Estados UnidosFil: Barbieri, Pablo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mar del Plata. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible. - Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Balcarce. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible.; Argentin

Using a genetic algorithm for energy demand management in microgrids through polygonal decomposition

[Resumen] Este trabajo propone una metodolog´ıa atractiva para la gestión energética orientada al lado de la demanda en la operación de microrredes. Esta metodología replica aspectos del rompecabezas chino Tangram, ya que los perfiles tanto de producción como de demanda de energía se construyen a través de polígonos y operan con geometría computacional. Por lo tanto, se desarrolla un problema de optimización, que a través de los perfiles de demanda de energía de n dispositivos (piezas) se intenta cubrir la totalidad, o la máxima área posible, de un perfil de producción de energía (forma). Así, el problema de optimización se resuelve con un algoritmo genético que calcula las posiciones óptimas de los polígonos de demanda para cubrir, es decir consumir, la máxima área del polígono equivalente a la producción de energía. Dado que la energía proviene de fuentes renovables de una microrred, este método permite reducir la dependencia del consumo de energía de la red pública y, por lo tanto, la factura de la energía eléctrica.[Abstract] This work proposes an attractive methodology for demand-side energy management in the operation of microgrids. This method replicates some aspects of the Chinese Tangram puzzle, as production and demand profiles are represented by polygonal shapes to handle through computational geometry. It defines an optimization problem in which the demand profiles (pieces) of n devices must be arranged to cover a production profile (shape) as well as possible. The optimization problem is addressed with a genetic algorithm that calculates the optimal positions of the demand polygons to consume the maximum energy production. Since the energy comes from renewable sources in a microgrid, this method allows reducing the dependence on the public energy grid and the electricity bills.Ministerio de Ciencia e Innovación; PID2021-126889OB-I00Junta de Andalucía; POSTDOC_21_0012

A two-layered solution for automatic heliostat aiming

The efficiency and safety of a solar central receiver system depend on the flux distribution reflected by the heliostat field on its receiver. Thus, the field must be carefully controlled to avoid dangerous radiation peaks and temperature gradients while also maximizing the efficiency of the system. Control tasks include deciding which heliostats to activate and where to aim them. The field is usually under direct human supervision, which is a potential limitation, and automatic aiming procedures are of great interest. This work proposes a general aiming methodology for flat-plate receivers. It intends to cover heliostat selection and aim point assignation to replicate any given reference flux distribution on the receiver. The methodology, which addresses this situation as a large-scale optimization problem, defines two consecutive stages. The first one handles heliostat selection by applying a specific genetic algorithm. The second one, based on a local gradient descent, assigns a final aim point to every active heliostat. The proposed methodology, in contrast to other existing methods in the literature, is not limited to achieve any specific target distribution. It exploits the analytical characterization of the considered field to minimize the accumulated squared error between any reference flux distribution and the achieved one. The results show very good replication quality and, considering its execution time, this method is suitable for preliminary and high-resolution field configuration

Optimizing the Heliostat Field Layout by Applying Stochastic Population-Based Algorithms

The heliostat field of Solar Central Receiver Systems takes up to 50% of the initial investment and can cause up to 40% of energetic loss in operation. Hence, it must be carefully optimized. Design procedures usually rely on particular heliostat distribution models. In this work, optimization of the promising biomimetic distribution model is studied. Two stochastic population-based optimizers are applied to maximize the optical efficiency of fields: a genetic algorithm, micraGA, and a memetic one, UEGO. As far as the authors know, they have not been previously applied to this problem. However, they could be a good option according to their structure. Additionally, a Brute-Force Grid is used to estimate the global optimum and a Pure-Random Search is applied as a baseline reference. Our empirical results show that many different configurations of the distribution model lead to very similar solutions. Although micraGA exhibits poor performance, UEGO achieves the best results in a reduced time and seems appropriate for the problem at hand